Vehicle intelligent evaluation method and system

Abstract

The invention provides an intelligent evaluation method and system for a vehicle, which are mainly applied to an equipment distribution box, the equipment distribution box comprises a plurality of display modules and pickup equipment plugged with the display modules, and relates to an intelligent evaluation technology. Generating guide data according to the position of the component to update each display module, responding to the back-insertion information of the pickup equipment, receiving audio data, obtaining a test time period corresponding to each test environment, splitting and analyzing the audio data according to the test time period to obtain an evaluation result, and displaying the evaluation result according to the evaluation result. And performing video fusion conversion on each evaluation result and the corresponding test environment to generate a vehicle simulation video, so that the actual performance of the vehicle can be evaluated more comprehensively and accurately.

Description

Technical Field

[0001] The present invention relates to intelligent evaluation technology, and in particular to a vehicle intelligent evaluation method and system. Background Art

[0002] With the rapid development of the automotive industry, in the fields of automobile manufacturing and after-sales maintenance, vehicle abnormal noise detection is a key link in evaluating the overall vehicle quality and component performance.

[0003] In the prior art, a relatively single audio acquisition device is usually used to evaluate some vehicle performances in a fixed environment. Since the vehicle areas covered by a single audio acquisition device are limited, relying solely on one audio acquisition device may not be able to collect the sound signals from each component simultaneously. For example, when the audio acquisition device is placed close to the engine, the engine sound may be captured well, but the sounds of other parts such as the door or chassis may be weak or even unable to be collected due to the long distance, resulting in an incomplete understanding of the overall vehicle sound situation and making it difficult to comprehensively and accurately evaluate the vehicle performance in a complex environment.

[0004] Therefore, how to more comprehensively and accurately evaluate the actual performance of a vehicle has become an urgent problem to be solved. Summary of the Invention

[0005] The present invention provides a vehicle intelligent evaluation method and system, which can more comprehensively and accurately evaluate the actual condition of a vehicle.

[0006] In a first aspect of the present invention, there is provided a vehicle intelligent evaluation method, which is characterized in that it is applied to an equipment distribution box. The equipment distribution box includes a plurality of display modules and a sound pickup device plugged into the display module, and includes: Determine the component positions corresponding to each sound pickup device based on a test request, and generate guidance data according to the component positions to update each display module; Respond to the plug-in information of the sound pickup device and receive audio data; Obtain the test time periods corresponding to each test environment, and perform split analysis on the audio data according to the test time periods to obtain an evaluation result; Perform video fusion transformation on each evaluation result and the corresponding test environment to generate a vehicle simulation video.

[0007] Optionally, in a possible implementation manner of the first aspect, the determining the component positions corresponding to each sound pickup device based on a test request, and generating guidance data according to the component positions to update each display module includes: Receive a test request of a target vehicle, and determine the vehicle model and pickup position corresponding to the target vehicle; Update the vehicle model according to the picking position to obtain an indication model; Split the indication model to generate guiding data and send it to each display module.

[0008] Optionally, in a possible implementation manner of the first aspect, it further includes: Mark the moment when the sound pickup device is unplugged as the first moment; Determine a verification period according to the first moment, verify the position image of the sound pickup device based on the verification period, and update the guiding data corresponding to the sound pickup device that meets the verification conditions to the first display mode.

[0009] Optionally, in a possible implementation manner of the first aspect, the obtaining the test periods corresponding to each test environment and splitting and analyzing the audio data according to the test periods to obtain an evaluation result includes: Respond to the test information, control each sound pickup device to pick up audio data; obtain the test periods corresponding to each test environment, and split the audio data according to the test periods to obtain multiple sub-data groups corresponding to the test environments; Perform audio analysis on the sub-data groups to obtain an evaluation result.

[0010] Optionally, in a possible implementation manner of the first aspect, the performing audio analysis on the sub-data groups to obtain an evaluation result includes: Perform spectral analysis on the sub-data in each sub-data group to obtain the spectral amplitudes corresponding to each sub-data group; Determine the sub-data with spectral amplitudes located in the abnormal amplitude range as abnormal data, and determine the sub-data with spectral amplitudes not located in the abnormal amplitude range as normal data; Generate an evaluation result corresponding to the sub-data group according to the abnormal data and normal data.

[0011] Optionally, in a possible implementation manner of the first aspect, performing video fusion transformation on each evaluation result and the corresponding test environment to generate a vehicle simulation video includes: Analyze the test environment corresponding to the sub-data group to obtain a speed environment and a road condition environment; Dynamically simulate the vehicle model according to the speed environment to generate a dynamic vehicle model, and generate a simulated road section according to the road condition environment; Update the dynamic vehicle model based on the evaluation result to generate a simulated vehicle; Combine the simulated vehicle and the simulated road section to obtain a sub-simulation video, and connect multiple sub-simulation videos to obtain a vehicle simulation video.

[0012] Optionally, in a possible implementation of the first aspect, the generating a dynamic vehicle model by dynamically simulating a vehicle model according to the speed environment includes: Retrieving a dynamic wheel model corresponding to the speed environment, and replacing the wheels of the vehicle model according to the dynamic wheel model to obtain an initial vehicle model; Adding labels to the dynamic wheel model of the initial vehicle model according to the speed parameters corresponding to the speed environment.

[0013] Optionally, in a possible implementation of the first aspect, the updating the dynamic vehicle model based on the evaluation result to generate a simulated vehicle includes: Determining the pickup component corresponding to the abnormal data and the update attributes corresponding to the pickup component, where the update attributes include real attributes and virtual attributes; Retrieving a video segment of a corresponding acquisition device based on the real attribute, and replacing and displaying the display area of the pickup component in the dynamic vehicle model according to the video segment to generate an abnormal model; Highlighting the pickup component in the dynamic vehicle model based on the virtual attribute according to a preset pixel value to generate an abnormal model; Binding the sub-data corresponding to the abnormal data to the pickup component of the abnormal model to obtain a simulated vehicle.

[0014] Optionally, in a possible implementation of the first aspect, the combining the simulated vehicle and the simulated road section to obtain a sub-simulated video, and connecting multiple sub-simulated videos to obtain a vehicle simulation video includes: Placing the simulated vehicle above the simulated road section to obtain the sub-simulated video; Extracting the speed environment corresponding to each sub-data group, sorting each sub-data group according to the speed environment to obtain a connection sequence; Connecting the sub-simulated videos based on the order of the connection sequence to obtain a vehicle simulation video.

[0015] In a second aspect of the present invention, there is provided a vehicle intelligent evaluation system, which is characterized in that it is applied to an equipment distribution box, and the equipment distribution box includes a plurality of display modules and a sound pickup device plugged into the display module, and includes: An update module, configured to determine the component positions corresponding to each sound pickup device based on a test request, and generate guidance data according to the component positions to update each display module; A response module, configured to respond to the back-insertion information of the sound pickup device and receive audio data; An analysis module, configured to obtain the test time periods corresponding to each test environment, and perform split analysis on the audio data according to the test time periods to obtain an evaluation result; A generation module, configured to perform video fusion transformation on each of the evaluation results and the corresponding test environments to generate a vehicle simulation video.

[0016] The beneficial effects of the present invention are as follows: 1. The present invention can comprehensively collect the sounds generated by a vehicle during driving through a device distribution box configured with multiple sound pickup devices, so as to more comprehensively and accurately evaluate the performance of the vehicle.

[0017] 2. During the process of sound collection, the present invention can guide the installation positions of the respective sound pickup devices through the display module on the device distribution box, so as to guide the user to accurately place the sound pickup devices on the corresponding vehicle components, thereby improving the accuracy of audio data collection.

[0018] 3. The present invention can use the acquisition device configured on the sound pickup device to collect position images during the verification period and compare and verify them with the guiding data. When the position image corresponding to the sound pickup device corresponds to the position shown in its guiding data, the guiding data is updated to the first display mode. This process ensures that the sound pickup device is correctly installed at the corresponding position, avoids affecting the accuracy of audio data collection due to installation position deviation, and thus guarantees the reliability of the collected audio data.

[0019] 4. By obtaining the test periods corresponding to each test environment, the present invention can accurately split the mixed audio data generated by the vehicle under complex and diverse operating conditions according to the time ranges of different test environments, thereby effectively preventing the confusion of sound data under different test environments, providing clear data samples for subsequent targeted analysis, and ensuring that the analysis results can accurately reflect the state of the vehicle under a specific test environment.

[0020] 5. By integrating multi-source data such as the audio data evaluation results, speed environment, and road condition environment information of the vehicle under different test environments, and performing video fusion transformation on this information into a vehicle simulation video, the present invention can reflect the operating state of the vehicle in various actual scenarios in a more comprehensive and intuitive manner. Description of the Drawings

[0021] Figure 1 is a flowchart of a vehicle intelligent evaluation method provided by an embodiment of the present invention; Figure 2 is a structural diagram of a vehicle intelligent evaluation system provided by an embodiment of the present invention; Figure 3 is a hardware structural diagram of a sound pickup device provided by an embodiment of the present invention. Detailed Embodiments

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] The following will detail the technical solutions of the present invention with specific embodiments. These several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0024] See Figure 1 , which is a schematic diagram of a vehicle intelligent evaluation method provided by an embodiment of the present invention. Figure 1 The execution subject of the shown method can be a software and / or hardware device. The execution subject of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. Among them, the user equipment can include, but is not limited to, a computer, a smart phone, a personal digital assistant (Personal Digital Assistant, abbreviated as: PDA), and the above-mentioned electronic devices, etc. The network equipment can include, but is not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing. Among them, cloud computing is a type of distributed computing, which consists of a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this.

[0025] The device distribution box in this solution includes multiple display modules and a sound pickup device plugged into each display module. Among them, the device distribution box is a device for uniformly managing multiple sound pickup devices, and the device distribution box can read and analyze the audio data collected by each sound pickup device. The display module refers to a module that can guide and display the installation positions corresponding to each sound pickup device. The sound pickup device refers to a device that collects the sounds generated during the vehicle driving process. It includes steps S1 to S4, specifically as follows: S1, determine the component positions corresponding to each sound pickup device based on a test request, and generate guiding data according to the component positions to update each display module.

[0026] Among them, the test request refers to a request for vehicle performance testing. The sound pickup device refers to a device that collects the sounds generated during the vehicle driving process. The component position refers to the installation position of the sound pickup device on the vehicle component. The guiding data refers to the indication data for guiding the installation of the sound pickup device to the corresponding vehicle component. The display module refers to the module in the device distribution box that is located below each sound pickup device and can display the guiding data, such as a display screen.

[0027] In practical applications, in the field of after-sales vehicle repair, it is often necessary to conduct performance tests on various vehicle components. For example, sound detection of components at different positions such as the engine compartment, doors, and chassis is carried out to determine whether their operating status is normal. However, currently, in the process of sound detection of vehicle components, usually only a small number of sound collection devices at fixed positions are used, which may be difficult to cover many vehicle components such as the engine compartment, doors, and chassis. This makes it impossible to comprehensively collect the sounds generated during vehicle driving, and key sound signals emitted by some components are easily missed, resulting in a one-sided assessment of vehicle performance and an inability to accurately grasp the overall operating status of the vehicle. Moreover, when maintenance personnel install sound collection devices, they mainly rely on personal experience to judge the component positions, lacking precise guidance, which easily leads to installation position deviations, resulting in the inability to accurately collect the sounds of target components, greatly affecting the accuracy of audio data collection, and thus may not be able to accurately evaluate vehicle performance based on reliable audio data.

[0028] This solution can comprehensively collect the sounds generated by a vehicle during driving through a device distribution box equipped with multiple sound pickup devices, thereby enabling a more comprehensive and accurate assessment of the vehicle's performance. During the process of sound collection, this solution can guide the installation positions of each sound pickup device through the display module on the device distribution box, so as to guide the user to accurately place the sound pickup device on the corresponding vehicle component, thereby improving the accuracy of audio data collection.

[0029] Specifically, when the user has a need to conduct a performance test on a vehicle, a corresponding test request can be sent. In the test request, the user can input the vehicle model. After receiving the request for vehicle performance testing, the vehicle model input by the user can be transmitted to the device distribution box. The device distribution box can accurately determine multiple vehicle components that need to be subjected to sound detection in combination with the vehicle model, and can determine the positions where each sound pickup device corresponds to the vehicle components, that is, the component positions. After determining the vehicle components corresponding to each sound pickup device, data for guiding and indicating the corresponding component positions, that is, guiding data, can be generated, and the corresponding guiding data can be displayed through the display module corresponding to each sound pickup device, such as a display screen.

[0030] In some embodiments, the specific implementation manner of step S1 may be: S11, receive a test request of a target vehicle, and determine the vehicle model and pickup position corresponding to the target vehicle.

[0031] Specifically, after receiving a request from the user to test a target vehicle, the virtual vehicle model corresponding to the target vehicle can be determined according to the vehicle model in the test request, and the optimal installation positions of each sound pickup device on the corresponding vehicle components, that is, the pickup positions, can be determined.

[0032] Among them, the target vehicle refers to the vehicle for which a performance test is requested, the vehicle model refers to the virtual model corresponding to the target vehicle, and the pickup position refers to the installation position of the sound pickup device on the corresponding vehicle component.

[0033] S12. Update the vehicle model according to the pickup position to obtain an indication model.

[0034] Specifically, after determining the installation positions of each sound pickup device, that is, the pickup positions, the position points corresponding to the pickup positions of each sound pickup device can be determined in the vehicle model, and then the vehicle model can be updated to obtain an indication model with clear installation instructions for the sound pickup devices. Among them, the indication model refers to a virtual model that can guide and indicate the installation position of the sound pickup device.

[0035] S13. Split the indication model to generate guiding data and send it to each display module.

[0036] Specifically, after obtaining the indication model, the indication model can be split according to the corresponding relationship between the sound pickup device and the corresponding vehicle component. Each sound pickup device and its corresponding vehicle component model can be split into independent sub-models. For example, if there are 5 sound pickup devices, the indication model will be split into 5 sub-models. Each sub-model corresponds to a sound pickup device and its installation position indication information on the vehicle model. According to each split sub-model, the guiding data corresponding to each sound pickup device can be obtained. For example, when a sound pickup device is installed on the left window of the target vehicle, the sub-model corresponding to the left window in the indication model can be split out as the guiding data corresponding to the sound pickup device on the left window, and the guiding data corresponding to each sound pickup device can be sent to the corresponding display module for display.

[0037] Through the above implementation methods, users can be guided to accurately install the sound pickup devices, thereby improving the accuracy of audio data collection, making the evaluation of vehicle performance more comprehensive and accurate, and providing a more reliable basis for subsequent maintenance and optimization.

[0038] Based on the above steps, the present solution further includes the following embodiments: A1. Mark the moment when the sound pickup device is pulled out as the first moment.

[0039] In practical applications, during the process of installing a sound pickup device to the corresponding pickup position, in order to determine whether the sound pickup device is correctly installed at the corresponding position, the actual installation position of the sound pickup device can be imaged by a micro collection device configured on the sound pickup device, such as a micro camera. The installation position of the sound pickup device can be verified according to the acquired image. If the actual installation position of the sound pickup device is inconsistent with the corresponding pickup position, the user can be reminded through the display module on the device distribution box.

[0040] Specifically, the moment when the sound pickup device is pulled out can be marked as the first moment. For example, when the moment when the sound pickup device is pulled out is 9:10:20, then 9:10:20 can be marked as the first moment. Here, the pull-out moment refers to the moment when the sound pickup device is taken out from the corresponding position slot in the device distribution box, and the first moment refers to the moment when the sound pickup device is pulled out.

[0041] A2. Determine a verification period according to the first moment, and verify the position image of the sound pickup device based on the verification period.

[0042] Specifically, according to the first moment, the time period for verifying the installation position of the sound pickup device can be determined, that is, the verification period. For example, when the time required to install the sound pickup device is 10 seconds, the time period from the first moment of 9:10:20 to 9:10:30 can be determined as the verification period. Obtain the position image of the sound pickup device collected by the micro collection device configured on the sound pickup device during the verification period. For example, when the pickup position of the sound pickup device is outside the left window, the collected position image can be an image corresponding to the outside of the left window. By comparing the collected position image with the guiding data corresponding to the sound pickup device, the actual installation situation of the sound pickup device can be verified.

[0043] Among them, the verification period refers to the time period for verifying the actual installation situation of the sound pickup device, and the position image refers to the image of the actual installation position of the sound pickup device collected by the micro collection device configured on the sound pickup device during the verification period.

[0044] A3. Update the guiding data corresponding to the sound pickup device that meets the verification conditions to the first display mode.

[0045] Specifically, when the position image corresponding to the sound pickup device corresponds to the position shown in its guiding data, it can be considered that the corresponding sound pickup device meets the verification conditions, and the guiding data corresponding to the sound pickup device can be updated to the first display mode.

[0046] Among them, the verification condition refers to that the guiding data corresponding to the sound pickup device and the position shown in the position image are the same installation position, and the first display mode refers to the display mode of the guiding data corresponding to the correctly installed sound pickup device.

[0047] Through the above embodiments, it is possible to ensure that the sound pickup device is correctly installed at the corresponding position, avoiding the influence of installation position deviation on the accuracy of audio data collection, thereby ensuring the reliability of the collected audio data.

[0048] S2. Respond to the reinsertion information of the sound pickup device and receive audio data.

[0049] Among them, the reinsertion information refers to the information of inserting the sound pickup device back into the device distribution box, and the audio data refers to the sound data of vehicle components collected by the sound pickup device during vehicle driving.

[0050] In the device distribution box, each sound pickup device has its corresponding position slot. After the sound pickup device completes the corresponding sound collection operation, the sound pickup device can be inserted back into the corresponding position slot in the device distribution box. After all the sound pickup devices are inserted back into the corresponding position slots, in response to the corresponding reinsertion information, the device distribution box can read the corresponding sound data of vehicle components collected by each sound pickup device during driving, so as to receive the audio data corresponding to each vehicle component.

[0051] S3. Obtain the test periods corresponding to each test environment, and perform splitting analysis on the audio data according to the test periods to obtain an evaluation result.

[0052] Among them, the test environment refers to specific physical scenarios that a vehicle may encounter during actual use, such as environments with different speeds, different road conditions, etc. The test period refers to the time period corresponding to different test environments, and the evaluation result refers to the result after analyzing each audio data.

[0053] In practical applications, vehicles may face diverse scenarios during actual use. Under these scenarios, the operating states and generated sound characteristics of vehicle components will be significantly different. For example, during the acceleration process, the load change of the engine will cause changes in sound frequency and amplitude, and when driving on a bumpy road section, components such as the chassis suspension system and body structure will generate unique sound signals due to vibration. Therefore, this solution can perform performance tests on the vehicle by setting different test environments. And in order to accurately capture the audio data generated by the vehicle under different test environments, it is possible to obtain the test periods corresponding to different test environments. The test periods can separate the sound data of the vehicle under different operating conditions, avoiding the confusion of sound data under different test environments and facilitating subsequent targeted analysis of the audio data. For example, when the vehicle accelerates from 0 to 10 kilometers per hour in this test environment, it may correspond to a relatively short test period, such as 0 - 5 seconds, while when accelerating from 0 to 100 kilometers per hour, since the acceleration process is longer, the test period may be a time period between 10 - 30 seconds.

[0054] After obtaining the test periods corresponding to each test environment, the audio data can be split according to each test period. This means that from the complete audio data collected by the pickup device, according to the time range of the test period, the audio segments corresponding to each test environment are extracted. For example, if there is a complete audio data with a duration of 60 seconds, where 0-3 seconds corresponds to the acceleration test environment of 0-10 km / h, 8-15 seconds corresponds to the acceleration test environment of 0-30 km / h, 20-28 seconds corresponds to the acceleration environment of 0-50 km / h, and 35-48 seconds corresponds to the acceleration test environment of 0-100 km / h, then the audio of these four test periods will be extracted respectively. By analyzing and evaluating the audio data of different periods, the corresponding evaluation results can be obtained.

[0055] Based on the above embodiments, the specific implementation manner of step S3 can be: S31. Respond to the test information and control each of the pickup devices to pick up audio data.

[0056] Specifically, after installing each pickup device at the corresponding position, it is possible to respond to the information for testing the vehicle, that is, the test information, and control each pickup device to collect the sound data generated by the corresponding vehicle components during the vehicle driving process, that is, the audio data. Among them, the test information refers to the information for testing the vehicle.

[0057] S32. Obtain the test periods corresponding to each test environment, and split the audio data according to the test periods to obtain multiple sub-data groups corresponding to the test environments.

[0058] Since different test environments correspond to different test periods, the test periods corresponding to each test environment can be obtained. After obtaining the test periods corresponding to each test environment, the complete audio data is split according to these periods. The specific operation is to accurately extract the audio segments corresponding to each test environment from the relatively long complete audio data collected by the pickup device according to the time range of the test period. For example, if there is a complete audio data with a duration of 60 seconds, where 0-3 seconds corresponds to the acceleration test environment of 0-10 km / h, 8-15 seconds corresponds to the acceleration test environment of 0-30 km / h, 20-28 seconds corresponds to the acceleration environment of 0-50 km / h, and 35-48 seconds corresponds to the acceleration test environment of 0-100 km / h, then the audio of these four test periods can be extracted respectively.

[0059] For each audio segment extracted from the test environment, it is further organized into sub-data groups. For example, assuming there are 10 pickup devices, in the acceleration test environment of 0-10 km / h, the audio segments corresponding to the test period collected from these 10 pickup devices form a sub-data group containing 10 sub-data. And so on, corresponding sub-data groups will be generated for different test environments.

[0060] Among them, the sub-data group refers to a data group composed of audio segments collected by multiple pickup devices in the same test environment.

[0061] Through the above implementation method, it can effectively prevent the confusion of sound data in different test environments, provide clear and pure data samples for subsequent targeted analysis, and ensure that the analysis results can accurately reflect the state of the vehicle in a specific test environment.

[0062] S33. Perform audio analysis on the sub-data group to obtain an evaluation result.

[0063] Specifically, by analyzing audio features in the sub-data group, such as frequency, amplitude, etc., to determine whether the operating state of vehicle components is normal in the corresponding test environment. For example, if there are abnormal frequency peaks or amplitude fluctuations in the audio spectrum of a certain sub-data group, it may mean that there is a problem with the corresponding vehicle component. Finally, based on the results of audio analysis, an evaluation result of the performance of vehicle components in the corresponding test environment is generated, providing a basis for the intelligent evaluation of the vehicle.

[0064] In some embodiments, the specific implementation manner of step S33 may be: S331. Perform spectrum analysis on the sub-data in each sub-data group to obtain the spectrum amplitudes corresponding to each sub-data group.

[0065] Specifically, from each of the obtained sub-data groups, one sub-data group is sequentially selected as the current analysis object, and each sub-data group contains audio data segments collected by multiple pickup devices in a specific test environment.

[0066] For each sub-data in the selected sub-data group, that is, the audio segment collected by each pickup device, the spectrum analysis algorithm is used for processing. The role of spectrum analysis is to convert the audio signal originally presented in the time domain to the frequency domain for observation. In the time domain, the audio signal is manifested as a voltage or current signal that changes with time, while in the frequency domain, the audio signal is represented by different frequency components and their corresponding amplitudes. Through spectrum analysis techniques such as Fourier transform, the complex time-domain audio signal can be decomposed into a combination of a series of sine waves and cosine waves with different frequencies, and then the amplitude corresponding to each sub-data at each frequency point, that is, the spectrum amplitude, can be obtained. The spectrum amplitudes obtained after spectrum analysis of all sub-data in the sub-data group are summarized, and these spectrum amplitudes together constitute the spectrum amplitude set corresponding to the sub-data group. This set comprehensively reflects the energy distribution of the audio signal represented by the sub-data group in the frequency domain, providing a key data basis for subsequent judgment of whether the sub-data is abnormal.

[0067] Among them, the sub-data refers to the audio segment collected by each pickup device during the test period corresponding to the test environment, and the spectrum amplitude refers to the signal intensity of the sub-data at a specific frequency point.

[0068] S332, determine the sub-data with spectrum amplitudes located within the abnormal amplitude range as abnormal data, and determine the sub-data with spectrum amplitudes not located within the abnormal amplitude range as normal data.

[0069] Before formally evaluating the audio data, based on the experience accumulated through a large number of experiments and data analysis of each vehicle component under normal operating conditions, a reasonable abnormal amplitude range can be preset in advance. The setting of this range is based on an accurate grasp of the audio spectrum amplitude range during normal vehicle operation, covering the spectrum amplitude fluctuation range that may occur under various normal operating conditions. The spectrum amplitudes exceeding this range are considered to possibly correspond to the abnormal operating state of the vehicle component.

[0070] Compare each spectrum amplitude in the spectrum amplitude set corresponding to the sub-data group with the preset abnormal amplitude range one by one in detail. The comparison process strictly judges according to the magnitude relationship of the amplitudes. After comparison, if the spectrum amplitude of the sub-data falls within the abnormal amplitude range, then the sub-data is determined to be abnormal data, indicating that the audio signal collected by the pickup device corresponding to the sub-data may originate from a vehicle component in an abnormal operating state. On the contrary, if the spectrum amplitude is outside the abnormal amplitude range, that is, within the normal amplitude range, then the sub-data is determined to be normal data, indicating that the corresponding vehicle component is operating normally in the current test environment. In this way, a preliminary judgment of the operating state of all sub-data in the entire sub-data group is completed.

[0071] Among them, the abnormal amplitude range refers to the pre-set spectral amplitude threshold range based on long-term experiments and data analysis of the vehicle under normal working conditions. Abnormal data refers to the sub-data in which at least one frequency point of the spectral amplitude falls within the abnormal amplitude range, indicating that the corresponding vehicle component may be in an abnormal operating state. Normal data refers to the sub-data in which the spectral amplitude of the entire frequency band is outside the abnormal amplitude range, indicating that the operating state of the corresponding vehicle component meets the normal expectations in the current test environment.

[0072] S333. Generate the evaluation result corresponding to the sub-data group according to the abnormal data and the normal data.

[0073] Specifically, according to the abnormal data and the normal data obtained after spectral analysis, the evaluation result corresponding to each sub-data group can be obtained.

[0074] Through the above implementation manner, the performance status of vehicle components in different test environments can be more comprehensively reflected.

[0075] S4. Perform video fusion transformation on each of the evaluation results and the corresponding test environment to generate a vehicle simulation video.

[0076] Among them, the vehicle simulation video refers to a dynamic display video that simulates the driving conditions of the vehicle by fusing multiple test data of the vehicle with the corresponding virtual model of the vehicle.

[0077] Specifically, the evaluation results corresponding to each test environment can be collected. These results include the judgment of the performance status of vehicle components in different test environments, such as whether there are abnormalities and other information. At the same time, the test environment information corresponding to them is collected, including the specific type of the test environment, such as the speed range in the acceleration scenario, the road surface condition in the road condition scenario, etc. Then, using video production technology and computer graphics simulation means, the evaluation results are fused and transformed with the test environment. In this process, the running state of the vehicle in different test environments can be simulated through animations, virtual scene construction, etc. At the same time, the evaluation results are incorporated into the simulated scene in an intuitive form, such as color marking. For example, the normally operating components are displayed in green, and the components with problems are marked with red flashing. Finally, these simulated video segments that integrate the evaluation results and the test environment can be integrated in a certain logical order to generate a complete vehicle simulation video. The generated vehicle simulation video can enable relevant personnel to more intuitively and comprehensively understand the performance of the vehicle in different actual scenarios, providing a powerful visual reference for vehicle maintenance, optimization, and further research and development.

[0078] Based on the above embodiments, the specific implementation manner of step S4 can be: S41. Analyze the test environment corresponding to the sub-data group to obtain the speed environment and road condition environment.

[0079] Specifically, each sub-data group is associated with specific test environment information, which was detailedly recorded during the previous test implementation process. The sub-data group contains audio segments collected by multiple pick-up devices in a specific test environment, and the corresponding test environment information describes the operating scenario where the vehicle was located at that time. Conduct a meticulous classification and analysis of the obtained test environment information, and mainly classify it into two key categories: speed environment and road condition environment. In terms of the speed environment, carefully identify the speed-related details of the vehicle during the test. For example, if the test environment is an acceleration scenario, it is necessary to clarify the starting speed, ending speed, and the time range of the entire acceleration process, such as accelerating from 0 to 10 kilometers per hour, or from 0 to 100 kilometers per hour and other specific situations in different speed intervals. For the road condition environment, focus on analyzing the road surface condition characteristics when the vehicle is driving, such as whether the road surface is flat, whether there are bumps and undulations, whether it is on a curve, and the curvature of the curve and other detailed information. Through this in-depth analysis, the complex test environment information is refined into two clear and operable elements: speed and road conditions, providing accurate data support for the subsequent construction of the simulation scenario.

[0080] Among them, the speed environment refers to the speed change situation of the vehicle during the test, and the road condition environment refers to the road conditions of the vehicle during the test.

[0081] S42. Dynamically simulate the vehicle model according to the speed environment to generate a dynamic vehicle model, and generate a simulated road section according to the road condition environment.

[0082] Specifically, according to the obtained speed environment data, conduct a dynamic simulation operation on the pre-established static vehicle model. Use advanced computer graphics algorithms and physical simulation technologies to precisely control the motion state parameters of the vehicle model according to different speed conditions, so as to simulate the dynamic driving effect of the vehicle during the speed change process and generate a dynamic vehicle model that fits the specific speed environment, enabling it to accurately reflect the actual operating state of the vehicle in this speed scenario.

[0083] For the analyzed road condition environment information, use geographic information modeling technology and virtual scene generation tools to construct a corresponding simulated road section. For example, for a bumpy road section, carefully set the terrain parameters of high and low undulations in the virtual terrain and reasonably distribute obstacles to simulate the strong vibration effect generated when the vehicle is driving on a bumpy road surface. For a flat road, create a smooth road surface model without obvious undulations to ensure that the vehicle presents a stable state when driving on it, thus truly restoring the driving conditions of the vehicle under different road conditions and providing a realistic scene basis for the subsequent simulation of the interaction between the vehicle and the road section.

[0084] Among them, the vehicle model refers to a virtual model corresponding to the target vehicle to be tested, the dynamic vehicle model refers to a model generated by dynamically simulating the vehicle model for driving, and the simulated road section refers to an interactive virtual road generated by combining road conditions and environments.

[0085] In some embodiments, "dynamically simulating the vehicle model according to the speed environment to generate a dynamic vehicle model" in step S42 includes the following steps: S421, retrieve the dynamic wheel model corresponding to the speed environment, and replace the wheels of the vehicle model according to the dynamic wheel model to obtain an initial vehicle model.

[0086] Specifically, according to the obtained speed environment information, it is possible to retrieve in a pre-constructed dynamic wheel model database, which stores dynamic wheel models corresponding to various different speed environments. Each model is optimized for a specific speed range to accurately simulate the motion characteristics of the wheels under that speed condition. For example, for a speed environment from 0 to 10 kilometers per hour, the database stores a specially designed dynamic wheel model, and its parameters such as rotational speed and inertia characteristics all conform to the actual motion of the wheels within this speed range. By identifying key parameters of the speed environment, such as the starting speed and the ending speed, it is possible to accurately retrieve the matching dynamic wheel model.

[0087] After successfully retrieving the dynamic wheel model corresponding to the speed environment, it can be applied to the pre-established static vehicle model. Through the model replacement algorithm of computer graphics, replace the original wheel model of the static vehicle model with the just retrieved dynamic wheel model. During the replacement process, ensure that the dynamic wheel model is perfectly adapted to the body part of the vehicle model in terms of geometric structure and connection relationship, so that the wheels can be correctly installed at the wheel axle position of the vehicle and be coordinated with other body parts in terms of spatial position. After this step, the static vehicle model initially has the basic conditions for dynamic operation in a specific speed environment, thus obtaining the initial vehicle model. Although this initial vehicle model has already replaced the dynamic wheel model, it still needs to be further optimized according to the speed parameters.

[0088] Among them, the dynamic wheel model refers to a wheel model that is pre-constructed and stored in the dynamic wheel model database and is optimized for a specific speed environment, and the initial vehicle model refers to the vehicle model obtained by replacing the original wheel model of the static vehicle model with a dynamic wheel model corresponding to the speed environment.

[0089] S422, add labels to the dynamic wheel model of the initial vehicle model according to the speed parameters corresponding to the speed environment.

[0090] By analyzing the obtained speed environment, corresponding speed parameters are obtained, including key information such as the starting speed, ending speed of the vehicle in this speed environment, and the corresponding test time period, etc. Through in-depth analysis of these parameters, the motion states and characteristics that the dynamic wheel model should present at different times can be determined. According to the results of the speed parameter analysis, using computer graphics and animation production techniques, corresponding labels are added to the dynamic wheel model of the initial vehicle model. These labels are used to mark the key states and motion information of the wheel at different speed stages. For example, an initial rotation speed label indicating the wheel at the starting speed (0 km / h) can be added, as well as a rotation speed label when the speed reaches 10 km / h. These labels can serve as important bases for subsequent animation driving and simulation of the dynamic wheel model. By adding these labels, detailed guidance can be provided for the accurate motion of the dynamic wheel model in different speed environments, enabling the dynamic wheel model to rotate realistically according to the actual speed change situation, thereby further improving the dynamic vehicle model and making it more accurately reflect the actual operating state of the vehicle in a specific speed scenario.

[0091] Among them, the speed parameter refers to the speed value of the vehicle in the corresponding speed environment.

[0092] Through the above implementation manner, detailed guidance can be provided for the accurate motion of the dynamic wheel model in different speed environments, enabling the dynamic wheel model to rotate realistically according to the actual speed change situation, thereby further improving the dynamic vehicle model and making it more accurately reflect the actual operating state of the vehicle in a specific speed scenario.

[0093] S43. Update the dynamic vehicle model based on the evaluation result to generate a simulated vehicle.

[0094] Specifically, according to the obtained evaluation result data, targeted update processing is performed on the generated dynamic vehicle model. If the evaluation result shows that an abnormal situation exists in a certain vehicle component, such as an abnormal frequency peak appears in the audio spectrum, which may mean that there is a mechanical fault inside the engine. At this time, in the dynamic vehicle model, this abnormal situation is visually presented in various ways. For example, the appearance display of this component can be changed, such as adding a prominent fault warning sign on the engine model to attract the attention of viewers. Through the update operation, a simulated vehicle that can accurately reflect the performance status of the vehicle in different test environments is generated, enabling viewers to intuitively understand the actual operating conditions of the vehicle components through the state of the vehicle model. Among them, the simulated vehicle refers to the virtual vehicle model obtained by updating the dynamic vehicle model according to the evaluation result.

[0095] In some embodiments, the specific implementation manner of step S43 may be: S431. Determine the pickup component corresponding to the abnormal data and the update attribute corresponding to the pickup component, where the update attribute includes a real attribute and a virtual attribute.

[0096] Among them, the pickup component refers to the vehicle component corresponding to the abnormal data, and the update attribute refers to the attribute for determining whether the pickup component corresponding to the identified abnormal data can update and display the dynamic vehicle model through real video data or virtual means during the vehicle performance evaluation process. The real attribute refers to the attribute corresponding to the vehicle component that can update the dynamic vehicle model through real video data, and the virtual attribute refers to the attribute corresponding to the vehicle component without real video data.

[0097] Specifically, after obtaining the evaluation result data, all abnormal data can be identified first. These abnormal data are the data whose spectral amplitudes are determined to be within a preset abnormal amplitude range after operations such as spectral analysis of the audio data. Each abnormal data is associated with a specific component on the vehicle because the sound pickup device collects audio data for different vehicle components. For each abnormal data, trace its source to determine the corresponding pickup component, that is, the vehicle component represented by the abnormal data. For example, if a sound pickup device is installed near the engine and the collected audio data is abnormal, then the engine is the corresponding pickup component.

[0098] Judge the update attribute corresponding to each pickup component. The real attribute means that there is real video data available for updating the dynamic vehicle model, which usually applies to some vehicle components that are easily directly photographed and recorded, such as the body appearance, some exposed mechanical components, etc. The corresponding collection device (such as a micro camera) can capture the state video of the component during actual operation. The virtual attribute is for those vehicle components without real video data, possibly because the component position is relatively hidden and it is difficult to obtain video through conventional collection devices. For example, some components inside the engine usually cannot directly capture the real-time video of their operation during operation, and such components have virtual attributes.

[0099] S432. Retrieve the video segment of the corresponding collection device based on the real attribute, and replace and display the display area of the pickup component in the dynamic vehicle model according to the video segment to generate an abnormal model.

[0100] Specifically, after determining the update attributes of each pickup component, the pickup components with real attributes are screened out. The acquisition devices corresponding to these components recorded relevant video data during the vehicle test. For the pickup components with real attributes, the video segments captured by the corresponding acquisition devices are retrieved. For example, if a door component has real attributes, the video captured by a micro camera installed near the door can be retrieved, and this video records the actual state of the door during vehicle operation.

[0101] After obtaining the corresponding video segments, in the dynamic vehicle model, the display area corresponding to the pickup component can be found, and using the image replacement technology of computer graphics, the retrieved video segments are embedded into the display area of the pickup component in the dynamic vehicle model. In this way, the original model display part is replaced with a real video image, making the display of the pickup component in the dynamic vehicle model more real and intuitively reflect its actual operating state. After this operation, a preliminary abnormal model can be generated. In this model, the pickup components with real attributes are presented with the actually captured video images, highlighting the possible abnormal component states.

[0102] Among them, the acquisition device refers to a device that can collect video data, such as a micro camera. The video segment refers to the relevant video data recorded by the acquisition device during the vehicle test. The display area refers to the area in the dynamic vehicle model used to display the state of vehicle components. The abnormal model refers to the model generated after updating the pickup components corresponding to abnormal data in the dynamic vehicle model.

[0103] S433, Based on the virtual attributes, highlight the pickup components in the dynamic vehicle model according to the preset pixel values to generate an abnormal model.

[0104] Specifically, the pickup components with virtual attributes are screened out. Since these components lack real video data, other methods are needed to highlight their abnormal states. Pixel values for highlighting the pickup components with virtual attributes are preset in advance. These pixel values usually choose colors that form a sharp contrast with the display color of normal components, such as eye-catching red, yellow, etc. In the dynamic vehicle model, for the pickup components with virtual attributes, using the model rendering technology of computer graphics, they are highlighted according to the preset pixel values. For example, by changing the color of the pickup component, it is made to stand out significantly in the dynamic vehicle model. For example, a component inside the engine with virtual attributes and judged to be abnormal is rendered red, making it easy to be observed in the entire vehicle model. After this step, the highlighting of the abnormal states of the pickup components with virtual attributes is also completed, and the abnormal model corresponding to the pickup components with virtual attributes is obtained. Among them, the preset pixel value refers to the pixel value preset in advance that can highlight the pickup component.

[0105] S434. Bind the sub - data corresponding to the abnormal data to the pickup components of the abnormal model to obtain a simulated vehicle.

[0106] Specifically, by establishing an association relationship between the sub - data corresponding to the abnormal data determined in the steps and the corresponding pickup components in the abnormal model. Each abnormal data is derived from the audio data collected by a certain pickup device in a specific test environment. These sub - data contain rich audio feature information, reflecting the operating state of the pickup components. By establishing this association, when relevant personnel view the simulated vehicle, they can quickly trace back to the corresponding source of abnormal data through the abnormal display of the pickup components. After binding the sub - data corresponding to the abnormal data to the pickup components of the abnormal model, the final simulated vehicle is generated. This simulated vehicle not only visually displays the abnormal states of each vehicle component on the appearance (highlighting the pickup components with real and virtual attributes in different ways), but also closely links the abnormal states with the corresponding audio sub - data at the data level. Thus, by observing the simulated vehicle, it is possible to clearly understand which components of the vehicle are abnormal in different test environments and the specific audio data characteristics corresponding to these abnormalities, providing a comprehensive, intuitive, and data - tightly - associated visualization tool for vehicle performance evaluation, fault diagnosis, etc.

[0107] Through the above - mentioned implementation manner, a comprehensive, intuitive, and data - tightly - associated visualization tool can be provided for vehicle performance evaluation, fault diagnosis, etc.

[0108] S44. Combine the simulated vehicle and the simulated road section to obtain a sub - simulated video, and connect multiple sub - simulated videos to obtain a vehicle simulated video.

[0109] Specifically, the generated simulated vehicle and the generated simulated road section can be organically combined in a virtual scene to generate a video clip of the simulated vehicle driving on the simulated road section, that is, a sub - simulated video. And in the sub - simulated video, the real - time speed of the simulated vehicle during the test process can be displayed. For example, it can be displayed in a fixed area in the upper left corner of the sub - simulated video. Connect multiple sub - simulated videos in a certain time sequence, so that each sub - simulated video can be spliced into a complete vehicle simulated video. This video comprehensively displays the operating state and performance evaluation results of the vehicle in different test environments, providing intuitive and visual reference materials for vehicle maintenance, optimization, and further research and development, helping relevant personnel more quickly and accurately understand the performance of the vehicle in various actual scenarios, and providing strong support for subsequent work.

[0110] Among them, the sub - simulated video refers to the simulated video data generated by combining the simulated vehicle and the simulated road section.

[0111] In some embodiments, the specific implementation of step S44 may be as follows: S441. Place the simulated vehicle above the simulated road section to obtain the sub-simulated video.

[0112] Specifically, the generated simulated vehicle is accurately placed above the simulated road section. This requires precise calculation of the position coordinates of the simulated vehicle to ensure that its wheels are in close contact with the road surface of the simulated road section and the posture of the vehicle conforms to the normal driving state. For example, according to the terrain undulation and slope change of the simulated road section, the tilt angle of the simulated vehicle is adjusted so that it looks like it is actually driving on this road section. Through this precise positioning and placement, the simulated vehicle and the simulated road section form a whole in the virtual scene. Using the video rendering technology of computer graphics, the scene of the simulated vehicle on the simulated road section is rendered to generate a video clip of the simulated vehicle driving on the simulated road section, that is, the sub-simulated video.

[0113] S442. Extract the speed environment corresponding to each sub-data group, and sort each sub-data group according to the speed environment to obtain a connection sequence.

[0114] Specifically, the speed environment corresponding to each sub-data group can be obtained. For example, when the speed environment corresponding to sub-data group 1 is accelerating from 0 to 10 kilometers per hour, the speed environment corresponding to sub-data group 2 is accelerating from 0 to 30 kilometers per hour, the speed environment corresponding to sub-data group 3 is accelerating from 0 to 50 kilometers per hour, and the speed environment corresponding to sub-data group 4 is accelerating from 0 to 100 kilometers per hour, then each sub-data group can be sorted according to the speed environment, and the obtained connection sequence can be sub-data group 1, sub-data group 2, sub-data group 3, sub-data group 4.

[0115] Among them, the connection sequence refers to the sequence obtained by sorting each sub-data group, and subsequently, the sub-simulated videos can be connected according to the connection sequence.

[0116] S443. Connect the sub-simulated videos in the order of the connection sequence to obtain a vehicle simulation video.

[0117] Specifically, according to the generated connection sequence, the sub-simulated videos corresponding to each sub-data group can be obtained in turn. Each sub-simulated video is generated by a simulated vehicle driving on the corresponding simulated road section under a specific speed environment. For example, if the speed environment corresponding to the first sub-data group in the connection sequence is accelerating from 0 to 10 kilometers per hour, then among all the sub-simulated videos, accurately find the sub-simulated video generated by the combination of the simulated vehicle and the simulated road section under this speed environment. According to the order corresponding to each sub-data group in the connection sequence, the corresponding sub-simulated videos are connected, and the simulation video data of the vehicle corresponding to different test environments can be obtained, that is, the vehicle simulation video.

[0118] Through the above embodiments, the running state of the vehicle in various actual scenarios can be reflected in a more comprehensive and intuitive manner.

[0119] Based on the above steps, the present solution further includes the following embodiments: B1. Based on the connection sequence, obtain the corresponding sub-simulation videos in sequence, compare the speed ranges of each sub-simulation video, and determine the target speed range corresponding to each sub-simulation video.

[0120] In practical applications, during the vehicle test simulation process, multiple sub-simulation videos corresponding to test environments with different speed ranges will be generated. However, these videos often contain overlapping parts. For example, the test video of accelerating from 0 to 30 kilometers per hour may contain the process of accelerating from 0 to 10 kilometers per hour. In order to more accurately analyze the vehicle performance in a specific speed range, the sub-simulation videos can be cropped and connected to obtain the required sub-video data.

[0121] Specifically, according to the determined connection sequence, the sub-simulation videos corresponding to each sub-data group can be obtained in sequence. After obtaining each sub-simulation video, in order to ensure that there are no overlapping parts between different sub-simulation videos, the target speed range corresponding to each sub-simulation video can be determined. For example, for the sub-simulation video corresponding to the test environment of accelerating from 0 to 30 kilometers per hour, it may cover the part from 0 to 10 kilometers per hour that already exists in a separate sub-simulation video. At this time, the speed environment corresponding to each sub-simulation video can be obtained, the speed ranges of the obtained sub-simulation videos can be compared, and the overlapping parts can be removed to determine the unique target speed range for each sub-simulation video. For example, for the sub-simulation video of accelerating from 0 to 30 kilometers per hour, if it is identified that the part from 0 to 10 kilometers per hour already exists in other sub-simulation videos, then its target speed range can be determined as 10 to 30 kilometers per hour. In this way, the subsequent cropping can accurately retain the content of the required speed range and avoid data redundancy.

[0122] The target speed range refers to the speed range determined for each sub-simulation video when processing sub-simulation videos corresponding to test environments with multiple different speed ranges, which does not include the parts overlapping with other sub-simulation videos.

[0123] B2. Obtain the starting frame and ending frame corresponding to the target speed range, and crop each sub-simulation video according to the starting frame and ending frame to obtain the target simulation video.

[0124] Specifically, after determining the target speed range, the starting frame and the ending frame corresponding to the target speed range can be determined. Through an image recognition algorithm, the sub-simulation video is analyzed frame by frame to identify the speed identification of the simulated vehicle in the sub-simulation video until the starting frame and the ending frame of the target speed range are found. Using a video processing tool, the corresponding sub-simulation video is cropped based on the starting frame and the ending frame. Starting from the starting frame, subsequent frames are extracted in sequence until the ending frame. These frames are recombined according to the original video frame rate and coding format to generate a target simulation video. For example, for a sub-simulation video with a target speed range of 10 to 30 kilometers per hour, after finding the starting frame corresponding to the speed reaching 10 kilometers per hour and the ending frame corresponding to the speed reaching 30 kilometers per hour, the video frames within this range are cropped to obtain a target simulation video that accurately reflects the vehicle state in this speed range.

[0125] Among them, the starting frame refers to the video frame in the sub-simulation video corresponding to the starting speed point of the target speed range, and the ending frame refers to the video frame in the sub-simulation video corresponding to the ending speed point of the target speed range. The target simulation video refers to the video that is generated after cropping the corresponding sub-simulation video according to the determined target speed range and recombined, which accurately reflects the vehicle running state within the target speed range.

[0126] B3. Connect the respective target video data according to the connection sequence to obtain a vehicle simulation video.

[0127] After completing the cropping of all sub-simulation videos to obtain their respective target simulation videos, video connection is performed according to the initially determined connection sequence. The connection sequence ensures the coherence of the video in terms of time, speed change, and test scenario, etc. For example, if the connection sequence is from low speed to high speed, the target simulation videos in the low speed range are processed first, and then the videos in the high speed range are processed in sequence. By splicing the respective target simulation videos in sequence, the corresponding vehicle simulation video can be obtained.

[0128] See Figure 2 , which is a schematic structural diagram of a vehicle intelligent evaluation system provided by an embodiment of the present invention. The data processing system based on the vehicle intelligent evaluation system includes: An update module, configured to determine the component positions corresponding to each sound pickup device based on a test request, and generate guiding data to update each display module according to the component positions; A response module, configured to respond to the reinsertion information of the sound pickup device and receive audio data; An analysis module, configured to obtain the test time period corresponding to each test environment, and perform split analysis on the audio data according to the test time period to obtain an evaluation result; A generation module, configured to perform video fusion conversion on each of the evaluation results and the corresponding test environment to generate a vehicle simulation video.

[0129] Figure 2 The device of the illustrated embodiment can correspondingly be used to execute Figure 1 the steps in the illustrated method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here.

[0130] See Figure 3 , which is a schematic diagram of the hardware structure of a sound pickup device provided by an embodiment of the present invention. The sound pickup device 30 includes: a processor 31, a memory 32, and a computer program; where The memory 32 is used to store the computer program, and this memory can also be a flash memory. The computer program is, for example, an application program, a functional module, etc. that implement the above method.

[0131] The processor 31 is used to execute the computer program stored in the memory to implement each step executed by the device in the above method. Specifically, reference can be made to the relevant descriptions in the foregoing method embodiments.

[0132] Optionally, the memory 32 can be either independent or integrated with the processor 31.

[0133] When the memory 32 is a device independent of the processor 31, the device may further include: A bus 33 for connecting the memory 32 and the processor 31.

[0134] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle intelligent evaluation method, characterized in that: Applied to a device distribution box, the device distribution box includes a plurality of display modules and a sound pickup device plugged into the display modules, including: Determine the component position corresponding to each sound pickup device based on the test request, and generate guidance data according to the component position to update each display module; In response to the back-insertion information of the sound pickup device, receiving audio data; Obtaining the test period corresponding to each test environment, and splitting and analyzing the audio data according to the test period to obtain an evaluation result; Perform video fusion conversion on each of the evaluation results and the corresponding test environment to generate a vehicle simulation video.

2. The method according to claim 1, characterized in that The determining the component position corresponding to each sound pickup device based on the test request, and generating guide data according to the component position to update each display module includes: Receive a test request for a target vehicle, and determine a vehicle model and a pickup position corresponding to the target vehicle; The vehicle model is updated according to the pickup position to obtain an indication model; The indication model is split into models, and guidance data is generated and sent to each display module.

3. The method according to claim 1 or 2, characterized in that: Also includes: Marking the time when the sound pickup device is unplugged as the first time; Determine a verification period according to the first moment, and verify the position image of the sound pickup device based on the verification period; The guidance data corresponding to the sound pickup device that meets the verification condition is updated to the first display mode.

4. The method according to claim 1, characterized in that: The obtaining of the test period corresponding to each test environment, and splitting and analyzing the audio data according to the test period to obtain the evaluation result, includes: In response to the test information, controlling each of the sound pickup devices to pick up audio data; Acquire a test period corresponding to each test environment, and split the audio data according to the test period to obtain a plurality of sub-data groups corresponding to the test environment; Audio analysis is performed on the sub-data group to obtain an evaluation result.

5. The method according to claim 4, characterized in that The performing audio analysis on the sub-data group to obtain an evaluation result includes: Performing spectrum analysis on the sub-data in each of the sub-data groups to obtain spectrum amplitudes corresponding to each of the sub-data groups; Determine that the sub-data whose spectrum amplitude is within the abnormal amplitude interval is abnormal data, and determine that the sub-data whose spectrum amplitude is not within the abnormal amplitude interval is normal data; An evaluation result corresponding to the sub-data group is generated according to the abnormal data and the normal data.

6. The method according to claim 5, characterized in that Perform video fusion conversion on each of the evaluation results and the corresponding test environment to generate a vehicle simulation video, including: Analyze the test environment corresponding to the sub-data group to obtain the speed environment and the road condition environment; Performing dynamic simulation on the vehicle model according to the speed environment to generate a dynamic vehicle model, and generating a simulated road section according to the road condition environment; Based on the evaluation result, the dynamic vehicle model is updated to generate a simulated vehicle; The simulated vehicle and the simulated road section are combined to obtain a sub-simulation video, and a plurality of the sub-simulation videos are connected to obtain a vehicle simulation video.

7. The method according to claim 6, characterized in that The dynamically simulating the vehicle model according to the speed environment to generate a dynamic vehicle model includes: Retrieving a dynamic wheel model corresponding to the speed environment, and replacing wheels of a vehicle model according to the dynamic wheel model to obtain an initial vehicle model; According to the speed parameter corresponding to the speed environment, a label is added to the dynamic wheel model of the initial vehicle model.

8. The method according to claim 7, characterized in that The step of updating the dynamic vehicle model based on the evaluation result to generate a simulated vehicle includes: Determine a pickup component corresponding to the abnormal data and an update attribute corresponding to the pickup component, wherein the update attribute includes a real attribute and a virtual attribute; Retrieving a video segment of a corresponding acquisition device based on the real attribute, replacing and displaying a display area of ​​a picked-up component in a dynamic vehicle model according to the video segment, and generating an abnormal model; highlighting the picked-up parts in the dynamic vehicle model according to preset pixel values ​​based on the virtual attributes to generate an abnormal model; The sub-data corresponding to the abnormal data are bound to the picking components of the abnormal model to obtain a simulated vehicle.

9. The method according to claim 6, characterized in that The step of combining the simulated vehicle and the simulated road section to obtain a sub-simulation video, and connecting a plurality of the sub-simulation videos to obtain a vehicle simulation video includes: Placing the simulated vehicle above the simulated road section to obtain the sub-simulation video; Extracting the speed environment corresponding to each of the sub-data groups, and sorting each of the sub-data groups according to the speed environment to obtain a connection sequence; The sub-simulation videos are connected based on the order of the connection sequence to obtain a vehicle simulation video.

10. A vehicle intelligent evaluation system, characterized in that: Applied to a device distribution box, the device distribution box includes a plurality of display modules and a sound pickup device plugged into the display modules, including: An updating module, used to determine the component position corresponding to each sound pickup device based on the test request, and to generate guidance data according to the component position to update each display module; A response module, used for responding to the back-insertion information of the sound pickup device and receiving audio data; An analysis module is used to obtain a test period corresponding to each test environment, and to split and analyze the audio data according to the test period to obtain an evaluation result; The generation module is used to perform video fusion conversion on each of the evaluation results and the corresponding test environment to generate a vehicle simulation video.

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