Method, system and equipment for testing stability of vehicle-mounted infotainment system and medium

Abstract

The invention relates to the field of in-vehicle infotainment testing, in particular to a stability testing method, system and device for an in-vehicle infotainment system and a medium, and the method comprises the steps: learning a first weight corresponding to each display area, support operation and a second weight corresponding to each support operation by using a Q-learning algorithm according to a priori knowledge rule set; executing a test step for a predetermined number of times, the test step comprising: determining a target area based on the first weight according to a predetermined rule; determining a target operation corresponding to a target area according to the second weight; and executing the target operation on the target area, judging the execution validity, and generating a stability test report according to an execution result. According to the characteristics of the vehicle machine system interface, the test event is intelligently generated, and the exploration probability is dynamically adjusted, so that the test coverage rate is improved, the test efficiency is also improved, and invalid and repeated tests are reduced.

Description

Technical Field

[0001] The present invention relates to the field of vehicle computer testing, and in particular to a stability testing method, system, equipment and medium for a vehicle computer system. Background Art

[0002] The in-vehicle computer system (IVI) is a crucial component of modern vehicles, integrating multiple functions such as navigation, entertainment, and communications, providing a convenient and comfortable driving experience for drivers and passengers. To ensure the reliability and stability of IVI systems, comprehensive testing, particularly stability testing, is required. Stability testing involves simulating user operations and running the system continuously for extended periods of time to verify its ability to operate normally under various conditions without experiencing abnormalities such as freezes and restarts. Stability testing is crucial for ensuring the quality and safety of IVI systems.

[0003] Currently, stability testing of in-vehicle systems typically uses the Monkey testing tool. Monkey is a stress testing tool native to the Android platform. It simulates user behavior by randomly generating user events (such as clicks, swipes, and keystrokes), thereby testing the stability of in-vehicle systems. However, Monkey testing has several drawbacks: First, the events generated by Monkey are completely random and cannot simulate real user operation patterns, resulting in insufficient test coverage; second, Monkey cannot generate effective test events tailored to the characteristics and interface structure of the in-vehicle system, resulting in low test efficiency. Summary of the Invention

[0004] To solve the above problems, the present invention provides a stability testing method, system, device and medium for a vehicle system.

[0005] A first aspect of the present invention discloses a stability testing method for a vehicle computer system, comprising:

[0006] Based on a pre-set set of prior knowledge rules, a Q-learning algorithm is used to learn a first weight corresponding to each display area, supported operations, and a second weight corresponding to each supported operation; wherein each display area corresponds to an area of the display interface, and the prior knowledge rule set includes the supported operations corresponding to the display interface and the weights corresponding to the supported operations;

[0007] Perform a predetermined number of test steps, the test steps comprising:

[0008] Based on the first weight and according to a predetermined rule, determining a target area from all display areas;

[0009] determining a target operation from all supporting operations corresponding to the target area according to the second weight;

[0010] Perform the target operation on the target area, judge the effectiveness of the execution, and count the historical operation effectiveness rate and the cumulative coverage rate of each display area;

[0011] Generate a stability test report based on the historical operation effectiveness rate and the cumulative coverage rate.

[0012] Furthermore, the prior knowledge rule set includes:

[0013] Determine the corresponding supported operation and the weight corresponding to the supported operation according to the type of the control on the display interface;

[0014] And / or determine the corresponding supported operation and the weight corresponding to the supported operation according to the position of the control in the display interface;

[0015] And / or determine the corresponding supported operation and the weight corresponding to the supported operation according to the geometric features of the control;

[0016] And / or determine the corresponding supported operation and the weight corresponding to the supported operation according to the contrast between the color of the control and the main color scheme of the display interface;

[0017] And / or determine the corresponding supported operation and the weight corresponding to the supported operation according to the text included in the control.

[0018] Furthermore, the steps of judging the effectiveness of the execution include:

[0019] Judge the effectiveness of performing the target operation on the target area by comparing the changes in the display interface before and after performing the target operation, and / or through the log content.

[0020] Furthermore, the steps of determining a target area from all display areas based on the first weight according to a pre-determined rule include:

[0021] }Calculate the determination mode of the target area according to an adjustable dynamic exploration probability:

[0022] When in the exploration mode, randomly select a display area as the target area;

[0023] When in the selection mode, select the one with the highest first weight from all display areas as the target area.

[0024] Furthermore, after the steps of counting the historical operation effectiveness rate and the cumulative coverage rate of each display area, the testing steps further include:

[0025] For each display area:

[0026] Determine whether the corresponding cumulative coverage rate exceeds a preset first threshold:

[0027] If it exceeds, reduce the first weight corresponding to this display area;

[0028] Determine whether the corresponding cumulative coverage rate is less than a preset second threshold:

[0029] If it is less, increase the first weight corresponding to this display area.

[0030] Further, after the steps of counting the historical operation efficiency and the cumulative coverage rate of each said display area, the test steps further include:

[0031] Determine whether the historical operation efficiency corresponding to the current test step exceeds a preset third threshold:

[0032] If it exceeds, reduce the dynamic exploration probability;

[0033] Otherwise, increase the dynamic exploration probability.

[0034] Further, after the steps of counting the historical operation efficiency and the cumulative coverage rate of each said display area, the test steps further include:

[0035] Count the frequency of each said display area being selected as the target area;

[0036] Calculate the number of display areas whose frequency of being selected as the target area is lower than a preset fourth threshold to obtain a first number;

[0037] Determine whether the first number exceeds a preset fifth threshold:

[0038] If it exceeds, increase the dynamic exploration probability.

[0039] The second aspect of the present invention discloses a stability test system for a vehicle-mounted system, including:

[0040] A learning module, configured to use the Q-learning algorithm to learn the first weight corresponding to each display area, the supported operations, and the second weight corresponding to each supported operation according to a preset prior knowledge rule set; wherein, each display area corresponds to an area of the display interface, and the prior knowledge rule set includes the supported operations corresponding to the display interface and the weights corresponding to the supported operations;

[0041] An execution module, configured to execute the test steps a predetermined number of times, and the test steps include:

[0042] Based on the first weight, determine a target area from all display areas according to a predetermined rule;

[0043] Determine a target operation from all supported operations corresponding to the target area according to the second weight;

[0044] Execute the target operation on the target area, judge the effectiveness of the execution, and count the historical operation effectiveness rate and the cumulative coverage rate of each display area;

[0045] A generation module, configured to generate a stability test report based on the historical operation effectiveness rate and the cumulative coverage rate.

[0046] The third aspect of the present invention discloses an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps of any one of the stability test methods of the vehicle-mounted system disclosed in the first aspect of the present invention.

[0047] The fourth aspect of the present invention discloses a storage medium, which stores a computer program. The computer program, when executed by a processor, implements the steps of any one of the stability test methods of the vehicle-mounted system disclosed in the first aspect of the present invention.

[0048] The present invention overcomes the defects of insufficient coverage and low efficiency in the stability test of the existing vehicle-mounted system by introducing the Q-learning algorithm and the prior knowledge rule set. According to the characteristics of the vehicle-mounted system interface, the present invention intelligently generates test events, and by dynamically adjusting the exploration probability, while improving the test coverage rate, it also improves the test efficiency, reduces invalid and repeated tests, and provides a more intelligent and efficient test means for ensuring the reliability and safety of the vehicle-mounted system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0050] Figure 1 is a schematic flow chart of a stability test method for a vehicle-mounted system disclosed in an embodiment of the present invention;

[0051] Figure 2 is a schematic structural diagram of a stability test system for a vehicle-mounted system disclosed in an embodiment of the present invention;

[0052] Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] The terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, or product end that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, devices, or product ends.

[0055] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0056] Please refer to Figure 1 as shown Figure 1 is a schematic flowchart of a method for testing the stability of a vehicle-mounted system disclosed in an embodiment of the present invention. As Figure 1 shown, the method for testing the stability of the vehicle-mounted system may include the following operations:

[0057] S100. According to a preset prior knowledge rule set, use the Q-learning algorithm to learn the first weight corresponding to each display area, the supported operations, and the second weight corresponding to each supported operation; wherein, each display area corresponds to an area of the display interface, and the prior knowledge rule set includes the supported operations corresponding to the display interface and the weight corresponding to the supported operation;

[0058] In this optional embodiment, the supported operations corresponding to the display interface included in the prior knowledge rule set refer to the supported operations corresponding to each control in the display interface. For example, the supported operations corresponding to the button control in the display interface include click, long press, double click, etc.; the prompt box control in the display interface does not correspond to any supported operation; the supported operation corresponding to the input box control in the display interface is text input.

[0059] The Q-learning algorithm is a reinforcement learning algorithm that optimizes the decision-making strategy of an agent by learning the rewards obtained by taking specific actions in a given state. In this embodiment, the Q-learning algorithm is used to learn the importance (first weight) of each display area, the support operations, and the importance (second weight) of each support operation.

[0060] Specifically, during the learning process, the Q-learning algorithm maintains a Q-table to store the Q-values of each state-action pair. In this embodiment, the state corresponds to the display area, and the action corresponds to the support operation. The Q-value represents the long-term cumulative reward for performing a specific support operation in a given display area. The learning process is as follows:

[0061] 1. Initialize the Q-table and set the initial Q-values according to the prior knowledge rule set. For each display area, set the corresponding Q-values according to the support operations and their weights defined in the prior knowledge rule set. For example, if the prior knowledge rule set indicates that the click operation of a certain button control has a high weight, then initialize the Q-value of the "click" operation in this display area to a relatively large value.

[0062] 2. In each training round, select a display area as the current state, and use the ε-greedy policy to select a support operation according to the current Q-table. The ε-greedy policy randomly selects an operation with a probability of ε and selects the operation with the largest Q-value with a probability of 1 - ε. This can achieve a balance between exploring new operations and exploiting existing knowledge.

[0063] 3. Execute the selected support operation and observe the execution result (effective or ineffective). Give a reward or punishment according to the execution result and update the corresponding Q-value in the Q-table. The reward can be a fixed value or a value related to the execution effect. For example, give a positive reward for executing an effective operation and a negative reward or zero reward for executing an ineffective operation.

[0064] 4. Update the Q-value of the current state-action pair according to the Q-value update formula.

[0065] 5. Repeat steps 2 - 4 until the preset number of training rounds or the convergence condition is reached.

[0066] In this optional embodiment, the display area is an area obtained by evenly dividing the display interface according to a pre-determined rule. The pre-determined rule can be an ergonomic-based equal division method, an interface resolution-based equal division method, a prior knowledge-based equal division method, etc. Among them, the ergonomic-based equal division method means dividing the display interface into several regions adapted to the size of a human finger according to the average size of a human finger; the interface resolution-based equal division method means evenly dividing the display interface into a fixed number of rectangular regions according to the resolution of the display interface.

[0067] In an optional embodiment, the prior knowledge rule set includes:

[0068] Determine the corresponding supported operations and the weights corresponding to the supported operations according to the type of the control on the display interface;

[0069] And / or determine the corresponding supported operations and the weights corresponding to the supported operations according to the position of the control in the display interface;

[0070] And / or determine the corresponding supported operations and the weights corresponding to the supported operations according to the geometric features of the control;

[0071] And / or determine the corresponding supported operations and the weights corresponding to the supported operations according to the contrast between the color of the control and the main color scheme of the display interface;

[0072] And / or determine the corresponding supported operations and the weights corresponding to the supported operations according to the text included in the control.

[0073] In this optional embodiment, determine the corresponding supported operations and the weights corresponding to the supported operations according to the type of the control on the display interface. For example, for a button-type control, the corresponding supported operation includes clicking, with a weight of 0.6. Determine the corresponding supported operations and the weights corresponding to the supported operations according to the position of the control in the display interface. For example, for a control in the bottom bar of the screen, the corresponding supported operation includes clicking, with a weight of 0.8. Determine the corresponding supported operations and the weights corresponding to the supported operations according to the geometric features of the control. For example, for a rectangular control with an aspect ratio exceeding 3, the corresponding supported operation includes swiping, with a weight of 0.7. Determine the corresponding supported operations and the weights corresponding to the supported operations according to the contrast between the color of the control and the main color scheme of the display interface. For example, for a control using the application theme color, the corresponding supported operation includes clicking, with a weight of 0.3. Determine the corresponding supported operations and the weights corresponding to the supported operations according to the text included in the control. For example, for a control containing action text such as "OK", "Cancel", "Submit", etc., the corresponding supported operation includes clicking, with a weight of 0.9.

[0074] It can be seen that in this alternative embodiment, by comprehensively considering various attributes such as the type, position, geometric features, color contrast, and text information of the controls, from different perspectives, the supported operations corresponding to each control and their weights are flexibly determined, forming a comprehensive, fine-grained, and multi-dimensional set of prior knowledge rules. This rule set not only makes full use of the visual and semantic features of the interface controls, but also takes into account common patterns of human-computer interaction and user habits, providing a rich and reliable prior knowledge basis for the subsequent Q-learning algorithm, helping to accelerate the convergence process of the algorithm, improve learning efficiency and test quality. At the same time, the construction method of this rule set has strong generality and scalability, and can flexibly adapt to in-vehicle interfaces with different styles and layouts, providing strong support for intelligent and automated stability testing.

[0075] S200. Execute the test steps a predetermined number of times, and the test steps include:

[0076] S210. Based on the first weight, determine a target area from all display areas according to pre-determined rules;

[0077] In an alternative embodiment, the step of determining a target area from all display areas based on the first weight according to pre-determined rules includes:

[0078] Calculate the determination mode of the target area according to an adjustable dynamic exploration probability:

[0079] When in the exploration mode, randomly select a display area as the target area;

[0080] When in the selection mode, select the one with the highest first weight from all display areas as the target area.

[0081] In this alternative embodiment, according to the currently set dynamic exploration probability, through the comparison result of random numbers, randomly switch between the exploration mode and the selection mode. The magnitude of the dynamic exploration probability directly determines the relative probabilities of the exploration mode and the selection mode being selected. The higher the dynamic exploration probability, the greater the probability of the exploration mode being selected, and vice versa, the greater the probability of the selection mode being selected.

[0082] It can be seen that this alternative embodiment achieves a good balance between exploring new possibilities and utilizing existing knowledge. It can not only discover potential stability problems, but also improve the pertinence and efficiency of testing. Moreover, by dynamically adjusting the exploration probability, the testing process shows a progressive characteristic from breadth to depth, which can cover all areas of the interface more comprehensively, and at the same time gradually focus on key problem areas.

[0083] S220. Based on the second weight, determine a target operation from all supported operations corresponding to the target area;

[0084] In this alternative embodiment, the second weight directly determines the probability that a supported operation in a display area is selected. For example, if a display area corresponds to two supported operations, the second weight corresponding to the first supported operation is 20%, and the second weight corresponding to the second supported operation is 80%, then when the first supported operation is selected in this display area, there is a 20% chance of it being executed.

[0085] S230. Perform the target operation on the target area, determine the effectiveness of the execution, and count the historical operation effectiveness rate and the cumulative coverage rate of each display area.

[0086] In an alternative embodiment, the step of determining the effectiveness of the execution includes:

[0087] Determine the effectiveness of performing the target operation on the target area by comparing the changes in the display interface before and after performing the target operation, and / or by means of the log content.

[0088] In this alternative embodiment, comparing the changes in the display interface before and after performing the target operation can be the proportion of pixels that have changed in the display interface.

[0089] In yet another alternative embodiment, after the step of counting the historical operation effectiveness rate and the cumulative coverage rate of each display area, the testing step further includes:

[0090] For each display area:

[0091] Determine whether its corresponding cumulative coverage rate exceeds a preset first threshold:

[0092] If it exceeds, then reduce the first weight corresponding to this display area;

[0093] Determine whether its corresponding cumulative coverage rate is less than a preset second threshold:

[0094] If it is less, then increase the first weight corresponding to this display area.

[0095] In this alternative embodiment, the cumulative coverage rate refers to the proportion of the cumulative number of times an operation is performed on a certain display area during the testing process to the total number of operations, which reflects the degree of sufficiency of this display area being tested.

[0096] It can be seen that in this alternative embodiment, by dynamically adjusting the first weight of each display area, the self-adaptive optimization of the testing process is achieved. When the cumulative coverage rate of a certain display area exceeds the preset first threshold, it indicates that this display area has been fully tested, and its corresponding first weight can be appropriately reduced to reduce the repeated testing of it. On the contrary, when the cumulative coverage rate of a certain display area is lower than the preset second threshold, it means that there may be testing blind spots in this display area, and its corresponding first weight needs to be increased to strengthen the testing intensity. Through this dynamic weight adjustment mechanism, the resource allocation can be continuously optimized during the testing process, and the testing focus can be gradually shifted to the display areas with lower coverage rates and higher probabilities of problems, thereby improving the pertinence and efficiency of testing and achieving more comprehensive and balanced coverage within a limited testing time.

[0097] In yet another alternative embodiment, after the steps of statistically calculating the historical operation effectiveness rate and the cumulative coverage rate of each of the said display areas, the testing steps further include:

[0098] Determine whether the historical operation effectiveness rate corresponding to the current testing step exceeds a preset third threshold:

[0099] If it exceeds, then reduce the dynamic exploration probability;

[0100] Otherwise, increase the dynamic exploration probability.

[0101] In this alternative embodiment, the historical operation effectiveness rate refers to the proportion of the number of operations that successfully trigger interface responses and state changes in the total number of executed operations during the testing process, which reflects the overall effectiveness of the testing operations and the rationality of the testing strategy.

[0102] It can be seen that in this alternative embodiment, by judging the relationship between the historical operation effectiveness rate and the preset third threshold and dynamically adjusting the exploration probability, the self-adaptive adjustment of the testing strategy is achieved. When the historical operation effectiveness rate exceeds the preset third threshold, it indicates that the current testing method can effectively trigger interface responses and discover problems, and the dynamic exploration probability can be appropriately reduced to increase the utilization degree of known effective operations. On the contrary, when the effectiveness rate is lower than the third threshold, it means that the current testing method may have limitations, and the dynamic exploration probability needs to be increased to strengthen the attempt of new operations and new areas. Through this feedback-based dynamic adjustment mechanism, the balance between exploration and utilization can be continuously optimized during the testing process, and the subsequent testing strategy selection can be guided according to the real-time testing effect, improving the self-adaptability and intelligent level of testing.

[0103] In yet another alternative embodiment, after the steps of statistically calculating the historical operation effectiveness rate and the cumulative coverage rate of each of the said display areas, the testing steps further include:

[0104] Count the frequency of each of the display areas being selected as the target area;

[0105] Calculate the number of display areas whose frequency of being selected as the target area is lower than a preset fourth threshold to obtain a first quantity;

[0106] Determine whether the first quantity exceeds a preset fifth threshold:

[0107] If it exceeds, increase the dynamic exploration probability.

[0108] It can be seen that in this optional embodiment, by counting the selection frequency of the target area and dynamically adjusting the exploration probability, key exploration of low-frequency areas is achieved. By calculating the number of display areas whose selection frequency is lower than the fifth threshold, it is possible to determine whether there are coverage blind spots and imbalance problems in the current test. When the number of low-frequency areas exceeds the preset warning threshold, it indicates that the current test is too concentrated on certain hot areas and ignores other areas, and it is necessary to strengthen the test of these blind spots by increasing the dynamic exploration probability. Through this frequency monitoring and dynamic adjustment mechanism, it is possible to timely discover and correct the imbalance problem of coverage during the test process, ensure that the test can comprehensively cover all display areas of the interface, and not miss any potential risk points. At the same time, this adjustment also helps to find the optimal balance between exploring new areas and utilizing hot areas, maximizing the efficiency and benefits of the test.

[0109] S300. Generate a stability test report based on the historical operation efficiency and the cumulative coverage rate.

[0110] Please refer to Figure 2 as shown in Figure 2 is a schematic structural diagram of a stability test system for a vehicle-mounted system disclosed in an embodiment of the present invention, including:

[0111] A learning module 201, configured to use the Q-learning algorithm to learn the first weight corresponding to each display area, the supported operations, and the second weight corresponding to each supported operation according to a preset prior knowledge rule set; wherein, each display area corresponds to an area of the display interface, and the prior knowledge rule set includes the supported operations corresponding to the display interface and the weights corresponding to the supported operations;

[0112] An execution module 202, configured to execute a predetermined number of test steps, and the test steps include:

[0113] Based on the first weight, determine a target area from all display areas according to a predetermined rule;

[0114] According to the second weight, determine a target operation from all supported operations corresponding to the target area;

[0115] Perform the target operation on the target area, judge the effectiveness of the execution, and count the historical operation effectiveness rate and the cumulative coverage rate of each display area;

[0116] A generation module 203, configured to generate a stability test report based on the historical operation effectiveness rate and the cumulative coverage rate.

[0117] For the specific limitations of the stability test system for the in-vehicle system, reference can be made to the limitations of the stability test method for the in-vehicle system in the above text, which will not be elaborated here. Each module in the above stability test system for the in-vehicle system can be implemented in whole or in part through software, hardware, and their combinations. The above modules can be embedded in the processor of the electronic device in a hardware format or independent of it, or stored in the memory of the electronic device in a software format, so as to facilitate the processor to call the corresponding operations of the above modules.

[0118] It should be noted that, in order to highlight the innovative part of the present invention, modules that are not closely related to solving the technical problems proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other modules in this embodiment.

[0119] As Figure 3 shown, the electronic device 1 provided by the present invention may include a memory 11, a processor 12, and a bus, and may also include a computer program stored in the memory 11 and operable on the processor 12, such as a stability test program for the in-vehicle system.

[0120] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as the mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 may include both the internal storage unit and the external storage device of the electronic device 1. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code for the stability test of the in-vehicle system, but also be used to temporarily store data that has been output or will be output.

[0121] In some embodiments, the processor 12 may be composed of an integrated circuit. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 12 is the control core of the electronic device 1, connecting various components of the entire electronic device 1 through various interfaces and circuits. By running or executing programs or modules stored in the memory 11 (such as the stability test program of the in-vehicle system), and by calling the data stored in the memory 11, it performs various functions of the electronic device 1 and processes data.

[0122] The processor 12 executes the operating system of the electronic device 1 and various installed application programs. The processor 12 executes the application programs to implement the steps in the above-mentioned stability test method of the in-vehicle system.

[0123] Exemplarily, the computer program may be divided into one or more modules. The one or more modules are stored in the memory 11 and executed by the processor 12 to complete the present application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into a learning module 201, an execution module 202, and a generation module 203.

[0124] The above-mentioned integrated unit implemented in the form of software function modules may be stored in a computer-readable storage medium. The storage medium may be non-volatile or volatile. The above-mentioned software function modules are stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute some functions of the stability test method of the in-vehicle system described in various embodiments of the present application.

[0125] In summary, a method, system, device, and medium for testing the stability of a vehicle-mounted system disclosed in the present invention overcome the deficiencies of insufficient coverage and low efficiency in the stability testing of existing vehicle-mounted systems by introducing the Q-learning algorithm and the prior knowledge rule set. According to the characteristics of the vehicle-mounted system interface, the present invention intelligently generates test events and improves the test efficiency while increasing the test coverage rate by dynamically adjusting the exploration probability, reducing invalid and repeated tests, and providing a more intelligent and efficient testing means for ensuring the reliability and safety of the vehicle-mounted system. Therefore, the present invention effectively overcomes various disadvantages in the prior art and has high industrial utilization value.

[0126] The above embodiments are only used to exemplarily illustrate the principles and effects of the present invention, rather than to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for testing the stability of a vehicle-mounted system, characterized in that, The method includes: According to a preset prior knowledge rule set, use the Q-learning algorithm to learn the first weight corresponding to each display area, the supported operations, and the second weight corresponding to each supported operation; wherein, each display area corresponds to an area of the display interface, and the prior knowledge rule set includes the supported operations corresponding to the display interface and the weight corresponding to the supported operation; Execute the test steps a predetermined number of times, and the test steps include: Based on the first weight, determine a target area from all display areas according to a predetermined rule; According to the second weight, determine a target operation from all supported operations corresponding to the target area; Execute the target operation on the target area, judge the effectiveness of the execution, and count the historical operation effectiveness rate and the cumulative coverage rate of each display area; Generate a stability test report based on the historical operation effectiveness rate and the cumulative coverage rate.

2. The stability testing method of a vehicle-mounted system according to claim 1, characterized in that, The prior knowledge rule set includes: According to the type of the control on the display interface, determine the corresponding supported operation and the weight corresponding to the supported operation; And / or according to the position of the control in the display interface, determine the corresponding supported operation and the weight corresponding to the supported operation; And / or according to the geometric features of the control, determine the corresponding supported operation and the weight corresponding to the supported operation; And / or according to the contrast between the color of the control and the main color scheme of the display interface, determine the corresponding supported operation and the weight corresponding to the supported operation; And / or according to the text included in the control, determine the corresponding supported operation and the weight corresponding to the supported operation.

3. The stability test method of a vehicle-mounted system according to claim 1, characterized in that The step of judging the effectiveness of the execution includes: By comparing the changes in the display interface before and after the execution of the target operation, and / or through the log content, judge the effectiveness of executing the target operation on the target area.

4. The stability test method for a vehicle-mounted system according to claim 1, wherein, The step of determining a target area from all display areas based on the first weight according to a predetermined rule includes: Calculate the determination mode of the target area according to an adjustable dynamic exploration probability: When in the exploration mode, randomly select a display area as the target area; When in the selection mode, select the one with the highest first weight from all display areas as the target area.

5. The stability test method for a vehicle-mounted system according to claim 4, characterized in that, After the step of counting the historical operation effectiveness rate and the cumulative coverage rate of each display area, the test steps further include: For each display area: Judge whether its corresponding cumulative coverage rate exceeds a preset first threshold: If it exceeds, reduce the first weight corresponding to the display area; Judge whether its corresponding cumulative coverage rate is less than a preset second threshold: If it is less, increase the first weight corresponding to the display area.

6. The stability test method of a vehicle-mounted system according to claim 4, characterized in that After the step of counting the historical operation effectiveness rate and the cumulative coverage rate of each display area, the test steps further include: Judge whether the historical operation effectiveness rate corresponding to the current test step exceeds a preset third threshold: If it exceeds, reduce the dynamic exploration probability; Otherwise, increase the dynamic exploration probability.

7. A stability test method for a vehicle-mounted system according to claim 4, characterized in that, After the step of counting the historical operation effectiveness rate and the cumulative coverage rate of each display area, the test steps further include: Count the frequency of each of the display areas being selected as the target area; Calculate the number of display areas whose frequency of being selected as the target area is lower than a preset fourth threshold to obtain a first number; Determine whether the first number exceeds a preset fifth threshold: If it exceeds, increase the dynamic exploration probability.

8. A stability test system for a vehicle-mounted system, characterized in that, Including: A learning module, configured to learn, according to a preset prior knowledge rule set, using the Q-learning algorithm, the first weight corresponding to each display area, the supported operations, and the second weight corresponding to each supported operation; wherein each display area corresponds to an area of the display interface, and the prior knowledge rule set includes the supported operations corresponding to the display interface and the weights corresponding to the supported operations; An execution module, configured to execute a test step a predetermined number of times, and the test step includes: Based on the first weight, determine a target area from all display areas according to a predetermined rule; According to the second weight, determine a target operation from all supported operations corresponding to the target area; Execute the target operation on the target area, judge the effectiveness of the execution, and count the historical operation effectiveness rate and the cumulative coverage rate of each of the display areas; A generation module, configured to generate a stability test report based on the historical operation effectiveness rate and the cumulative coverage rate.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the stability test method of the in-vehicle system according to any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the stability test method of the in-vehicle system according to any one of claims 1 to 7.

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