Vehicle-mounted display screen full-scene automatic testing method and system based on intelligent interaction
Through full-scenario automated testing methods and dynamic resource allocation, the resource preemption problem under multimodal interaction of in-vehicle display screens is solved, efficient resource optimization and stability improvement are achieved, interaction jams are avoided, and testing costs are reduced.
Patent Information
- Application Number
- CN202510858954.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-25
- Publication Date
- 2025-09-26
AI Technical Summary
Existing technologies lack systematic testing of multimodal interactions of in-vehicle displays, and are unable to detect resource preemption issues, resulting in response delays or resource waste, and are unable to meet the real-time and stability requirements of all scenarios.
Through a full-scenario automated testing method, signals are divided into those that meet or do not meet the standards based on feedback time, and multiple tests are performed on randomly combined signals. The computing power resource allocation is dynamically adjusted, resource usage is optimized, and the optimal resource allocation plan is generated.
Effectively discover resource conflicts in multi-tasking parallel processing, avoid interaction freezes, dynamically adjust resource allocation, improve intelligent interaction efficiency, and reduce testing costs.
Smart Images
Figure CN120703489A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of vehicle-mounted display screens, and specifically to a full-scenario automated testing method and system for vehicle-mounted display screens based on intelligent interaction. Background Art
[0002] With the development of intelligent connected vehicles, in-vehicle displays serve as the core entrance for human-computer interaction, and their intelligent interactive performance (such as voice recognition, gesture control, and multi-tasking) directly affects driving safety and user experience.
[0003] Existing solutions lack systematic testing of different signal combinations and are unable to detect resource preemption issues during parallel processing of multiple tasks. For example, when speech recognition and image rendering occupy CPU resources at the same time, traditional testing makes it difficult to quantify the impact of different task priority settings on response time, causing the in-vehicle system to freeze in complex interactive scenarios.
[0004] In existing technologies, the allocation of computing power resources in vehicle systems is mostly based on preset rules (such as a fixed allocation of 30% of computing power to the voice module), and lacks a mechanism for dynamic adjustment based on real-time interactive load. When users trigger multimodal commands at a high frequency, static allocation may cause high-priority tasks (such as safety prompts) to respond late due to insufficient resources, or low-priority tasks to occupy too many resources, resulting in waste.
[0005] Existing technologies cannot meet the real-time and stability requirements of in-vehicle displays in full-scene, multi-modal interactions. There is an urgent need for an automated testing method that takes into account single function verification, multi-modal combination testing, and dynamic resource optimization to improve intelligent interaction efficiency and reduce testing costs. Summary of the Invention
[0006] In response to the shortcomings of the existing technology, the present invention provides a full-scene automated testing method and system for vehicle-mounted display screens based on intelligent interaction, which solves the problem of not optimizing the fast output of vehicle-mounted display screens.
[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a full-scene automated testing method for an in-vehicle display screen based on intelligent interaction, comprising the following steps: Step 1: Perform a single test on the vehicle display screen based on the preset analog signal. Based on the feedback process of the vehicle display screen, the analog signal is divided into a qualified signal or a non-qualified signal. The specific method is as follows: The preset analog signals are sent to the vehicle display screen in sequence, and the feedback time T generated by the vehicle display screen after receiving the analog signals is recorded. i , where i represents different analog signals; If T i≤Y1, where Y1 is the preset value, the current analog signal is recorded as the standard signal. If T i >Y1, the analog signal is recorded as a substandard signal; Step 2: For the confirmed qualified signals, several groups of qualified signals are randomly combined to generate several signal combination columns, and multiple test processes are performed on the vehicle display screen through the signal combination columns. From the test processing process, a test pass signal or a test fail signal is generated. The specific method is as follows: Based on the confirmed multiple groups of qualified signals, the multiple groups of qualified signals are randomly combined to generate multiple signal combination columns with different sorting characteristics; Each signal combination sequence is sent to the vehicle display screen in turn, and the total feedback time ZT associated with the vehicle display screen from receiving the signal combination sequence and completing all feedback is calculated. k , where k represents a sequence of different signal combinations, and the total time ZT from the confirmed multiple feedback groups k In the k max and ZT k min associated with the signal combination column, ZT k The signal combination sequence associated with max is recorded as the longest signal combination sequence, and ZT k The signal combination sequence associated with min is recorded as the shortest time signal combination sequence, and ZT k max and ZT k Recheck the time features associated with min and identify ZT k max and ZT k Does min satisfy: (ZT k max-ZT k min)≤Y2, where Y1 is a preset value. If it is satisfied, it means that the different signal combinations have met the test standards during the test process, and a pass signal is generated. If it is not satisfied, it means that the different signal combinations have failed to meet the test standards during the test process, and a fail signal is generated. Step 3: Based on the generated test failure signal, extract the percentage of computing power resources associated with each different qualified signal of the vehicle display screen, lock the extracted resources, and allocate the extracted resources to the feedback process executed by the vehicle display screen. Then, lock the different feedback features associated with different extracted resources. The specific method is as follows: The different computing resources associated with different compliance signals for the vehicle display are recorded and calibrated as SLq, where q represents different compliance signals; Based on the preset extraction ratio B, which gradually increases from 1% to 30%, the pending resources are locked from the computing power resources SLq associated with each different compliance signal according to the confirmed extraction ratio B: TQq = SLq × B. The different pending resources determined by each different compliance signal are summed up to confirm the extraction resource; Repeat step 2 to assign different extraction resources to the feedback process being executed by the corresponding vehicle display screen, and confirm the longest signal combination column and the shortest signal combination column associated with different extraction resources, and assign the ZT associated with the longest signal combination column and the shortest signal combination column. k max and ZT k min to perform mean processing, confirm the first feature, and then use (ZT k max-ZT k min) Confirm the second feature and use: first feature × C1 + second feature × C2 = feedback feature to lock the feedback feature associated with the current extraction resource, where C1 and C2 are both preset fixed coefficient factors, and record the feedback features associated with different extraction resources in turn; Step 4: Based on the different feedback features associated with different feedback processes, identify the different extraction resources associated with different feedback features. Then, sort the feedback features from smallest to largest extraction resources to determine the feature sequence. Select the optimal extraction resource from the feature sequence and execute it. The specific method is as follows: According to the confirmed feature sequence, the single feedback feature in the feature sequence is recorded as FK, the previous set of feedback features corresponding to the feedback feature FK is recorded as FK1, and the next set of feedback features corresponding to the feedback feature FK is recorded as FK2. The calibration feature BZ associated with the corresponding feedback feature is confirmed using: FK+(FK-FK1)+(FK2-FK)=BZ; According to the different calibration features BZ associated with different feedback features, the minimum value is selected, and the feedback feature associated with the minimum value is recorded as the optimal feature. The extraction resources associated with the optimal feature are used as the optimal extraction resources. Based on the confirmed optimal extraction resources, the computing power resources associated with different standard signals on the vehicle display are re-extracted, and then the corresponding optimal extraction resources are executed in different feedback processes.
[0008] Preferably, the full-scenario automated testing system for vehicle-mounted display screens based on intelligent interaction includes: At the preliminary test end, the vehicle display screen is subjected to a single test based on a preset analog signal. Based on the feedback process of the vehicle display screen, the analog signal is classified as a qualified signal or a non-qualified signal. The combination test end randomly combines several groups of qualified signals for the confirmed qualified signals to generate several signal combination trains, and performs multiple test processes on the vehicle display screen through the signal combination trains. From the test processing process, a test pass signal or a test fail signal is generated; The feedback feature confirmation end, based on the generated test failure signal, extracts a percentage of the computing power resources associated with each different passing signal of the vehicle display screen, locks the extracted resources, and allocates the extracted resources to the feedback process executed by the vehicle display screen, and then locks the different feedback features associated with different extracted resources; The comprehensive locking end confirms the different extraction resources associated with different feedback features based on the different feedback features associated with different feedback processes, and then sorts the feedback features according to the order of extraction resources from small to large to confirm the feature sequence, and selects the optimal extraction resource from the feature sequence and executes it.
[0009] The present invention provides a full-scenario automated testing method and system for vehicle-mounted display screens based on intelligent interaction. Compared with the existing technology, it has the following advantages: The present invention generates a signal combination sequence by randomly combining the standard signals and compares the total feedback time ZT of different combinations. k The difference (ZT k max-ZT k min) satisfies the threshold Y2 to verify the system resource scheduling capability during multimodal interaction (such as voice + gesture collaboration). This mechanism can effectively detect resource conflicts during multi-tasking parallel processing (such as rendering delays caused by CPU / GPU preemption) and avoid interaction freezes caused by unreasonable signal combination order or priority settings.
[0010] By extracting different proportions of computing resources (gradually increasing from 1% to 30%) and quantifying feedback features (such as ZT k A weighted combination of the mean and difference) can automatically search for the optimal resource allocation plan; for example, when the speech recognition module occupies too much computing power and causes gesture processing delays, the system dynamically adjusts the resource proportion of each module by calculating the feedback feature FK, so that high-priority interaction tasks (such as navigation instructions) can obtain more computing power support. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] Figure 1 Schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION
[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0013] First embodiment See also Figure 1 , this application provides a full-scene automated testing method for an in-vehicle display screen based on intelligent interaction, comprising the following steps: Step 1: Perform a single test on the vehicle display screen according to the preset analog signal. According to the feedback process of the vehicle display screen, the analog signal is divided into a qualified signal or a non-qualified signal. Specifically, during the intelligent interaction process, the corresponding vehicle display screen generally converts the corresponding voice signal into a corresponding digital signal. Different voice signals correspond to different digital signals. The analog signal associated here is the analog signal that the vehicle display screen can normally perform intelligent interaction. It is a preset signal and is prepared in advance by the relevant operator. According to the specific interaction process of such analog signal, the feedback rate of the corresponding vehicle display screen is evaluated. If the feedback rate meets the standard, it means that the corresponding analog signal is a qualified signal. If the feedback rate does not meet the standard, it means that the corresponding analog signal is a non-qualified signal. The specific method of dividing the analog signal is as follows: The preset analog signals are sent to the vehicle display screen in sequence, and the feedback time T generated by the vehicle display screen after receiving the analog signals is recorded. i , where i represents different analog signals; If T i ≤Y1, where Y1 is a preset value, the specific value of which is determined by the operator based on experience. The current analog signal is recorded as a qualified signal. Otherwise, the corresponding analog signal is recorded as a non-qualified signal and directly displayed. External relevant personnel adjust and optimize the parameters of the vehicle display based on the displayed non-qualified signal, so that the corresponding non-qualified signal is converted into a qualified signal in the subsequent test process; Step 2: For the confirmed qualified signals, several groups of qualified signals are randomly combined to generate several signal combination columns, and multiple test processes are performed on the vehicle display screen through the signal combination columns. From the test process, a test pass signal or a test failure signal is generated. Specifically, when performing multiple test processes, several qualified signals are combined, and the feedback time generated by each qualified combination column is recorded. If the recorded time difference belonging to different signal combination columns is small, it means that the corresponding signal combination column can achieve a better combination processing effect regardless of the combination state. If better time feedback cannot be performed, the qualified signal cannot achieve a better combination processing effect. The specific method of generating the test pass signal or the test fail signal is as follows: Based on the confirmed multiple groups of qualified signals, the multiple groups of qualified signals are randomly combined to generate multiple signal combination sequences with different sorting characteristics, that is, each signal combination sequence is different; Each signal combination sequence is sent to the vehicle display screen in turn, and the total feedback time ZT associated with the vehicle display screen from receiving the signal combination sequence and completing all feedback is calculated. k , where k represents a sequence of different signal combinations, and the total time ZT from the confirmed multiple feedback groups k In the k max and ZT k min associated with the signal combination column, ZT k The signal combination sequence associated with max is recorded as the longest signal combination sequence, and ZT k The signal combination sequence associated with min is recorded as the shortest time signal combination sequence, and ZT k max and ZT k Recheck the time features associated with min and identify ZT k max and ZT k Does min satisfy: (ZT k max-ZT k min)≤Y2, where Y1 is a preset value, and its specific value is determined by the operator based on experience. If it is satisfied, it means that the different signal combinations have met the test standards during the test process, and a test pass signal is generated. If it is not satisfied, it means that the different signal combinations have failed to meet the test standards during the test process, and a test fail signal is generated. Specifically, the so-called test failure signal requires subsequent secondary verification processing, which requires specific allocation of relevant resources in the signal combination column during the verification process. From the specific allocation process, the optimal resource allocation ratio is locked in to ensure the feedback time during the actual use of the corresponding in-vehicle display. In the state of multiple voice command interactions, the fastest response effect can be achieved, shortening the reaction time; Step 3: Based on the generated test failure signal, a percentage of the computing power resources associated with each different compliance signal of the vehicle display screen is extracted, the extracted resources are locked, and the extracted resources are allocated to the feedback process executed by the vehicle display screen. Then, different feedback features associated with different extraction resources are locked. Specifically, the so-called feedback feature is the total feedback time and the corresponding time difference generated by the corresponding vehicle display screen in the specific execution of the feedback process. Based on the corresponding total time and time difference features, the comprehensive feedback feature associated with the corresponding feedback process is comprehensively confirmed. Subsequently, the optimal state is locked from the corresponding feedback features, so that the subsequent vehicle display screen reaches the optimal state in the specific execution of the feedback process; The specific method for confirming different feedback features associated with different extraction resources is as follows: The different computing resources associated with different compliance signals for the vehicle display are recorded and calibrated as SLq, where q represents different compliance signals; According to the preset extraction ratio B, the extraction ratio B gradually increases from 1% to 30%. Based on the confirmed extraction ratio B, the pending resources are locked from the computing power resources SLq associated with each different compliance signal: TQq = SLq × B, and the different pending resources determined by each different compliance signal are summed to confirm the extraction resource. During extraction, the extraction ratio B associated with each compliance signal is the same, that is, in the process of gradually increasing from 1% to 30%, there are several gradually increasing extraction resources; Repeat step 2 to assign different extraction resources to the feedback process being executed by the corresponding vehicle display screen (for different analog signals, there are different feedback processes, and for the corresponding signal combination columns, signal feedback confirmation is also performed process by process), and confirm the longest signal combination column and the shortest signal combination column associated with different extraction resources, and assign the ZT associated with the longest signal combination column and the shortest signal combination column k max and ZT k min to perform mean processing, confirm the first feature, and then use (ZT k max-ZT k min) Confirm the second feature and use the formula: first feature × C1 + second feature × C2 = feedback feature to lock the feedback feature associated with the current extraction resource. C1 and C2 are both preset fixed coefficient factors. Their specific values are determined by the operator based on experience. C1 is generally 0.687 and C2 is generally 0.313. The feedback features associated with different extraction resources are recorded in turn; Specifically, according to the set extraction ratio, from 1% to 30% gradually increase in the process, different extraction ratios are associated with different extraction resources. Different extraction resources have different feedback characteristics in different feedback processing processes. Therefore, different extraction resources correspond to different feedback characteristics. When the corresponding feedback characteristic value is the smallest, it means that the corresponding ZT k max and ZT k The mean of min is also small, and the synchronous ZT k max and ZT k min is also the smallest. Then, by selecting the minimum value from the different feedback features associated with different extraction resources, the optimal extraction resource can be determined, and the corresponding vehicle display screen can execute the corresponding extraction resource to ensure that the corresponding vehicle display screen is in the optimal operating state and achieve the optimal operating effect. Step 4: Based on the different feedback features associated with different feedback processes, identify the different extraction resources associated with the different feedback features. Then, sort the feedback features from smallest to largest extraction resources to determine a feature sequence. Select the optimal extraction resource from the feature sequence and execute it. The specific method for selecting the optimal extraction resource is: According to the confirmed feature sequence, the single feedback feature in the feature sequence is recorded as FK, the previous set of feedback features corresponding to the feedback feature FK is recorded as FK1, and the next set of feedback features corresponding to the feedback feature FK is recorded as FK2. The calibration feature BZ associated with the corresponding feedback feature is confirmed using: FK+(FK-FK1)+(FK2-FK)=BZ; Based on the different calibration features BZ associated with different feedback features, the minimum value is selected, and the feedback feature associated with the minimum value is recorded as the optimal feature. The extraction resource associated with the optimal feature is used as the optimal extraction resource. Based on the confirmed optimal extraction resource, the computing power resources associated with different compliance signals of the vehicle display screen are re-extracted, and then the corresponding optimal extraction resource is executed in different feedback processes; Specifically, different extraction resources have different feedback characteristics. In the numerical change process of the corresponding feedback characteristics, there are different change characteristics. Then, the optimal value is selected from the associated different change characteristics, and the corresponding optimal extraction resource is executed from the selected optimal value, so that the corresponding vehicle display screen reaches the optimal operating state.
[0014] Second embodiment Combine Figure 2 , a full-scenario automated test system for in-vehicle displays based on intelligent interaction, including: At the preliminary test end, the vehicle display screen is subjected to a single test based on a preset analog signal. Based on the feedback process of the vehicle display screen, the analog signal is classified as a qualified signal or a non-qualified signal. The combination test end randomly combines several groups of qualified signals for the confirmed qualified signals to generate several signal combination trains, and performs multiple test processes on the vehicle display screen through the signal combination trains. From the test processing process, a test pass signal or a test fail signal is generated; The feedback feature confirmation end, based on the generated test failure signal, extracts a percentage of the computing power resources associated with each different passing signal of the vehicle display screen, locks the extracted resources, and allocates the extracted resources to the feedback process executed by the vehicle display screen, and then locks the different feedback features associated with different extracted resources; The comprehensive locking end confirms the different extraction resources associated with different feedback features based on the different feedback features associated with different feedback processes, and then sorts the feedback features according to the order of extraction resources from small to large to confirm the feature sequence, and selects the optimal extraction resource from the feature sequence and executes it.
[0015] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.
[0016] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.
Claims
1. A full-scenario automated testing method for vehicle-mounted display screens based on intelligent interaction, characterized in that: The following steps are involved: Step 1: Perform a single test on the vehicle display screen according to a preset analog signal, and classify the analog signal as a qualified signal or a non-qualified signal according to the feedback process of the vehicle display screen; Step 2: For the confirmed qualified signals, several groups of qualified signals are randomly combined to generate several signal combination columns, and multiple test processes are performed on the vehicle display screen through the signal combination columns. From the test processing process, a test pass signal or a test fail signal is generated; Step 3: Based on the generated test failure signal, extract the percentage of computing power resources associated with each different passing signal of the vehicle display screen, lock the extracted resources, and allocate the extracted resources to the feedback process executed by the vehicle display screen, and then lock the different feedback features associated with different extracted resources; Step 4: Based on the different feedback features associated with different feedback processes, identify the different extraction resources associated with different feedback features. Then, based on the sorting method of extraction resources from small to large, sort the feedback features to confirm the feature sequence, and select the optimal extraction resource from the feature sequence and execute it.
2. The full-scene automated testing method for vehicle-mounted display screens based on intelligent interaction according to claim 1 is characterized in that: In step 1, the specific method of dividing the analog signal is: The preset analog signals are sent to the vehicle display screen in sequence, and the feedback time T generated by the vehicle display screen after receiving the analog signals is recorded. i , where i represents different analog signals; If T i ≤Y1, where Y1 is the preset value, the current analog signal is recorded as the standard-reaching signal.
3. The full-scene automated testing method for vehicle-mounted display screens based on intelligent interaction according to claim 2 is characterized in that: If T i >Y1, the analog signal will be recorded as a substandard signal.
4. The full-scene automated testing method for vehicle-mounted display screens based on intelligent interaction according to claim 1 is characterized in that: In step 2, the specific method for confirming the test pass signal is: Based on the confirmed multiple groups of qualified signals, the multiple groups of qualified signals are randomly combined to generate multiple signal combination columns with different sorting characteristics; Each signal combination sequence is sent to the vehicle display screen in turn, and the total feedback time ZT associated with the vehicle display screen from receiving the signal combination sequence and completing all feedback is calculated. k , where k represents a sequence of different signal combinations, and the total time ZT from the confirmed multiple feedback groups k In the k max and ZT k min associated signal combination column, ZT k The signal combination sequence associated with max is recorded as the longest signal combination sequence, and ZT k The signal combination sequence associated with min is recorded as the shortest time signal combination sequence, and ZT k max and ZT k Recheck the time features associated with min and identify ZT k max and ZT k Does min satisfy: (ZT k max-ZT k min)≤Y2, where Y1 is the preset value. If it is met, it means that the different signal combinations are tested to meet the standards during the test process and a test pass signal is generated.
5. The full-scene automated testing method for vehicle-mounted display screens based on intelligent interaction according to claim 4 is characterized in that: If ZT k max and ZT k min does not satisfy (ZT k max-ZT k min)≤Y2, it means that different signal combinations fail to meet the test standards during the test process and a test failure signal is generated.
6. The full-scene automated testing method for vehicle-mounted display screens based on intelligent interaction according to claim 1 is characterized in that: In step 3, the specific method for confirming different feedback features associated with different extraction resources is: The different computing resources associated with different compliance signals for the vehicle display are recorded and calibrated as SLq, where q represents different compliance signals; Based on the preset extraction ratio B, which gradually increases from 1% to 30%, the pending resources are locked from the computing power resources SLq associated with each different compliance signal according to the confirmed extraction ratio B: TQq = SLq × B. The different pending resources determined by each different compliance signal are summed up to confirm the extraction resource; Repeat step 2 to assign different extraction resources to the feedback process being executed by the corresponding vehicle display screen, and confirm the longest signal combination column and the shortest signal combination column associated with different extraction resources, and assign the ZT associated with the longest signal combination column and the shortest signal combination column. k max and ZT k min to perform mean processing, confirm the first feature, and then use (ZT k max-ZT k min) confirm the second feature and use: first feature × C1 + second feature × C2 = feedback feature to lock the feedback feature associated with the current extraction resource, where C1 and C2 are both preset fixed coefficient factors, and record the feedback features associated with different extraction resources in turn.
7. The full-scenario automated testing method for vehicle-mounted display screens based on intelligent interaction according to claim 1 is characterized in that: In step 4, the specific method for selecting the optimal extraction resource is: According to the confirmed feature sequence, the single feedback feature in the feature sequence is recorded as FK, the previous set of feedback features corresponding to the feedback feature FK is recorded as FK1, and the next set of feedback features corresponding to the feedback feature FK is recorded as FK2. The calibration feature BZ associated with the corresponding feedback feature is confirmed using: FK+(FK-FK1)+(FK2-FK)=BZ; According to the different calibration features BZ associated with different feedback features, the minimum value is selected, and the feedback feature associated with the minimum value is recorded as the optimal feature. The extraction resources associated with the optimal feature are used as the optimal extraction resources. Based on the confirmed optimal extraction resources, the computing power resources associated with different standard signals on the vehicle display are re-extracted, and then the corresponding optimal extraction resources are executed in different feedback processes.
8. A vehicle-mounted display screen full-scenario automated testing system based on intelligent interaction, the system being operated according to the vehicle-mounted display screen full-scenario automated testing method based on intelligent interaction according to any one of claims 1 to 7, characterized in that: include: At the preliminary test end, the vehicle display screen is subjected to a single test based on a preset analog signal. Based on the feedback process of the vehicle display screen, the analog signal is classified as a qualified signal or a non-qualified signal. The combination test end randomly combines several groups of qualified signals for the confirmed qualified signals to generate several signal combination trains, and performs multiple test processes on the vehicle display screen through the signal combination trains. From the test processing process, a test pass signal or a test fail signal is generated; The feedback feature confirmation end, based on the generated test failure signal, extracts a percentage of the computing power resources associated with each different passing signal of the vehicle display screen, locks the extracted resources, and allocates the extracted resources to the feedback process executed by the vehicle display screen, and then locks the different feedback features associated with different extracted resources; The comprehensive locking end confirms the different extraction resources associated with different feedback features based on the different feedback features associated with different feedback processes, and then sorts the feedback features according to the order of extraction resources from small to large to confirm the feature sequence, and selects the optimal extraction resource from the feature sequence and executes it.