Laser radar module and accuracy detection method thereof

Abstract

The invention relates to the technical field of laser radars, and discloses a laser radar module and an accuracy detection method thereof, and the method comprises the steps: setting a plurality of detection plates with known positions, placing a to-be-detected laser radar module at detection points with known positions, comparing a detection distance value with an actual distance value, and adjusting a data processing period; when the data processing period meets the condition, acquiring detection data and establishing a time sequence data set, and judging whether the laser radar module is accurate or not; if an over-threshold data point exists, the module is judged to be inaccurate, otherwise, the module is judged to be qualified; when the module is inaccurate, extracting an imprecise feature and comparing the imprecise feature with a historical data set to determine an imprecise reason; calculating a tolerable value according to the inaccurate reason, and sorting the modules according to the tolerable value; the imprecise features comprise imprecise data and imprecise types. According to the method, the detection precision and the production reliability of the laser radar module are effectively improved through real-time monitoring and accurate identification of error sources.

Description

Technical Field

[0001] The present invention relates to the field of laser radar technology, and in particular to a laser radar module and a method for detecting its accuracy. Background Art

[0002] As a high-precision environmental perception technology, LiDAR is widely used in autonomous driving, drone mapping, security monitoring and other fields. The core optical structure of the radar module usually includes a lens barrel, a front shell, a lens group and related electronic components. Its assembly process has a direct impact on the performance, stability and yield of the product. In addition, the existing LiDAR accuracy detection methods mainly rely on static calibration and laboratory measurements, such as using standard reflective surfaces or targets of known size for distance measurement comparison. Although these methods can provide certain detection capabilities under ideal experimental conditions, in complex actual application environments, the measurement accuracy of LiDAR may be affected by various dynamic factors, making it difficult for traditional methods to accurately evaluate its true working status. In addition, some detection methods only focus on a single factor (such as distance error), while ignoring the comprehensive impact of multiple factors such as signal strength and ambient temperature on the accuracy of LiDAR, resulting in insufficient reliability of the detection results.

[0003] Therefore, there is an urgent need for a more comprehensive and intelligent lidar accuracy detection method to improve the accuracy, robustness and adaptability of detection, thereby providing a more scientific basis for the optimization and screening of lidar modules. Summary of the invention

[0004] In view of this, the present invention proposes a laser radar module and an accuracy detection method thereof, aiming to solve the problem that the accuracy of the laser radar is difficult to ensure in the current technology.

[0005] On the one hand, the present invention proposes a method for detecting the accuracy of a laser radar module, comprising: setting a number of detection boards with known positions, placing the laser radar module to be tested at a detection point at a known position, comparing the detection distance value of the laser radar module to be tested with the actual distance value calculated based on the detection board and the detection point, and adjusting the data processing cycle of the detection distance value according to the distance difference between the detection distance value and the actual distance value.

[0006] When the data processing cycle is met, the detection data within the data processing cycle is obtained, and a detection data set is established according to the time series. The detection data set is processed to determine whether the laser radar module to be tested is accurate; when there is at least one over-threshold data point, the laser radar module to be tested is determined to be inaccurate; when the over-threshold data point does not exist, the laser radar module to be tested is determined to be qualified.

[0007] When it is determined that the laser radar module to be tested is inaccurate, inaccurate features are extracted, and the inaccurate features are compared with historical detection data sets to determine the cause of the inaccuracy.

[0008] A tolerable value is calculated according to the inaccurate cause and the inaccurate feature, and the laser radar modules to be tested are sorted according to the size of the tolerable value.

[0009] Furthermore, the inaccurate feature includes: inaccurate data and inaccurate types, the inaccurate data is the detection data corresponding when it is judged that the laser radar module to be tested is inaccurate, and the inaccurate type is the type of the corresponding detection data.

[0010] Furthermore, the data processing cycle is determined by the following relationship: ; in, is the data processing cycle, is the maximum distance difference of the laser radar module to be tested, is the error correction coefficient, is the maximum sampling frequency within the detection period, is the minimum sampling frequency within the detection period, is the real-time data update rate, is the measurement accuracy coefficient, is the signal strength factor of the laser radar module to be tested, is the temperature influence coefficient.

[0011] Further, when judging whether the laser radar module to be tested is accurate, it includes: obtaining the detection data set, the detection data in the detection data set is divided into four categories, including: distance difference, temperature, received signal strength and detection time of the laser radar module to be tested, and the detection data obtained when the detection time in the detection data set is the same establish a data group to obtain N data groups, each of which includes the distance difference, temperature, received signal strength and detection time of the laser radar module to be tested, and the data groups are arranged to obtain an N*M matrix, wherein M=4, M is the number of types of the detection data, and the N*M matrix is ​​obtained by represents any element in the matrix, is the element of the jth class at detection time i.

[0012] Select any element in the N*M matrix as the center point, set the data judgment range, the radius of the data judgment range is R, select all elements of the same type within the data judgment range, and calculate the average value.

[0013] Set the error threshold. When the difference between the average value and the average value is greater than or equal to the error threshold, it is determined that is the above-threshold data point, when When the difference between the average value and the average value is less than the error threshold, it is judged that is a normal data point.

[0014] When there is at least one over-threshold data point, it is determined that the laser radar module to be tested is inaccurate.

[0015] Furthermore, the error threshold is calculated by the following relationship: ; in, is the error threshold, is the variance of all distance differences within the data judgment range, is the average value of all distance differences within the data judgment range, is the average value of all temperatures within the data judgment range, is the average value of the received signal strength within the data judgment range, , and is the average value of the received signal strength within the data judgment range, .

[0016] Furthermore, when determining the inaccurate cause, it includes: extracting the inaccurate feature, and judging the inaccurate cause according to the overlap between the inaccurate feature and the historical detection data set.

[0017] The overlap satisfies the following relationship: ; in, is the overlap, is the i-th data point in the inexact data, is the mean of all inexact data, is the i-th data point in the historical data set, is the mean of all data points in the historical data set, is the number of all inexact data.

[0018] The inaccurate data with the highest similarity is matched with the historical data set, and the inaccurate cause of the inaccurate data is determined according to the inaccurate cause in the historical data set.

[0019] Further, when calculating the tolerable value according to the inaccurate cause and the inaccurate feature, it includes: ; in, is the tolerable value, is the maximum distance difference in the inexact data, is the maximum distance difference in the historical data set, is the minimum received signal strength in the inaccurate data, is the minimum received signal strength in the historical data set, is the average temperature in the inaccurate data, is the average temperature in the historical data set, is the distance difference influence coefficient, is the signal strength influence coefficient, is the temperature influence coefficient.

[0020] Furthermore, the distance difference influences the coefficient Obtained through the following relationship: ; Signal strength influence coefficient Obtained through the following relationship: ; Temperature influence coefficient Obtained through the following relationship: ; ; in, is the variance of all distance differences in the inexact data, is the variance of all signal strength values ​​in the inexact data, is the variance of all temperature values ​​in the inexact data, is the total variance.

[0021] On the other hand, the present invention proposes a laser radar module, which is obtained by adopting the accuracy detection method of the above-mentioned laser radar module, and the laser radar module includes.

[0022] The front shell is connected with the light-transmitting cover plate at its top, the front shell is provided with a plurality of lens barrel holes, and the lower surface of the front shell is provided with a lens barrel fixing pile and a first threaded hole.

[0023] The lens barrel assembly comprises a lens seat and a lens barrel, wherein the lens seat and the lens barrel are an integrated structure, the lens seat is provided with a second threaded hole and a lens barrel fixing hole, and a PIN fixing groove is provided on the lower surface of the lens seat; the lens barrel fixing pile is inserted into the lens barrel fixing hole to fix the lens barrel assembly.

[0024] A fixing screw passes through the first threaded hole and the second threaded hole and is fixed on the front shell.

[0025] A PCBA board and a PIN needle, one end of the PIN needle is fixedly connected to the PCBA board, and the other end of the PIN needle is connected to the PIN fixing groove.

[0026] A heat conducting plate and a rear shell, wherein the heat conducting plate is located in the interlayer between the rear shell and the PCBA board, the rear shell is connected to the front shell, and a connector port is disposed on the lower surface of the rear shell.

[0027] Furthermore, the front shell and the rear shell are connected by laser welding, and the PIN pin and the PIN fixing groove are interference fit.

[0028] Compared with the prior art, the invention has the following beneficial effects: the invention can monitor the accuracy of the laser radar module in real time by dynamically adjusting the data processing cycle and combining time series and multi-dimensional data, such as distance difference, temperature, signal strength, etc. By identifying over-threshold data points, it can quickly determine whether the radar module has accuracy problems, avoiding the lag of traditional static detection methods and making the detection process more efficient.

[0029] When module inaccuracy is detected, the present invention can help developers accurately identify the root cause of module performance degradation by extracting inaccurate features and comparing them with historical detection data, analyzing the source of the error, and helping developers accurately identify the root cause of module performance degradation. This diagnostic process can provide data support for subsequent optimization and improve product stability and reliability.

[0030] By combining environmental parameters such as temperature influence coefficient and signal strength factor, the accuracy detection method of the present invention can effectively adapt to the impact of different environmental conditions on the performance of the laser radar module, especially in an environment with large temperature fluctuations, and can still maintain a high detection accuracy. This flexibility makes the method more adaptable in practical applications.

[0031] By optimizing the module structure design, such as using an integrated lens barrel assembly and an optimized screw fixing structure, the number of parts and complex assembly steps are reduced, thereby simplifying the production process and reducing production costs. At the same time, the need for debugging and rework is reduced, and production efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flowchart of a method for detecting the accuracy of a laser radar module provided in an embodiment of the present invention.

[0033] Figure 2An exploded view of a laser radar module at a certain angle provided in an embodiment of the present invention.

[0034] Figure 3 An exploded view of a laser radar module provided in an embodiment of the present invention from another angle.

[0035] In the figure, 10, light-transmitting cover plate; 20, front shell; 21, lens barrel hole; 22, lens barrel fixing pile; 23, first threaded hole; 30, lens barrel assembly; 31, lens base; 32, lens barrel; 33, lens barrel fixing hole; 34, second threaded hole; 35, PIN fixing slot; 36, fixing screw; 37, PIN pin; 38, PCBA board; 40, rear shell; 41, connector port. DETAILED DESCRIPTION

[0036] The exemplary embodiments disclosed in the present application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] See also Figure 1 As shown, an embodiment of the present invention provides a method for detecting the accuracy of a laser radar module, including: S1, setting a number of detection boards with known positions, placing the laser radar module to be tested at a detection point at a known position, comparing the detection distance value of the laser radar module to be tested with the actual distance value calculated based on the detection board and the detection point, and adjusting the data processing cycle of the detection distance value according to the distance difference between the detection distance value and the actual distance value.

[0038] S2, when the data processing cycle is met, the detection data within the data processing cycle is obtained, and a detection data set is established according to the time series, and the detection data set is processed to determine whether the laser radar module to be tested is accurate; when there is at least one over-threshold data point, the laser radar module to be tested is determined to be inaccurate, and when there is no over-threshold data point, the laser radar module to be tested is determined to be qualified.

[0039] S3, when it is determined that the laser radar module to be tested is inaccurate, the inaccurate features are extracted, and the inaccurate features are compared with the historical detection data set to determine the cause of the inaccuracy.

[0040] S4, calculating the tolerable value according to the inaccurate causes and inaccurate features, and sorting the laser radar modules to be tested according to the size of the tolerable value.

[0041] The inaccurate features include: inaccurate data and inaccurate types. The inaccurate data refers to the detection data corresponding to when the laser radar module to be tested is judged to be inaccurate, and the inaccurate type refers to the type of the corresponding detection data.

[0042] First, by comparing the laser radar module to be tested with the detection board at a known position, the difference between the actual distance value and the detection distance value is automatically calculated, and the data processing cycle can be accurately adjusted to ensure the accuracy and stability of the data. This real-time, automated adjustment mechanism can significantly improve detection efficiency.

[0043] Secondly, when a data point exceeding the threshold is detected, it can promptly determine whether the module is inaccurate, avoiding errors caused by manual judgment. When inaccuracies are found, the system can extract inaccurate features and analyze them in combination with historical detection data sets to accurately determine the cause of the inaccuracy. This process not only helps locate the problem, but also takes targeted solutions to specific problems.

[0044] In addition, through the calculation and sorting mechanism of tolerable values, the modules are sorted according to the imprecise features and causes, which can further optimize the production process, reduce the defective rate, and improve the quality and reliability of the LiDAR module. Overall, this method greatly improves the detection accuracy and production efficiency, reduces the production cost, can effectively ensure the accuracy of the LiDAR module under different environmental conditions, and has broad application prospects.

[0045] In some embodiments of the present application, the data processing period is determined by the following relationship: ; in, is the data processing cycle, is the maximum distance difference of the laser radar module to be tested, is the error correction coefficient, is the maximum sampling frequency within the detection period, is the minimum sampling frequency within the detection period, is the real-time data update rate, is the measurement accuracy coefficient, is the signal strength factor of the laser radar module to be tested, is the temperature influence coefficient.

[0046] Specifically, the formula for determining the data processing cycle involves the following key factors: Maximum distance difference of the laser radar module to be tested: This is the maximum ranging error that may occur during the detection process of the laser radar module to be tested. This data reflects the maximum error range of the module and directly affects the calculation of the data processing cycle. Error correction coefficient: This coefficient is used to correct errors during the data processing process to ensure that the measured value is more accurate. The error correction coefficient takes into account potential errors caused by equipment accuracy, environmental factors, etc., and adjusts the data processing cycle. Maximum sampling frequency and minimum sampling frequency: These two frequency values ​​affect the frequency of data collection, which in turn determines the accuracy of the data. In the calculation of the data processing cycle, the higher the sampling frequency, the faster the data update, and the higher the processing accuracy may be, but it also increases the calculation burden. Therefore, the maximum and minimum sampling frequencies have an important impact on the cycle.

[0047] Real-time data update rate: This parameter determines the frequency of data updates and directly affects the choice of data processing cycle. If the real-time data update rate is high, it means that the data needs to be processed more frequently, thereby adjusting the length of the data processing cycle. Measurement accuracy coefficient: This coefficient is set according to the measurement accuracy of the lidar module and is used to measure the reliability of its measurement results. The higher the measurement accuracy, the shorter the required data processing cycle may be. Signal strength factor of the lidar module to be tested: The signal strength of the lidar will affect the quality of the data. When the signal strength is weak, a longer data processing cycle may be required to ensure the accuracy of the data. Temperature influence coefficient: Changes in ambient temperature will affect the measurement accuracy of the lidar module. The performance of the lidar may be different under different temperature conditions. Therefore, the temperature influence coefficient helps to adjust the data processing cycle to ensure accuracy stability at different temperatures.

[0048] In some embodiments of the present application, when determining whether the laser radar module to be tested is accurate, it includes: obtaining a detection data set, the detection data in the detection data set is divided into four categories, including: distance difference, temperature, received signal strength and detection time of the laser radar module to be tested, and the detection data acquired at the same detection time in the detection data set are used to establish a data group to obtain N data groups, each data group includes distance difference, temperature, received signal strength and detection time of the laser radar module to be tested, and an N*M matrix is ​​obtained according to the data group arrangement, wherein M=4, M is the number of types of detection data, and the N*M matrix is ​​obtained by represents any element in the matrix, is the element of the jth category when the detection time is i; select any element in the N*M matrix as the center point, set the data judgment range, the radius of the data judgment range is R, select all elements of the same type within the data judgment range, and calculate the average value; set the error threshold, when When the difference between the average value and the average value is greater than or equal to the error threshold, it is determined that is the above-threshold data point, when When the difference between the average value and the average value is less than the error threshold, it is judged that is a normal data point; when there is at least one over-threshold data point, it is judged that the laser radar module to be tested is inaccurate.

[0049] Specifically, first, obtain the detection data set from the lidar module. The data set contains four types of data, each of which represents a specific measurement feature: Distance difference: It indicates the difference between the actual measured distance and the expected distance, reflecting the measurement accuracy.

[0050] Temperature: The operating temperature of the LiDAR module. Temperature has a significant impact on the performance and accuracy of the LiDAR.

[0051] Received signal strength: The signal strength received by the radar. The signal strength directly affects the accuracy of the measurement.

[0052] Detection time: The timestamp of each detection, used to arrange data by time.

[0053] The detection data at the same timestamp is divided into a data group, and each data group contains these four types of data. Through these data groups, an N×M matrix can be obtained, where N represents the number of data groups and M represents the number of data types (4 in this example). The elements of the matrix correspond to specific data at a certain moment.

[0054] Determine the data judgment range and average value calculation: Next, select any element in the N×M matrix as the center point and set a data judgment range with a radius of R. Select the same type of data within this range (for example, all distance differences or all temperature values) and calculate the average value of these data. In this way, the stability and consistency of the data at that moment can be evaluated.

[0055] Set the error threshold and judge the over-threshold data points: Set an error threshold based on the calculated average value. When the difference between the detected data and the average value is greater than or equal to the error threshold, it is judged as an over-threshold data point, that is, the data point has a large anomaly or error. When the difference with the average value is less than the error threshold, it is judged as a normal data point.

[0056] Ultimately, any over-threshold data points in the detection data set are considered abnormal, resulting in the determination that the LiDAR module under test is “inaccurate”. If there are no over-threshold data points in the detection data set, the module is considered qualified under the test conditions and the accuracy meets the requirements.

[0057] This method can automatically determine the accuracy of the LiDAR module, reduce manual intervention, and automatically detect whether the system meets the accuracy requirements. This helps to improve detection efficiency and accuracy and reduce human errors. By setting the error threshold and judging the over-threshold data points, abnormal data can be effectively identified, especially when the signal strength is weak or the temperature fluctuation is large. In this way, the detection system can detect potential problems in advance and avoid inaccurate detection results affecting the performance of the system. The use of multiple data dimensions (such as distance difference, temperature, signal strength and time) for comprehensive analysis can more comprehensively evaluate the accuracy of the LiDAR module and improve the reliability and stability of the system. Through the processing and matrix analysis of time series data, not only can the accuracy of the detection data be evaluated in real time, but also the detection parameters (such as error threshold and judgment range) can be optimized according to historical data, thereby improving the long-term detection accuracy and efficiency. By setting the data judgment range and calculating the average value, the system can effectively reduce the impact of single data fluctuations or instantaneous errors on the judgment accuracy, enhance the fault tolerance of the overall test, and ensure the stability of accuracy detection. Because this method takes into account the effects of temperature and signal strength, it can automatically adjust the detection standard according to environmental changes, ensuring that the lidar module can maintain high accuracy in a variety of different working environments.

[0058] In some embodiments of the present application, the error threshold is calculated by the following relationship: ; in, is the error threshold, is the variance of all distance differences within the data judgment range, is the average value of all distance differences within the data judgment range, is the average value of all temperatures within the data judgment range, is the average value of the received signal strength within the data judgment range, , and is the average value of the received signal strength within the data judgment range, .

[0059] It should be noted that Indicates the variance of all distance differences within the data judgment range. It reflects the degree of dispersion or fluctuation between data. The larger the variance, the more drastic the data changes and the greater the error may be. Therefore, variance is used to measure the instability or inconsistency of data.

[0060] It indicates the average value of all distance differences within the data judgment range. It reflects the overall deviation of the measured data. The closer it is to zero, the more accurate the measurement. If the distance difference is large, it means that there is a problem with the accuracy of the LiDAR module.

[0061] Indicates the average value of all temperatures within the data judgment range. Temperature affects the performance of the LiDAR module, so temperature fluctuations may cause errors. If the temperature is too high or too low, it may cause inaccurate measurements and affect the module accuracy.

[0062] Indicates the average value of the received signal strength within the data judgment range. Signal strength affects the reflection and ranging accuracy of the LiDAR. Weaker signal strength may result in inaccurate measurement data, so the error may increase when the signal strength is weak.

[0063] , and ,These weight coefficients are used to adjust the contribution of each factor to the error threshold. Different weight coefficients can be adjusted according to different application scenarios to adapt to different detection conditions and requirements.

[0064] This formula calculates the error threshold by integrating multiple influencing factors (such as the variance of the distance difference, temperature, and signal strength). The error threshold is a key criterion for determining whether the test data is normal. Only when the deviation of the test data exceeds this threshold will it be considered an "over-threshold data point" or "abnormal data". Therefore, the error threshold is an important parameter in accuracy testing.

[0065] The influence of the variance of the distance difference on the error threshold can be weighted by the weight coefficient. If the distance measurement results of the LiDAR module have large fluctuations (i.e. large variance), then The impact will be amplified, leading to stricter error standards.

[0066] The effect of temperature on radar accuracy is Adjustment. If the temperature fluctuates greatly, it will affect the propagation speed of the signal, thus introducing measurement errors. The influence of temperature on the error threshold can be controlled by the weight coefficient.

[0067] The signal strength is affected by Weak signals can cause inaccurate measurements, so the error threshold is increased when the signal strength is low.

[0068] By taking into account multiple factors (such as distance difference, temperature, and signal strength), this formula can more accurately reflect the accuracy of the LiDAR module under different conditions. A single factor is often not enough to fully characterize the error of the system, so by combining these factors, the reliability of error detection can be improved.

[0069] This method can flexibly adjust the error threshold according to different application scenarios (such as temperature changes, signal strength fluctuations, etc.), ensuring accurate detection in different environments. By adjusting the weight coefficient, the contribution of each factor to the error can be adjusted according to actual needs, further optimizing the flexibility and accuracy of detection.

[0070] In some embodiments of the present application, when determining the inaccurate cause, it includes: extracting inaccurate features, and judging the inaccurate cause according to the overlap between the inaccurate features and the historical detection data set.

[0071] The overlap satisfies the following relationship: ; in, is the overlap, is the i-th data point in the inexact data, is the mean of all inexact data, is the i-th data point in the historical data set, is the mean of all data points in the historical data set, is the number of all inexact data.

[0072] The inaccurate data with the highest similarity is matched with the historical data set, and the inaccurate cause of the inaccurate data is determined according to the inaccurate cause in the historical data set.

[0073] It should be noted that the historical detection data set contains several known causes of abnormal performance of lidar modules and their characteristic data.

[0074] By calculating the overlap between imprecise data and historical data, the similarity between the two can be accurately quantified, thereby matching the closest historical imprecise data. This data-driven method is more objective and stable than simple rule matching or empirical judgment. This method dynamically adjusts the matching rules based on historical data. Even if the distribution of imprecise data changes, it can still adaptively identify different types of imprecise causes through the continuously updated historical detection data set, thereby improving the generalization ability and robustness of the system. By calculating the similarity, the most similar imprecise data in the historical data set can be quickly located, and the cause of the current imprecise data can be inferred based on the historical records, avoiding the inefficient process of manual analysis or trial and error required by traditional methods, greatly improving the efficiency of fault diagnosis and problem tracing. Using mathematical formulas to quantify the similarity between imprecise data and historical data can effectively reduce misjudgments caused by subjective judgment, noise interference or random errors, and improve the consistency and reliability of the system in identifying imprecise causes. This method can be widely used in scenarios such as equipment fault detection, data quality analysis, and automatic control systems. It is suitable for any occasion where data accuracy needs to be analyzed and the cause of abnormalities needs to be found, and has good versatility.

[0075] In some embodiments of the present application, when calculating the tolerable value according to the inaccurate cause and the inaccurate feature, it includes: ; in, is the tolerable value, is the maximum distance difference in the inexact data, is the maximum distance difference in the historical data set, is the minimum received signal strength in the inaccurate data, is the minimum received signal strength in the historical data set, is the average temperature in the inaccurate data, is the average temperature in the historical data set, is the distance difference influence coefficient, is the signal strength influence coefficient, is the temperature influence coefficient.

[0076] It should be noted that the formula takes into account factors such as distance difference, signal strength and temperature, and assigns a corresponding influence coefficient to each factor, making the calculation result more comprehensive and accurate. By comparing the imprecise data with the historical data set (maximum / minimum value or mean), the degree to which the imprecise data deviates from the normal situation can be effectively measured. Because the formula uses normalization processing, data of different scales can be compared uniformly, avoiding the deviation caused by different numerical values ​​of certain factors.

[0077] The influence coefficients α, β, and η can be dynamically adjusted according to the specific scenario, so that the system can adapt to different application environments and improve the generalization ability. The formula is simple in form and has a small amount of calculation. It is suitable for real-time analysis and rapid detection, especially when the amount of data is large, and can effectively reduce the calculation overhead. By comparing the degree of deviation between imprecise data and historical data, it is possible to effectively identify which data is tolerable and which is abnormal, thereby reducing false positives and false negatives. Combined with historical data, existing experience can be used to optimize the detection logic and improve the intelligence level of the system. This calculation method can be widely used in many fields such as wireless communications, environmental monitoring, and sensor data analysis, which helps to optimize equipment performance and improve data quality and stability.

[0078] In some embodiments of the present application, the distance difference influences the coefficient Obtained through the following relationship: ; Signal strength influence coefficient Obtained through the following relationship: ; Temperature influence coefficient Obtained through the following relationship: ; ; in, is the variance of all distance differences in the inexact data, is the variance of all signal strength values ​​in the inexact data, is the variance of all temperature values ​​in the inexact data, is the total variance.

[0079] It should be noted that the traditional method usually requires manual setting of the weights of each influencing factor, while this method automatically determines the weights through data statistics methods (based on variance calculation), which reduces human intervention and improves the level of intelligence. This data-driven approach can better adapt to different scenarios, such as wireless communication environments, sensor data analysis, industrial measurement, etc. Since the variance of imprecise data and precise data directly affects the calculation of each influencing coefficient, it can more accurately identify which factors have the greatest impact on the error, thereby being more accurate in anomaly detection. For example, when the variance of the distance difference is significantly greater than the variance of the signal strength or temperature, the system will automatically increase the weight of α to ensure that the factor receives greater attention and reduce misjudgments caused by improper weight setting. The calculation method is based on variance normalization, involving only simple addition, division and variance calculation, with low computational complexity, suitable for real-time data analysis and large-scale data processing, and can improve the real-time performance and response speed of the system. This method can be widely used in wireless communication, environmental monitoring, intelligent sensing, data quality control and other fields, especially in scenarios involving error analysis and data accuracy optimization, which can significantly improve the accuracy and stability of data processing.

[0080] See also Figure 2-Figure 3 As shown, an embodiment of the present invention further provides a laser radar module, which includes: a front shell 20, which is connected to the transparent cover plate 10 at the top, the front shell 20 is provided with a plurality of lens barrel holes 21, and the lower surface of the front shell 20 is provided with a lens barrel fixing pile 22 and a first threaded hole 23.

[0081] The lens barrel assembly 30 includes a lens base 31 and a lens barrel 32. The lens base 31 and the lens barrel 32 are an integrated structure. The lens base 31 is provided with a second threaded hole 34 and a lens barrel fixing hole 33. The lower surface of the lens base 31 is provided with a PIN fixing groove 35. The lens barrel fixing pile 22 is inserted into the lens barrel fixing hole 33 to fix the lens barrel assembly 30.

[0082] The fixing screw 36 passes through the first threaded hole 23 and the second threaded hole 34 and is fixed on the front housing 20 .

[0083] The PCBA board 38 and the PIN pin 37 , one end of the PIN pin 37 is fixedly connected to the PCBA board 38 , and the other end of the PIN pin 37 is connected to the PIN fixing groove 35 .

[0084] The heat conducting plate and the rear shell 40 , the heat conducting plate is located in the interlayer between the rear shell 40 and the PCBA board 38 , the rear shell 40 is connected to the front shell 20 , and a connector port 41 is disposed on the lower surface of the rear shell 40 .

[0085] In some embodiments of the present application, the front shell 20 and the rear shell 40 are connected by laser welding, and the PIN pin 37 and the PIN fixing groove 35 are interference fit.

[0086] It should be noted that the lens barrel assembly 30 can be two or three. When there are two lens barrel assemblies 30, one lens is used for receiving and one lens is used for sending. When there are three lens barrel assemblies 30, one lens is used for receiving and two lenses are used for sending. Figure 2 and Figure 3 Not shown in the figure, in this embodiment, the heat conducting plate is preferably a graphene sheet, and its function is to dissipate heat. Other plate-like material structures for the purpose of heat dissipation may also be used.

[0087] Compared with traditional screw fixing or gluing methods, laser welding has higher strength, sealing and durability, can effectively improve the structural strength of the module, and reduce displacement or loosening caused by vibration, impact or temperature change. This method can also effectively prevent the entry of external factors such as dust and water vapor, improve dustproof and waterproof performance, and is suitable for various complex environments (such as autonomous driving, industrial inspection, etc.). The integration of the lens seat 31 and the lens barrel 32 reduces the tolerance accumulation during the assembly process, improves the alignment accuracy of the optical system, and thus improves the measurement accuracy and stability of the laser radar. The lens barrel fixing pile 22 is fixed by plugging, which improves the seismic resistance of the lens barrel assembly 30, prevents the lens barrel 32 from shifting due to long-term use, and ensures the long-term stability of the laser radar. The lens barrel assembly 30 is fixed to the front shell 20 through the lens barrel fixing pile 22, and the fixing screw 36 passes through the front shell 20 and the lens seat 31 to ensure precision alignment. The fixing screw 36 is used for multi-point fixing to ensure the close connection and optical axis consistency between the lens barrel assembly 30 and the front shell 20, reduce the optical deviation caused by external force or temperature change, and ensure long-term stable laser radar ranging accuracy. The interference fit keeps the PIN pin 37 and the PIN fixing groove 35 tightly connected, which can effectively avoid poor contact caused by vibration, thermal expansion and contraction, and improve the stability of signal transmission. This design reduces the contact resistance of the traditional plug-in connection, improves the electrical reliability of the laser radar module, and ensures the integrity of data transmission. The fixed connection method avoids the cracking of the solder joints caused by mechanical fatigue or vibration, improves the reliability of the PCBA board 38, and ensures the stable operation of the circuit. This structure can effectively improve the efficiency of heat conduction, quickly conduct the heat on the PCBA board 38 to the rear shell 40, and dissipate it to the external environment through the rear shell 40, so as to avoid the performance degradation or failure of the components due to high temperature. This design is particularly suitable for laser radars that run for a long time and at high loads, such as application scenarios such as autonomous driving and industrial inspection. The front shell 20 has multiple barrel holes 21, which can adapt to different types of barrel assemblies 30. This design allows the laser radar module to be modularly configured in different application scenarios, and the barrel assembly 30 can be flexibly replaced to meet the needs of different focal lengths, field of view (FOV) or detection distances, thereby improving the versatility and scalability of the product. The connector port 41 is located on the lower surface of the rear housing 40 to facilitate the connection of external devices. The design of the connector port 41 optimizes the wiring method, reduces cable bending, and improves the overall installation convenience.

[0088] The traditional lens barrel 32 and lens base 31 need to be glued together. The present invention directly produces the lens barrel 32 and lens base 31 as a whole, saving the glue assembly process and production process flow. The PIN pin 37 is fixed by the crimping process and the aluminum shell for PCBA positioning; the PIN pin 37 and the bottom hole have an interference fit, and will not loosen in high and low temperature environments and long-term vibration. The connection between the front shell 20 and the rear shell 40 is achieved by laser welding. If the front shell 20 and the rear shell 40 are encapsulated with sealant, there is a risk of aging after long-term use, affecting the lens. The front and rear shells 40 are encapsulated using a laser welding process, which permanently achieves acid-proof, waterproof, fog-proof, and dust-proof effects.

[0089] After the PCBA board 38 and the optical component AA are aligned, only simple soldering is required for fixing, and the operation is simple and efficient.

[0090] The soldering process does not require curing, and rapid welding after AA ensures the accuracy and consistency of the optical axis.

[0091] Through multi-head laser welding, multiple positions are welded simultaneously to ensure uniform heat distribution.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting the accuracy of a laser radar module, characterized in that: include: Setting up several detection boards with known positions, placing the laser radar module to be tested at the detection point at the known position, comparing the detection distance value of the laser radar module to be tested with the actual distance value calculated according to the detection board and the detection point, and adjusting the data processing cycle of the detection distance value according to the distance difference between the detection distance value and the actual distance value; When the data processing cycle is satisfied, the detection data within the data processing cycle is acquired, and a detection data set is established according to the time series, and the detection data set is processed to determine whether the laser radar module to be tested is accurate; when there is at least one over-threshold data point, the laser radar module to be tested is determined to be inaccurate, and when there is no over-threshold data point, the laser radar module to be tested is determined to be qualified; When it is determined that the laser radar module to be tested is inaccurate, extracting inaccurate features, comparing the inaccurate features with historical detection data sets, and determining the cause of the inaccuracy; A tolerable value is calculated according to the inaccurate cause and the inaccurate feature, and the laser radar modules to be tested are sorted according to the size of the tolerable value.

2. The accuracy detection method of the laser radar module according to claim 1 is characterized in that: The inaccurate features include: inaccurate data and inaccurate types. The inaccurate data is the detection data corresponding to when the laser radar module to be tested is judged to be inaccurate, and the inaccurate type is the type of the corresponding detection data.

3. The accuracy detection method of the laser radar module according to claim 2 is characterized in that: The data processing cycle is determined by the following relationship: ； in, is the data processing cycle, is the maximum distance difference of the laser radar module to be tested, is the error correction coefficient, is the maximum sampling frequency within the detection period, is the minimum sampling frequency within the detection period, is the real-time data update rate, is the measurement accuracy coefficient, is the signal strength factor of the laser radar module to be tested, is the temperature influence coefficient.

4. The accuracy detection method of the laser radar module according to claim 3 is characterized in that: When judging whether the laser radar module to be tested is accurate, it includes: The detection data set is obtained, and the detection data in the detection data set is divided into four categories, including: distance difference, temperature, received signal strength of the laser radar module to be tested, and detection time. The detection data obtained at the same detection time in the detection data set are used to establish a data group to obtain N data groups, each of which includes distance difference, temperature, received signal strength of the laser radar module to be tested, and detection time. According to the data groups, an N*M matrix is ​​obtained by arranging, wherein M=4, M is the number of types of the detection data, and the N*M matrix is ​​obtained by represents any element in the matrix, is the element of the jth class at the detection time i; Select any element in the N*M matrix as the center point, set the data judgment range, the radius of the data judgment range is R, select all elements of the same type within the data judgment range, and calculate the average value; Set the error threshold. When the difference between the average value and the average value is greater than or equal to the error threshold, it is determined is the above-threshold data point, when When the difference between the average value and the average value is less than the error threshold, it is judged that is a normal data point; When there is at least one over-threshold data point, it is determined that the laser radar module to be tested is inaccurate.

5. The method for detecting the accuracy of a laser radar module according to claim 4, characterized in that: The error threshold is calculated by the following relationship: ； in, is the error threshold, is the variance of all distance differences within the data judgment range, is the average value of all distance differences within the data judgment range, is the average value of all temperatures within the data judgment range, is the average value of the received signal strength within the data judgment range, , and is the average value of the received signal strength within the data judgment range, .

6. The method for detecting the accuracy of a laser radar module according to claim 5, characterized in that: When an imprecise cause is determined, include: Extracting the inaccurate features, and determining the inaccurate causes according to the overlap between the inaccurate features and the historical detection data set; The overlap satisfies the following relationship: ； in, is the overlap, is the i-th data point in the inexact data, is the mean of all inexact data, is the i-th data point in the historical data set, is the mean of all data points in the historical data set, is the number of all inexact data; The inaccurate data with the highest similarity is matched with the historical data set, and the inaccurate cause of the inaccurate data is determined according to the inaccurate cause in the historical data set.

7. The method for detecting the accuracy of a laser radar module according to claim 6, characterized in that: When calculating the tolerable value based on the inaccurate causes and inaccurate characteristics, it includes: ； in, is the tolerable value, is the maximum distance difference in the inexact data, is the maximum distance difference in the historical data set, is the minimum received signal strength in the inaccurate data, is the minimum received signal strength in the historical data set, is the average temperature in the inaccurate data, is the average temperature in the historical data set, is the distance difference influence coefficient, is the signal strength influence coefficient, is the temperature influence coefficient.

8. The method for detecting the accuracy of a laser radar module according to claim 7, characterized in that: Distance difference influence coefficient Obtained through the following relationship: ； Signal strength influence coefficient Obtained through the following relationship: ； Temperature influence coefficient Obtained through the following relationship: ； ； in, is the variance of all distance differences in the inexact data, is the variance of all signal strength values ​​in the inexact data, is the variance of all temperature values ​​in the inexact data, is the total variance.

9. A laser radar module, obtained by using the laser radar module accuracy detection method according to any one of claims 1 to 8, characterized in that: The laser radar module includes: A front shell, the upper part of which is connected to the light-transmitting cover plate, the front shell is provided with a plurality of lens barrel holes, and the lower surface of the front shell is provided with a lens barrel fixing pile and a first threaded hole; The lens barrel assembly comprises a lens seat and a lens barrel, wherein the lens seat and the lens barrel are an integrated structure, the lens seat is provided with a second threaded hole and a lens barrel fixing hole, and a PIN fixing groove is provided on the lower surface of the lens seat; the lens barrel fixing pile is inserted into the lens barrel fixing hole to fix the lens barrel assembly; A fixing screw passing through the first threaded hole and the second threaded hole and fixed on the front shell; A PCBA board and a PIN pin, one end of the PIN pin is fixedly connected to the PCBA board, and the other end of the PIN pin is connected to the PIN fixing groove; A heat conducting plate and a rear shell, wherein the heat conducting plate is located in the interlayer between the rear shell and the PCBA board, the rear shell is connected to the front shell, and a connector port is disposed on the lower surface of the rear shell.

10. The laser radar module according to claim 9, characterized in that: The front shell and the rear shell are connected by laser welding, and the PIN pin and the PIN fixing groove are interference fit.

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