Intelligent testing method for car navigation PCBA

Abstract

The present invention relates to an intelligent testing method for an in-vehicle navigation PCBA in the field of information technology. The method comprises: determining, based on the richness of electronic map data of the in-vehicle navigation PCBA and the GPS positioning accuracy requirements, a test range and specific parameters of environmental stress factors such as temperature, humidity, and vibration in an accelerated aging test, such as a temperature range of -40°C to 85°C, a humidity level of 85% RH, a vibration frequency of 10 Hz to 500 Hz, and a vibration amplitude of 2g; and adopting an orthogonal experimental design method to comprehensively consider different level combinations of environmental stress factors such as temperature, humidity, and vibration, construct an orthogonal table, select appropriate factor levels, and obtain an accelerated aging test scheme for multiple stress factor combinations through simulation analysis and expert review. The test scheme must cover the functional modules of the automatic voice navigation function and the optimal path search algorithm of the in-vehicle navigation PCBA.

Description

Technical Field

[0001] The present invention relates to the field of information technology, specifically to the field of testing technology of vehicle-mounted navigation PCBA, and in particular to an intelligent testing method for vehicle-mounted navigation PCBA. Background Art

[0002] Designing an accelerated aging test protocol is a key technical challenge in intelligent testing methods for in-car navigation PCBAs. First, it's important to clarify that the purpose of accelerated aging testing is to simulate the various extreme conditions a PCBA may encounter during actual use and assess its long-term reliability and lifespan. However, designing a comprehensive and targeted accelerated aging test protocol presents numerous challenges. First, the operating environment of in-car navigation PCBAs is complex and highly variable. Selecting appropriate environmental stress factors (such as high temperature, high humidity, and vibration) and determining their test range and parameters requires comprehensive consideration of actual usage scenarios and failure mechanisms. Second, different environmental stress factors may interact with each other, making the design of an accelerated aging test that combines multiple stress factors to more comprehensively assess PCBA reliability a pressing issue. Third, accelerated aging testing is often time-consuming and costly. Optimizing the test protocol and balancing test efficiency and coverage within limited resources is also a worthy challenge.

[0003] Therefore, in the intelligent testing method of in-vehicle navigation PCBA, there is an urgent need for a scientific and systematic accelerated aging test scheme design method, which comprehensively considers factors such as the actual use environment, failure mechanism, and the interaction of multiple stress factors. While meeting the test coverage, it also takes into account the test efficiency and cost, providing reliable data support for product quality control and improvement. Summary of the Invention

[0004] The present invention provides an intelligent testing method for a vehicle-mounted navigation PCBA, the method comprising the following steps:

[0005] S101. Based on the richness of the electronic map data and the GPS positioning accuracy requirements of the vehicle navigation PCBA, determine the test range and specific parameters of the environmental stress factors of temperature, humidity, and vibration in the accelerated aging test, such as a temperature range of -40°C to 85°C, a humidity level of 85% RH, a vibration frequency of 10Hz to 500Hz, and a vibration amplitude of 2g;

[0006] S102. Use an orthogonal experimental design method to comprehensively consider different level combinations of environmental stress factors such as temperature, humidity, and vibration, construct an orthogonal table, select appropriate factor levels, and, through simulation analysis and expert review, obtain an accelerated aging test plan for multiple stress factor combinations. The test plan must cover the functional modules of the automatic voice navigation function and the optimal path search algorithm of the in-vehicle navigation PCBA.

[0007] S103. The vehicle navigation PCBA test frame adopts an integrated stamping process. The test frame panel adopts a downward-extending inclined design, and the back panel and side panels adopt a connected structure. This structural design reduces the cost and size of the test frame. The test carrier adopts a positioning function, and the push rod reinforcement ribs are arranged horizontally on the outer surface of the top plate. This increases the structural force without increasing the volume and weight, thereby improving the stability of the test frame.

[0008] S104. Determine the priority and sampling ratio of test items based on the key performance parameters of the in-vehicle navigation PCBA, including voice recognition rate and path planning time, and the relationship between the accelerated aging test time and actual service life. Set the test coverage requirement for the functional modules of the automatic voice navigation and path search algorithm to 95%. Calculate the test coverage by dividing the number of tested key items by the total number of key items.

[0009] S105. During the accelerated aging test, monitor the key performance parameters of the vehicle navigation PCBA, such as voice recognition rate and path planning time, in real time. Analyze the test data to identify performance anomalies and degradation trends. Based on the monitoring results, trigger corresponding failure analysis and improvement measures, such as adjusting the test frame bevel angle and carrier positioning accuracy, and dynamically optimize the test plan, including adjusting the stress level, test time and sampling frequency, and adding or reducing test items.

[0010] S106. Use an accelerated lifespan model and Bayesian network algorithm to comprehensively analyze the accelerated aging test data of the in-vehicle navigation PCBA, evaluate the reliability level and failure patterns, identify the failure modes and influencing factors of the functional modules of the automatic voice navigation and path search algorithms, such as decreased voice recognition rate and increased path planning time, formulate reliability improvement recommendations, and propose preliminary recommendations for quality control standards for key performance parameters, such as a voice recognition rate of no less than 95% and a path planning time of no more than 5 seconds;

[0011] S107. Based on the reliability assessment results and quality control standard recommendations, establish quality control standards and monitoring specifications for vehicle navigation PCBAs. Incorporate the key failure modes and influencing factors of speech recognition rate and path planning time into the quality control standards. Develop corresponding monitoring indicators and thresholds. Embed the quality control standards and monitoring specifications into the product life cycle management process. Through continuous reliability verification and iterative improvement, improve the quality and user experience of vehicle navigation PCBAs, and provide reliable protection for the safe and stable operation of vehicle navigation systems.

[0012] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0013] The present invention discloses an intelligent testing method for an in-vehicle navigation PCBA. The method determines the test range and parameters of environmental stress factors such as temperature, humidity, and vibration based on the electronic map data and GPS positioning accuracy requirements of the in-vehicle navigation PCBA. An orthogonal experimental design method is used to construct an accelerated aging test scheme combining multiple stress factors, covering key functional modules such as automatic voice navigation and path search. The test frame adopts an integrated stamping process and a downward-extending inclined surface design to reduce costs and improve stability. Test coverage requirements are set and monitored in real time based on key performance parameters such as voice recognition rate and path planning time. Accelerated life models and Bayesian networks are used to analyze test data and evaluate reliability levels and failure patterns. Failure modes and influencing factors of key functional modules are identified, and reliability improvement recommendations and quality control standards are formed. Standards are embedded in the full life cycle management process. Through continuous verification and improvement, product quality and user experience are improved, providing reliable guarantees for the safe and stable operation of the in-vehicle navigation system. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to specific embodiments.

[0015] The intelligent testing method for the vehicle navigation PCBA of this embodiment may specifically include:

[0016] S101. Based on the richness of the electronic map data and the GPS positioning accuracy requirements of the vehicle navigation PCBA, determine the test range and specific parameters of the environmental stress factors of temperature, humidity, and vibration in the accelerated aging test, such as a temperature range of -40°C to 85°C, a humidity level of 85% RH, a vibration frequency of 10 Hz to 500 Hz, and a vibration amplitude of 2g.

[0017] S101 includes: obtaining the richness of the electronic map data of the vehicle navigation PCBA and determining the temperature, humidity, and vibration parameter ranges of the accelerated aging test. Using a support vector machine algorithm, based on pre-acquired historical accelerated aging test data, a mapping relationship model is established between the temperature, humidity, vibration frequency, vibration amplitude, the richness of the electronic map data, and the GPS positioning accuracy. The richness of the electronic map data and the GPS positioning accuracy requirements of the vehicle navigation PCBA to be tested are input into the mapping relationship model to obtain corresponding test parameters. After the test is completed, the test results of the richness of the electronic map data and GPS positioning accuracy of the vehicle navigation PCBA are compared with the indicators before the test to determine the effectiveness of the accelerated aging test, and the mapping relationship model is optimized based on the judgment results.

[0018] Specifically, based on the richness of the electronic map data for the vehicle navigation PCBA, the temperature range for the accelerated aging test was determined to be -40°C to 85°C, the humidity range to be 5% RH to 95% RH, and the vibration frequency range to be 10Hz to 2000Hz. By analyzing the GPS positioning accuracy requirements for the vehicle navigation PCBA, it was determined that the vibration frequency and amplitude for the accelerated aging test should be set to 55Hz and 35mm, respectively. Using a support vector machine algorithm, based on 1000 sets of historical accelerated aging test data, a nonlinear mapping model was established between temperature, humidity, vibration frequency, and vibration amplitude, as well as the richness of electronic map data and GPS positioning accuracy. The model achieved an accuracy rate exceeding 95%. By inputting the 95% richness of the electronic map data for the vehicle navigation PCBA under test and the 5m GPS positioning accuracy requirement into the established mapping model, the corresponding test parameters were obtained: a temperature range of -20°C to 70°C, a humidity level of 30% RH, a vibration frequency of 60Hz, and a vibration amplitude of 30mm. Based on the test parameters obtained in the previous step, the accelerated aging test equipment was set to a temperature range of -20°C to 70°C, a humidity range of 30% RH, a vibration frequency of 60Hz, and a vibration amplitude of 30mm. The accelerated aging test equipment was activated and subjected to a 15-day accelerated aging test on the in-vehicle navigation PCBA. Parameters such as temperature, humidity, vibration frequency, and vibration amplitude were monitored and recorded in real time every hour during the test. After the test, the in-vehicle navigation PCBA's electronic map data richness and GPS positioning accuracy were tested. The results showed that the electronic map data richness was 93% and the GPS positioning accuracy was 2m, which were basically consistent with the pre-test indicators, indicating that the accelerated aging test was effective. Based on the test results, the established mapping relationship model was optimized, increasing its accuracy to 97%, providing more accurate parameter references for subsequent accelerated aging tests.

[0019] S102. Utilize an orthogonal experimental design method, comprehensively considering different combinations of environmental stress factors, including temperature, humidity, and vibration, to construct an orthogonal table and select appropriate factor levels. Through simulation analysis and expert review, develop an accelerated aging test plan for multiple stress factor combinations. This test plan must cover the functional modules of the in-vehicle navigation PCBA's automatic voice navigation function and optimal path search algorithm.

[0020] S102 includes: determining the temperature, humidity, and vibration environmental stress factors and their level ranges based on the functional requirements of the vehicle navigation PCBA, and constructing a multi-factor, multi-level orthogonal table using an orthogonal experimental design method. Using the constructed orthogonal table, simulation analysis is performed to simulate the performance of the vehicle navigation PCBA under different combinations of environmental stress factors, obtaining accelerated aging effect data for each combination. The obtained simulation analysis results are statistically processed to calculate the accelerated aging coefficient for each environmental stress factor combination. Based on the calculated accelerated aging coefficients, the analytic hierarchy process is used to determine the accelerated aging effect weights for each combination. Experts in relevant fields are invited to review the obtained simulation analysis results and the determined accelerated aging effect weights. Based on the expert opinions obtained, the orthogonal experimental design is optimized and adjusted to obtain an optimized accelerated aging test plan for multiple stress factor combinations. Test cases are developed for the automatic voice navigation function of the vehicle navigation PCBA. The automatic voice navigation function is tested under the optimized accelerated aging test plan, and key performance indicators such as speech recognition accuracy and speech synthesis response time are recorded. Developed test cases for the optimal path search algorithm for the vehicle navigation PCBA, the algorithm was tested under an optimized accelerated aging test protocol, recording key performance indicators such as path search time and path planning accuracy. Combining the test results of the automatic voice navigation function and the optimal path search algorithm, the overall performance of the vehicle navigation PCBA under the optimized accelerated aging test protocol was determined. A multi-stress factor combined accelerated aging test report was generated, providing a reference for the reliability design of the vehicle navigation PCBA.

[0021] Specifically, according to the functional requirements of the vehicle navigation PCBA, the temperature range is determined to be -40℃~85℃, the humidity range is 5%~95%, and the vibration frequency range is 10Hz~2000Hz. The L9(34) orthogonal table is used to select three factors: temperature, humidity, and vibration frequency. Each factor is selected with three levels to construct a multi-factor and multi-level orthogonal table. Using ANSYS simulation software, the vehicle navigation PCBA is subjected to a thermal-structural coupling analysis under the nine environmental stress factor combination conditions designed by the orthogonal table. The displacement cloud map, stress cloud map, and temperature cloud map of the PCBA are obtained, and the maximum displacement, maximum stress, and maximum temperature data of the PCBA under the nine working conditions are extracted. The simulation analysis results are subjected to a range analysis, and the weight coefficients of the accelerated aging effect of the three environmental stress factors are calculated. The results show that the weight coefficient of temperature is 5, the weight coefficient of humidity is 3, and the weight coefficient of vibration frequency is 2. Five reliability experts were invited to review the simulation analysis results and the weighting coefficients for the accelerated aging effect. The average expert score was 2 out of 5. Based on their opinions, the vibration frequency range was adjusted to 50Hz to 1500Hz. Two test cases were designed for the automatic voice navigation function of the vehicle navigation PCBA: speech recognition accuracy and speech synthesis response time. Under the optimized accelerated aging test scheme, speech recognition accuracy decreased from 95% to 90%, while speech synthesis response time increased from 1s to 5s. Two test cases were designed for the optimal path search algorithm of the vehicle navigation PCBA: path search time and path planning accuracy. Under the optimized accelerated aging test scheme, path search time increased from 5s to 8s, while path planning accuracy decreased from 98% to 95%. Comprehensive test results show that under the optimized accelerated aging test scheme, the performance of both the automatic voice navigation function and the optimal path search algorithm of the vehicle navigation PCBA decreased slightly, but still met design requirements. The resulting accelerated aging test report with multiple stress factors can provide a reference for the reliability design of vehicle navigation PCBAs.

[0022] S103. The in-vehicle navigation PCBA test frame utilizes an integrated stamping process. The test frame panel features a downward-extending slope, and the back and side panels are connected. This structural design reduces test frame cost and size. The test carrier board incorporates a positioning function, and push rod reinforcement ribs are arranged horizontally on the outer surface of the top plate. This increases structural stress without increasing size or weight, improving test frame stability.

[0023] S103 includes: obtaining the three-dimensional model data of the vehicle navigation PCBA test frame, and using an integrated stamping process to manufacture the test frame according to the three-dimensional model data to obtain an integrated test frame main structure; for the test frame panel, adopting a downward extension design of the inclined surface, and assembling the panel with the back panel and the side panel using a connecting structure; obtaining the structural parameters of the test carrier board, and setting a positioning structure that matches the test carrier board in the test frame according to the structural parameters of the test carrier board, and realizing the precise positioning of the test carrier board in the test frame through the positioning structure; obtaining the structural parameters of the push rod reinforcement rib, and setting the push rod reinforcement rib on the outer surface of the top plate in a transverse arrangement according to the structural parameters of the push rod reinforcement rib; for the assembled test frame, obtaining the test parameters of its structural stability. Measure data, and judge whether the structural stability of the test frame meets the requirements according to a preset stability threshold. If the stability requirements are met, the design and manufacture of the test frame are completed; if the stability requirements are not met, the structural design parameters of the test frame are adjusted and optimized; for the manufactured test frame, obtain its volume and weight parameters, and judge whether the volume and weight of the test frame meet the requirements according to the preset volume and weight thresholds. If the requirements are met, the test frame is put into use; if not, the structural design of the test frame is further optimized; during the test frame being put into use, the stress state data of the test frame is obtained in real time, and the arrangement of the push rod reinforcement ribs is dynamically adjusted according to the stress state data of the test frame. By optimizing the arrangement of the push rod reinforcement ribs, the stability of the test frame during use is dynamically improved.

[0024] Specifically, the 3D model data of the vehicle navigation PCBA test frame is first obtained using 3D modeling software such as SolidWorks and exported in STEP or IGES format. The 3D model data is then imported into CAM software such as UG. The test frame is manufactured using an integrated stamping process, performed using a CNC punch press with a press pressure set to 10-20 tons and a stamping speed of 10-30 times per minute, resulting in an integrated test frame main structure. The test frame panel is designed with an inclined surface extending downward by 15-30 degrees and bolted to the back and side panels. Finite element analysis is used to optimize the test frame structure design, ensuring strength and stability while reducing manufacturing costs by 20%-30% and overall volume by 10%-20%. The structural parameters of the test carrier, such as length, width, and height, are obtained, and matching bosses and slots are installed within the test frame. The positioning accuracy is controlled to 1-2 mm, ensuring precise positioning of the test carrier within the test frame. The structural parameters of the push rod reinforcement ribs, such as cross-sectional dimensions and length, are obtained and arranged horizontally on the outer surface of the top plate with a spacing of 50-100mm. The layout of the push rod reinforcement ribs is optimized through simulation analysis to increase the stress strength by 30%-50% without increasing the volume and weight of the test frame. For the assembled test frame, the dimensional accuracy test data is obtained using a three-coordinate measuring machine. Based on the preset accuracy tolerance of ±2mm, it is determined whether it meets the requirements. If it does, the design and manufacture of the test frame is completed. If not, the design parameters are iteratively optimized until the requirements are met. For the manufactured test frame, its weight and dimensional parameters are obtained using an electronic scale and vernier caliper. Based on the preset volume and weight thresholds, it is determined whether the volume and weight of the test frame meet the requirements. If it does, the test frame is put into use. If it does not, the optimization design is returned and the weight and volume are further reduced through methods such as topology optimization. During the use of the test frame, the strain data of the test frame is obtained in real time through the strain gauge sensor. Based on the stress state reflected by the strain data, the layout of the push rod reinforcement is optimized through the genetic algorithm, which dynamically improves the stability of the test frame during use and extends its service life by 20%-30%.

[0025] S104. Determine the priority and sampling ratio of test items based on the key performance parameters of the in-vehicle navigation PCBA, including voice recognition rate and path planning time, and the relationship between the accelerated aging test time and actual service life. For the functional modules of the automatic voice navigation and path search algorithm, set the test coverage requirement to 95%. Calculate the test coverage by dividing the number of tested key items by the total number of key items.

[0026] S104 includes: obtaining key performance parameters of the vehicle navigation PCBA, the key performance parameters including speech recognition rate and path planning time; establishing a mapping model between the accelerated aging test time and the actual service life according to the corresponding relationship between the accelerated aging test time and the actual service life; determining the priority of each test item according to the importance of the key performance parameters and the mapping model, and allocating more test resources to the test items with a priority higher than a preset threshold; determining a sampling ratio for each test item according to the priority, and the sampling ratio of the test items with a priority higher than the preset threshold is greater than the preset threshold, and the sampling ratio of the test items with a priority lower than the preset threshold is less than the preset threshold. The ratio is less than a preset threshold; obtain a set of test cases for automatic voice navigation and path search algorithm, and determine the execution priority of each test case according to the importance of the test case; execute the test case, count the number of key items that have been tested, and calculate the test coverage; if the test coverage does not reach the preset threshold, continue to execute the remaining test cases according to the execution priority of the test case until the test coverage reaches the preset threshold; based on the test results, determine whether the key performance indicators of the in-vehicle navigation PCBA meet the preset requirements; if the key performance indicators do not meet the preset requirements, perform optimization and improvement, and retest until the preset requirements are met.

[0027] Specifically, to determine the key performance parameters of an in-vehicle navigation PCBA, actual testing can be used to obtain metrics such as speech recognition rate and path planning time. For example, by testing 1,000 voice commands and counting the number of correctly recognized commands, a speech recognition rate of 95% can be calculated. By performing path planning for 100 different starting and ending points and counting the planning time, an average path planning time of 8 seconds can be calculated. A mapping model can be established based on the correspondence between accelerated aging test time and actual service life. For example, by subjecting the PCBA to high-temperature and high-humidity testing, where every 100 hours corresponds to one year of actual service life, a mapping relationship can be established between accelerated aging test time and actual service life. Based on the importance of the key performance parameters and the mapping model, a hierarchical analysis method can be used to prioritize each test item. Important metrics such as speech recognition rate and path planning time are assigned higher weights and thus higher priority. For each test item, a stratified sampling method can be used to determine the sampling ratio based on the priority level. For example, for the high-priority voice recognition rate test, a sampling ratio of 20% can be used; for the medium-priority path planning time test, a sampling ratio of 10% can be used; and for the low-priority memory usage test, a sampling ratio of 5% can be used. After obtaining a set of test cases for the functional modules of the automatic voice navigation and path search algorithms, a fault tree analysis method can be used to determine the execution priority of each test case based on the degree of fault impact on the system. During test case execution, the test management tool can be used to count the number of key items tested and calculate test coverage. For example, if the total number of key items is 200 and the number of tested items is 180, the test coverage is 90%. If the test coverage does not reach 95%, higher-priority test cases are prioritized according to their execution priority until the test coverage meets the required level. Based on the test results, the analytic hierarchy process can be used to compare the key performance indicators and calculate the compliance rate of each indicator to assess whether the key performance of the in-vehicle navigation PCBA meets the requirements. If the requirements are not met, the brainstorming method is used to optimize the speech recognition algorithm, path search algorithm, etc. to form an optimization plan, and the optimization effect is verified through simulation. The optimization is continuously iterated until the performance requirements are met.

[0028] S105. During the accelerated aging test, monitor the key performance parameters of the vehicle navigation PCBA, such as speech recognition rate and path planning time, in real time. Analyze the test data to identify performance anomalies and degradation trends. Based on the monitoring results, trigger corresponding failure analysis and improvement measures, adjust the test fixture bevel angle, carrier positioning accuracy, etc., and dynamically optimize the test plan, including adjusting the stress level, test time, sampling frequency, and adding or removing test items.

[0029] S105 includes obtaining key performance parameters of the in-vehicle navigation PCBA, including speech recognition rate and path planning time, establishing a real-time monitoring mechanism for these performance parameters, and collecting and recording performance data. The collected test data is analyzed using data mining algorithms such as decision trees and support vector machines to determine whether there are performance anomalies or degradation trends. If anomalies or degradation are detected, an early warning is triggered. Based on the early warning information of performance anomalies and degradation, an expert knowledge base and case-based reasoning are used to automatically diagnose and analyze the causes of the anomalies and degradation, and to identify improvement measures. From the failure analysis results, factors requiring optimization in the test fixture parameters and carrier board accuracy are extracted. Through simulation and optimization algorithms, dynamically adjusted parameter values ​​are obtained, and an improved test plan is generated. The optimized test fixture parameters and carrier board accuracy are applied to the accelerated aging test process. A feedback control mechanism is used to dynamically adjust the test stress level, test time, and sampling frequency. During the test, key performance parameters are continuously monitored. If performance anomalies or degradation trends are detected again, the above analysis, diagnosis, and dynamic optimization steps are repeated, forming a closed-loop control system to continuously improve the test plan. Summarize all test data and analysis results, generate test reports through data visualization technology, evaluate the effectiveness of accelerated aging tests, and provide data support and decision-making basis for subsequent reliability improvements.

[0030] Specifically, key performance parameters of the vehicle navigation PCBA, such as speech recognition rate and path planning time, are collected in real time via the CAN bus, recorded once per second. The collected data is analyzed using the CART decision tree algorithm. A recognition rate below 90% or a planning time exceeding 500ms is considered an anomaly, triggering an alert. Based on an expert knowledge base, anomaly patterns are matched and CBR reasoning is used to diagnose the causes of anomalies, such as poor test fixture contact and a carrier board accuracy deviation of ±1mm. Test fixture parameters are optimized through Ansys Maxwell simulation, improving carrier board processing accuracy to ±1mm. The optimized results are applied to accelerated aging tests at 85°C and 85% humidity, adjusted every 10 minutes, and performance parameters continuously monitored. If an anomaly is detected again, the closed-loop control process is repeated. Finally, a test report is generated using Tableau. Visual analysis, such as radar charts and scatter plots, is used to evaluate the test results, providing data support for subsequent reliability improvements.

[0031] S106. Utilize an accelerated lifespan model and Bayesian network algorithms to comprehensively analyze the accelerated aging test data for the in-vehicle navigation PCBA to assess reliability and failure patterns. Identify failure modes and influencing factors for the functional modules of the automatic voice navigation and path search algorithms, such as decreased voice recognition rate and increased path planning time. Develop recommendations for reliability improvements and propose preliminary recommendations for quality control standards for key performance parameters, such as a voice recognition rate of no less than 95% and a path planning time of no more than 5 seconds.

[0032] S106 includes: obtaining a unified format data set, wherein the unified format data set includes status information of each functional module at multiple time points; establishing an accelerated life model based on the unified format data set, wherein the accelerated life model uses a Weibull distribution function, wherein β represents a shape parameter, η represents a scale parameter, t represents time, F(t) represents a failure rate, and the formula of the Weibull distribution function is F(t)=1exp(-(t / η)^β); determining the average life of key functional modules through the accelerated life model; adopting the average life of key functional modules, integrating key functional module information, and constructing a Bayesian network topology structure, wherein the parent node is the interface type and the child node is the key functional module; expressing the relationship between the interface type and the key functional module through probability, and obtaining a conditional probability table of each node; obtaining speech recognition Module status information, input the speech recognition module status information into the Bayesian network, and obtain the speech recognition rate through Bayesian network reasoning; determine whether the speech recognition rate is lower than the preset speech recognition rate threshold, and if so, trigger the early warning mechanism; obtain the path planning module status information, input the path planning module status information into the Bayesian network, and obtain the path planning time through Bayesian network reasoning; determine whether the path planning time exceeds the preset path planning time threshold, and if so, trigger the early warning mechanism; according to the triggering status of the early warning mechanism, count the number of faults and calculate the failure rate, and the failure rate is the total number of faults in the statistical period divided by the length of the statistical period; according to the failure rate, generate a reliability improvement suggestion report, and the reliability improvement suggestion report includes the failure rate and failure time to determine the direction of reliability improvement.

[0033] Specifically, assume that data is collected from an in-vehicle navigation PCBA after 1000, 2000, and 3000 hours of accelerated aging testing. This data contains status information for functional modules such as the Bluetooth module, WiFi module, voice recognition module, and path planning module. Status information is represented by 0 and 1, with 0 representing normal and 1 representing failure. This data is organized into a unified format, such as a CSV file, with each row representing the status of all modules at a given time point, such as "1000, 0, 0, 0, 0," "2000, 0, 1, 0, 0," and "3000, 1, 1, 1, 0." For the WiFi module, failure data is collected at different time points and fitted using a Weibull distribution function. Assuming that parameter estimation yields β = 5 and η = 2500, the failure rate function for the WiFi module is F(t) = 1 - exp(-(t / 2500)^5). This function can be used to calculate the failure rate of the WiFi module at any time point and further infer its average lifespan of approximately 2200 hours. Similarly, we can get the average lifespan of the Bluetooth module to be 2800 hours, the average lifespan of the voice recognition module to be 2500 hours, and the average lifespan of the path planning module to be 2300 hours. Based on the average lifespans of these key functional modules and their relationship with the interface type, a Bayesian network is constructed. For example, the WiFi module and the Bluetooth module are both connected through a wireless interface. We can use "wireless interface" as the parent node and "WiFi module" and "Bluetooth module" as child nodes. Assume that historical data statistics show that when the wireless interface is normal (status is 0), the probability of the WiFi module failing is 1, and the probability of the Bluetooth module failing is 0.5; when the wireless interface is abnormal (status is 1), the probability of the WiFi module failing is 6, and the probability of the Bluetooth module failing is 4. These probability values ​​constitute a conditional probability table. Now input the current status information of the voice recognition module as normal (0). Through Bayesian network reasoning, combined with the historical correlation data of the voice recognition module and other modules and interfaces, it is assumed that the current voice recognition rate is 96%. Since 96% is greater than 95%, no warning is triggered. Then obtain the status information of the path planning module. Assuming that its status is that the response time is 6 seconds, combined with the historical correlation data of the path planning module and other modules and interfaces, through Bayesian network reasoning, it is obtained that the current path planning time is expected to be 5 seconds. Since 5 seconds is greater than 5 seconds, the early warning mechanism is triggered. Assuming that a total of 10 early warnings are triggered within a statistical period of 168 hours, the failure rate is 10 / 168=0595 times / hour. Based on the failure rate and the time of failure, a reliability improvement report is generated. For example, the report shows: "Failure rate: 0595 times / hour, failures mainly occur around 3000 hours. It is recommended to conduct an in-depth analysis of the failure mode in this time period, such as optimizing the heat dissipation design or replacing more reliable components.

[0034] S107. Based on the reliability assessment results and quality control standard recommendations, establish quality control standards and monitoring specifications for in-vehicle navigation PCBAs. Incorporate the key failure modes and influencing factors of speech recognition rate and path planning time into the quality control standards, and develop corresponding monitoring indicators and thresholds. Embed quality control standards and monitoring specifications into the product lifecycle management process. Through continuous reliability verification and iterative improvement, enhance the quality and user experience of in-vehicle navigation PCBAs, and provide reliable assurance for the safe and stable operation of in-vehicle navigation systems.

[0035] S107 includes: obtaining a voice data stream from the vehicle navigation PCBA during actual operation, preprocessing the voice data stream, the preprocessing including removing environmental noise and irrelevant sound information; extracting acoustic feature parameters from the preprocessed voice data stream, and decoding the acoustic feature parameters using a pre-trained acoustic model to obtain preliminary text recognition results. Based on the connectivity and distance information between nodes in the road network topology diagram, an A* search algorithm is used to perform a path search, calculate at least one candidate path from the starting point to the end point, and determine the total length of the candidate path. The occurrence frequency and influencing factors of the vehicle navigation PCBA's failure mode are obtained. Based on the occurrence frequency and influencing factors, the preset quality control standards and monitoring specifications are updated, and the design and production process of the vehicle navigation PCBA are adjusted to obtain an improved vehicle navigation PCBA.

[0036] Specifically, the system collects voice data streams from the in-vehicle navigation system PCBA during actual operation. It then uses Qualcomm's QCC5100 series chips for voice preprocessing. An adaptive filtering algorithm removes ambient noise, improving the signal-to-noise ratio by over 15dB and eliminating 95% of irrelevant sound information. 39-dimensional MFCC acoustic feature parameters are extracted from the preprocessed voice data stream and decoded using a deep neural network-based acoustic model with 98% accuracy, resulting in preliminary text recognition results. This preliminary text recognition result is then compared with the in-vehicle navigation system's instruction set, which contains 500 commonly used instructions. With a 90% matching success rate, the corresponding instructions are executed. Text recognition results and corresponding voice data that fail to match are marked as negative samples and added to the training set for model optimization. The optimized model's speech recognition accuracy improves by 3%. When a user initiates a navigation request, the system collects the user's input start and end point information, calls the AutoNavi Map API, and retrieves the returned map data. Using graph theory algorithms, the system performs topological analysis on the map data, constructing a 5,000-node road network topology diagram and determining the connectivity and distance between nodes. Based on this topology, the A* search algorithm is used to search for paths, generating 10 candidate paths from the start point to the end point and calculating the length of each candidate path. By integrating the candidate path lengths with real-time traffic information, including congestion index and speed limit information, the system uses a weighted scoring model to comprehensively score each candidate path. The weight coefficients are trained using historical data, and the highest-scoring path is selected as the optimal navigation path, reducing average path planning time by 20%. The system monitors key parameters such as CPU utilization, memory usage, and chip temperature during operation of the in-vehicle navigation PCBA. Warning thresholds are set, triggering alarms and logging information when these parameters exceed the thresholds. By analyzing log information with big data, we summarized the common failure modes and influencing factors of vehicle navigation PCBAs, updated quality control standards and monitoring specifications, and optimized PCBA design and production processes. This reduced the failure rate by 10% and increased the mean time between failures by 5,000 hours.

[0037] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. Intelligent testing method for vehicle navigation PCBA, characterized by: The method comprises the following steps: S101. Determine the test range and specific parameters of the environmental stress factors of temperature, humidity, and vibration in the accelerated aging test based on the richness of the electronic map data and the GPS positioning accuracy requirements of the vehicle navigation PCBA; S102. Based on the functional requirements of the vehicle navigation PCBA and taking into account different combinations of temperature, humidity, and vibration, a multi-factor, multi-level orthogonal table is constructed. The orthogonal test scheme is optimized and adjusted through simulation analysis and expert review to obtain an optimized accelerated aging test scheme for multiple stress factor combinations. The automatic voice navigation function and optimal path search algorithm of the vehicle navigation PCBA are tested according to the accelerated aging test scheme to determine the overall performance of the vehicle navigation PCBA. S103. Based on the three-dimensional model data of the vehicle navigation PCBA test frame, the main structure of the test frame is obtained by integrated stamping. The test frame panel adopts a design with an inclined downward extension. The test frame panel, back panel, and side panels are assembled using a connecting structure. A positioning structure that matches the test carrier is provided within the test frame. Push rod reinforcement ribs are arranged horizontally on the outer surface of the top plate. The structure and design parameters of the test frame and the arrangement of the push rod reinforcement ribs are optimized and adjusted to improve the stability of the test frame. S104. Obtain key performance parameters of the vehicle navigation PCBA, including voice recognition rate and path planning time. Determine the priority of each test item based on the relationship between the accelerated aging test time and the actual service life, as well as the importance of the key performance parameters. Determine a sampling ratio based on the priority. Obtain a set of test cases for the automatic voice navigation function and the optimal path search algorithm. Test the test cases based on their execution priority until the test coverage reaches a preset threshold. Calculate the test coverage by dividing the number of tested key items by the total number of key items. S105. During the accelerated aging test, monitor the key performance parameters of the vehicle navigation PCBA, such as speech recognition rate and path planning time, in real time. Analyze the test data to identify performance anomalies and degradation trends. Based on the monitoring results, trigger corresponding failure analysis and improvement measures, and dynamically optimize the test plan, including adjusting the stress level, test time, and sampling frequency, as well as adding or removing test items. S106. Comprehensively analyze the accelerated aging test data of the in-vehicle navigation PCBA using an accelerated lifespan model and Bayesian network algorithm to assess reliability levels and failure patterns, identify failure modes and influencing factors of the automatic voice navigation function and optimal path search algorithm, formulate reliability improvement recommendations, and propose preliminary recommendations for quality control standards for key performance parameters. S107. Based on the reliability improvement suggestions and preliminary quality control standards, establish quality control standards and monitoring specifications for vehicle navigation PCBAs, incorporate the key failure modes and influencing factors of voice recognition rate and path planning time into the quality control standards, formulate corresponding monitoring indicators and thresholds, embed the quality control standards and monitoring specifications into the product life cycle management process, and continuously conduct reliability verification and iterative improvement.

2. The intelligent testing method for vehicle navigation PCBA according to claim 1, characterized in that: The S101 includes: Obtain the richness of the electronic map data of the vehicle navigation PCBA and determine the temperature, humidity and vibration parameter ranges of the accelerated aging test; Using a support vector machine algorithm, based on pre-acquired historical accelerated aging test data, a mapping relationship model between the temperature, humidity, vibration frequency, vibration amplitude, the richness of the electronic map data, and the GPS positioning accuracy is established; Input the richness of the electronic map data and the GPS positioning accuracy requirements of the vehicle navigation PCBA to be tested into the mapping relationship model to obtain corresponding test parameters; After the test is completed, the test results of the electronic map data richness and GPS positioning accuracy of the vehicle navigation PCBA are compared with the indicators before the test to determine the effect of the accelerated aging test, and the mapping relationship model is optimized based on the determination results.

3. The intelligent testing method for vehicle navigation PCBA according to claim 1, characterized in that: The S102 includes: According to the functional requirements of the vehicle navigation PCBA, the temperature, humidity and vibration environmental stress factors and their level ranges are determined, and the orthogonal experimental design method is used to construct a multi-factor and multi-level orthogonal table; Based on the constructed orthogonal table, a simulation analysis method was used to simulate the performance of the vehicle navigation PCBA under different combinations of environmental stress factors, and the accelerated aging effect data under each combination was obtained; Perform statistical processing on the obtained simulation analysis results, calculate the accelerated aging coefficient of each combination of environmental stress factors, and use the hierarchical analysis method to determine the accelerated aging effect weight of each combination based on the calculated accelerated aging coefficient; Invite experts in related fields to review the obtained simulation analysis results and the determined accelerated aging effect weights. Based on the obtained expert opinions, the orthogonal experimental design scheme is optimized and adjusted to obtain the optimized multi-stress factor combination accelerated aging test scheme; Develop test cases for the automatic voice navigation function of the vehicle navigation PCBA. Test the automatic voice navigation function under the optimized accelerated aging test plan, and record the key performance indicators of speech recognition accuracy and speech synthesis response time. Develop corresponding test cases for the optimal path search algorithm of the vehicle navigation PCBA. Test the optimal path search algorithm under the optimized accelerated aging test scheme, and record the key performance indicators of path search time and path planning accuracy. Based on the test results of the automatic voice navigation function and the optimal path search algorithm, the comprehensive performance of the vehicle navigation PCBA under the optimized accelerated aging test scheme is judged, and a multi-stress factor combination accelerated aging test report is formed to provide a reference for the reliability design of the vehicle navigation PCBA.

4. The intelligent testing method for vehicle navigation PCBA according to claim 1, characterized in that: The S103 further includes: Acquire three-dimensional model data of a vehicle navigation PCBA test frame, and manufacture the test frame using an integrated stamping process based on the three-dimensional model data to obtain an integrated test frame main structure; Acquiring structural parameters of the test carrier, and setting a positioning structure matching the test carrier in the test frame according to the structural parameters of the test carrier, so as to achieve precise positioning of the test carrier in the test frame through the positioning structure; Obtaining structural parameters of the push rod reinforcement ribs, and arranging the push rod reinforcement ribs on the outer surface of the top plate in a transverse arrangement according to the structural parameters of the push rod reinforcement ribs; For the assembled test frame, obtain the test data of its structural stability, and determine whether the structural stability of the test frame meets the requirements based on the preset stability threshold. If the stability requirements are met, complete the design and manufacture of the test frame; if not, adjust and optimize the structural design parameters of the test frame; For the manufactured test frame, obtain its volume and weight parameters, and determine whether the volume and weight of the test frame meet the requirements based on the preset volume and weight thresholds. If the requirements are met, put the test frame into use; if not, further optimize the structural design of the test frame; During the use of the test frame, the stress status data of the test frame is obtained in real time. According to the stress status data of the test frame, the arrangement of the push rod reinforcement ribs is dynamically adjusted. By optimizing the arrangement of the push rod reinforcement ribs, the stability of the test frame during use is dynamically improved.

5. The intelligent testing method for vehicle navigation PCBA according to any one of claims 1 to 4, characterized in that: The S104 further includes: According to the corresponding relationship between the accelerated aging test time and the actual service life, a mapping model between the accelerated aging test time and the actual service life is established; Determine the priority of each test item according to the importance of the key performance parameter and the mapping model, and allocate more test resources to the test items whose priority is higher than a preset threshold; For each of the test items, a sampling ratio is determined according to the priority level, wherein the sampling ratio of the test items whose priority level is higher than a preset threshold is greater than the preset threshold, and the sampling ratio of the test items whose priority level is lower than the preset threshold is less than the preset threshold; Obtaining a set of test cases for automatic voice navigation and path search algorithms, and determining an execution priority of each test case based on the importance of the test case; Execute the test cases, count the number of key items tested, and calculate the test coverage; If the test coverage rate does not reach the preset threshold, the remaining test cases are continued to be executed according to the execution priority of the test cases until the test coverage rate reaches the preset threshold; According to the test results, determine whether the key performance indicators of the vehicle navigation PCBA meet the preset requirements; If the key performance indicators do not meet the preset requirements, optimization and improvement will be carried out and retesting will be carried out until the preset requirements are met.

6. The intelligent testing method for vehicle navigation PCBA according to any one of claims 1 to 4, characterized in that: The S107 includes: Acquire the voice data stream of the vehicle navigation PCBA during actual operation, and preprocess the voice data stream, wherein the preprocessing includes removing environmental noise and irrelevant sound information; Extracting acoustic feature parameters from the preprocessed speech data stream and decoding the acoustic feature parameters using a pre-trained acoustic model to obtain preliminary text recognition results; Based on the connectivity and distance information between nodes in the road network topology diagram, an A* search algorithm is used to perform a path search, calculate at least one candidate path from the starting point to the end point, and determine the total length of the candidate path; Obtain the occurrence frequency and influencing factors of the failure mode of the vehicle navigation PCBA, update the preset quality control standards and monitoring specifications based on the occurrence frequency and influencing factors, and adjust the design and production process of the vehicle navigation PCBA to obtain an improved vehicle navigation PCBA.