A method for measuring packet loss rate of tire pressure monitoring system

Abstract

The present invention discloses a method for measuring the packet loss rate of a tire pressure monitoring system, which relates to the technical field of packet loss rate measurement. The method includes: constructing a physical communication model of the tire pressure monitoring system; comparing the sequence numbers of the original data packet and the received data packet with the expected sequence numbers to calculate a first packet loss rate; constructing a channel compensation model based on vehicle status data; calculating the channel interference error based on the physical communication model and the channel compensation model, compensating for the first packet loss rate, and generating a second packet loss rate; statistically analyzing the second packet loss rate using an outlier detection algorithm, identifying and correcting measurement errors caused by transient interference or external factors, performing smoothing processing to generate a third packet loss rate, and displaying the third packet loss rate as the tire pressure monitoring system packet loss rate. The channel compensation model is constructed by combining the physical communication model with vehicle status data, and an anomaly detection and correction algorithm is further employed. Combined with smoothing processing, continuous nonlinear smoothing of the packet loss rate is achieved, ensuring the authenticity of the final output data.

Description

Technical Field

[0001] The present invention relates to the technical field of packet loss rate measurement, and in particular to a method for measuring the packet loss rate of a tire pressure monitoring system. Background Art

[0002] In recent years, with the continuous improvement of vehicle safety, fuel economy, and environmental protection requirements, tire pressure monitoring systems (TPMS) have become widely used in modern vehicles. By monitoring tire pressure in real time, these systems effectively prevent accidents and vehicle performance degradation caused by abnormal tire pressure. However, during dynamic driving, wireless communication channels often experience multipath fading, signal interference, vibration, and transient external interference due to the combined effects of vehicle structure, tire dynamic characteristics, and environmental interference. These phenomena can lead to packet loss during transmission, compromising monitoring accuracy and system stability.

[0003] Currently, existing technologies primarily use simple packet sequence number comparison methods to calculate packet loss rates. However, this method often overlooks channel interference effects caused by vehicle motion, tire rotation, and speed variations. Furthermore, existing technologies struggle to effectively identify and correct abnormal data caused by transient interference or external environmental factors, resulting in significant uncertainty in packet loss rate statistics. This is particularly true under conditions such as high speeds, complex road conditions, or severe vehicle vibration. Traditional methods are unable to fully compensate for nonlinear and complex interference in wireless channels, making them incapable of meeting the requirements for high-precision monitoring. Summary of the Invention

[0004] Based on the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method for measuring the packet loss rate of a tire pressure monitoring system to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for measuring the packet loss rate of a tire pressure monitoring system, comprising:

[0006] S1: Construct a physical communication model of the tire pressure monitoring system based on the vehicle structure and tire dynamic characteristics;

[0007] S2: Compare the sequence numbers of the original data packet and the received data packet with the expected sequence numbers to calculate and obtain the first packet loss rate;

[0008] S3: Build a channel compensation model based on the vehicle status data, calculate the channel interference error based on the physical communication model and the channel compensation model, compensate the first packet loss rate based on the channel interference error, and generate a second packet loss rate;

[0009] S4: Performing statistical analysis on the second packet loss rate using an outlier detection algorithm to identify and correct measurement errors caused by transient interference or external factors, performing smoothing processing to generate a third packet loss rate, and displaying the third packet loss rate as the tire pressure monitoring system packet loss rate.

[0010] The present invention is further configured such that step S1 comprises:

[0011] Construct a local two-dimensional coordinate system for the vehicle and define the relative offset between the tire center position and the sensor;

[0012] Calculate the radiation field distribution function according to the radiation direction vector of the sensor antenna;

[0013] Characterize the propagation signal according to the path integral method, calculate the attenuation of a single path through the nonlinear attenuation function, integrate the multipath effect, and calculate the channel transfer function;

[0014] Calculate the instantaneous phase correction factor based on the tire's vibration angular frequency and instantaneous rotation angle through nonlinear phase perturbation;

[0015] The physical communication model of the tire pressure monitoring system is constructed based on the radiation field distribution function, channel transmission function and instantaneous phase correction factor.

[0016] The present invention is further configured such that the logical relationship between the tire center position and the relative offset of the sensor is: , For sensors The global coordinates of For tires The center coordinates of is the rotation matrix, For tires The instantaneous turning angle, is the fixed offset of the sensor in the tire local coordinate system;

[0017] The calculation logic of the radiation field distribution function is: , is the radiation field distribution function, and are the polar angle and azimuth angle of the antenna direction, is the wave number corresponding to the sensor operating frequency, is the imaginary unit, is the direction vector of the sensor antenna, is the unit direction vector;

[0018] The calculation logic of the channel transfer function is: , is the channel transfer function, is the number of valid paths, For the The geometric distance of the paths, For the The phase constant of the path, For the The decay function of the path, , For the The attenuation factor of the path, is the scale parameter, is an exponential parameter describing the attenuation characteristics;

[0019] The calculation logic of the instantaneous phase correction factor is: , is the instantaneous phase correction factor, is the vibration amplitude factor, is the vibration angular frequency, is the initial phase, is the tire rotation modulation coefficient, For the moment The instantaneous turning angle of

[0020] The construction logic of the physical communication model of the tire pressure monitoring system is: , is the physical communication model, is the angular domain of the antenna radiation integral, is the angular domain integral element.

[0021] The present invention is further configured such that step S2 includes:

[0022] Construct an expected sequence number based on the sequence number of the original data packet and the data transmission convention;

[0023] Construct a missing interval based on the expected sequence number and the sequence number of the received data packet, and calculate the missing contribution function based on the missing interval;

[0024] Aggregate the missing contributions of each missing interval, calculate the total missing error coefficient, and normalize it to obtain the first packet loss rate.

[0025] The present invention is further configured to record the sequence number of the original data packet as: , is the sequence number set of the original data packet, For the The sequence number of the packet, is the number of original data packets; the sequence number of the received data packet is recorded as: , is the sequence number set of the received data packet, For the The sequence number of the packet, is the number of received packets; the expected sequence number is: , is the expected sequence number set, For the The sequence number of the packet, is the sequence number of the transmitted data packet, is the number of transmitted data packets;

[0026] The missing intervals are: , is the missing interval; the missing contribution function is: , is the missing contribution function, is the sensitivity adjustment parameter, is the modulation index;

[0027] The total missing error coefficient is: , is the total missing error coefficient; the first packet loss rate is: , is the first packet loss rate.

[0028] The present invention is further configured such that the vehicle status data includes vehicle speed and tire angular velocity.

[0029] The present invention is further configured such that step S3 includes:

[0030] A channel compensation model is constructed based on the vehicle speed and tire angular velocity, and a channel compensation factor is calculated;

[0031] Calculate the function value based on the physical communication model and take the amplitude as the expected channel quality coefficient;

[0032] The channel interference error is calculated based on the expected channel quality coefficient, the actual measured channel quality and the channel compensation factor;

[0033] The first packet loss rate is compensated according to the channel interference error to generate a second packet loss rate.

[0034] The present invention is further configured such that the construction logic of the channel compensation model is: , is the channel compensation factor, is the amplitude adjustment parameter, is the vehicle speed, is the tire angular velocity, and is the modulation index, is the adjustment index, is a small constant;

[0035] The calculation logic of channel interference error is: , is the channel interference error, is the expected channel quality coefficient, To actually measure the channel quality;

[0036] The calculation logic of the second packet loss rate is: , is the second packet loss rate, is the first packet loss rate, To compensate for the intensity adjustment coefficient, is the adjustment coefficient, is the adjustment index.

[0037] The present invention is further configured such that step S4 includes:

[0038] Sort the second packet loss rate sequence corresponding to the serial number of the received data packet in ascending order, and perform outlier detection on the second packet loss rate sequence according to a preset anomaly scoring function;

[0039] Correcting the outliers according to a preset correction function to obtain a corrected second packet loss rate sequence;

[0040] The corrected second packet loss rate sequence is smoothed to generate a third packet loss rate.

[0041] The present invention is further configured such that the calculation logic of the abnormality scoring function is: , is the anomaly scoring function, The first The rank function of the second packet loss rate, is the second packet loss rate sequence length, To adjust the parameters, is the exponential parameter;

[0042] The calculation logic of the correction function is: , is the corrected second packet loss rate, is the neighborhood interval of the outlier, , is the local kernel regulation parameter, is the decay exponent, is the integral variable;

[0043] The calculation logic of smoothing is: , is the third packet loss rate, is the target data index, is the length of the integration interval, is the corrected second packet loss rate, , is the anomaly scoring threshold, is the sliding bandwidth parameter, is the modulation index, is the integration variable, is the normalization factor, .

[0044] The present invention provides a method for measuring the packet loss rate of a tire pressure monitoring system. The method comprises the following steps: constructing a physical communication model of the tire pressure monitoring system based on a vehicle structure and tire dynamic characteristics; comparing the sequence numbers of original data packets and received data packets with expected sequence numbers to calculate a first packet loss rate; constructing a channel compensation model based on vehicle status data; calculating a channel interference error based on the physical communication model and the channel compensation model; compensating the first packet loss rate based on the channel interference error to generate a second packet loss rate; performing a statistical analysis on the second packet loss rate using an outlier detection algorithm to identify and correct measurement errors caused by transient interference or external factors; and performing a smoothing process to generate a third packet loss rate. The third packet loss rate is then displayed as the packet loss rate of the tire pressure monitoring system. The beneficial effects produced include:

[0045] 1. High-precision packet loss rate measurement: A physical communication model based on the vehicle structure and tire dynamic characteristics is constructed. By comparing the sequence numbers of original and received data packets, the system can accurately construct the expected sequence and calculate the preliminary packet loss rate, thereby accurately quantifying data transmission loss.

[0046] 2. Effective channel interference compensation: A channel compensation model is constructed based on vehicle status data. By calculating the channel interference error, nonlinear compensation is performed on the initial packet loss rate, effectively offsetting channel fluctuations caused by vehicle motion and tire rotation, and improving the robustness of data transmission.

[0047] 3. Abnormal data detection and correction: An outlier detection algorithm is used to perform statistical analysis on the compensated packet loss rate, promptly identifying and correcting measurement errors caused by transient interference or external environmental factors, ensuring that abnormal data does not have a significant impact on the overall statistical results.

[0048] 4. Smoothing and data stability: The corrected packet loss rate is continuously and nonlinearly smoothed by the smoothing method, so that the final output third packet loss rate can truly reflect the long-term stability of data transmission.

[0049] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. In the drawings:

[0051] Figure 1 The flowchart of a method for measuring the packet loss rate of a tire pressure monitoring system is shown as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0053] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0054] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0055] A method for measuring the packet loss rate of a tire pressure monitoring system, such as Figure 1 Shown, including:

[0056] S1: Construct a physical communication model of the tire pressure monitoring system based on the vehicle structure and tire dynamic characteristics;

[0057] S2: Compare the sequence numbers of the original data packet and the received data packet with the expected sequence numbers to calculate and obtain the first packet loss rate;

[0058] S3: Build a channel compensation model based on the vehicle status data, calculate the channel interference error based on the physical communication model and the channel compensation model, compensate the first packet loss rate based on the channel interference error, and generate a second packet loss rate;

[0059] S4: Performing statistical analysis on the second packet loss rate using an outlier detection algorithm to identify and correct measurement errors caused by transient interference or external factors, performing smoothing processing to generate a third packet loss rate, and displaying the third packet loss rate as the tire pressure monitoring system packet loss rate.

[0060] The present invention is further configured such that step S1 comprises:

[0061] A local two-dimensional coordinate system of the vehicle is constructed to define the relative offset between the tire center position and the sensor. The present invention is further configured such that the logical relationship between the tire center position and the relative offset between the sensor is: , For sensors The global coordinates of For tires The center coordinates of is the rotation matrix, For tires The instantaneous turning angle, is the fixed offset of the sensor in the tire local coordinate system; specifically, a two-dimensional coordinate system is established inside the vehicle, where the origin is selected as the center of the vehicle or a reference point. Recorded as , For tires The center coordinates of are fixed coordinates in the global coordinate system. Recorded as , sensor relative offset is the fixed position offset vector of the sensor relative to the tire center in the tire local coordinate system, Recorded as ,when For tires When the instantaneous angle is for The above calculation logic describes the process of converting the sensor from a fixed offset to a global coordinate under the action of tire rotation. It can reflect the impact of tire rotation on the sensor position in real time, thereby ensuring the global positioning accuracy of the sensor;

[0062] According to the radiation direction vector of the sensor antenna, the radiation field distribution function is calculated; the calculation logic of the radiation field distribution function is: , is the radiation field distribution function, and are the polar angle and azimuth angle of the antenna direction, is the wave number corresponding to the sensor operating frequency, is the imaginary unit, is the direction vector of the sensor antenna, is a unit direction vector; specifically, the radiation field distribution function Describes the sensor antenna at a specific polar angle and azimuth Function of the radiant energy distribution in the direction, wave number The sensor operating frequency Calculated, , is the speed of light, the direction vector of the sensor antenna Reflects the direction of the actual radiation main lobe of the sensor antenna. Its value is determined by the antenna design. In one embodiment of the present invention, the direction vector of all sensor antennas is Make unified settings, unit direction vector for , the polar angle and azimuth angle of the antenna direction and The antenna's angles in the horizontal and vertical planes are determined separately. The aforementioned calculation logic uses the sensor antenna's directional vector to determine its radiation field distribution in all directions in space, accurately characterizing the antenna's directional characteristics. By taking the inner product of the antenna's actual radiation direction and the unit directional vector and modulating the phase using the wave number, a radiation field distribution function containing both amplitude and phase information is derived. This accurately reflects the antenna's radiation characteristics in different angular domains, providing precise input for wireless channel modeling.

[0063] The propagation signal is characterized by the path integral method. The attenuation of a single path is calculated using a nonlinear attenuation function. The multipath effect is integrated to calculate the channel transfer function. The calculation logic of the channel transfer function is: , is the channel transfer function, is the number of valid paths, For the The geometric distance of the paths, For the The phase constant of the path, For the The decay function of the path, , For the The attenuation factor of the path, is the scale parameter, is an exponential parameter describing the attenuation characteristics; specifically, the channel transfer function It represents the comprehensive impact of the entire wireless channel on the signal, which is the superposition of the attenuation and phase change of all effective paths. The decay function of the path Describing the path The nonlinear relationship between the signal strength and the propagation distance attenuation, the phase constant Indicates the path Phase changes caused by distance and medium characteristics during propagation are multiplied by the geometric distance to give the phase offset. The signal contributions of each path are multiplied together to form the overall transfer function. The above calculation logic is based on the path integral method, which treats the propagating signal as consisting of multiple independent paths, each of which contributes its attenuation and phase information. For each path, a nonlinear attenuation function is used to describe the attenuation of its signal strength with distance, while also considering the phase changes of the path propagation. The contributions of each path are integrated in the form of a product to form the overall channel transfer function, which truly reflects the signal characteristics in a multipath propagation environment.

[0064] The instantaneous phase correction factor is calculated based on the tire's vibration angular frequency and instantaneous rotation angle through nonlinear phase perturbation. The calculation logic of the instantaneous phase correction factor is: , is the instantaneous phase correction factor, is the vibration amplitude factor, is the vibration angular frequency, is the initial phase, is the tire rotation modulation coefficient, For the moment Specifically, the calculation logic aims to use the dynamic phase disturbance caused by tire vibration and rotation to correct the phase error of the transmitted signal. Tire vibration is described as a sine function, whose vibration frequency and initial phase reflect the periodic characteristics of the vibration. At the same time, the phase modulation of tire rotation is introduced. The vibration amplitude is nonlinearly modulated by a cosine term based on the instantaneous rotation angle, thereby forming an overall instantaneous phase correction factor to compensate for the phase offset caused by mechanical vibration and rotation.

[0065] The physical communication model of the tire pressure monitoring system is constructed based on the radiation field distribution function, channel transmission function, and instantaneous phase correction factor. The construction logic of the physical communication model of the tire pressure monitoring system is as follows: , is the physical communication model, is the angular domain of the antenna radiation integral, is the angular domain integral element. Specifically, the angular domain of the antenna radiation integral Refers to the angular domain of antenna radiation integration, covering all effective radiation directions, the angular domain integration element Represents the area element of a small area in the angular domain, which is used to accumulate the contributions of each direction in the integral. The above physical communication model aims to comprehensively consider the antenna directivity, multipath propagation effect and instantaneous phase disturbance, and builds an overall model through three key modules. First, in the antenna radiation integral domain, the radiation contribution of each direction is integrated according to the radiation field distribution function of the sensor antenna to obtain the overall radiation characteristics of the antenna; secondly, through the channel transfer function Integrate the nonlinear attenuation and phase changes in the multipath propagation process; finally, use the instantaneous phase correction factor Real-time compensation of dynamic phase errors ensures that the model accurately reflects actual transmission conditions.

[0066] The present invention is further configured such that step S2 includes:

[0067] The expected sequence number is constructed based on the sequence number of the original data packet and the data transmission agreement; the present invention is further configured to record the sequence number of the original data packet as: , is the sequence number set of the original data packet, For the The sequence number of the packet, is the number of original data packets; the sequence number of the received data packet is recorded as: , is the sequence number set of the received data packet, For the The sequence number of the packet, is the number of received packets; the expected sequence number is: , is the expected sequence number set, For the The sequence number of the packet, is the sequence number of the transmitted data packet, The number of transmitted data packets; specifically, the calculation logic is based on the data transmission agreement, taking the sequence number of the original data packet as the ideal sending order, and fixing the starting sequence number Incrementally construct the expected sequence number set At the same time, the actual data packet sequence number set is extracted from the sender and receiver respectively and ;

[0068] The missing interval is constructed based on the expected sequence number and the sequence number of the received data packet, and the missing contribution function is calculated based on the missing interval; the missing interval is: , is the missing interval; the missing contribution function is: , is the missing contribution function, is the sensitivity adjustment parameter, is the modulation index; specifically, the missing interval Indicates the number of missing data packets between the sequence actually obtained by the receiver and the expected sequence during data transmission. The values ​​correspond to the missing conditions at different positions, such as starting missing, middle continuous missing, and end missing, and the missing contribution function The number of missing packets in each missing interval It is converted into a contribution value between 0 and 1 through nonlinear mapping. When the number of missing items is small, the exponential function value is close to 1, making is smaller; when the number of missing items increases, Tends to 1, emphasizing the impact of large missing intervals, and determines the missing packet interval during data transmission based on comparing the expected sequence number with the actual received sequence number. By defining the missing interval For different situations, the number of missing data packets in each segment can be accurately quantified, and then a nonlinear function is used to assign a weight to each missing interval to form a missing contribution function;

[0069] Aggregate the missing contributions of each missing interval, calculate the total missing error coefficient, and normalize it to obtain the first packet loss rate; the total missing error coefficient is: , is the total missing error coefficient; the first packet loss rate is: , Specifically, the first packet loss rate is obtained by converting the packet loss in each missing interval into a contribution value, and then aggregating these contribution values ​​and normalizing them. First, for each missing interval Calculate missing contribution function , and then sum up all missing contributions to get the total missing error coefficient ; Finally, normalize the total value by dividing it by the total number of expected packets , get the first packet loss rate indicating the degree of transmission loss , reflecting the accumulated missing information during the entire data transmission process.

[0070] The present invention is further configured such that the vehicle status data includes vehicle speed and tire angular velocity.

[0071] The present invention is further configured such that step S3 includes:

[0072] A channel compensation model is constructed based on the vehicle speed and tire angular velocity, and a channel compensation factor is calculated. The present invention is further configured such that the construction logic of the channel compensation model is: , is the channel compensation factor, is the amplitude adjustment parameter, is the vehicle speed, is the tire angular velocity, and is the modulation index, is the adjustment index, is a small constant; specifically, the amplitude adjustment parameter It is a positive number used to adjust the weight of the vehicle speed's impact on the channel. The larger the value, the faster the compensation factor decreases and the modulation index decreases. and is a positive number, which controls the degree of nonlinear influence of vehicle speed and tire angular velocity on the compensation factor, and adjusts the index It is a positive number, which is used to further adjust the sensitivity of the overall nonlinear mapping, making the compensation factor smoother or steeper in the face of different motion states. To prevent the denominator from being zero, the protection parameter should be less than , based on vehicle speed and tire angular velocity Two key dynamic parameters, channel compensation factor constructed through nonlinear mapping , used to adjust for channel attenuation and interference effects caused by vehicle motion. Using an exponential decay formula, it combines the positive impact of vehicle speed with the buffering effect of tire angular velocity to output a compensation factor between 0 and 1 to dynamically compensate for adverse effects during channel measurement and data transmission.

[0073] The function value is calculated based on the physical communication model, and the amplitude is taken as the expected channel quality coefficient. The channel interference error is calculated based on the expected channel quality coefficient, the actual measured channel quality and the channel compensation factor. The calculation logic of the channel interference error is: , is the channel interference error, is the expected channel quality coefficient, The actual measurement channel quality; specifically, the actual measurement channel quality The channel signal amplitude measured by the actual receiving device reflects the channel conditions in the actual transmission environment. The above calculation logic quantifies the channel interference by comparing the difference between the expected channel quality and the actual measured channel quality, and incorporating dynamic compensation caused by the vehicle's motion state. Specifically, the expected channel quality coefficient (its amplitude) is obtained from the physical communication model and then compared with the actual measured channel quality. The absolute value of the difference between the two reflects the deviation of the channel after interference. This difference is then multiplied by the channel compensation factor composed of vehicle speed and tire angular velocity to calculate the final channel interference error.

[0074] The first packet loss rate is compensated according to the channel interference error to generate a second packet loss rate. The calculation logic of the second packet loss rate is: , is the second packet loss rate, is the first packet loss rate, To compensate for the intensity adjustment coefficient, is the adjustment coefficient, is the adjustment index. Specifically, the compensation intensity adjustment coefficient Controls the overall magnitude of the channel interference error in the compensation function. This parameter determines how strongly the compensation factor adjusts the first packet loss rate under the same interference level. The larger the value, the more obvious the compensation effect, thereby reducing the second packet loss rate more. The value range is [0.1, 10]. The adjustment coefficient Used to amplify or reduce the impact of channel interference error in the compensation function. This parameter determines the scale of the interference error before it enters the exponential function, so that the system can adjust the sensitivity appropriately for smaller or larger errors. The value range is [0.01, 10]. The adjustment exponent The degree of nonlinear mapping of interference error in the compensation function is defined, which determines the shape of the curve of the compensation function's response to interference error. The value range is [1, 3]. The first packet loss rate, which is initially calculated, is dynamically compensated by the channel interference error to generate a corrected second packet loss rate. Nonlinear modulation is used to divide the first packet loss rate by the compensation factor, which increases with the increase of channel interference error. This appropriately corrects the packet loss rate to more realistically reflect the impact of interference on data transmission.

[0075] The present invention is further configured such that step S4 includes:

[0076] The second packet loss rate sequence corresponding to the sequence number of the received data packet is sorted in ascending order, and an outlier detection is performed on the second packet loss rate sequence according to a preset anomaly scoring function. The present invention is further configured such that the calculation logic of the anomaly scoring function is: , is the anomaly scoring function, The first The rank function of the second packet loss rate, is the second packet loss rate sequence length, To adjust the parameters, is the index parameter; specifically, The rank function of the second packet loss rate represents the The rank corresponding to the data, that is, the position of the data in the sequence, and the adjustment parameters Controls the sensitivity of the anomaly scoring function to the normalized rank, with a value range of [0.1, 5]. The exponential parameter Determine the degree of nonlinearity of the normalized rank mapping, with a value range of [0.1, 3]. The second packet loss rate sequence corresponding to the received data packets is sorted in ascending order, and then a preset anomaly scoring function is used to map the normalized rank of the sorted data to an anomaly score value. This anomaly score reflects the position of each data point in the overall distribution, thereby identifying those data points that are on the edge of the ranking. The anomaly scoring function uses an exponential mapping to achieve a nonlinear amplification effect, resulting in higher scores for high-rank data and lower scores for low-rank data.

[0077] The outliers are corrected according to a preset correction function to obtain a corrected second packet loss rate sequence. The calculation logic of the correction function is: , is the corrected second packet loss rate, is the neighborhood interval of the outlier, , is the local kernel regulation parameter, is the decay exponent, is the integral variable; specifically, the local kernel adjustment parameter Determines the decay rate of the weight function, the value range is [0.1,10], The attenuation exponent controls the degree of nonlinear mapping of the deviation from the outlier in the kernel function, with a value range of [0.1, 3]. The outliers in the second packet loss rate sequence are corrected using a preset local correction function. Specifically, within the neighborhood of the outlier, the local kernel function is used as the weight, and a weighted integral is performed on the data within that interval to calculate a local smoothed value, which replaces the original outlier data. This process can be viewed as local kernel smoothing or weighted averaging, aiming to suppress extreme data caused by transient interference or occasional anomalies, thereby making the corrected second packet loss rate sequence more consistent with actual transmission conditions.

[0078] The corrected second packet loss rate sequence is smoothed to generate a third packet loss rate. The calculation logic of the smoothing process is: , is the third packet loss rate, is the target data index, is the length of the integration interval, is the corrected second packet loss rate, , is the anomaly scoring threshold, is the sliding bandwidth parameter, is the modulation index, is the integration variable, is the normalization factor, ; Specifically, the target data index is the position index of each data point in the corrected second packet loss rate sequence, and the length of the integration interval Index of target data The integral range selected on both sides is the local window of the smoothing kernel, the sliding bandwidth parameter The scale parameter that controls the decay rate of the smoothing kernel function, the modulation index Used to control distance in kernel function The nonlinear index of the influence of the deviation on the weight, the value range is [1,3], the normalization factor It is used to ensure that the sum of the integral weights is 1, so that the smoothing result is the weighted average of the local data. The local kernel smoothing technology is used to smooth the second packet loss rate sequence after correction, thereby generating the third packet loss rate. The basic idea is to Select an integral interval near the distance, and calculate the correction value in the interval according to the distance The weights are given by an exponential kernel function, which can quickly attenuate the impact of distant data on the smoothed result, ensuring that the smoothed result can eliminate noise fluctuations while retaining local trends.

[0079] The third packet loss rate is displayed as the tire pressure monitoring system packet loss rate. After anomaly detection, local correction, and kernel smoothing, the third packet loss rate can more accurately reflect the actual packet loss situation during data transmission. Therefore, the third packet loss rate is displayed as the final packet loss rate of the tire pressure monitoring system, providing a reliable basis for system performance evaluation, alarm triggering, and subsequent channel optimization.

[0080] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0081] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0082] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0083] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0084] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0085] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0087] 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0088] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0089] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0090] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for measuring the packet loss rate of a tire pressure monitoring system, characterized in that: include: S1: Construct a physical communication model of the tire pressure monitoring system based on the vehicle structure and tire dynamic characteristics; S2: Comparing the sequence numbers of the original data packet and the received data packet with the expected sequence numbers to calculate a first packet loss rate; including: constructing an expected sequence number based on the sequence number of the original data packet and the data transmission agreement; constructing a missing interval based on the expected sequence number and the sequence number of the received data packet, and calculating a missing contribution based on the missing interval; aggregating the missing contributions of each missing interval, calculating a total missing error coefficient, and normalizing the result to obtain the first packet loss rate; S3: Build a channel compensation model based on the vehicle status data, calculate the channel interference error based on the physical communication model and the channel compensation model, compensate the first packet loss rate based on the channel interference error, and generate a second packet loss rate; S4: Performing statistical analysis on the second packet loss rate using an outlier detection algorithm to identify and correct measurement errors caused by transient interference or external factors, performing smoothing processing to generate a third packet loss rate, and displaying the third packet loss rate as the tire pressure monitoring system packet loss rate.

2. The tire pressure monitoring system packet loss rate measurement method according to claim 1, characterized in that: Step S1 includes: Construct a local two-dimensional coordinate system for the vehicle and define the relative offset between the tire center position and the sensor; Calculate the radiation field distribution function according to the radiation direction vector of the sensor antenna; Characterize the propagation signal according to the path integral method, calculate the attenuation of a single path through the nonlinear attenuation function, integrate the multipath effect, and calculate the channel transfer function; Calculate the instantaneous phase correction factor based on the tire's vibration angular frequency and instantaneous rotation angle through nonlinear phase perturbation; The physical communication model of the tire pressure monitoring system is constructed based on the radiation field distribution function, channel transmission function and instantaneous phase correction factor.

3. The tire pressure monitoring system packet loss rate measurement method according to claim 2, characterized in that: The logical relationship between the tire center position and the relative offset of the sensor is: , For sensors The global coordinates of For tires The center coordinates of is the rotation matrix, For tires The instantaneous turning angle, is the fixed offset of the sensor in the tire local coordinate system; The calculation logic of the radiation field distribution function is: , is the radiation field distribution function, and are the polar angle and azimuth angle of the antenna direction, is the wave number corresponding to the sensor operating frequency, is the imaginary unit, is the direction vector of the sensor antenna, is the unit direction vector; The calculation logic of the channel transfer function is: , is the channel transfer function, is the number of valid paths, For the The geometric distance of the paths, For the The phase constant of the path, For the The decay function of the path, , For the The attenuation factor of the path, is the scale parameter, is an exponential parameter describing the attenuation characteristics; The calculation logic of the instantaneous phase correction factor is: , is the instantaneous phase correction factor, is the vibration amplitude factor, is the vibration angular frequency, is the initial phase, is the tire rotation modulation coefficient, For the moment The instantaneous turning angle of The construction logic of the physical communication model of the tire pressure monitoring system is: , is the physical communication model, is the angular domain of the antenna radiation integral, is the angular domain integral element.

4. The tire pressure monitoring system packet loss rate measurement method according to claim 1, characterized in that: The sequence number of the original data packet is recorded as: , is the sequence number set of the original data packet, For the The sequence number of the packet, is the number of original data packets; the sequence number of the received data packet is recorded as: , is the sequence number set of the received data packet, For the The sequence number of the packet, is the number of received packets; the expected sequence number is: , is the expected sequence number set, For the The sequence number of the packet, is the sequence number of the transmitted data packet, is the number of transmitted data packets; The missing intervals are: , is the missing interval; the missing contribution is: , Contribute to the missing is the sensitivity adjustment parameter, is the modulation index; The total missing error coefficient is: , is the total missing error coefficient; the first packet loss rate is: , is the first packet loss rate.

5. The tire pressure monitoring system packet loss rate measurement method according to claim 3, characterized in that: Vehicle status data includes vehicle speed and tire angular velocity.

6. The tire pressure monitoring system packet loss rate measurement method according to claim 5, characterized in that: Step S3 includes: A channel compensation model is constructed based on the vehicle speed and tire angular velocity, and a channel compensation factor is calculated; Calculate the function value based on the physical communication model and take the amplitude as the expected channel quality coefficient; The channel interference error is calculated based on the expected channel quality coefficient, the actual measured channel quality and the channel compensation factor; The first packet loss rate is compensated according to the channel interference error to generate a second packet loss rate.

7. The tire pressure monitoring system packet loss rate measurement method according to claim 6, characterized in that: The construction logic of the channel compensation model is: , is the channel compensation factor, is the amplitude adjustment parameter, is the vehicle speed, is the tire angular velocity, and is the modulation index, is the adjustment index, is a small constant; The calculation logic of channel interference error is: , is the channel interference error, is the expected channel quality coefficient, To actually measure the channel quality; The calculation logic of the second packet loss rate is: , is the second packet loss rate, is the first packet loss rate, To compensate for the intensity adjustment coefficient, is the adjustment coefficient, is the adjustment index.

8. The tire pressure monitoring system packet loss rate measurement method according to claim 1, characterized in that: Step S4 includes: Sort the second packet loss rate sequence corresponding to the serial number of the received data packet in ascending order, and perform outlier detection on the second packet loss rate sequence according to a preset anomaly scoring function; Correcting the outliers according to a preset correction function to obtain a corrected second packet loss rate sequence; The corrected second packet loss rate sequence is smoothed to generate a third packet loss rate.

9. The tire pressure monitoring system packet loss rate measurement method according to claim 8, characterized in that: The calculation logic of the anomaly scoring function is: , is the anomaly scoring function, The first The rank function of the second packet loss rate, is the second packet loss rate sequence length, To adjust the parameters, is the exponential parameter; The calculation logic of the correction function is: , is the corrected second packet loss rate, is the neighborhood interval of the outlier, , is the local kernel regulation parameter, is the decay exponent, is the integral variable; The calculation logic of smoothing is: , is the third packet loss rate, is the target data index, is the length of the integration interval, , is the anomaly scoring threshold, is the sliding bandwidth parameter, is the modulation index, is the integration variable, is the normalization factor, .

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