Vehicle Intelligent Evaluation Method and System

Abstract

The present invention provides a vehicle intelligent evaluation method and system, which are mainly 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 relates to intelligent evaluation technology. By determining the component positions corresponding to each sound pickup device based on a test request, generating guidance data according to the component positions to update each display module, responding to the reinsertion information of the sound pickup device, receiving audio data, obtaining the 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 performing video fusion transformation on each evaluation result and the corresponding test environment to generate a vehicle simulation video, 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 that a single audio acquisition device can cover are limited, relying on only one audio acquisition device may not be able to collect the sound signals from each component at the same time. 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, which leads to an incomplete understanding of the overall vehicle sound situation, and thus it is 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, a vehicle intelligent evaluation method is provided, 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:

[0007] 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;

[0008] Respond to the plug-in information of the sound pickup device and receive audio data;

[0009] 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;

[0010] Perform video fusion conversion on each evaluation result and the corresponding test environment to generate a vehicle simulation video.

[0011] 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:

[0012] Receive a test request from the target vehicle, determine the vehicle model corresponding to the target vehicle and the pickup position;

[0013] Update the vehicle model according to the pickup position to obtain an indication model;

[0014] Perform model splitting on the indication model, generate guiding data and send it to each display module.

[0015] Optionally, in a possible implementation manner of the first aspect, it further includes:

[0016] Mark the moment when the pickup device is pulled out as the first moment;

[0017] Determine a verification period according to the first moment, verify the position image of the pickup device based on the verification period, and update the guiding data corresponding to the pickup device that meets the verification conditions to the first display mode.

[0018] Optionally, in a possible implementation manner of the first aspect, the obtaining of 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:

[0019] Respond to the test information, control each pickup device to pick up audio data; obtain the test periods corresponding to each test environment, split the audio data according to the test periods to obtain a plurality of sub-data groups corresponding to the test environments;

[0020] Perform audio analysis on the sub-data groups to obtain an evaluation result.

[0021] Optionally, in a possible implementation manner of the first aspect, the performing of audio analysis on the sub-data groups to obtain an evaluation result includes:

[0022] Perform spectrum analysis on the sub-data in each sub-data group to obtain the spectrum amplitudes corresponding to each sub-data group;

[0023] 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;

[0024] Generate an evaluation result corresponding to the sub-data group according to the abnormal data and the normal data.

[0025] 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:

[0026] Analyze the test environment corresponding to the sub-data group to obtain a speed environment and a road condition environment;

[0027] 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;

[0028] Update the dynamic vehicle model based on the evaluation result to generate a simulated vehicle;

[0029] 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.

[0030] Optionally, in a possible implementation manner of the first aspect, the dynamically simulating the vehicle model according to the speed environment to generate a dynamic vehicle model includes:

[0031] 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;

[0032] Add labels to the dynamic wheel model of the initial vehicle model according to the speed parameters corresponding to the speed environment.

[0033] Optionally, in a possible implementation manner of the first aspect, the updating the dynamic vehicle model based on the evaluation result to generate a simulated vehicle includes:

[0034] Determine the picking component corresponding to the abnormal data and the update attribute corresponding to the picking component, where the update attribute includes a real attribute and a virtual attribute;

[0035] Retrieve the video segment of the corresponding acquisition device based on the real attribute, and replace and display the display area of the picking component in the dynamic vehicle model according to the video segment to generate an abnormal model;

[0036] Highlight the picking component in the dynamic vehicle model based on the virtual attribute according to the preset pixel value to generate an abnormal model;

[0037] Bind the sub-data corresponding to the abnormal data to the picking component of the abnormal model to obtain a simulated vehicle.

[0038] Optionally, in a possible implementation manner 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 simulated video includes:

[0039] Place the simulated vehicle above the simulated road section to obtain the sub-simulated video;

[0040] 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;

[0041] The sub-simulation videos are connected based on the order of the connection sequence to obtain a vehicle simulation video.

[0042] A second aspect of the present invention provides a vehicle intelligent assessment system, characterized in that it is 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:

[0043] 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;

[0044] A response module, used for responding to the back-insertion information of the sound pickup device and receiving audio data;

[0045] 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;

[0046] 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.

[0047] The beneficial effects of the present invention are as follows:

[0048] 1. The present invention can comprehensively collect the sound generated by a vehicle during driving by configuring a device distribution box with multiple sound pickup devices, so as to make a more comprehensive and accurate evaluation of the vehicle's performance.

[0049] 2. During the sound collection process, the present invention can guide the installation position of each sound pickup device through the display module on the device distribution box, thereby guiding the user to accurately place the sound pickup device on the corresponding vehicle component, thereby improving the accuracy of audio data collection.

[0050] 3. The present invention can utilize the acquisition device configured on the sound pickup device to acquire the position image within the verification period, and compare and verify it with the guidance data. When the position image corresponding to the sound pickup device corresponds to the position displayed in its guidance data, the guidance 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 acquisition due to installation position deviation, and thus ensures the reliability of the collected audio data.

[0051] 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 vehicle state under a specific test environment.

[0052] 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 converting these information into a vehicle simulation video through video fusion, the present invention can reflect the operating state of the vehicle in various actual scenarios in a more comprehensive and intuitive manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a schematic flowchart of a vehicle intelligent evaluation method provided by an embodiment of the present invention;

[0054] Figure 2 is a schematic structural diagram of a vehicle intelligent evaluation system provided by an embodiment of the present invention;

[0055] Figure 3 is a schematic hardware structure diagram of a sound pickup device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0058] See Figure 1 , which is a schematic diagram of a vehicle intelligent evaluation method provided by an embodiment of the present invention, Figure 1The execution entity of the method shown can be a software and / or hardware device. The execution entity 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, computers, smart phones, personal digital assistants (Personal Digital Assistant, abbreviated as: PDA), and the electronic devices mentioned above, 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 kind of distributed computing, which is composed of a group of loosely coupled computers to form a super virtual computer. This embodiment does not make any restrictions on this.

[0059] 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 position corresponding to each sound pickup device. The sound pickup device refers to a device that collects the sounds generated during the vehicle driving process, including steps S1 to S4, specifically as follows:

[0060] S1, determine the component positions corresponding to each sound pickup device based on a test request, and update each display module according to the component positions to generate guiding data.

[0061] 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.

[0062] 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 states are normal. However, currently, during 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 numerous 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 state of the vehicle. Moreover, when maintenance personnel install the sound collection devices, they mainly rely on personal experience to judge the positions of the components, lacking precise guidance, which easily leads to installation position deviations, resulting in the inability to accurately collect the sounds of the 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.

[0063] 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 the respective sound pickup devices through the display module on the device distribution box, thereby guiding the user to accurately place the sound pickup devices on the corresponding vehicle components, thus improving the accuracy of audio data collection.

[0064] Specifically, when a 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 a vehicle performance test, 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 modules corresponding to the respective sound pickup devices, such as a display screen.

[0065] In some embodiments, the specific implementation manner of step S1 may be:

[0066] S11, receive a test request for a target vehicle, and determine the vehicle model and pickup position corresponding to the target vehicle.

[0067] 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 the respective sound pickup devices on the corresponding vehicle components, i.e., the pickup positions, can be determined.

[0068] 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.

[0069] S12. Update the vehicle model according to the pickup position to obtain an indication model.

[0070] Specifically, after determining the installation positions of the respective sound pickup devices, i.e., the pickup positions, the position points corresponding to the pickup positions of the respective sound pickup devices 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 the virtual model that can guide and indicate the installation position of the sound pickup device.

[0071] S13. Split the indication model to generate guidance data and send it to each display module.

[0072] Specifically, after obtaining the indication model, the indication model can be split according to the correspondence 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, and each sub-model corresponds to a sound pickup device and its installation position indication information on the vehicle model. According to the split sub-models, the guidance 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 guidance data corresponding to the sound pickup device on the left window, and the guidance data corresponding to each sound pickup device can be sent to the corresponding display module for display.

[0073] Through the above implementation method, the user can be guided to accurately install the sound pickup device, 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.

[0074] On the basis of the above steps, the present solution further includes the following embodiments:

[0075] A1. Mark the moment when the sound pickup device is pulled out as the first moment.

[0076] 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 acquisition device configured on the sound pickup device, such as a micro camera. The installation position of the sound pickup device can be verified based on the acquired image. If the actual installation position of the sound pickup device does not match the corresponding pickup position, the user can be alerted through the display module on the device distribution box.

[0077] 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.

[0078] A2. Determine a verification period based on the first moment, and verify the position image of the sound pickup device based on the verification period.

[0079] Specifically, according to the first moment, the time period for verifying the installation position of the sound pickup device, that is, the verification period, can be determined. 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 acquisition 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 the 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.

[0080] Here, 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 acquisition device configured on the sound pickup device during the verification period.

[0081] A3. Update the guiding data corresponding to the sound pickup device that meets the verification conditions to the first display mode.

[0082] 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.

[0083] Here, the verification condition means that the guiding data corresponding to the sound pickup device and the position image show 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.

[0084] Through the above implementation manners, it can be ensured 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.

[0085] S2. Respond to the reinsertion information of the sound pickup device and receive audio data.

[0086] Wherein, 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 during vehicle driving collected by the sound pickup device.

[0087] 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, the device distribution box can respond to the corresponding reinsertion information, and the device distribution box can read the corresponding sound data of vehicle components during driving collected by each sound pickup device, so as to receive the audio data corresponding to each vehicle component.

[0088] S3. 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.

[0089] Wherein, the test environment refers to specific physical scenarios that the vehicle may encounter during actual use, such as environments with different speeds, different road conditions, etc. The test time period refers to the time period corresponding to different test environments, and the evaluation result refers to the result after analyzing each audio data.

[0090] In practical applications, the vehicle may face diverse scenarios during actual use. In 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, the test time periods corresponding to different test environments can be obtained. The test time 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 the 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 time period, such as 0 - 5 seconds, while when accelerating from 0 to 100 kilometers per hour, since the acceleration process is longer, the test time period may be a time period between 10 - 30 seconds.

[0091] 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 correspond to the acceleration test environment of 0 - 10 km / h, 8 - 15 seconds correspond to the acceleration test environment of 0 - 30 km / h, 20 - 28 seconds correspond to the acceleration environment of 0 - 50 km / h, and 35 - 48 seconds correspond 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.

[0092] Based on the above embodiments, the specific implementation manner of step S3 can be:

[0093] S31. Respond to the test information and control each of the pickup devices to pick up audio data.

[0094] 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's driving, that is, the audio data. Among them, the test information refers to the information for testing the vehicle.

[0095] 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.

[0096] 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 - duration 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 correspond to the acceleration test environment of 0 - 10 km / h, 8 - 15 seconds correspond to the acceleration test environment of 0 - 30 km / h, 20 - 28 seconds correspond to the acceleration environment of 0 - 50 km / h, and 35 - 48 seconds correspond to the acceleration test environment of 0 - 100 km / h, then the audio of these four test periods can be extracted respectively.

[0097] 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 collected from these 10 pickup devices during the corresponding test period form a sub-data group containing 10 sub-data. By analogy, corresponding sub-data groups will be generated for different test environments.

[0098] Among them, a sub-data group refers to a data group composed of audio segments collected by multiple pickup devices in the same test environment.

[0099] Through the above implementation manner, it is possible to 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.

[0100] S33. Perform audio analysis on the sub-data group to obtain an evaluation result.

[0101] Specifically, by analyzing audio features in the sub-data group, such as frequency, amplitude, etc., 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.

[0102] In some embodiments, the specific implementation manner of step S33 may be:

[0103] S331. Perform spectrum analysis on the sub-data in each sub-data group to obtain the spectrum amplitudes corresponding to each sub-data group.

[0104] Specifically, from each of the obtained sub-data groups, sequentially select a sub-data group as the current analysis object. Each sub-data group contains audio data segments collected by multiple pickup devices in a specific test environment.

[0105] For each sub - data in the selected sub - data group, that is, the audio segments collected by each pickup device, a 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. Summarize the spectrum amplitudes obtained after spectrum analysis of all sub - data in the sub - data group. These spectrum amplitudes together constitute the spectrum amplitude set corresponding to this sub - data group, and this set comprehensively reflects the energy distribution of the audio signal represented by this sub - data group in the frequency domain, providing a key data basis for subsequent judgment of whether the sub - data is abnormal.

[0106] Among them, sub - data refers to the audio segments collected by each pickup device during the test period corresponding to the test environment, and spectrum amplitude refers to the signal intensity of sub - data at a specific frequency point.

[0107] 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.

[0108] 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 possible spectrum amplitude fluctuation ranges under various normal operating conditions. Spectrum amplitudes outside this range are considered to possibly correspond to abnormal operating states of vehicle components.

[0109] 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 this 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 states of all sub - data in the entire sub - data group is completed.

[0110] 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 where 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 where the spectral amplitude in the entire frequency band is outside the abnormal amplitude range, indicating that the operating state of the corresponding vehicle component meets the normal expectations under the current test environment.

[0111] S333, generate the evaluation result corresponding to the sub-data group according to the abnormal data and the normal data.

[0112] Specifically, according to the abnormal data and normal data obtained after performing spectral analysis, the evaluation result corresponding to each sub-data group can be obtained.

[0113] Through the above implementation manner, the performance status of vehicle components under different test environments can be more comprehensively reflected.

[0114] S4, perform video fusion transformation on each of the evaluation results and the corresponding test environment to generate a vehicle simulation video.

[0115] Among them, the vehicle simulation video refers to a dynamic display video that simulates the driving condition of the vehicle by fusing multiple test data of the vehicle with the corresponding virtual model of the vehicle.

[0116] Specifically, the evaluation results corresponding to each test environment can be collected. These results contain the judgment of the performance status of vehicle components under different test environments, such as whether there are abnormalities and other information. At the same time, collect the corresponding test environment information, 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, use video production technology and computer graphics simulation means to fuse and transform the evaluation results with the test environment. In this process, the operating state of the vehicle under different test environments can be simulated through animations, virtual scene construction, etc. At the same time, the evaluation results are incorporated into the simulation 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 simulation 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 under different actual scenarios, providing a powerful visual reference for vehicle maintenance, optimization, and further research and development.

[0117] Based on the above embodiments, the specific implementation manner of step S4 can be:

[0118] S41. Analyze the test environment corresponding to the sub-data group to obtain the speed environment and road condition environment.

[0119] 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 pickup devices under a specific test environment, and the corresponding test environment information describes the running scenario of the vehicle at that time. Conduct a detailed 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 characteristics of the road surface 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, etc. Through this in-depth analysis, the complex test environment information is refined into two clear and operable elements: speed and road condition, providing accurate data support for constructing the subsequent simulation scenario.

[0120] 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.

[0121] 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.

[0122] Specifically, according to the obtained speed environment data, perform dynamic simulation operations on the pre-established static vehicle model. Using advanced computer graphics algorithms and physical simulation technologies, accurately 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 running state of the vehicle in this speed scenario.

[0123] 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 subsequent simulation of the interaction between the vehicle and the road section.

[0124] 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 in combination with the road conditions and environment.

[0125] 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:

[0126] 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.

[0127] 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 has been 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, there is a specially designed dynamic wheel model stored in the database, 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, the dynamically matching wheel model can be accurately retrieved.

[0128] 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, the original wheel model of the static vehicle model is replaced 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 axle position of the vehicle and are coordinated with other body components 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.

[0129] 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.

[0130] S422, add labels to the dynamic wheel model of the initial vehicle model according to the speed parameters corresponding to the speed environment.

[0131] 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 period. 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.

[0132] Among them, the speed parameter refers to the speed value of the vehicle in the corresponding speed environment.

[0133] 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.

[0134] S43. Update the dynamic vehicle model based on the evaluation result to generate a simulated vehicle.

[0135] 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 a certain vehicle component is abnormal, 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 label to 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.

[0136] In some embodiments, the specific implementation manner of step S43 may be:

[0137] S431. Determine 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.

[0138] Herein, 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 in a virtual manner 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.

[0139] Specifically, after obtaining the evaluation result data, all abnormal data can be identified first. These abnormal data are determined to have spectral amplitudes within a pre-set 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.

[0140] Judge the update attributes 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, perhaps 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 during operation, and such components have virtual attributes.

[0141] 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.

[0142] 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 certain door component has real attributes, the video captured by the micro camera installed near the door can be retrieved, and this video records the actual state of the door during the vehicle operation.

[0143] 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 picture, 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 pictures, highlighting the possible abnormal component states.

[0144] 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 vehicle component state. The abnormal model refers to the model generated after updating the pickup component corresponding to the abnormal data in the dynamic vehicle model.

[0145] 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.

[0146] 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. The 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, making it stand out significantly in the dynamic vehicle model. For example, a certain 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 state 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.

[0147] S434. Bind the sub - data corresponding to the abnormal data to the pickup component of the abnormal model to obtain a simulated vehicle.

[0148] 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 and reflect 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 (by highlighting the pickup components with real and virtual attributes in different ways), but also closely links the abnormal state 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 under different test environments and the specific audio data characteristics corresponding to these abnormalities, providing a comprehensive, intuitive, and data - closely - related visualization tool for vehicle performance evaluation, fault diagnosis, etc.

[0149] Through the above - mentioned implementation method, a comprehensive, intuitive, and data - closely - related visualization tool can be provided for vehicle performance evaluation, fault diagnosis, etc.

[0150] 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.

[0151] 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 under 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.

[0152] Among them, the sub - simulated video refers to the simulated video data generated by combining the simulated vehicle and the simulated road section.

[0153] In some embodiments, the specific implementation of step S44 may be as follows:

[0154] S441, place the simulated vehicle above the simulated road section to obtain the sub-simulated video.

[0155] 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 the road section. Through this precise positioning and placement, the simulated vehicle and the simulated road section form an integral 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.

[0156] 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.

[0157] 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.

[0158] Among them, the connection sequence refers to the sequence obtained by sorting each sub-data group, and subsequently, each sub-simulated video can be connected according to the connection sequence.

[0159] S443, connect the sub-simulated videos based on the order of the connection sequence to obtain a vehicle simulation video.

[0160] Specifically, according to the generated connection sequence, the sub-simulation videos corresponding to each sub-data group can be obtained in sequence. Each sub-simulation 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-simulation videos, accurately find the sub-simulation 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, connect the corresponding sub-simulation videos to obtain the simulated video data corresponding to the vehicle under different test environments, that is, the vehicle simulation video.

[0161] Through the above implementation manner, the running state of the vehicle in various actual scenarios can be reflected in a more comprehensive and intuitive way.

[0162] On the basis of the above steps, this solution further includes the following embodiments:

[0163] B1. Based on the connection sequence, obtain the corresponding sub-simulation videos in sequence, compare the speed intervals of each sub-simulation video, and determine the target speed interval corresponding to each sub-simulation video.

[0164] In practical applications, during the vehicle test simulation process, multiple sub-simulation videos corresponding to test environments with different speed intervals 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 interval, the sub-simulation videos can be cropped and connected to obtain the sub-video data that meets the requirements.

[0165] 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 interval 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 0 to 10 kilometers per hour part of an existing separate sub-simulation video. At this time, the speed environment corresponding to each sub-simulation video can be obtained, the speed intervals of the obtained sub-simulation videos can be compared, and the overlapping parts can be removed to determine the unique target speed interval 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 0 to 10 kilometers per hour part already exists in other sub-simulation videos, then its target speed interval 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 interval and avoid data redundancy.

[0166] Among them, the target speed range refers to the speed range determined for each sub-simulation video when processing sub-simulation videos corresponding to multiple different speed range test environments, which does not include the overlapping part with other sub-simulation videos.

[0167] 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.

[0168] Specifically, after determining the target speed range, the starting frame and ending frame corresponding to the target speed range can be determined. Through an image recognition algorithm, each frame of the sub-simulation video is analyzed frame by frame to identify the speed identifier of the simulated vehicle in the sub-simulation video until the starting frame and ending frame of the target speed range are found. Using a video processing tool, the corresponding sub-simulation video is cropped according to the starting frame and 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 encoding format to generate the 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.

[0169] 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, the ending frame refers to the video frame in the sub-simulation video corresponding to the ending speed point of the target speed range, and the target simulation video refers to the video that is re-combined after cropping the corresponding sub-simulation video according to the determined target speed range and accurately reflects the vehicle running state within the target speed range.

[0170] B3. Connect each target video data according to the connection sequence to obtain the vehicle simulation video.

[0171] 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 of the low speed range are processed first, and then the videos of the high speed range are processed in sequence. By splicing each target simulation video in sequence, the corresponding vehicle simulation video can be obtained.

[0172] 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:

[0173] An update module, configured to 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;

[0174] A response module, configured to respond to the re-insertion information of the sound pickup device and receive audio data;

[0175] An analysis module, configured to obtain the test periods corresponding to each test environment, and perform split analysis on the audio data according to the test periods to obtain an evaluation result;

[0176] 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.

[0177] Figure 2 The device in the illustrated embodiment can correspondingly be used to execute Figure 1 the steps in the method embodiment shown, and its implementation principle and technical effects are similar, and will not be elaborated here.

[0178] See Figure 3 , which is a schematic hardware structure diagram 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

[0179] The memory 32 is used to store the computer program, and the memory can also be a flash memory. The computer program is, for example, an application program or a functional module that implements the above method.

[0180] The processor 31 is configured to execute the computer program stored in the memory to implement each step performed by the device in the above method. Specifically, reference can be made to the relevant descriptions in the foregoing method embodiments.

[0181] Optionally, the memory 32 can be either independent or integrated with the processor 31.

[0182] When the memory 32 is a device independent of the processor 31, the device may further include:

[0183] A bus 33, configured to connect the memory 32 and the processor 31.

[0184] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended 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 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 reinsertion information of the sound pickup device and receive audio data; Obtain the test periods corresponding to each test environment, and perform split analysis on the audio data according to the test periods to obtain an evaluation result, including: Respond to test information and 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 a plurality of sub-data groups corresponding to the test environments; Perform audio analysis on the sub-data groups to obtain an evaluation result; Perform video fusion conversion on each evaluation result and the corresponding test environment to generate a vehicle simulation video, including: 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 a plurality of the sub-simulation videos to obtain a vehicle simulation video.

2. The method according to claim 1, characterized in that, 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 the pickup position corresponding to the target vehicle; Update the vehicle model according to the pickup position to obtain an indication model; Perform model splitting on the indication model, and generate guidance data to be sent to each display module.

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

4. The method according to claim 1, wherein The performing audio analysis on the sub-data groups to obtain an evaluation result includes: Perform spectrum analysis on the sub-data in each sub-data group to obtain the spectrum amplitudes corresponding to each sub-data group; Determine the sub-data with spectrum amplitudes located in the abnormal amplitude range as abnormal data, and determine the sub-data with spectrum 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 the normal data.

5. The method according to claim 4, wherein The dynamically simulating the vehicle model according to the speed environment to generate a dynamic vehicle model includes: Retrieve a 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; Add labels to the dynamic wheel model of the initial vehicle model according to the speed parameters corresponding to the speed environment.

6. The method according to claim 5, wherein The updating the dynamic vehicle model based on the evaluation result to generate a simulated vehicle includes: Determine 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; Retrieve the video segment of the corresponding acquisition 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; Highlight the pickup component in the dynamic vehicle model based on the virtual attribute according to the preset pixel value to generate an abnormal model; Bind the sub-data corresponding to the abnormal data to the pickup component of the abnormal model to obtain a simulated vehicle.

7. The method according to claim 1, characterized in that, The combination of the simulated vehicle and the simulated road section to obtain a sub-simulated video, and the connection of multiple sub-simulated videos to obtain a vehicle simulation video includes: Place the simulated vehicle above the simulated road section to obtain the sub-simulated video; Extract the speed environment corresponding to each sub-data group, sort each sub-data group according to the speed environment to obtain a connection sequence; Connect the sub-simulated videos based on the order of the connection sequence to obtain a vehicle simulation video.

8. A vehicle intelligent evaluation system according to the method described in any one of claims 1-7, 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 module, including: An update module for determining the component positions corresponding to each sound pickup device based on a test request and generating guidance data to update each display module according to the component positions; A response module for responding to the re-insertion information of the sound pickup device and receiving audio data; An analysis module for 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; A generation module for performing video fusion transformation on each evaluation result and the corresponding test environment to generate a vehicle simulation video.

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