Intelligent test method of vehicle-mounted navigation PCBA (Printed Circuit Board Assembly)

Abstract

The invention relates to an intelligent test method of a vehicle-mounted navigation PCBA (Printed Circuit Board Assembly) in the technical field of information, which comprises the following steps of: determining test ranges and specific parameters of environmental stress factors of temperature, humidity and vibration in an accelerated aging test according to data richness of an electronic map of the vehicle-mounted navigation PCBA and GPS (Global Position System) positioning accuracy requirements, for example, the temperature range is-40 DEG C to 85 DEG C, the humidity level is 85% RH, and the temperature range is-40 DEG C to 85 DEG C; the vibration frequency is 10 Hz to 500 Hz, and the vibration amplitude is 2 g; an orthogonal test design method is adopted, different level combinations of environmental stress factors such as temperature, humidity and vibration are comprehensively considered, an orthogonal table is constructed, proper factor levels are selected, and an accelerated aging test scheme of multiple stress factor combinations is obtained through simulation analysis and expert review. The test scheme needs to cover an automatic voice navigation function of the vehicle-mounted navigation PCBA and a function module of an optimal path search algorithm.

Description

Technical Field

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

[0002] In the intelligent testing method of vehicle navigation PCBA, how to design an accelerated aging test scheme is a key technical issue. First of all, it is necessary to clarify that the purpose of accelerated aging test is to simulate various extreme conditions that PCBA may encounter in actual use and evaluate its long-term reliability and life. However, how to design a comprehensive and targeted accelerated aging test scheme faces many challenges. First, the use environment of vehicle navigation PCBA is complex and changeable. How to select appropriate environmental stress factors (such as high temperature, high humidity, vibration, etc.) and determine its test range and parameters requires comprehensive consideration of actual use scenarios and failure mechanisms. Second, there may be interactions between different environmental stress factors. How to design an accelerated aging test with a combination of multiple stress factors to more comprehensively evaluate the reliability of PCBA is an urgent problem to be solved. Third, the time and cost of accelerated aging test are often high. How to optimize the test scheme and balance test efficiency and test coverage under limited resource conditions is also a problem worth exploring.

[0003] Therefore, in the intelligent testing method of vehicle navigation PCBA, a scientific and systematic accelerated aging test scheme design method is urgently needed, which comprehensively considers factors such as the actual use environment, failure mechanism, and the interaction of multiple stress factors, while meeting the test coverage, taking into account the test efficiency and cost, and 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. According to the richness of electronic map data and GPS positioning accuracy requirements of the vehicle navigation PCBA, determine the test range and specific parameters of 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 the orthogonal test design method to comprehensively consider the different level combinations of environmental stress factors such as temperature, humidity and vibration, construct an orthogonal table, select the appropriate factor level, and obtain the 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 vehicle navigation PCBA;

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

[0008] S104. According to the key performance parameters of the vehicle navigation PCBA, including the voice recognition rate and the path planning time, and in combination with the relationship between the accelerated aging test time and the actual service life, the priority and sampling ratio of the test items are determined. For the functional modules of the automatic voice navigation and path search algorithm, the test coverage requirement is set to 95%, and the test coverage is calculated by dividing the number of tested key items by the total number of key items.

[0009] S105. During the accelerated aging test, the key performance parameters of the vehicle navigation PCBA, such as the voice recognition rate and path planning time, are monitored in real time. The test data is analyzed to find performance anomalies and degradation trends. According to the monitoring results, corresponding failure analysis and improvement measures are triggered, such as adjusting the angle of the test frame bevel, the positioning accuracy of the carrier, etc., and the test plan is dynamically optimized, including adjusting the stress level, test time and sampling frequency, as well as adding or reducing test items.

[0010] S106. Comprehensively analyze the accelerated aging test data of the vehicle navigation PCBA using the accelerated life model and Bayesian network algorithm, evaluate the reliability level and failure pattern, identify the failure mode and influencing factors of the functional modules of the automatic voice navigation and path search algorithm, such as the decline in voice recognition rate and the increase in path planning time, form reliability improvement suggestions, and put forward preliminary suggestions for quality control standards such as key performance parameters such as voice recognition rate not less than 95% and path planning time not 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 PCBA, incorporate the key failure modes and influencing factors of speech recognition rate and path planning time into the quality control standards, formulate corresponding monitoring indicators and thresholds, and embed 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 PCBA, and provide reliable protection for the safe and stable operation of the vehicle navigation system.

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

[0013] The invention discloses an intelligent testing method for a vehicle navigation PCBA. The test range and parameters of environmental stress factors such as temperature, humidity and vibration are determined according to the electronic map data and GPS positioning accuracy requirements of the vehicle navigation PCBA. An orthogonal experimental design method is adopted to construct an accelerated aging test scheme with a combination of multiple stress factors, covering key functional modules such as automatic voice navigation and path search. The test frame adopts an integrated stamping molding process and a downward extension design of the inclined plane to reduce costs and improve stability. According to the key performance parameters of voice recognition rate and path planning time, the test coverage requirements are set and monitored in real time. Accelerated life model and Bayesian network are used to analyze test data to evaluate reliability level and failure law. Failure modes and influencing factors of key functional modules are identified to form reliability improvement suggestions and quality control standards. The standards are embedded in the full life cycle management process, and product quality and user experience are improved through continuous verification and improvement, providing reliable guarantee for the safe and stable operation of the 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 in conjunction with specific embodiments.

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

[0016] S101. According to the richness of the electronic map data of the vehicle navigation PCBA and the requirements for GPS positioning accuracy, 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.

[0017] S101 includes: obtaining the richness of the electronic map data of the vehicle navigation PCBA, and determining the temperature, humidity and vibration parameter range of the accelerated aging test. Using the support vector machine algorithm, based on the historical accelerated aging test data acquired in advance, a mapping relationship model between the temperature, humidity, vibration frequency, vibration amplitude and the richness of the electronic map data and GPS positioning accuracy is established. 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 the corresponding test parameters. After the test is completed, the detection results of the richness of the electronic map data and the GPS positioning accuracy of the vehicle navigation PCBA are compared with the indicators before the test to judge the effect of the accelerated aging test, and the mapping relationship model is optimized based on the judgment result.

[0018] Specifically, according to the richness of the electronic map data of the vehicle navigation PCBA, it can be determined that the temperature range in the accelerated aging test is -40℃ to 85℃, the humidity range is 5%RH to 95%RH, and the vibration frequency range is 10Hz to 2000Hz. By analyzing the GPS positioning accuracy requirements of the vehicle navigation PCBA, it can be determined that the vibration frequency in the accelerated aging test is set to 55Hz and the vibration amplitude is set to 35mm. Using the support vector machine algorithm, based on 1000 sets of historical accelerated aging test data, a nonlinear mapping relationship model between temperature, humidity, vibration frequency and vibration amplitude and the richness of electronic map data and GPS positioning accuracy is established, and the model accuracy rate reaches more than 95%. The richness of the electronic map data of the vehicle navigation PCBA to be tested is 95% and the GPS positioning accuracy requirement is 5m. Input into the established mapping relationship model, the corresponding test parameters of the temperature range is -20℃ to 70℃, the humidity level is 30%RH, the vibration frequency is 60Hz, and the vibration amplitude is 30mm. According to the test parameters obtained in the previous step, the temperature of the accelerated aging test equipment is set to -20℃ to 70℃, the humidity is 30%RH, the vibration frequency is 60Hz, and the vibration amplitude is 30mm. Start the accelerated aging test equipment and conduct a 15-day accelerated aging test on the vehicle navigation PCBA. During the test, the temperature, humidity, vibration frequency and vibration amplitude and other parameters are monitored and recorded in real time every 1 hour. After the test is completed, the richness of the electronic map data and the GPS positioning accuracy of the vehicle navigation PCBA are tested. The results show that the richness of the electronic map data is 93% and the GPS positioning accuracy is 2m, which is basically consistent with the indicators before the test, indicating that the accelerated aging test is effective. At the same time, according to the test results, the established mapping relationship model is optimized to improve the model accuracy to 97%, providing a more accurate parameter reference for subsequent accelerated aging tests.

[0019] S102. Use the orthogonal test design method to comprehensively consider the different level combinations of environmental stress factors such as temperature, humidity and vibration, construct an orthogonal table, and select the appropriate factor level. Through simulation analysis and expert review, obtain the accelerated aging test scheme for multiple stress factor combinations. The test scheme must cover the functional modules of the automatic voice navigation function and the optimal path search algorithm of the vehicle navigation PCBA.

[0020] S102 includes: according to the functional requirements of the vehicle navigation PCBA, determine the temperature, humidity, vibration environmental stress factors and their level ranges, and use the orthogonal test design method to construct a multi-factor multi-level orthogonal table. For the constructed orthogonal table, a simulation analysis method is 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 is obtained. The obtained simulation analysis results are statistically processed, and the accelerated aging coefficients of each combination of environmental stress factors are calculated. According to the size of the calculated accelerated aging coefficient, the hierarchical analysis method is used to determine the accelerated aging effect weights of each combination. Invite experts in related fields to review the obtained simulation analysis results and the determined accelerated aging effect weights, and optimize and adjust the orthogonal test design scheme based on the obtained expert opinions to obtain the optimized multi-stress factor combination accelerated aging test scheme. For the automatic voice navigation function of the vehicle navigation PCBA, a corresponding test case is formulated, and the automatic voice navigation function is tested under the optimized accelerated aging test scheme, and the key performance indicators of speech recognition accuracy and speech synthesis response time are recorded. For the optimal path search algorithm of the vehicle navigation PCBA, the corresponding test cases are formulated, and the optimal path search algorithm is tested under the optimized accelerated aging test scheme, and the key performance indicators of path search time and path planning accuracy are recorded. The test results of the automatic voice navigation function and the optimal path search algorithm are comprehensively obtained to judge the comprehensive performance of the vehicle navigation PCBA under the optimized accelerated aging test scheme, and a multi-stress factor combined accelerated aging test report is formed to provide 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 of temperature, humidity and vibration frequency, and three levels are selected for each factor to construct a multi-factor multi-level orthogonal table. Using ANSYS simulation software, the vehicle navigation PCBA is subjected to thermal-structural coupling analysis under the 9 environmental stress factor combination conditions designed by the orthogonal table, and the displacement cloud map, stress cloud map and temperature cloud map of the PCBA are obtained. The maximum displacement, maximum stress and maximum temperature data of the PCBA under the 9 working conditions are extracted. The simulation analysis results are subjected to 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 weight coefficient of the accelerated aging effect. The average expert score was 2 points (out of 5 points). According to the expert opinions, the vibration frequency range was adjusted to 50Hz~1500Hz. For the automatic voice navigation function of the vehicle navigation PCBA, two test cases were designed for voice recognition accuracy and voice synthesis response time. Under the optimized accelerated aging test scheme, the voice recognition accuracy dropped from 95% to 90%, and the voice synthesis response time increased from 1s to 5s. For the best path search algorithm of the vehicle navigation PCBA, two test cases were designed for path search time and path planning accuracy. Under the optimized accelerated aging test scheme, the path search time increased from 5s to 8s, and the path planning accuracy dropped from 98% to 95%. The comprehensive test results show that under the optimized accelerated aging test scheme, the performance of the automatic voice navigation function and the best path search algorithm of the vehicle navigation PCBA has declined, but still meets the design requirements. The formed multi-stress factor combined accelerated aging test report can provide a reference for the reliability design of the vehicle navigation PCBA.

[0022] S103. The PCBA test frame for vehicle navigation adopts an integrated stamping process. The test frame panel adopts a downward extending inclined design, and the back panel and side panels adopt a connecting structure. The structural design reduces the cost and volume 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, which increases the structural force without increasing the volume and weight, and improves the stability of the test frame.

[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, using an inclined downward extension design, and assembling the panel with the back panel and the side panel using a connecting structure; obtaining the structural parameters of the test carrier, and according to the structural parameters of the test carrier, setting a positioning structure matching the test carrier in the test frame, and realizing the precise positioning of the test carrier in the test frame through the positioning structure; obtaining the structural parameters of the push rod reinforcement ribs, and according to the structural parameters of the push rod reinforcement ribs, setting the push rod reinforcement ribs on the outer surface of the top plate in a transverse arrangement; for the assembled test frame, obtaining the test parameters of its structural stability. The test frame is designed and manufactured according to the preset stability threshold. If the stability requirement is met, the design and manufacture of the test frame is completed. If the stability requirement is not met, the structural design parameters of the test frame are adjusted and optimized. For the manufactured test frame, its volume and weight parameters are obtained. According to the preset volume and weight thresholds, it is determined whether the volume and weight of the test frame meet the requirements. If the requirements are met, the test frame is put into use. If the requirements are not met, the structural design of the test frame is further optimized. During the use of the test frame, the stress state data of the test frame is obtained in real time. According to the stress state 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.

[0024] Specifically, firstly, the 3D model data of the vehicle navigation PCBA test frame is obtained through 3D modeling software such as SolidWorks, and exported to STEP or IGES format. Then the 3D model data is imported into CAM software such as UG, and the test frame is manufactured and processed by an integrated stamping process. The stamping is completed by a CNC punch press, and the stamping pressure is set to 10-20 tons and the stamping speed is 10-30 times / minute to obtain an integrated test frame main structure. For the test frame panel, a design of a 15-30 degree downward extension of the inclined plane is adopted, and the back plate and side plate are connected by bolts. The test frame structure design is optimized by finite element analysis, while ensuring the strength and stability of the test frame, the manufacturing cost is reduced by 20%-30%, and the overall volume is reduced by 10%-20%. The structural parameters of the test carrier, such as the length, width and height, are obtained, and a boss and slot positioning structure matching it is set in the test frame, and the positioning accuracy is controlled at 1-2mm to achieve accurate positioning of the test carrier in the test frame. Obtain the structural parameters of the push rod reinforcement, such as cross-sectional dimensions and length, and set them on the outer surface of the top plate in a horizontal arrangement with a spacing of 50-100mm. Optimize the arrangement of the push rod reinforcement through simulation analysis, and increase the force strength by 30%-50% without increasing the volume and weight of the test frame. For the assembled test frame, obtain the test data of its dimensional accuracy through a three-coordinate measuring instrument, and judge whether it meets the requirements according to the preset accuracy tolerance of ±2mm. If it meets the requirements, complete the design and manufacture of the test frame. If not, iterate and optimize the design parameters until the requirements are met. For the manufactured test frame, obtain its weight and dimensional parameters through an electronic scale and a vernier caliper, and judge whether the volume and weight of the test frame meet the requirements according to the preset volume and weight thresholds. If it meets the requirements, put the test frame into use. If it does not meet the requirements, return to the optimized design, and further reduce the weight and volume through methods such as topological optimization. When the test frame is put into use, the strain data of the test frame is obtained in real time through the strain gauge sensor. According to the stress state reflected by the strain data, the arrangement of the push rod reinforcement ribs 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. According to the key performance parameters of the vehicle navigation PCBA, including voice recognition rate and path planning time, and in combination with the relationship between the accelerated aging test time and the actual service life, determine the priority and sampling ratio of the test items. For the functional modules of the automatic voice navigation and path search algorithm, set the test coverage requirement to 95%, and 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, wherein the key performance parameters include 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 greater 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 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, in order to obtain the key performance parameters of the vehicle navigation PCBA, the voice recognition rate, path planning time and other indicator data can be obtained through actual testing. For example, by performing recognition tests on 1,000 voice commands, the number of correctly recognized commands is counted, and the voice recognition rate is calculated to be 95%; by performing path planning for 100 different starting points and end points, the planning time is counted, and the average path planning time is calculated to be 8 seconds. According to the correspondence between the accelerated aging test time and the actual service life, a mapping model can be established. For example, by conducting high temperature and high humidity tests on PCBA, every 100 hours corresponds to 1 year of actual service life, and the mapping relationship between the accelerated aging test time and the actual service life can be established. Based on the importance of key performance parameters and the mapping model, the hierarchical analysis method can be used to determine the priority of each test item, and for important indicators such as voice recognition rate and path planning time, a higher weight is given, and the priority is higher. For each test item, the sampling ratio can be determined by the stratified sampling method according to the priority level. For example, for the high-priority speech 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; for the low-priority memory usage test, a sampling ratio of 5% can be used. After obtaining the test case set of the functional modules of the automatic voice navigation and path search algorithm, the fault tree analysis method can be used to determine the execution priority of each test case according to the degree of impact of the fault on the system. When executing the test case, the number of key items tested can be counted through the test management tool to calculate the 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%, the high-priority test cases are executed first according to the execution priority of the test case until the test coverage reaches the standard. According to the test results, the hierarchical analysis method can be used to compare the key performance indicators, calculate the compliance rate of each indicator, and evaluate whether the key performance of the 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 and other aspects to form an optimization plan. The optimization effect is verified through simulation, and the optimization is continuously iterated until the performance requirements are met.

[0028] S105. During the accelerated aging test, the key performance parameters of the voice recognition rate and path planning time of the vehicle navigation PCBA are monitored in real time, and the test data is analyzed to find performance anomalies and degradation trends. According to the monitoring results, the corresponding failure analysis and improvement measures are triggered, and the angle of the test frame and the positioning accuracy of the carrier are adjusted to dynamically optimize the test plan, including adjusting the stress level, test time and sampling frequency, and adding or reducing test items.

[0029] S105 includes: obtaining key performance parameters of the vehicle navigation PCBA, including voice recognition rate and path planning time, establishing a real-time monitoring mechanism for performance parameters, and collecting and recording performance data. The collected test data is analyzed, and data mining algorithms such as decision trees and support vector machines are used to determine whether there are performance anomalies and degradation trends. If anomalies and degradation are detected, an early warning is triggered. According to the early warning information of performance anomalies and degradation, the expert knowledge base and case reasoning methods are used to automatically diagnose and analyze the causes of performance anomalies and degradation, and determine improvement measures. From the results of the failure analysis, the elements that need to be optimized in the test frame parameters and the board accuracy are extracted, and the dynamically adjusted parameter values ​​are obtained through simulation and optimization algorithms to generate an improved test plan. The optimized test frame parameters and board accuracy are applied to the accelerated aging test process, and the test stress level, test time and sampling frequency are dynamically adjusted through the feedback control mechanism. During the test process, the key performance parameters are continuously monitored. If performance anomalies and degradation trends are found again, the above analysis, diagnosis and dynamic optimization steps are repeated to form a closed-loop control and continuously improve the test plan. Summarize all test data and analysis results, generate test reports through data visualization technology, evaluate the effect of accelerated aging test, and provide data support and decision-making basis for subsequent reliability improvement.

[0030] Specifically, the key performance parameters of the vehicle navigation PCBA, such as the speech recognition rate and path planning time, are collected in real time through the CAN bus and recorded once per second. The collected data is analyzed using the CART decision tree algorithm. If the recognition rate is lower than 90% or the planning time is greater than 500ms, it is considered abnormal and triggers an early warning. Based on the expert knowledge base to match the abnormal pattern, CBR reasoning is used to diagnose the abnormal cause, such as poor contact of the test frame and ±1mm deviation of the carrier board accuracy. The test frame parameters are optimized through Ansys Maxwell simulation, and the carrier board processing accuracy is improved to ±1mm. The optimization results are applied to the accelerated aging test, with a temperature of 85℃ and a humidity of 85%, adjusted every 10 minutes, and the performance parameters are continuously monitored. If an abnormality is detected again, the above closed-loop control process optimization is repeated. Finally, Tableau is used to generate a test report, and the test effect is evaluated through visual analysis such as radar charts and scatter plots, providing data support for subsequent reliability improvements.

[0031] S106. Comprehensively analyze the accelerated aging test data of the vehicle navigation PCBA using the accelerated life model and Bayesian network algorithm to evaluate the reliability level and failure pattern. Identify the failure modes and influencing factors of the functional modules of the automatic voice navigation and path search algorithm, such as the decline in voice recognition rate and the increase in path planning time, form reliability improvement suggestions, and put forward preliminary suggestions for quality control standards such as key performance parameters such as voice recognition rate not less than 95% and path planning time not more than 5 seconds.

[0032] S106 includes: obtaining a unified format data set, the unified format data set includes status information of each functional module at multiple time points; establishing an accelerated life model according to the unified format data set, using the Weibull distribution function in the accelerated life model, 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; using the average life of the key functional modules, integrating the 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, 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, 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, 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, the reliability improvement suggestion report contains the failure rate and failure time, and determines the reliability improvement direction.

[0033] Specifically, assume that data of the vehicle navigation PCBA under 1000 hours, 2000 hours, and 3000 hours of accelerated aging tests are collected, including the status information of functional modules such as the Bluetooth module, WiFi module, voice recognition module, and path planning module. The status information is represented by 0 and 1, 0 represents normal, and 1 represents failure. These data are organized into a unified format, such as a CSV file, and each row represents the status of all modules at a time point, such as "1000, 0, 0, 0, 0", "2000, 0, 1, 0, 0", and "3000, 1, 1, 1, 0". For the Wi F i module, its failure data at different time points are collected, and its failure data is fitted using the Weibull distribution function. Assuming that β = 5 and η = 2500 are obtained through parameter estimation, the failure rate function of the WiFi module is F(t) = 1-exp(-(t / 2500)^5). Through this function, the failure rate of the WiFi module at any time point can be calculated, and its average life span is further deduced to be about 2200 hours. Similarly, it can be obtained that the average life of the Bluetooth module is 2800 hours, the average life of the speech recognition module is 2500 hours, and the average life of the path planning module is 2300 hours. According to the average life 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. The "wireless interface" can be used as the parent node, and the "WiFi module" and "Bluetooth module" can be used as child nodes. Assume that historical data statistics show that when the wireless interface is normal (status is 0), the probability of failure of the WiFi module is 1, and the probability of failure of the Bluetooth module is 05; when the wireless interface is abnormal (status is 1), the probability of failure of the WiFi module is 6, and the probability of failure of the Bluetooth module is 4. These probability values ​​constitute a conditional probability table. Now input the current state information of the speech recognition module as normal (0). Through Bayesian network reasoning, combined with the historical association data of the speech recognition module with other modules and interfaces, it is assumed that the current speech recognition rate is derived to be 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, combine the historical correlation data of the path planning module with other modules and interfaces, and use Bayesian network reasoning to obtain 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. Generate a reliability improvement report based on the failure rate and the time of failure. 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 vehicle navigation PCBA, incorporate the key failure modes and influencing factors of speech recognition rate and path planning time into the quality control standards, and formulate corresponding monitoring indicators and thresholds. Embed quality control standards and monitoring specifications into the product life cycle management process, improve the quality and user experience of vehicle navigation PCBA through continuous reliability verification and iterative improvement, and provide reliable protection for the safe and stable operation of the vehicle navigation system.

[0035] S107 includes: obtaining the voice data stream of 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. According to the connectivity relationship and distance information between the nodes in the road network topology diagram, the A* search algorithm is used to search for paths, at least one candidate path from the starting point to the end point is calculated, and the total length of the candidate path is determined. The occurrence frequency and influencing factors of the failure mode of the vehicle navigation PCBA are obtained, and according to 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 voice data stream of the vehicle navigation PCBA during actual operation is collected, and the Qualcomm QCC5100 series chip is used for voice preprocessing. The environmental noise is removed by the adaptive filtering algorithm, the signal-to-noise ratio is increased by more than 15dB, and the rejection rate of irrelevant sound information reaches 95%. The 39-dimensional MFCC acoustic feature parameters are extracted from the preprocessed voice data stream, and the acoustic feature parameters are decoded using an acoustic model based on a deep neural network. The decoding accuracy rate reaches 98%, and the preliminary text recognition results are obtained. The preliminary text recognition results are compared with the instruction set of the vehicle navigation system. The instruction set contains 500 commonly used instructions, and the matching success rate is 90%. The corresponding instructions are executed. The text recognition results and the corresponding voice data that failed to match are marked as negative samples, added to the training set for model optimization, and the voice recognition accuracy of the optimized model is increased by 3%. When the user initiates a navigation request, the start and end point information entered by the user is collected, the Amap API interface is called, the returned map data is obtained, and the topological structure of the map data is analyzed using graph theory algorithms. A road network topology diagram containing 5,000 nodes is constructed to determine the connectivity and distance information between the nodes. According to the road network topology diagram, the A* search algorithm is used to search for paths, 10 candidate paths from the start point to the end point are calculated, and the length of each candidate path is calculated. The candidate path length and real-time traffic information, including road congestion index and speed limit information, are integrated, and each candidate path is comprehensively scored using a weighted scoring model. The weight coefficient is obtained through historical data training, and the path with the highest score is selected as the optimal navigation path, shortening the average path planning time by 20%. The key parameters such as CPU utilization, memory occupancy, and chip temperature during the operation of the vehicle navigation PCBA are monitored, and warning thresholds are set. When the parameters exceed the threshold, an alarm is triggered and log information is recorded. By analyzing log information with big data, we summarized the common failure modes and influencing factors of vehicle navigation PCBA, updated the quality control standards and monitoring specifications, optimized the PCBA design and production process, 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 is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. Intelligent testing method for vehicle navigation PCBA, characterized in that: The method comprises the following steps: S101. Determine the test range and specific parameters of environmental stress factors such as temperature, humidity and vibration in the accelerated aging test according to the richness of the electronic map data of the vehicle navigation PCBA and the requirements for GPS positioning accuracy; S102. Use the orthogonal test design method to comprehensively consider the different level combinations of environmental stress factors such as temperature, humidity and vibration, construct an orthogonal table, select the appropriate factor level, and obtain the 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 vehicle navigation PCBA; S103. The PCBA test frame for vehicle navigation adopts an integrated stamping process. The test frame panel adopts a downward extending inclined surface design. The back panel and the side panel adopt a connecting structure. The structural design reduces the cost and volume of the test frame. The test carrier adopts a positioning function. The push rod reinforcement ribs are arranged horizontally on the outer surface of the top plate. The structural force is increased without increasing the volume and weight, thereby improving the stability of the test frame. S104. According to the key performance parameters of the vehicle navigation PCBA, including the voice recognition rate and the path planning time, and in combination with the relationship between the accelerated aging test time and the actual service life, the priority and sampling ratio of the test items are determined. For the functional modules of the automatic voice navigation and path search algorithm, the test coverage requirement is set to 95%, and the test coverage is calculated 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 voice recognition rate and path planning time of the vehicle navigation PCBA in real time, analyze the test data, find performance anomalies and degradation trends, trigger corresponding failure analysis and improvement measures based on the monitoring results, and dynamically optimize the test plan, including adjusting the stress level, test time and sampling frequency, and adding or reducing test items; S106. Use the accelerated life model and Bayesian network algorithm to comprehensively analyze the accelerated aging test data of the vehicle navigation PCBA, evaluate the reliability level and failure pattern, identify the failure mode and influencing factors of the functional modules of the automatic voice navigation and path search algorithm, form reliability improvement suggestions, and put forward preliminary suggestions for the quality control standards of key performance parameters; S107. Based on the reliability assessment results and quality control standard recommendations, establish quality control standards and monitoring specifications for vehicle navigation PCBA, incorporate the key failure modes and influencing factors of speech recognition rate and path planning time into the quality control standards, formulate corresponding monitoring indicators and thresholds, and embed 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 PCBA, and provide reliable protection for the safe and stable operation of the vehicle navigation system.

2. The intelligent testing method for vehicle navigation PCBA according to claim 1 is 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 the previously acquired historical accelerated aging test data, a mapping relationship model between the temperature, humidity, vibration frequency, vibration amplitude and 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 richness of the electronic map data and the 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 is 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, the simulation analysis method is 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 is 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 weight of the accelerated aging effect of each combination according to the calculated accelerated aging coefficient; Invite experts in related fields to review the obtained simulation analysis results and the determined accelerated aging effect weights, and optimize and adjust the orthogonal test design scheme based on the obtained expert opinions to obtain the optimized multi-stress factor combination accelerated aging test scheme; For the automatic voice navigation function of the vehicle navigation PCBA, formulate corresponding test cases, test the automatic voice navigation function under the optimized accelerated aging test scheme, and record the key performance indicators of voice recognition accuracy and voice synthesis response time; Formulate 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 best 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 combined 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 is characterized in that: The S103 includes: Acquire the three-dimensional model data of the vehicle navigation PCBA test frame, and use the 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 rack panel, a downward extending inclined surface design is adopted, and the panel is assembled with the back panel and the side panels using a connecting structure; Acquire structural parameters of the test carrier, and according to the structural parameters of the test carrier, set a positioning structure matching the test carrier in the test frame, and achieve accurate positioning of the test carrier in the test frame through the positioning structure; Acquire structural parameters of the push rod reinforcement ribs, and arrange 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, the test data of its structural stability is obtained, and according to the preset stability threshold, whether the structural stability of the test frame meets the requirements is judged. 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 determine 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, put the test frame into use; if not, further optimize the structural design of the test frame; When the test frame is put into use, the stress state data of the test frame is obtained in real time. According to the stress state 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 includes: Obtain key performance parameters of the vehicle navigation PCBA, wherein the key performance parameters include speech recognition rate and path planning time; 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 with a priority higher than a preset threshold; For each of the test items, a sampling ratio is determined according to the priority level, 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 the execution priority of each of the test cases according to the importance of the test cases; Execute the test cases, count the number of key items 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 cases until the test coverage 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 indicator does not meet the preset requirements, optimization and improvement are performed and retesting is performed 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 pre-process the voice data stream, wherein the pre-processing 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 a preliminary text recognition result; According to the connectivity relationship and distance information between nodes in the road network topology diagram, an A* search algorithm is used to perform 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 failure mode of the vehicle navigation PCBA are obtained, and according to 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.