Method for measuring packet loss rate of tire pressure monitoring system

Abstract

The 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 and comprises the following steps: constructing a physical communication model of the tire pressure monitoring system; comparing the serial numbers of the original data packet and the accepted data packet with an expected serial number, and calculating to obtain a first packet loss rate; constructing a channel compensation model according to the vehicle state data, calculating a channel interference error according to the physical communication model and the channel compensation model, compensating the first packet loss rate, and generating a second packet loss rate; and carrying out statistical analysis on the second packet loss rate through an abnormal value detection algorithm, identifying and correcting a measurement error caused by transient interference or external factors, carrying out smooth processing to generate a third packet loss rate, and displaying the third packet loss rate as the tire pressure monitoring system packet loss rate. A physical communication model is combined with vehicle state data to construct a channel compensation model, an anomaly detection and correction algorithm is further adopted, smooth processing is combined to realize continuous nonlinear smoothing of the packet loss probability, and the authenticity of final output data is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of packet loss rate measurement, and specifically 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 automotive safety, fuel economy, and environmental protection requirements, tire pressure monitoring systems (TPMS) have been widely used in modern vehicles. This system effectively prevents safety accidents and vehicle performance degradation caused by abnormal tire pressure by monitoring tire pressure in real time. However, during the dynamic driving of a vehicle, due to the combined effects of vehicle structure, tire dynamic characteristics, and environmental interference, wireless communication channels often exhibit phenomena such as multipath fading, signal interference, vibration, and transient external interference, resulting in packet loss during data transmission, thereby affecting monitoring accuracy and system stability.

[0003] Currently, the existing technology mainly calculates the packet loss rate using a simple method of comparing packet sequence numbers. However, this method often ignores the channel interference effects caused by vehicle motion state, tire rotation, and vehicle speed changes. At the same time, for abnormal data caused by transient interference or external environmental factors, the existing technology is difficult to effectively identify and correct, resulting in a large uncertainty in the statistical results of the packet loss rate. Especially under working conditions such as high-speed driving, complex road conditions, or severe vehicle vibration, the traditional method cannot fully compensate for the non-linear and complex interference in the wireless channel and cannot meet the requirements of high-precision monitoring. Summary of the Invention

[0004] Based on the above-mentioned shortcomings of the existing technology, the purpose 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 technical problems.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for measuring the packet loss rate of a tire pressure monitoring system, including: S1: Construct a physical communication model of the tire pressure monitoring system according to the vehicle structure and tire dynamic characteristics; S2: Compare the sequence numbers of the original data packet and the received data packet with the expected sequence number, and calculate and obtain the first packet loss rate; S3: Construct a channel compensation model according to the vehicle state data. According to the physical communication model and the channel compensation model, calculate the channel interference error, and compensate the first packet loss rate according to the channel interference error to generate the second packet loss rate; S4: Perform statistical analysis on the second packet loss rate through an outlier detection algorithm, identify and correct the measurement error caused by transient interference or external factors, perform smoothing processing to generate the third packet loss rate, and display the third packet loss rate as the packet loss rate of the tire pressure monitoring system.

[0006] The present invention is further configured such that step S1 includes: Construct a local two-dimensional coordinate system of 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 a non-linear attenuation function, integrate the multipath effect, and calculate the channel transfer function; Calculate the instantaneous phase correction factor through non-linear phase perturbation based on the vibration angular frequency and instantaneous rotation angle of the tire; Construct a physical communication model of the tire pressure monitoring system according to the radiation field distribution function, the channel transfer function, and the instantaneous phase correction factor.

[0007] The present invention is further configured such that the logical relationship of the relative offset between the tire center position and the sensor is: , is the global coordinate of the sensor , is the center coordinate of the tire , is the rotation matrix, is the tire 's instantaneous rotation angle, is the fixed offset of the sensor in the local coordinate system of the tire; The calculation logic of the radiation field distribution function is: , is the radiation field distribution function, and are the polar angle and azimuth angle where the antenna direction is located, is the wave number corresponding to the operating frequency of the sensor, 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 effective paths, is the th path's geometric distance, is the th path's phase constant, is the th path's attenuation function, , is the th path's attenuation factor, is the scale parameter, is the exponential parameter describing the attenuation characteristic; The calculation logic of the instantaneous phase correction factor is as follows: , 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, is the moment of the instantaneous rotation angle; 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.

[0008] The present invention is further configured such that step S2 includes: Construct an expected sequence number according to the sequence number of the original data packet and the data transmission convention; Construct a missing interval according to the expected sequence number and the sequence number of the received data packet, and calculate the missing contribution function according to the missing interval; Aggregate the missing contributions of each missing interval, calculate the total missing error coefficient, and perform normalization to obtain the first packet loss rate.

[0009] The present invention is further configured such that the sequence number of the original data packet is denoted as: , is the set of sequence numbers of the original data packets, is the sequence number of the th data packet, is the number of original data packets; the sequence number of the received data packet is denoted as: , is the set of sequence numbers of the received data packets, is the sequence number of the th data packet, is the number of received data packets; the expected sequence number is: , is the set of expected sequence numbers, is the sequence number of the th data packet, is the sequence number of the transmitted data packet, is the number of transmitted data packets; 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; The total missing error coefficient is: , is the total missing error coefficient; the first packet loss rate is: , is the first packet loss rate.

[0010] The present invention is further configured such that the vehicle state data includes vehicle speed and tire angular velocity.

[0011] The present invention is further configured such that step S3 includes: Construct a channel compensation model based on the vehicle speed and tire angular velocity, and calculate the channel compensation factor; Calculate the function value according to the physical communication model, and take the amplitude as the expected channel quality coefficient; Calculate the channel interference error based on the expected channel quality coefficient, the actually measured channel quality, and the channel compensation factor; Compensate the first packet loss rate according to the channel interference error to generate the second packet loss rate.

[0012] 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 tiny constant; The calculation construction logic of the channel interference error is: , is the channel interference error, is the expected channel quality coefficient, is the actually measured channel quality; The calculation construction logic of the second packet loss rate is: , is the second packet loss rate, is the first packet loss rate, is the compensation intensity adjustment coefficient, is the adjustment coefficient, is the adjustment index.

[0013] The present invention is further configured such that step S4 includes: Sort the second packet loss rate sequence corresponding to the sequence numbers of the received data packets in ascending order, and perform outlier detection on the second packet loss rate sequence according to a preset anomaly scoring function; Correct the outliers according to a preset correction function to obtain a corrected second packet loss rate sequence; Smoothing the corrected second packet loss rate sequence to generate a third packet loss rate.

[0014] The present invention is further configured such that the calculation logic of the anomaly scoring function is: , is the anomaly scoring function, is the rank function of the th second packet loss rate in the sequence, is the length of the second packet loss rate sequence, is the adjustment parameter, 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 adjustment parameter, is the attenuation exponent, is the integration variable; 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 exponent, is the integration variable, is the normalization factor, .

[0015] The present invention provides a method for measuring the packet loss rate of a tire pressure monitoring system. By constructing a physical communication model of the tire pressure monitoring system according to the vehicle structure and tire dynamic characteristics; comparing the sequence numbers of the original data packets and the received data packets with the expected sequence numbers, calculating and obtaining the first packet loss rate; constructing a channel compensation model according to the vehicle state data, calculating the channel interference error according to the physical communication model and the channel compensation model, compensating the first packet loss rate according to the channel interference error to generate the second packet loss rate; performing statistical analysis on the second packet loss rate through an outlier detection algorithm, identifying and correcting the measurement errors caused by transient interference or external factors, performing smoothing processing to generate the third packet loss rate, and displaying the third packet loss rate as the packet loss rate of the tire pressure monitoring system. The beneficial effects generated include: 1. High-precision packet loss rate measurement: By constructing a physical communication model based on the vehicle structure and tire dynamic characteristics, and using the comparison between the original data packet and the received data packet sequence number, the expected sequence can be accurately constructed and the preliminary packet loss rate can be calculated, thus realizing the accurate quantification of data transmission loss.

[0016] 2. Effective channel interference compensation: Combining vehicle state data to construct a channel compensation model, and non-linearly compensating the preliminary packet loss rate by calculating the channel interference error, effectively canceling the channel fluctuations caused by vehicle movement and tire rotation, and improving the robustness of data transmission.

[0017] 3. Abnormal data detection and correction: Using an outlier detection algorithm to statistically analyze the compensated packet loss rate, timely identifying and correcting measurement errors caused by transient interference or external environmental factors, ensuring that abnormal data will not have too much impact on the overall statistical results.

[0018] 4. Smoothing processing and data stability: Continuously non-linearly smoothing the corrected packet loss rate through a smoothing processing method, so that the finally output third packet loss rate can truly reflect the long-term stability of data transmission.

[0019] The above description is only an overview of the technical solution of this application. In order to be able to more clearly understand the technical means of this application, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically illustrates the specific implementation manners of this application. Brief Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 It is a flowchart of a method for measuring the packet loss rate of a tire pressure monitoring system shown in an exemplary embodiment of the present invention. Detailed Embodiments

[0021] The following will illustrate the embodiments of the present invention with reference to the drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, rather than for limiting the protection scope of the present invention.

[0022] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention schematically. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0023] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0024] A method for measuring the packet loss rate of a tire pressure monitoring system, as Figure 1 shown, includes: S1: Construct a physical communication model of the tire pressure monitoring system according to the vehicle structure and tire dynamic characteristics; S2: Compare the sequence numbers of the original data packet and the received data packet with the expected sequence number, and calculate and obtain the first packet loss rate; S3: Construct a channel compensation model according to the vehicle state data, calculate the channel interference error according to the physical communication model and the channel compensation model, and compensate the first packet loss rate according to the channel interference error to generate the second packet loss rate; S4: Perform statistical analysis on the second packet loss rate through an outlier detection algorithm, identify and correct the measurement errors caused by transient interference or external factors, perform smoothing processing to generate the third packet loss rate, and display the third packet loss rate as the packet loss rate of the tire pressure monitoring system.

[0025] The present invention is further configured such that step S1 includes: Construct a local two-dimensional coordinate system of the vehicle, and define the relative offset between the tire center position and the sensor; The present invention is further configured such that the logical relationship of the relative offset between the tire center position and the sensor is: , is the global coordinate of the sensor, is the center coordinate of the tire, is the rotation matrix, is the instantaneous rotation angle of the tire, is the fixed offset of the sensor in the local coordinate system of the tire; Specifically, establish a two-dimensional coordinate system inside the vehicle, where the origin is selected as the vehicle center or a certain reference point, and is denoted as , is the The fixed coordinates of the center in the global coordinate system, and is denoted as , the relative offset of the sensor is the fixed position offset vector of the sensor relative to the tire center in the local coordinate system of the tire. Denote as . When is the instantaneous rotation angle of the tire , the rotation matrix is . The above calculation logic describes the process of the sensor converting from a fixed offset to the global coordinate under the action of tire rotation, which can reflect the influence of tire rotation on the sensor position in real time, thereby ensuring the global positioning accuracy of the sensor; According to the radiation direction vector of the sensor antenna, calculate the radiation field distribution function; the calculation logic of the radiation field distribution function is: , is the radiation field distribution function, and are the polar angle and azimuth angle where the antenna direction is located, is the wave number corresponding to the working frequency of the sensor, is the imaginary unit, is the direction vector of the sensor antenna, is the unit direction vector; specifically, the radiation field distribution function describes the function of the radiation energy distribution of the sensor antenna in the direction of a specific polar angle and azimuth angle . The wave number is calculated from the working frequency of the sensor, , 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, and its value is determined by the antenna design. In an embodiment of the present invention, the direction vectors of all sensor antennas are uniformly set. The unit direction vector is . The polar angle and azimuth angle and where the antenna direction is located respectively determine the angles of the antenna direction in the horizontal and vertical planes. The above calculation logic determines the radiation field distribution in all directions in space through the direction vector of the sensor antenna, thereby accurately characterizing the directivity characteristics of the antenna. By taking the inner product of the actual radiation direction of the antenna and the unit direction vector and modulating the phase using the wave number, a radiation field distribution function that includes both amplitude and phase information is obtained, which can accurately reflect the radiation characteristics of the antenna in different angular domains and provide accurate input for wireless channel modeling; Characterize the propagated signal according to the path integral method, calculate the attenuation of a single path through a non-linear attenuation function, integrate the multipath effect, and calculate the channel transfer function; the calculation logic of the channel transfer function is as follows: , is the channel transfer function, is the number of effective paths, is the geometric distance of the path, is the phase constant of the path, is the attenuation function of the , is the attenuation factor of the path, is the scale parameter, is the exponential parameter describing the attenuation characteristic; specifically, the channel transfer function represents the comprehensive influence of the entire wireless channel on the signal, which is composed of the superposition of the attenuation and phase changes of all effective paths. The attenuation function of the path describes the non-linear relationship between the signal strength and the propagation distance on path , and the phase constant represents the phase change caused by the distance and medium characteristics during the propagation of path . Its multiplication by the geometric distance gives the phase shift. The contributions of the signals of each path are multiplied in the form of a product to obtain the overall transfer function. The above calculation logic is based on the path integral method, considering the propagated signal as composed of multiple independent paths, and each path contributes its attenuation and phase information. For each path, a non-linear attenuation function is used to describe the attenuation of its signal strength with distance, and the phase change during path propagation is also considered; the contributions of each path are integrated in the form of a product to form the overall channel transfer function, thus truly reflecting the signal characteristics in the multipath propagation environment; , 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, is the The instantaneous rotation angle; specifically, the above calculation logic aims to correct the phase error of the transmission signal by using the dynamic phase perturbation generated by tire vibration and rotation. The tire vibration is described as a sine function, and its vibration frequency and initial phase reflect the vibration periodic characteristics. At the same time, the modulation of the phase by tire rotation is introduced, and the vibration amplitude is non-linearly modulated through a cosine term based on the instantaneous rotation angle, thereby forming an overall instantaneous phase correction factor to compensate for the phase shift caused by mechanical vibration and rotation. Construct a physical communication model of the tire pressure monitoring system according to the radiation field distribution function, the channel transmission function, and the 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 integration element. Specifically, the angular domain of the antenna radiation integral refers to the angular domain of the antenna radiation integral, covering all effective radiation directions. The angular domain integration element represents the area element of a tiny region within the angular domain, which is used to accumulate the contributions in each direction during the integration. The above physical communication model aims to comprehensively consider the antenna directivity, the multipath propagation effect, and the instantaneous phase perturbation, and construct an overall model through three key modules. First, within the antenna radiation integral domain, the radiation contributions in each direction are integrated according to the radiation field distribution function of the sensor antenna to obtain the overall radiation characteristics of the antenna. Second, the non-linear attenuation and phase change during the multipath propagation process are integrated through the channel transmission function ; finally, the dynamic phase error is compensated in real time by using the instantaneous phase correction factor to ensure that the model can accurately reflect the actual transmission conditions.

[0026] The present invention is further configured such that step S2 includes: Construct an expected sequence number according to the sequence number of the original data packet and the data transmission convention; the present invention is further configured such that the sequence number of the original data packet is denoted as: , is the set of sequence numbers of the original data packets, is the sequence number of the th data packet, , is the set of sequence numbers of the received data packets, is the sequence number of the th received data packet, , is the set of expected sequence numbers, is the The sequence number of a data packet, is the sequence number of the transmitted data packet, and is the number of transmitted data packets; specifically, based on the data transmission convention, the calculation logic uses the sequence number of the original data packet as the ideal transmission order, and constructs the expected sequence number set by incrementing from a fixed starting sequence number simultaneously, the actual data packet sequence number sets are extracted from the sender and the receiver respectively and ; ; 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 according to 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 represents the number of missing data packets between the sequence actually obtained at the receiver and the expected sequence during data transmission. Different values correspond to different missing situations, such as starting missing, middle consecutive missing, and ending missing. The missing contribution function converts the number of missing data packets in each missing interval into a contribution value between 0 and 1 through a non-linear mapping. When the number of missing packets is small, the exponential function value is close to 1, making small; while when the number of missing packets increases, tends to 1, emphasizing the impact of large missing intervals. Based on comparing the expected sequence number with the actual received sequence number, the missing data packet interval during data transmission is determined. By defining different situations of the missing interval , the number of missing data packets in each segment can be accurately quantified, and then a non-linear function is used to assign weights to each missing interval to form the missing contribution function; Aggregate the missing contributions of each missing interval, calculate the total missing error coefficient, and perform normalization 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: , is the first packet loss rate; specifically, by converting the missing situation of data packets in each missing interval into a contribution value, then aggregating these contribution values and performing normalization processing, the first packet loss rate is finally obtained. First, for each missing interval calculate the missing contribution function , and then sum all the missing contributions to obtain the total missing error coefficient ; Finally, normalize the total value, i.e., divide it by the total number of expected data packets to obtain a first packet loss rate representing the degree of transmission loss , reflecting the cumulative loss situation during the entire data transmission process.

[0027] The present invention is further configured such that the vehicle state data includes vehicle speed and tire angular velocity.

[0028] The present invention is further configured such that step S3 includes: Construct a channel compensation model based on the vehicle speed and tire angular velocity, and calculate a channel compensation factor; 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 is a positive number, used to adjust the weight of the vehicle speed's influence on the channel; the larger the value, the faster the compensation factor decreases, and the modulation index and are positive numbers, respectively controlling the degree of the non-linear influence of the vehicle speed and tire angular velocity on the compensation factor. The adjustment index is a positive number, used to further adjust the sensitivity of the overall non-linear mapping, making the compensation factor smoother or steeper in the face of different motion states. The small constant is a protection parameter to prevent the denominator from being zero, and its value is less than , based on the vehicle speed and the tire angular velocity two key dynamic parameters, construct the channel compensation factor through non-linear mapping, which is used to adjust the channel attenuation and interference effects caused by vehicle movement. Adopting an exponential decay form, combine the positive influence of the vehicle speed with the buffering effect of the tire angular velocity, and output a compensation factor between 0 and 1 to dynamically compensate for the adverse effects during channel measurement and data transmission; Calculate the function value according to the physical communication model, and take the amplitude as the expected channel quality coefficient; calculate the channel interference error based on the expected channel quality coefficient, the actually 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, To actually measure the channel quality; specifically, the actual measured channel quality The amplitude of the channel signal measured by the actual receiving device reflects the channel condition in the real 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 combining the dynamic compensation caused by the vehicle motion state. Specifically, the expected channel quality coefficient (taking its amplitude) is obtained from the physical communication model and then compared with the actually measured channel quality. The absolute value of the difference between the two reflects the deviation of the channel after being interfered. This difference is then multiplied by the channel compensation factor composed of the vehicle speed and the tire angular velocity to calculate the final channel interference error; Compensate the first packet loss rate according to the channel interference error to generate the 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, is the compensation intensity adjustment coefficient, is the adjustment coefficient, is the adjustment exponent. Specifically, the compensation intensity adjustment coefficient controls the overall amplitude of the influence of the channel interference error in the compensation function. This parameter determines the adjustment strength of the compensation factor on the first packet loss rate under the same interference level; the larger the value, the more obvious the compensation effect, so that the second packet loss rate drops more. The value range is [0.1, 10]. The adjustment coefficient is used to amplify or reduce the influence amplitude of the channel interference error in the compensation function. This parameter determines the scale of the interference error before entering the exponential function, so that the system can appropriately adjust the sensitivity to smaller or larger errors. The value range is [0.01, 10]. The adjustment exponent defines the degree of non-linear mapping of the interference error in the compensation function, that is, determines the curve shape of the response of the compensation function to the interference error. The value range is [1, 3]. By using the channel interference error to dynamically compensate the initially calculated first packet loss rate, the corrected second packet loss rate is generated. Non-linear modulation is adopted, and the first packet loss rate is divided by the compensation factor, which becomes larger as the channel interference error increases, so as to appropriately correct the packet loss rate to more realistically reflect the interference influence in data transmission; The present invention is further set to, step S4 includes: Sort the second packet loss rate sequence corresponding to the serial numbers of the received data packets in ascending order, and perform outlier detection on the second packet loss rate sequence according to the preset outlier scoring function; the present invention is further set to, the calculation logic of the outlier scoring function is: , is the outlier scoring function, is the The rank function of the second packet loss rate is the length of the second packet loss rate sequence is the adjustment parameter is the exponential parameter; specifically, the rank function of the th second packet loss rate in the sequence represents the rank corresponding to the th data in the sorted sequence, that is, the position of the data in the sequence. The adjustment parameter controls the sensitivity of the anomaly scoring function to the normalized rank, and its value range is [0.1, 5]. The exponential parameter determines the non - linear degree of the normalized rank mapping, and its value range is [0.1, 3]. By ascendingly sorting the second packet loss rate sequence corresponding to the received data packets, and then using a preset anomaly scoring function, the normalized rank of the sorted data is mapped to an anomaly score value. This anomaly score reflects the position of each data point in the overall distribution, thereby enabling the identification of data points that are at the edge in the sorting. The anomaly scoring function adopts exponential mapping, achieving a non - linear amplification effect, such that data with high ranks obtain higher scores, while data with low ranks have lower scores; Correct the outliers 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 adjustment parameter is the decay exponent is the integration variable; specifically, the local kernel adjustment parameter determines the decay speed of the weight function, and its value range is [0.1, 10], The decay exponent controls the non - linear mapping degree of the deviation from the outlier in the kernel function, and its value range is [0.1, 3]. Through a preset local correction function, the outliers in the second packet loss rate sequence are corrected. The specific approach is to perform weighted integration on the data within the neighborhood interval of the outlier with the local kernel function as the weight to calculate a local smoothing value, thereby replacing the original abnormal data. This process can be regarded as local kernel smoothing or weighted averaging, aiming to suppress extreme data caused by transient interference or occasional anomalies, so that the corrected second packet loss rate sequence is more in line with the actual transmission situation; Perform smoothing processing on the corrected second packet loss rate sequence to generate the third packet loss rate. The calculation logic of the smoothing processing 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 score 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 integration interval length is the integration range selected on both sides of the target data index , that is, the local window where the smoothing kernel acts, and the sliding bandwidth parameter is the scale parameter that controls the decay rate of the smoothing kernel function, and the modulation index is used to control the distance in the kernel function, and the deviation of the weight affected by the non - linear index, and the value range is [1, 3]. The normalization factor is used to ensure that the sum of the integration weights is 1, so that the smoothing result is the weighted average of the local data. Through the local kernel smoothing technology, the corrected second packet loss rate sequence is smoothed to generate the third packet loss rate. The basic idea is to select an integration interval near each target data index , and weight - average the corrected values within this interval according to the distance . The weight is given by an exponential kernel function, which can quickly attenuate the influence of data farther away on the smoothing result, ensuring that the smoothed result can eliminate noise fluctuations and retain local trends; The third packet loss rate is displayed as the packet loss rate of the tire pressure monitoring system. After anomaly detection, local correction, and kernel smoothing processing, the third packet loss rate can more accurately reflect the actual packet loss situation during data transmission. Therefore, this third packet loss rate is used as the final packet loss rate of the tire pressure monitoring system for display, providing a reliable basis for system performance evaluation, alarm triggering, and subsequent channel optimization.

[0029] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. 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 programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. 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 wired (such as infrared, wireless, microwave, etc.) means. 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 collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0030] It should be understood that the term "and / or" in this document is merely a description of the associated relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context.

[0031] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0032] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0033] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0034] Those skilled in the art can 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 foregoing method embodiments and will not be elaborated herein.

[0035] In the several embodiments provided in this application, it should be understood that the disclosed systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0036] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0038] When the above-described 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 this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0039] As described above, the above are only specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for measuring the packet loss rate of a tire pressure monitoring system, characterized in that, Including: S1: Construct a physical communication model of the tire pressure monitoring system according to the vehicle structure and tire dynamic characteristics; S2: Compare the sequence numbers of the original data packet and the received data packet with the expected sequence number, and calculate to obtain the first packet loss rate; S3: Construct a channel compensation model according to the vehicle state data, calculate the channel interference error according to the physical communication model and the channel compensation model, and compensate the first packet loss rate according to the channel interference error to generate the second packet loss rate; S4: Statistically analyze the second packet loss rate through an outlier detection algorithm, identify and correct the measurement errors caused by transient interference or external factors, perform smoothing processing to generate the third packet loss rate, and display the third packet loss rate as the packet loss rate of the tire pressure monitoring system.

2. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 1, characterized in that Step S1 includes: Construct a local two-dimensional coordinate system of 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 a non-linear attenuation function, integrate the multi-path effect, and calculate the channel transfer function; Calculate the instantaneous phase correction factor through non-linear phase perturbation according to the vibration angular frequency and instantaneous rotation angle of the tire; Construct a physical communication model of the tire pressure monitoring system according to the radiation field distribution function, the channel transfer function and the instantaneous phase correction factor.

3. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 2, characterized in that, The logical relationship between the relative offset of the tire center position and the sensor is as follows: , is the global coordinate of the sensor , is the center coordinate of the tire , is the rotation matrix is the instantaneous rotation angle of the tire , is the fixed offset of the sensor in the local coordinate system of the tire; The calculation logic of the radiation field distribution function is as follows: , is the radiation field distribution function, and are the polar angle and azimuth angle where the antenna direction is located, is the wave number corresponding to the working frequency of the sensor, 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 as follows: , is the channel transfer function, is the number of effective paths, is the geometric distance of the th path, is the phase constant of the th path, is the attenuation function of the , is the attenuation factor of the th path, is the scale parameter, is the exponential parameter describing the attenuation characteristic; The calculation logic of the instantaneous phase correction factor is as follows: , 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, is the moment of the instantaneous rotation angle; 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.

4. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 1, characterized in that, Step S2 includes: Construct an expected sequence number according to the sequence number of the original data packet and the data transmission convention; Construct a missing interval according to the expected sequence number and the sequence number of the received data packet, and calculate the missing contribution function according to the missing interval; Aggregate the missing contributions of each missing interval, calculate the total missing error coefficient, and normalize it to obtain the first packet loss rate.

5. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 4, characterized in that Denote the sequence number of the original data packet as: , is the set of sequence numbers of the original data packets, is the sequence number of the th data packet, is the number of original data packets; Denote the sequence number of the received data packet as: , is the set of sequence numbers of the received data packets, is the sequence number of the th data packet, is the number of received data packets; The expected sequence number is: , is the set of expected sequence numbers, is the sequence number of the th data packet, is the sequence number of the transmitted data packet, is the number of transmitted data packets; 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; The total missing error coefficient is: , is the total missing error coefficient; The first packet loss rate is: , is the first packet loss rate.

6. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 1, characterized in that, The vehicle state data includes the vehicle speed and the tire angular velocity.

7. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 6, characterized in that, Step S3 includes: Construct a channel compensation model according to the vehicle speed and the tire angular velocity, and calculate the channel compensation factor; Calculate the function value according to the physical communication model, and take the amplitude as the expected channel quality coefficient; Calculate the channel interference error according to the expected channel quality coefficient, the actually measured channel quality and the channel compensation factor; Compensate the first packet loss rate according to the channel interference error to generate the second packet loss rate.

8. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 7, characterized in that, The construction logic of the channel compensation model is as follows: , is the channel compensation factor, is the amplitude adjustment parameter, is the vehicle speed, is the angular velocity of the tire, and is the modulation index, is the adjustment index, is a tiny constant; The calculation logic of the channel interference error is as follows: , is the channel interference error, is the expected channel quality coefficient, is the actually measured channel quality; The calculation logic of the second packet loss rate is as follows: , is the second packet loss rate, is the first packet loss rate, is the compensation intensity adjustment coefficient, is the adjustment coefficient, is the adjustment exponent.

9. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 1, characterized in that, Step S4 includes: Sort the second packet loss rate sequence corresponding to the sequence numbers of the received data packets in ascending order, and perform outlier detection on the second packet loss rate sequence according to a preset anomaly scoring function; Correct the outliers according to a preset correction function to obtain the corrected second packet loss rate sequence; Perform smoothing processing on the corrected second packet loss rate sequence to generate the third packet loss rate.

10. A method for measuring the packet loss rate of a tire pressure monitoring system according to claim 9, characterized in that, The calculation logic of the anomaly scoring function is as follows: , is the anomaly scoring function, is the rank function of the -th second packet loss rate in the sequence, is the length of the second packet loss rate sequence, is the adjustment parameter, is the exponential parameter; The calculation logic of the correction function is as follows: , is the second packet loss rate after correction, is the neighborhood interval of the outlier, , is the local kernel adjustment parameter, is the attenuation exponent, is the integration variable; The calculation logic of the smoothing process is as follows: , is the third packet loss rate, is the target data index, is the integral interval length, is the corrected second packet loss rate, , is the anomaly score threshold, is the sliding bandwidth parameter, is the modulation index, is the integral variable, is the normalization factor, .

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