Ultrasonic threshold calibration method and device, electronic equipment and computer storage medium

Abstract

The present application relates to ultrasonic radar technology, provide a kind of ultrasonic threshold calibration method, device, electronic equipment and computer storage medium, the method comprises: the target detection distance is divided into multiple continuous detection intervals;According to the initial threshold function of each detection interval that is fitted out according to preset threshold model, preset threshold model is obtained according to the multiple historical sampling echo points in target detection distance, the initial threshold function of each detection interval that is fitted out according to the modeling of target detection distance segmentation;For the last detection interval, the initial threshold function of each detection interval is sequentially smoothed according to the initial threshold function of the next detection interval adjacent to each detection interval, and the segmented threshold function of each detection interval is obtained.The present application improves the accuracy of target detection by calibrating reasonable ultrasonic threshold.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic radar technology, and more specifically, to an ultrasonic threshold calibration method, apparatus, electronic device, and computer storage medium. Background Technology

[0002] Ultrasonic radar is a sensor based on ultrasonic echo ranging. It determines the target distance by emitting high-frequency sound waves and receiving the echo signals reflected from the target, and then calculating the propagation time of the ultrasonic waves. It is currently widely used in target detection for parking assistance and blind spot monitoring. The calibration of the ultrasonic threshold is crucial to ensuring the accuracy of target detection.

[0003] The existing fixed ultrasonic threshold cannot adapt to dynamic environmental changes, which can easily lead to misjudgment of target detection in complex and ever-changing environments. Summary of the Invention

[0004] The purpose of this invention is to provide an ultrasonic threshold calibration method, apparatus, electronic device, and computer storage medium, which improves the accuracy of target detection by calibrating a reasonable ultrasonic threshold.

[0005] The embodiments of the present invention can be implemented as follows:

[0006] In a first aspect, the present invention provides an ultrasonic threshold calibration method, the method comprising:

[0007] The target detection range is divided into multiple continuous detection intervals;

[0008] An initial threshold function is fitted to each of the detection intervals according to a preset threshold model, wherein the preset threshold model is obtained by segmenting the target detection distance based on multiple historical sampling echo points within the target detection distance.

[0009] For non-last detection intervals, the initial threshold function of each detection interval is smoothed sequentially based on the initial threshold function of the next detection interval adjacent to each detection interval, to obtain the piecewise threshold function of each detection interval.

[0010] In an optional implementation, each detection interval includes a start point and an end point, and the step of fitting an initial threshold function for each detection interval according to a preset threshold model includes:

[0011] Based on the preset threshold model, the threshold for the start point and the threshold for the end point of each detection interval are calculated respectively.

[0012] Based on the threshold values ​​of the starting and ending points of each detection interval and the interval distance of each detection interval, an initial threshold function for each detection interval is fitted.

[0013] In an optional implementation, the step of smoothing the initial threshold function of each detection interval according to the initial threshold function of the next detection interval adjacent to each detection interval to obtain the piecewise threshold function of each detection interval includes:

[0014] For any adjacent first detection interval and second detection interval, a weighted process is performed according to a preset weight function, the initial threshold function of the first detection interval and the initial threshold function of the second detection interval to obtain the piecewise threshold function of the first detection interval, and the starting point of the second detection interval is the ending point of the first detection interval.

[0015] Each pair of adjacent detection intervals is taken as the first detection interval and the second detection interval, respectively, to obtain the segmented threshold function of each detection interval.

[0016] In an optional implementation, the historical sampling echo points are sampling points collected from the echoes of ultrasonic waves emitted by the sensor, and the method further includes:

[0017] Obtain the multiple historical sampling echo points;

[0018] According to a preset reference distance, the target detection distance is divided into multiple consecutive reference intervals. The number of historical sampling echo points included in each reference interval is greater than the preset number. The reference intervals and the detection intervals correspond one-to-one.

[0019] Generate the noise distribution of historical sampled echo points within each of the aforementioned reference interval segments;

[0020] Based on the noise distribution of each reference interval, the preset distance attenuation model, and the characteristic parameters of the sensor, a reference threshold function for each reference interval is constructed, and finally the preset threshold model including all reference threshold functions is obtained.

[0021] In an optional implementation, the step of generating the noise distribution of historical sampled echo points within each of the reference interval segments includes:

[0022] Calculate the mean and standard deviation of historical sampled echo points within each of the aforementioned reference intervals;

[0023] The noise distribution of historical sampled echo points within each reference interval is determined based on the mean and standard deviation of each reference interval.

[0024] In an optional implementation, the characteristic parameters of the sensor include the signal-to-noise ratio. Before the step of constructing a reference threshold function for each reference interval based on the noise distribution of each reference interval, a preset distance attenuation model, and the characteristic parameters of the sensor, and finally obtaining the preset threshold model including all reference threshold functions, the method includes:

[0025] Noise points and signal points are selected from multiple historical sampled echo points within the target detection range;

[0026] The signal-to-noise ratio is the ratio of the minimum signal power to the maximum noise power, where the minimum signal power is the minimum power of all signal points and the maximum noise power is the maximum power of all noise points.

[0027] In an optional implementation, the method further includes:

[0028] The piecewise threshold function for each of the detection intervals is verified to obtain the verification results;

[0029] If the verification result of any of the detection intervals fails, the preset threshold model is readjusted until the verification results of all the detection intervals pass.

[0030] In a second aspect, the present invention provides an ultrasonic threshold calibration device, the device comprising:

[0031] The segmentation module is used to divide the target detection range into multiple continuous detection intervals;

[0032] The fitting module is used to fit an initial threshold function for each detection interval according to a preset threshold model. The preset threshold model is obtained by segmenting the target detection distance based on multiple historical sampling echo points within the target detection distance.

[0033] The processing module is used to smooth the initial threshold function of each detection interval according to the initial threshold function of the next detection interval adjacent to each detection interval for non-last detection intervals, so as to obtain the segmented threshold function of each detection interval.

[0034] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory being used to store a program, and the processor being used to implement the ultrasonic threshold calibration method as described in the first aspect when executing the program.

[0035] Fourthly, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the ultrasonic threshold calibration method as described in the first aspect.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] By dividing the target detection range into multiple continuous detection intervals, and using a preset threshold model obtained by segmenting the target detection range based on multiple historical sampling echo points within the target detection range, an initial threshold function is fitted to each detection interval. Then, for each detection interval other than the last one, the initial threshold function of the next adjacent detection interval is used to smooth each detection interval, ultimately obtaining a segmented threshold function for each detection interval. Since the preset threshold model is obtained by segmenting based on historical sampling echo points, the fitted initial threshold function of each detection interval is best matched to that detection interval, avoiding the impact of unreasonable thresholds on the accuracy of target detection. By using the initial threshold function of the next adjacent detection interval to smooth the initial threshold function of each detection interval, misjudgments in the final target detection are avoided due to threshold jumps at the boundary between two adjacent detection intervals. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a logic example diagram for the automated calibration of ultrasonic thresholds provided in this embodiment.

[0040] Figure 2 A flowchart of the ultrasonic threshold calibration method provided in this embodiment Figure 1 .

[0041] Figure 3 A flowchart of the ultrasonic threshold calibration method provided in this embodiment Figure 2 .

[0042] Figure 4 This is a block diagram illustrating the ultrasonic threshold calibration device provided in this embodiment.

[0043] Figure 5 This is a block diagram of the electronic device provided in this embodiment.

[0044] Icons: 10-Electronic device; 11-Processor; 12-Memory; 13-Bus; 100-Ultrasonic threshold calibration device; 110-Division module; 120-Fitting module; 130-Processing module; 140-Construction module; 150-Verification module. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0046] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0047] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0048] In the description of this invention, it should be noted that if terms such as "upper," "lower," "inner," or "outer" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed, they are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0049] Furthermore, the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0050] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0051] Traditional ultrasonic threshold calibration methods that rely on manual operation typically involve: 1. The operator placing the target obstacle (such as a 75cm PVC pipe) at a specific location, collecting data point by point, and calculating the echo threshold; 2. Driving the vehicle to various road surfaces to collect static and dynamic data, and calculating the echo threshold. This method has at least the following drawbacks: 1. Subjective error: Differences in the operator's experience may affect the calibration results; 2. Low efficiency: A single calibration can take up to two weeks, which is difficult to keep up with the current pace of technological advancements.

[0052] In addition, when ultrasonic radar operates in complex environments (such as rain, snow, and various reflectors), the echo signal is easily affected by noise (such as environmental clutter and circuit noise), leading to misjudgment of target detection. Furthermore, the fixed threshold values ​​calibrated in the traditional way cannot adapt to dynamic environmental changes.

[0053] In view of this, this embodiment provides an ultrasonic threshold calibration method, apparatus, electronic device, and computer storage medium, which improves the accuracy of target detection by calibrating a reasonable ultrasonic threshold. It will be described in detail below.

[0054] Please refer to Figure 1 , Figure 1 This is a logic example diagram for the automated calibration of ultrasonic thresholds provided in this embodiment. Figure 1 In China, the automated calibration of ultrasonic thresholds includes the following stages:

[0055] 1. Start-up and calibration phase: The vehicle or test platform starts the radar echo signal acquisition equipment, checks the hardware status, and initializes parameter storage and communication links;

[0056] 2. Data Acquisition Stage: Static and dynamic raw data are acquired and written to the buffer through ultrasonic radar transmission and reception; the data stream in the buffer is filtered in real time to remove some abrupt echoes, resulting in filtered echoes.

[0057] 3. Automatic threshold calculation stage: Modeling the ultrasonic threshold using the filtered echo and determining the model parameters;

[0058] 4. Threshold generation stage: Generate ultrasonic thresholds based on model parameters;

[0059] 5. Verification phase: The generated ultrasonic threshold is verified using the true value; if the verification fails, the ultrasonic threshold can be regenerated in the automatic calculation phase of the error feedback value threshold until the generated ultrasonic threshold passes the verification.

[0060] 6. Output Integration Stage: Integrate the verified ultrasonic thresholds into the ultrasonic radar application system.

[0061] This embodiment describes the ultrasonic threshold calibration method based on the above-mentioned stages. Please refer to [link / reference]. Figure 2 , Figure 2 A flowchart of the ultrasonic threshold calibration method provided in this embodiment Figure 1 The method includes the following steps:

[0062] Step S101: Divide the target detection range into multiple continuous detection intervals.

[0063] In this embodiment, the target detection range refers to the actual effective detection range of the radar. The target detection range is related to the performance of the radar itself, and different models of radar may have different effective detection ranges.

[0064] Step S102: Fit the initial threshold function for each detection interval according to the preset threshold model. The preset threshold model is obtained by segmenting the target detection distance based on multiple historical sampling echo points within the target detection distance.

[0065] In this embodiment, the detection intervals and the reference intervals used in segmented modeling are in one-to-one correspondence, and the start and end points of each detection interval are the same as the start and end points of the corresponding reference interval. For example, if there are 10 reference intervals in segmented modeling, then there are also 10 detection intervals. If the distance range of the first reference interval is 0 meters to 0.3 meters, then the distance range of the first detection interval is also 0 meters to 0.3 meters, and the remaining detection intervals are similar.

[0066] In this embodiment, the historical sampling echo points can be obtained from the sampling points where the radar measures and samples the echoes under various environmental conditions. Each sampling point can obtain corresponding echo data. The various environmental conditions can cover typical application scenarios such as different temperatures, humidity, and road surface types.

[0067] In this embodiment, the preset threshold model is obtained by segmenting multiple historical sampled echo points. Each segment corresponds to a detection interval, and each detection interval has its own threshold calculation method. Given a specific distance point, the theoretical threshold given by the model at that specific distance point can be directly calculated using the threshold calculation method corresponding to that detection interval, based on the detection interval in which that specific distance point is located. By fitting the initial threshold function for each detection interval, it is ensured that the initial threshold function of all detection intervals satisfies a monotonically decreasing trend globally, while also taking into account the impact of local noise fluctuations.

[0068] Step S103: For non-last detection intervals, the initial threshold function of each detection interval is smoothed according to the initial threshold function of the next detection interval adjacent to each detection interval, so as to obtain the piecewise threshold function of each detection interval.

[0069] In this embodiment, the piecewise threshold function for each detection interval is obtained by smoothing the initial threshold function for each detection interval. The purpose of the smoothing is to avoid target detection misjudgment caused by abrupt threshold changes at the boundary of adjacent detection intervals.

[0070] The method provided in this embodiment obtains a preset threshold model by segmenting and modeling using historical sampling echo points, thereby ensuring that the initial threshold function of each fitted detection interval best matches the detection interval, avoiding the impact of unreasonable thresholds on the accuracy of target detection. By using the initial threshold function of the next detection interval adjacent to each detection interval, the initial threshold function of each detection interval is smoothed to avoid misjudgment of the final target detection due to threshold jumps at the boundary between two adjacent detection intervals.

[0071] In an optional implementation, to make the initial threshold function of each detection interval more accurately reflect the noise environment characteristics and signal attenuation law of each detection interval, this embodiment provides a fitting method for the initial threshold function:

[0072] Based on the preset threshold model, calculate the threshold at the start point and the threshold at the end point of each detection interval.

[0073] Based on the threshold values ​​at the start and end points of each detection interval and the interval distance of each detection interval, an initial threshold function for each detection interval is fitted.

[0074] In this embodiment, the start and end points of each detection interval constitute the upper and lower limits of each detection interval. The start point is the endpoint of a detection interval closest to the sensor, and the end point is the endpoint furthest from the sensor. The interval distance of each detection interval can be calculated from the distances corresponding to the start and end points. For detection interval D... i =[d i d i+1 ), d i and d i+1 These represent the distances corresponding to the start and end points of the detection interval, D. i The interval distance can be obtained through d i+1 -d i The initial threshold function for each detection interval is used to describe the trend of ultrasonic threshold variation at different detection distances d within that detection interval.

[0075] In this embodiment, if the scene state at the time of application and at the time of model construction is not significantly different, the initial threshold function for each detection interval should be linear. In this case, linear interpolation can be used to fit the initial threshold function. For the i-th detection interval, one way to represent its initial threshold function is as follows:

[0076] ,in, Let be the initial threshold function for the i-th detection interval. and These are the threshold values ​​for the i-th detection interval and the (i+1)-th detection interval, respectively. and are the distance of the starting point of the (i + 1)-th detection interval relative to the end point of the sensor and the distance of the starting point of the i-th detection interval relative to the end point of the sensor, respectively, is the interval distance of the i-th detection interval. If the application scenario is too different from the scenario during model construction, re-model construction can be considered at this time, or polynomial fitting or look-up table method can be used for fitting.

[0077] In an optional implementation, due to modeling differences, there may be threshold jumps between adjacent detection intervals. This jump phenomenon will cause misjudgment or missed detection of target detection at the interval junction, thus affecting the measurement stability and reliability of the entire ultrasonic radar system. This embodiment provides an implementation method for smoothing the initial threshold function:

[0078] For any adjacent first detection interval and second detection interval, weighted processing is performed according to a preset weight function, the initial threshold function of the first detection interval, and the initial threshold function of the second detection interval to obtain the piecewise threshold function of the first detection interval. The starting point of the second detection interval is the ending point of the first detection interval;

[0079] Each pair of adjacent detection intervals is respectively used as the first detection interval and the second detection interval to obtain the piecewise threshold function of each detection interval.

[0080] In this embodiment, for example, D1 and D2 are the first detection interval and the second detection interval respectively. The range of D1 is [d1, d2), and the range of D2 is [d2, d3). The preset weight function dynamically adjusts the influence ratio of the initial threshold functions corresponding to the adjacent first detection interval and second detection interval according to the distance deviation. For any distance d within the first detection interval and the second detection interval, the distance deviation can be calculated by d - d c calculated, and d c is the junction point of the first detection interval and the second detection interval. For D1 and D2, d c is d2.

[0081] In this embodiment, in order to make the smoothing degree more flexible to control, a smoothing degree control parameter can be introduced to adjust the steepness of the smoothing. As an implementation method, the Sigmoid function can be used as the smoothing function, so that when d < dc, the initial threshold function of the first detection interval dominates; when d > dc, the initial threshold function of the second detection interval gradually increases its influence, and finally forms a progressive transition relationship. The smoothing function can be expressed as:

[0082] , where , is the smoothness control parameter. When d < dc, is θ i (dstart) and θ i (dc), the slope determined by these two points. When d > dc, is θ i+1 (dc) and θ i+1 (dend), the slope determined by these two points. d c is the intersection point of the first detection interval and the second detection interval. dstart is the distance corresponding to the starting point of the first detection interval, and dend is the distance corresponding to the ending point of the second detection interval.

[0083] After obtaining the piecewise threshold function for each detection interval, the threshold function of the target detection distance can be expressed in the following way:

[0084]

[0085] where, is the threshold function of the target detection distance, d is any distance within the target detection distance, , , …, are n detection intervals, , , … are the piecewise threshold functions of the n detection intervals respectively, and this function satisfies:

[0086] (1) , which is used to suppress noise and achieve high robustness. Among them, and are the mean and standard deviation of the detection interval i respectively, and s is the serial number of the detection interval i among the n detection intervals;

[0087] (2) , which is used to retain the effective signal and achieve high sensitivity;

[0088] (3) As d increases, it decreases smoothly to achieve distance adaptability.

[0089] In an optional implementation manner, in order to make the preset threshold model accurately reflect the noise distribution characteristics of each detection interval, the echo physical propagation law, and the influence of the sensor characteristic parameters on the ultrasonic threshold, and make the ultrasonic threshold predicted by the preset threshold model more in line with the actual scenario, this embodiment also provides a construction method for the preset threshold model. Please refer to Figure 3 , Figure 3 is the process example of the ultrasonic threshold calibration method provided by this embodiment Figure 2 , and this method includes the following steps:

[0090] Step S201: Obtain multiple historical sampling echo points.

[0091] In this embodiment, historical sampling echo points are sampling points that collect echoes of ultrasonic waves emitted by the sensor. Radar is a type of sensor, and the sampling data of historical sampling echo points reflects complex factors such as noise interference, signal attenuation, and multipath effects in real application scenarios.

[0092] Step S202: According to the preset reference distance, the target detection distance is divided into multiple continuous reference intervals. The number of historical sampling echo points included in each reference interval is greater than the preset number. The reference intervals and detection intervals correspond one-to-one.

[0093] In this embodiment, the division of the reference interval is based on, but is not limited to, differences in acoustic characteristics, such as different attenuation patterns between the near and far fields, and the evenness of data sample distribution. It is necessary to ensure that each detection interval has a sufficient number of historical sampling echo points for statistical analysis. The near field is typically an interval less than 1 meter, and the far field is typically an interval greater than 3 meters. Multiple consecutive detection intervals can be non-overlapping. As one implementation method, when segmenting the target detection distance, three initial intervals are first divided: an interval less than 1 meter, an interval greater than 3 meters, and then the intervals greater than 1 meter and less than 3 meters are further subdivided based on the evenness of the historical echo point distribution. For example, each subdivided interval is guaranteed to include at least a preset number of historical sampling echo points. The preset number can be set according to the actual sampling results and accuracy requirements, ultimately resulting in multiple detection intervals. For example, there are N detection intervals, represented as D. i =[d i d i+1 ), i=1,2,…,N, where d1=d min d N+1 =d max d min and d max These represent the distances corresponding to the start and end points of the target detection range, respectively.

[0094] Step S203: Generate the noise distribution of historical sampled echo points within each reference interval segment.

[0095] In this embodiment, the noise distribution characterizes the background noise level within the reference interval. The noise distribution pattern (such as Gaussian distribution, multimodal distribution, etc.) can be determined by combining the probability density function or histogram fitting method.

[0096] This embodiment provides a method for determining noise distribution based on mean and standard deviation: calculate the mean and standard deviation of historical sampled echo points within each reference interval; and determine the noise distribution of historical sampled echo points within each reference interval based on the mean and standard deviation of each reference interval.

[0097] For any target reference interval, its mean can be expressed as: ,in, The mean noise value of historical sampled echo points within the target reference interval is used to estimate the DC component of the noise, where Q is the number of historical sampled echo points within the reference interval. The noise of the j-th historical sampled echo point within the reference interval; its variance can be expressed as: , The variance of the noise at historical sampled echo points within the target reference interval is used to estimate the noise fluctuation intensity.

[0098] Based on the mean and standard deviation of the target reference interval, the noise distribution of the target reference interval can be expressed as: ,in, The noise distribution of historical sampled echo points within the target reference interval. The noise of historical sampled echo points within the target reference interval.

[0099] Combine the noise distribution of all reference intervals into a function of any distance d:

[0100] Its mean can be expressed as: ,in, As a switching function, it means that it only applies to the reference interval segment to which d belongs. Let M be the mean noise of the historical sampled echo points of the i-th reference interval segment, and M be the number of reference interval segments.

[0101] Its standard deviation can be expressed as: .

[0102] Its noise distribution can be expressed as: ,in, Let be the standard deviation of the noise of the historical sampled echo points of the i-th reference interval segment.

[0103] It should be noted that, to avoid abrupt changes between reference intervals, the mean can be smoothed using the following method: for distance d, its smoothed mean for: .

[0104] Step S204: Based on the noise distribution of each reference interval, the preset distance attenuation model, and the characteristic parameters of the sensor, construct a reference threshold function for each reference interval, and finally obtain a preset threshold model that includes all reference threshold functions.

[0105] In this embodiment, the preset distance attenuation model is used to characterize the trend of echo intensity changing with the detection distance d, and can be expressed as: ,in, The reference echo intensity is set at a reference distance, which can be 1m or a relatively accurate echo intensity value measured in advance at other distances. The dielectric attenuation coefficient can be obtained through experimental fitting.

[0106] In this embodiment, the characteristic parameters of the sensor include, but are not limited to, key indicators such as transmit power Pt, receive sensitivity Sr, and lower limit of signal-to-noise ratio SNRmin. The stronger the transmit power, the farther the detection distance; the farther the distance, the weaker the sensitivity. Sensitivity is also related to transmit power; the stronger the transmit power, the higher the receive sensitivity. In short, these parameters together determine the minimum detectable strength of the effective echo signal.

[0107] In this embodiment, as a specific construction method, when constructing the reference threshold function for any target reference interval t, it is necessary to ensure that the distance d within the reference interval t satisfies the following condition:

[0108] and ,in, Let be the reference threshold function for the reference interval segment t. and Let be the mean and standard deviation of the reference interval t, respectively. The minimum signal-to-noise ratio of the effective signal. For the preset distance attenuation model, Noise distribution for reference interval t , It needs to satisfy the constraint that it monotonically decreases with d, where s is the index of the reference interval segment among all reference interval segments.

[0109] In addition, depending on the needs of the scenario, a safety factor can also be utilized. Allow for environmental fluctuations. ,For example, Take 0.7.

[0110] In this embodiment, since the reference interval and the detection interval correspond one-to-one, the initial threshold function of each detection interval will be directly based on the reference threshold function constructed from its corresponding reference interval during the subsequent threshold fitting process.

[0111] In an optional implementation, to ensure that the signal-to-noise ratio accurately reflects the minimum resolvability of the target signal under a specific environment, this embodiment also provides a method for calculating the signal-to-noise ratio:

[0112] Noise points and signal points are filtered out from multiple historical sampling echo points within the target detection range;

[0113] The ratio of minimum signal power to maximum noise power is used as the signal-to-noise ratio (SNR). The minimum signal power is the minimum power of all signal points, and the maximum noise power is the maximum power of all noise points.

[0114] In this embodiment, noise points refer to low-intensity echo samples that are not affected by reflection from the target object and are caused only by environmental interference or circuit noise; signal points are echo samples with significant energy characteristics generated by reflection from the target object. In actual processing, methods such as amplitude thresholding, statistical distribution analysis, or machine learning classification can be used to distinguish the two types of samples based on power or amplitude characteristics.

[0115] It should be noted that, Figure 3 The steps and implementation methods of ultrasonic threshold calibration in [the context of the text] Figure 4 The steps and implementation methods for constructing the preset threshold model can be run on the same device or on different devices.

[0116] In an optional implementation, to avoid performance degradation due to modeling errors, sudden environmental changes, or hardware differences, this embodiment also provides a closed-loop verification mechanism to achieve feedback adjustment between theoretical modeling and actual application effects. This improves the accuracy and consistency of the threshold function, thereby ensuring stable and reliable detection performance in different application scenarios. One implementation method is as follows:

[0117] The piecewise threshold function for each detection interval is verified to obtain the verification results;

[0118] If the verification result of any detection interval fails, the preset threshold model is readjusted until the verification results of all detection intervals pass.

[0119] In this embodiment, the piecewise threshold function of each detection interval can be verified based on actual test results or simulation data. The verification result can be a quantitative evaluation data such as the false alarm rate, false alarm rate, and detection accuracy obtained by comparing and analyzing the theoretical results calculated based on the piecewise threshold function of each detection interval with the actual test results. For example, if the piecewise threshold function in a certain detection interval causes the number of false alarms to exceed the set threshold or the false alarm probability to be higher than the expected standard, then the threshold function of that detection interval is considered to have failed verification. When it is found that the verification result of any detection interval does not meet the expected standard, the entire calibration process will not end directly, but will automatically enter the model iteration and optimization stage. In this stage, the adjustment operations may include, but are not limited to: recalculating the mean and standard deviation of the reference interval segment, updating the signal-to-noise ratio estimate, adjusting the fitting parameters of the distance attenuation model, and even re-dividing the detection interval structure to improve the matching degree and stability of the overall threshold function. This process will continue until the piecewise threshold function of all detection intervals has passed verification.

[0120] To perform the corresponding steps in the above embodiments and various possible implementations, an implementation of the ultrasonic threshold calibration device 100 is given below. Please refer to... Figure 4 , Figure 4 This is a block diagram of the ultrasonic threshold calibration device provided in this embodiment. It should be noted that the ultrasonic threshold calibration device 100 provided by the present invention has the same basic principle and technical effect as the corresponding embodiment described above. For the sake of brevity, this embodiment does not mention or point out some of these aspects.

[0121] The ultrasonic threshold calibration device 100 includes a division module 110, a fitting module 120, and a processing module 130.

[0122] The segmentation module 110 is used to divide the target detection range into multiple continuous detection intervals;

[0123] The fitting module 120 is used to fit the initial threshold function of each detection interval according to the preset threshold model. The preset threshold model is obtained by segmenting the target detection distance based on multiple historical sampling echo points within the target detection distance.

[0124] The processing module 130 is used to smooth the initial threshold function of each detection interval according to the initial threshold function of the next detection interval adjacent to each detection interval for non-last detection intervals, so as to obtain the piecewise threshold function of each detection interval.

[0125] In an optional implementation, each detection interval includes a start point and an end point, and the fitting module 120 is specifically used for:

[0126] Based on the preset threshold model, calculate the threshold at the start point and the threshold at the end point of each detection interval.

[0127] Based on the threshold values ​​at the start and end points of each detection interval and the interval distance of each detection interval, an initial threshold function for each detection interval is fitted.

[0128] In an optional implementation, the processing module 130 is specifically used for:

[0129] For any adjacent first and second detection intervals, a weighted process is performed based on a preset weight function, the initial threshold function of the first detection interval, and the initial threshold function of the second detection interval to obtain a piecewise threshold function for the first detection interval. The starting point of the second detection interval is the ending point of the first detection interval.

[0130] Each pair of adjacent detection intervals is taken as the first detection interval and the second detection interval, respectively, to obtain the piecewise threshold function of each detection interval.

[0131] In an optional implementation, the historical sampling echo points are sampling points collected from the echoes of the ultrasonic waves emitted by the sensor. The ultrasonic threshold calibration device also includes a construction module 140, which is used for:

[0132] Acquire multiple historical sampling echo points;

[0133] According to the preset reference distance, the target detection distance is divided into multiple continuous reference intervals. The number of historical sampling echo points included in each reference interval is greater than the preset number. The reference intervals and detection intervals correspond one-to-one.

[0134] Generate the noise distribution of historical sampled echo points within each reference interval;

[0135] Based on the noise distribution of each reference interval, the preset distance attenuation model, and the characteristic parameters of the sensor, a reference threshold function for each reference interval is constructed, and finally a preset threshold model including all reference threshold functions is obtained.

[0136] In an optional implementation, the construction module 140 is specifically used for:

[0137] Calculate the mean and standard deviation of historical sampled echo points within each reference interval;

[0138] The noise distribution of historical sampled echo points within each reference interval is calibrated based on the mean and standard deviation of each reference interval.

[0139] In an optional implementation, the sensor's characteristic parameters include the signal-to-noise ratio, and the building module 140 is further configured to:

[0140] Noise points and signal points are filtered out from multiple historical sampling echo points within the target detection range;

[0141] The ratio of minimum signal power to maximum noise power is used as the signal-to-noise ratio (SNR). The minimum signal power is the minimum power of all signal points, and the maximum noise power is the maximum power of all noise points.

[0142] In an optional embodiment, the ultrasonic threshold calibration device further includes a verification module 150, which is used for:

[0143] The piecewise threshold function for each detection interval is verified to obtain the verification results;

[0144] If the verification result of any detection interval fails, the preset threshold model is readjusted until the verification results of all detection intervals pass.

[0145] This invention also provides a block diagram of an electronic device 10, which implements the ultrasonic threshold calibration method described in the foregoing embodiments. Please refer to... Figure 5 , Figure 5 This is a block diagram of the electronic device 10 provided in this embodiment. The electronic device 10 includes a processor 11, a memory 12 and a bus 13. The processor 11 and the memory 12 are connected through the bus 13.

[0146] The processor 11 can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the ultrasonic threshold calibration method described above can be completed by the integrated logic circuitry in the processor 11 or by software instructions. The processor 11 can be a general-purpose processor, including a CPU (Central Processing Unit), an NP (Network Processor), etc.; it can also be a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Logic Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0147] The memory 12 is used to store the program for implementing the ultrasonic threshold calibration method. The program can be a software function module stored in the memory 12 in the form of software or firmware or embedded in the OS (Operating System) of the electronic device 10.

[0148] After receiving the execution instruction, the processor 11 executes the program to implement the ultrasonic threshold calibration method of the aforementioned embodiment.

[0149] This embodiment provides a computer storage medium storing a computer program that, when executed by a processor, implements the ultrasonic threshold calibration method as described in any of the foregoing embodiments.

[0150] In summary, the embodiments of the present invention provide an ultrasonic threshold calibration method, apparatus, electronic device, and computer storage medium. The method includes: dividing the target detection distance into multiple continuous detection intervals; fitting an initial threshold function for each detection interval according to a preset threshold model, wherein the preset threshold model is obtained by segmenting the target detection distance based on multiple historical sampling echo points within the target detection distance; and for non-last detection intervals, smoothing the initial threshold function of each detection interval sequentially according to the initial threshold function of the next detection interval adjacent to each detection interval to obtain a segmented threshold function for each detection interval. Compared with the prior art, this embodiment has at least the following advantages: (1) By using historical sampling echo points to perform segmented modeling to obtain a preset threshold model, the initial threshold function of each fitted detection interval is best matched with the detection interval, avoiding the impact of unreasonable threshold on the accuracy of target detection. By using the initial threshold function of the next detection interval adjacent to each detection interval, the initial threshold function of each detection interval is smoothed to avoid the misjudgment of the final target detection due to the threshold jump at the junction of two adjacent detection intervals; (2) Based on the noise distribution of each reference interval segment, the preset distance attenuation model and the characteristic parameters of the sensor, each reference interval segment is constructed. The reference threshold function of the reference interval segment is used to obtain the preset threshold model, which can accurately capture the characteristics of multi-peak or non-stationary noise and realize the automatic adjustment of the threshold with noise fluctuation; (3) Provide a closed-loop verification mechanism to realize the feedback adjustment between theoretical modeling and actual application effect, improve the accuracy and consistency of the threshold function, and ensure that it always maintains stable and reliable detection performance in different application scenarios, avoiding performance degradation caused by modeling error, environmental change or hardware difference; (4) Automatic threshold adjustment avoids the need for manual resampling every time the environment changes, which is time-consuming and labor-intensive, has a long deployment cycle, is difficult to meet the requirements of rapid iteration, and is affected by subjective experience, resulting in low accuracy.

[0151] The above descriptions are merely various embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calibrating an ultrasonic threshold, characterized in that, The method includes: The target detection range is divided into multiple continuous detection intervals; An initial threshold function is fitted to each detection interval according to a preset threshold model. The preset threshold model is obtained by segmenting the target detection distance based on multiple historical sampling echo points within the target detection distance. The historical sampling echo points are sampling points collected for the echoes of ultrasonic waves emitted by the sensor. For non-last detection intervals, the initial threshold function of each detection interval is smoothed according to the initial threshold function of the next detection interval adjacent to each detection interval, so as to obtain the piecewise threshold function of each detection interval. The steps for constructing the preset threshold model include: Obtain the multiple historical sampling echo points; According to a preset reference distance, the target detection distance is divided into multiple consecutive reference intervals. The number of historical sampling echo points included in each reference interval is greater than the preset number. The reference intervals and the detection intervals correspond one-to-one. Generate the noise distribution of historical sampled echo points within each of the aforementioned reference interval segments; Based on the noise distribution of each reference interval, the preset distance attenuation model, and the characteristic parameters of the sensor, a reference threshold function for each reference interval is constructed, and finally the preset threshold model including all reference threshold functions is obtained.

2. The method according to claim 1, characterized in that, Each of the aforementioned detection intervals includes a start point and an end point. The step of fitting an initial threshold function for each of the aforementioned detection intervals according to a preset threshold model includes: Based on the preset threshold model, the threshold for the start point and the threshold for the end point of each detection interval are calculated respectively. Based on the threshold values ​​of the starting and ending points of each detection interval and the interval distance of each detection interval, an initial threshold function for each detection interval is fitted.

3. The method according to claim 1, characterized in that, The step of smoothing the initial threshold function of each detection interval according to the initial threshold function of the next detection interval adjacent to each detection interval to obtain the piecewise threshold function of each detection interval includes: For any adjacent first detection interval and second detection interval, a weighted process is performed according to a preset weight function, the initial threshold function of the first detection interval and the initial threshold function of the second detection interval to obtain the piecewise threshold function of the first detection interval, and the starting point of the second detection interval is the ending point of the first detection interval. Each pair of adjacent detection intervals is taken as the first detection interval and the second detection interval, respectively, to obtain the segmented threshold function of each detection interval.

4. The method according to claim 1, characterized in that, The step of generating the noise distribution of historical sampled echo points within each of the reference interval segments includes: Calculate the mean and standard deviation of historical sampled echo points within each of the aforementioned reference intervals; The noise distribution of historical sampled echo points within each reference interval is determined based on the mean and standard deviation of each reference interval.

5. The method according to claim 1, characterized in that, The characteristic parameters of the sensor include the signal-to-noise ratio. Before the step of constructing a reference threshold function for each reference interval based on the noise distribution of each reference interval, a preset distance attenuation model, and the characteristic parameters of the sensor, and finally obtaining the preset threshold model including all reference threshold functions, the following steps are included: Noise points and signal points are selected from multiple historical sampled echo points within the target detection range; The signal-to-noise ratio is the ratio of the minimum signal power to the maximum noise power, where the minimum signal power is the minimum power of all signal points and the maximum noise power is the maximum power of all noise points.

6. The method according to claim 1, characterized in that, The method further includes: The piecewise threshold function for each of the detection intervals is verified to obtain the verification results; If the verification result of any of the detection intervals fails, the preset threshold model is readjusted until the verification results of all the detection intervals pass.

7. An ultrasonic threshold calibration device, characterized in that, The device includes: The segmentation module is used to divide the target detection range into multiple continuous detection intervals; The fitting module is used to fit an initial threshold function for each detection interval according to a preset threshold model. The preset threshold model is obtained by segmenting the target detection distance based on multiple historical sampling echo points within the target detection distance. The historical sampling echo points are sampling points collected for the echoes of ultrasonic waves emitted by the sensor. The processing module is used to smooth the initial threshold function of each detection interval according to the initial threshold function of the next detection interval adjacent to each detection interval for non-last detection intervals, so as to obtain the segmented threshold function of each detection interval. The construction module is used for: acquiring the plurality of historical sampling echo points; dividing the target detection distance into a plurality of continuous reference intervals according to a preset reference distance, wherein the number of historical sampling echo points included in each reference interval is greater than a preset number, and the reference intervals correspond one-to-one with the detection intervals; generating the noise distribution of the historical sampling echo points in each reference interval; and constructing a reference threshold function for each reference interval based on the noise distribution of each reference interval, a preset distance attenuation model, and the characteristic parameters of the sensor, and finally obtaining the preset threshold model including all reference threshold functions.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory being used to store a program, and the processor being used to implement the ultrasonic threshold calibration method as described in any one of claims 1-6 when executing the program.

9. A computer storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the ultrasonic threshold calibration method as described in any one of claims 1-6.

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