Vehicle cabin air pressure impact control method and system, electronic equipment and storage medium

Abstract

The invention discloses an air pressure impact control method and system in a vehicle cabin, electronic equipment and a storage medium, and relates to the field of cabin pressure, and the method comprises the following steps: obtaining and defining key parameters: obtaining a sampling object and parameter item information associated with air pressure transmission in the vehicle cabin; the step of data acquisition and database establishment comprises the following substeps: setting an experimental process, and acquiring air pressure impact data in a vehicle cabin in a sampling object parameter adjustment state; the step of constructing the human ear perception equivalent model comprises the substeps of setting a simulation model in a vehicle cabin, and constructing the human ear perception equivalent model; the step of establishing the rapid prediction model comprises the following steps of: performing multiple linear regression analysis according to air pressure impact data in a vehicle cabin in a collected sampling object parameter adjustment state, and constructing a regression equation; the step of result verification and application comprises the substep of recognizing the state of air pressure impact in a vehicle cabin in the vehicle door closing process on the basis of building a regression equation and building a human ear perception equivalent model.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cabin pressure, in particular to a vehicle cabin air pressure impact control method, a vehicle cabin air pressure impact control system, an electronic device, a storage medium and a cabin test platform. BACKGROUND

[0002] With the continuous improvement of vehicle cabin sealing process level, when the user quickly closes the door, the air is rapidly compressed, forming a positive pressure pulse in the vehicle, acting on the eardrum of the human ear, causing "ear pressure", "tinnitus" and even temporary pain. If the pressure relief channel is not reasonably set in the early stage of vehicle body design, the ear pressure will seriously affect the comfort of the driver and passenger.

[0003] The traditional method is to use real people to subjectively evaluate or use test instruments (such as pressure sensors and acoustic mannequins) to measure in the cab after the sample vehicle is manufactured. However, at this time, the vehicle body structure and sealing system have been finalized, and the modification cost is extremely high, the cycle is long, the real person evaluation has individual differences, is not objective and quantitative, and belongs to "trial and error" adjustment, such as repeatedly adjusting the door lock and sealing strip, which lacks precise guidance and is inefficient. Some enterprises use computational fluid dynamics (CFD) software to simulate air flow and pressure changes during door closing. The fine fluid-structure coupling simulation has a huge amount of calculation and takes a long time, which is not suitable for rapid iteration in the early stage of design. The simulation result is usually a pressure-time curve, but how to directly link this curve with the "uncomfortable" degree of the human body lacks a recognized and efficient evaluation index and prediction model.

[0004] Therefore, there is a need for a vehicle cabin air pressure impact control scheme that can guide the early design of the vehicle cabin, quickly and accurately identify the design scheme risk of the ear pressure impact during door closing at a low cost. SUMMARY

[0005] The purpose of the present application is to provide a vehicle cabin air pressure impact control method, a vehicle cabin air pressure impact control system, an electronic device, a storage medium and a cabin test platform, which at least solve the problem of how to construct a human ear perception equivalent model, solve the problem of how to establish a rapid prediction model, and solve one of the technical problems of how to determine the key data in the current cabin design.

[0006] The present application provides the following scheme:

[0007] According to a first aspect of the present application, a vehicle cabin air pressure impact control method is provided, which comprises the steps of key parameter acquisition and definition, data acquisition and database establishment, construction of a human ear perception equivalent model, establishment of a rapid prediction model, and result verification and application.

[0008] The step of acquiring and defining key parameters includes acquiring information of sampling objects and parameter items associated with vehicle cabin air pressure transmission;

[0009] The step of data acquisition and database establishment includes setting an experimental process and acquiring vehicle cabin air pressure impact data under parameter adjustment states of sampling objects;

[0010] The step of constructing a human ear perception equivalent model includes setting a simulation model in the vehicle cabin and constructing a human ear perception equivalent model;

[0011] The step of establishing a rapid prediction model includes performing multiple linear regression analysis and constructing a regression equation based on the acquired vehicle cabin air pressure impact data under parameter adjustment states of sampling objects;

[0012] The step of result verification and application includes identifying the state of vehicle cabin air pressure impact during the vehicle door closing process based on the constructed regression equation and the constructed human ear perception equivalent model.

[0013] Further, the step of acquiring and defining key parameters includes acquiring information of sampling objects and parameter items associated with vehicle cabin air pressure transmission, which includes:

[0014] Based on the vehicle cabin air pressure conduction model, sampling objects associated with vehicle cabin air pressure transmission are selected, including vehicle doors, holes, valves, and volume sampling objects;

[0015] The sampling object parameter item information includes parameter items such as door closing speed, door area, cabin volume, body panel airflow passage hole area, airflow passage direction, interior grille opening area, pressure relief valve performance, pressure relief valve arrangement number, pressure relief valve arrangement position, and vehicle air tightness.

[0016] Further, the step of data acquisition and database establishment includes setting an experimental process and acquiring vehicle cabin air pressure impact data under parameter adjustment states of sampling objects, which includes:

[0017] Setting sampling positions and pressure threshold intervals in the vehicle cabin;

[0018] Pre-selecting sampling objects and corresponding initial parameter values of preset parameter items;

[0019] Setting an experimental group that changes the parameter values of the corresponding preset parameter items of the sampling objects;

[0020] According to the experimental group, sampling pressure data at the set sampling positions in the vehicle cabin;

[0021] According to the sampling pressure data at the set sampling positions in the vehicle cabin and the set pressure threshold intervals at the sampling positions in the vehicle cabin, the experimental group is classified and recorded;

[0022] According to the classification and record of the experimental group, key data is screened and defined.

[0023] Further, the step of constructing the human ear perception equivalent model comprises setting an imitation model in the vehicle cabin, and the step of constructing the human ear perception equivalent model comprises:

[0024] Based on setting the sampling position as the human ear position and the pressure threshold interval as the human ear pressure interval in the vehicle cabin, a door speed instrument and a pressure sensor are set, and a simulation model of the air pressure impact on the human ear in the running state of the vehicle door is constructed;

[0025] According to the door speed instrument control vehicle door opening and closing operation state, the air pressure impact data of the pressure sensor is collected, and the human ear perception equivalent model is constructed.

[0026] Further, the step of establishing a rapid prediction model comprises adjusting the air pressure impact data in the vehicle cabin under the state of the collected sampling object parameters, performing multiple linear regression analysis, and constructing a regression equation comprising:

[0027] The set changed sampling object and the corresponding parameter item parameter value are taken as independent variables, and the pressure data at the set sampling position in the vehicle cabin is taken as dependent variable, multiple linear regression analysis is performed, and a regression equation is constructed.

[0028] Further, the set changed sampling object and the corresponding parameter item parameter value are taken as independent variables, and the pressure data at the set sampling position in the vehicle cabin is taken as dependent variable, multiple linear regression analysis is performed, and a regression equation is constructed comprising:

[0029] According to the screened and defined key data, the actual pressure relief area X1 of the pressure relief valve, the air tightness X2 of the whole vehicle, the door hole Y projection area / seat volume X3 and the door closing speed X4 are taken as independent variables, and the pressure peak value at the set sampling position in the vehicle cabin is taken as dependent variable, multiple linear regression analysis is performed, and a regression equation is constructed;

[0030] The regression equation comprises: pressure peak value = 75.67-0.012X1+0.204X2+532.1X3+47.3X4;

[0031] Among them, according to the through hole area of the body sheet metal air flow channel, the opening area of the interior grille and the number of pressure relief valve arrangement, the actual pressure relief area X1 of the pressure relief valve is generated;

[0032] According to the door area and the vehicle cabin volume, the door hole Y projection area / seat volume X3 is generated.

[0033] According to a second aspect of the present application, a vehicle cabin air pressure impact control system is provided, comprising: a key parameter acquisition and definition module, a data acquisition and database establishment module, a human ear perception equivalent model construction module, a rapid prediction model establishment module, and a result verification and application module;

[0034] The key parameter acquisition and definition module is configured to acquire information of sampling objects and parameter items associated with vehicle cabin air pressure transmission.

[0035] The data acquisition and database establishment module is configured to set an experiment process and acquire vehicle cabin air pressure impact data under a parameter adjustment state of a sampling object.

[0036] The human ear perception equivalent model construction module is configured to set a simulation model in a vehicle cabin and construct a human ear perception equivalent model.

[0037] The rapid prediction model establishment module is configured to perform multiple linear regression analysis and construct a regression equation based on the acquired vehicle cabin air pressure impact data under the parameter adjustment state of the sampling object.

[0038] The result verification and application module is configured to identify a state of vehicle cabin air pressure impact during a vehicle door closing process based on the constructed regression equation and the constructed human ear perception equivalent model.

[0039] According to a third aspect of the present application, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete mutual communication through the communication bus.

[0040] The memory stores a computer program, and when the computer program is executed by the processor, the processor executes steps of a vehicle cabin air pressure impact control method.

[0041] According to a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes steps of a vehicle cabin air pressure impact control method.

[0042] According to a fifth aspect of the present application, a cockpit test platform is provided, comprising:

[0043] The electronic device is configured to implement steps of a vehicle cabin air pressure impact control method.

[0044] The processor runs a program, and when the program runs, the data output from the electronic device executes steps of a vehicle cabin air pressure impact control method.

[0045] A storage medium for storing a program, which, when executed, performs the steps of the vehicle cabin air pressure impact control method on data output from the electronic device.

[0046] Through the above scheme, the following beneficial technical effects are obtained:

[0047] The present application locks the possible problem source through key parameter acquisition and definition, and prepares for later problem analysis and design modification direction.

[0048] The present application further locks the problem source by setting an experimental process to collect vehicle cabin air pressure impact data under the parameter adjustment state of the sampling object.

[0049] The present application constructs a human ear perception equivalent model by setting a simulation model in the vehicle cabin, and clearly defines the action mechanism of the problem source, so that there is a rule to follow when avoiding problems.

[0050] The present application provides an applicable tool for vehicle design by performing multiple linear regression analysis and constructing a regression equation according to the vehicle cabin air pressure impact data collected under the parameter adjustment state of the sampling object.

[0051] The present application further modifies and improves the scheme of the present application by verifying and applying the results, and more widely identifying the state of the vehicle cabin air pressure impact during the vehicle door closing process in various vehicle models. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is a flowchart of a vehicle cabin air pressure impact control method provided by one or more embodiments of the present application.

[0053] Figure 2 is a structural diagram of a vehicle cabin air pressure impact control system provided by one or more embodiments of the present application.

[0054] Figure 3 is a schematic diagram of a commercial vehicle door closing process ear pressure impact problem identification and prediction overall process provided by one specific embodiment of the present application.

[0055] Figure 4 is a schematic diagram of a commercial vehicle cab and ear pressure measurement point position provided by one specific embodiment of the present application.

[0056] Figure 5 is a schematic diagram of a typical door closing process ear pressure-time curve provided by one specific embodiment of the present application.

[0057] Figure 6 is a schematic diagram of an "ear discomfort index" construction model provided by one specific embodiment of the present application.

[0058] Figure 7is a schematic diagram of an input-output relationship of a fast prediction model provided by one embodiment of the present application.

[0059] Figure 8 is a structural block diagram of an electronic device of a vehicle cabin air pressure impact control method provided by one or more embodiments of the present application. DETAILED DESCRIPTION

[0060] The technical solutions of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0061] Figure 1 is a flowchart of a vehicle cabin air pressure impact control method provided by one or more embodiments of the present application.

[0062] As Figure 1 shown, the vehicle cabin air pressure impact control method includes: a step A1 of acquiring and defining key parameters, a step A2 of data acquisition and database establishment, a step A3 of constructing a human ear perception equivalent model, a step A4 of establishing a fast prediction model, and a step A5 of result verification and application.

[0063] The step A1 of acquiring and defining key parameters includes acquiring vehicle cabin air pressure transmission associated sampling object and parameter item information.

[0064] The step A2 of data acquisition and database establishment includes setting an experimental procedure, and collecting vehicle cabin air pressure impact data under a sampling object parameter adjustment state.

[0065] The step A3 of constructing a human ear perception equivalent model includes setting a simulation model in the vehicle cabin, and constructing a human ear perception equivalent model.

[0066] The step A4 of establishing a fast prediction model includes performing multiple linear regression analysis according to the collected vehicle cabin air pressure impact data under the sampling object parameter adjustment state, and constructing a regression equation.

[0067] The step A5 of result verification and application includes identifying a vehicle cabin air pressure impact state during a vehicle door closing process based on the constructed regression equation and the constructed human ear perception equivalent model.

[0068] Specifically, in one embodiment, a commercial vehicle door closing process ear pressure impact problem identification and prediction method is provided, which aims to solve the defects of late problem discovery, high cost, and inaccurate prediction in the prior art.

[0069] This embodiment is not limited by the stage of vehicle development. For example, during the vehicle body digital model design stage, it fully considers parameters such as the vehicle's airtightness and pressure relief channels. Combining the door closing CFD simulation results with subjective and objective data collected on the test bench, it utilizes correlation analysis and multiple linear regression analysis to establish a model for identifying and predicting pressure problems near the door closing ears in commercial vehicles. This patented calculation and analysis process is efficient and accurate, which is beneficial for meeting development needs.

[0070] like Figure 3 The diagram illustrates the overall process for identifying and predicting ear-side pressure impact during the closing process of a commercial vehicle door. S1 is the automated pre-processing stage, which transforms initial data into an analyzable mesh by setting the door's geometric model and mesh parameters. S2-S4 involve establishing the pressure impact simulation model and setting parameters. S5-S6 represent post-processing and prediction optimization.

[0071] The method for identifying and predicting ear pressure impact during the closing process of commercial vehicles includes key parameter acquisition and definition, data collection and database establishment, construction of an equivalent model of human ear perception, establishment of a rapid prediction model, and result verification and application. This embodiment achieves the identification and prediction of ear pressure comfort issues during the closing process of commercial vehicles.

[0072] Rapid Prediction Model: Based on key parameters and test data of ear pressure comfort during the door closing process of commercial vehicles, this embodiment builds a multivariate linear regression rapid prediction model and develops a method for identifying and predicting ear pressure comfort issues during the door closing process.

[0073] Key parameter acquisition and definition: such as Figure 7 As shown, in this embodiment, the parameters are: ① door closing speed, ② door area / cabin volume, ③ airflow channel perforation area of ​​the body sheet metal, ④ airflow channel direction, ⑤ interior grille opening area, ⑥ pressure relief valve performance parameters, ⑦ number of pressure relief valves, ⑧ pressure relief valve placement, and ⑨ overall vehicle air tightness.

[0074] Initially, manual adjustments to the vehicle's structural parameters can be made using sampled designs to test peak ear pressure comfort and study the relationship between performance indicators and structural parameters. This application designs a total of 16 experimental groups for real-vehicle testing (the number of experimental groups can be flexibly increased or decreased according to the experimental objectives). Figure 4 The locations of the pressure measurement points in the cab and near the ear of the commercial vehicle shown are used to generate the following experimental data in different experimental groups.

[0075] X1: Actual pressure relief area of ​​the pressure relief valve, including ③ airflow channel orifice area, ⑤ interior grille opening area, and ⑦ number of pressure relief valves; Adjustment method: Block part of the pressure relief channel; Parameter variable: 3002mm 2 9294mm 2 .

[0076] X2: Whole vehicle air tightness includes 9 whole vehicle air tightness; adjustment method: block the door gap, door handle and other body leakage points; parameter variable: 201, 176, 120 SCFM@125Pa.

[0077] X3: Door hole Y direction projection area / cabin volume includes 2 vehicle door area / cabin volume, that is, filling a closed box as a placeholder in the cabin; parameter variable: 0.2043 / 0.1858 / 0.1748 m-1.

[0078] X4: Door closing speed includes 1 door closing speed; adjust the door closing speed; parameter variable: 1.1 / 1.2 / 1.3 / 1.4 m / s.

[0079] Using statistical software, 16 groups of data X1-X4 are used as independent variables, and the measured pressure peak value is used as the dependent variable, and multiple linear regression analysis is performed to build the regression equation as follows:

[0080] Pressure peak value = 75.67-0.012X1+0.204X2+532.1X3+47.3X4.

[0081] Human ear perception equivalent model: in this embodiment, as shown in the typical door closing process ear pressure-time curve, a door speed instrument and a pressure sensor are used to measure the dynamic pressure of the ear of the driver side door at 1.2 m / s. The ear pressure-time curve is shown in Figure 6 The "ear discomfort index" model is constructed as shown in Figure 5 The correlation analysis is used to analyze the significance of each ear pressure comfort influencing factor, and the correlation result shows that the maximum ear pressure, minimum ear pressure, ear pressure amplitude and ear pressure recovery time are the most relevant to the subjective score of ear pressure comfort. The pressure peak value and the subjective evaluation score are significantly negatively correlated, so the pressure peak value is used as the objective evaluation index of ear pressure comfort in the research. The characteristic pressure interval (human ear pressure bearing interval) is shown in Table 1.

[0082] Table 1 Characteristic pressure interval

[0083] Description Subjective score Peak pressure at ear (Pa) Unqualified Below 5.5 ＞230 Qualified 5.5-6.5 200~230 Excellent 6.5-8 ＜200

[0084] The present application verifies and applies the steps, more widely identifies the state of the vehicle cabin air pressure impact in the vehicle door closing process of various vehicle models, and can further modify and improve the scheme of the present application by constructing models of different vehicle models or a unified large model.

[0085] In this embodiment, the steps of obtaining and defining key parameters include obtaining vehicle cabin air pressure transmission associated sampling objects and parameter item information, including:

[0086] Based on the vehicle cabin air pressure conduction model, the vehicle cabin air pressure transmission associated sampling objects are selected, including the sampling objects of the vehicle door, hole, valve and volume;

[0087] The sampling object parameter item information includes the door closing speed, vehicle door area, vehicle cabin volume, vehicle body sheet metal airflow passage hole area, airflow passage direction, interior trim grille opening area, pressure relief valve performance, pressure relief valve arrangement number, pressure relief valve arrangement position and vehicle air tightness parameter items.

[0088] Specifically, in one specific embodiment, when the vehicle body uses a material with poor rigidity or good elasticity, the deformation can affect the air pressure, so the deformation and initial shape are also associated data. When the vehicle body uses a material with strong rigidity or poor elasticity, the deformation and initial shape can be ignored.

[0089] In this embodiment, the data collection and database establishment steps include setting an experimental process, collecting vehicle cabin air pressure impact data under the parameter adjustment state of the sampling object, including:

[0090] Setting a sampling position and a pressure threshold interval in the vehicle cabin;

[0091] Preselecting the initial parameter values of the sampling objects and the corresponding preset parameter items;

[0092] Setting an experimental group that changes the parameter values of the sampling objects and the corresponding preset parameter items;

[0093] According to the experimental group, sampling the pressure data at the sampling position set in the vehicle cabin;

[0094] According to the pressure data sampled at the sampling position set in the vehicle cabin and the pressure threshold interval set at the sampling position in the vehicle cabin, the experimental group is classified and recorded;

[0095] According to the classification and recording of the experimental group, the key data is screened and defined.

[0096] Specifically, by classifying and recording the experimental group, the key data of ① door closing speed, ② vehicle door area / vehicle cabin volume, ③ vehicle body sheet metal airflow passage hole area, ④ airflow passage direction, ⑤ interior trim grille opening area, ⑥ pressure relief valve performance parameter, ⑦ pressure relief valve arrangement number, ⑨ vehicle air tightness, ⑧ pressure relief valve arrangement position, and the pressure relief valve arrangement position does not obviously affect the vehicle cabin air pressure impact data, which can be ignored in the subsequent model.

[0097] In this embodiment, the step of constructing a human ear perception equivalent model includes setting a simulation model in the vehicle cabin, and constructing a human ear perception equivalent model, including:

[0098] Based on setting the sampling position in the vehicle cabin as the human ear position and the pressure threshold interval as the human ear pressure bearing interval, a door speed instrument and a pressure sensor are arranged, and a simulation model of the air pressure impact on the human ear in the running state of the door is constructed.

[0099] According to the door speed instrument, the running state of the door is controlled, the air pressure impact data of the pressure sensor is collected, and the human ear perception equivalent model is constructed.

[0100] Specifically, the human ear pressure bearing interval can be marked by scoring at different pressure values to reflect the actual feeling of the personnel, specifically: subjective score 5.5 or less (unqualified) corresponds to ear pressure peak value > 230Pa; subjective score 5.5-6.5 (qualified) corresponds to ear pressure peak value 200~230Pa; subjective score 6.5-8 (excellent) corresponds to ear pressure peak value < 200Pa. The subjective score data of the user can be collected to evaluate the user feedback and take remedial measures. For example, for controllable electric doors, the speed can be reduced when the door is hit at the end, or the door can be hit with a gap in the car window, and the speed can be reduced when the door is hit without a gap. For non-electrically controllable doors, the door seal can also be partially replaced, adjusted, etc. through maintenance opportunities.

[0101] In the embodiment, the step of establishing a rapid prediction model includes collecting air pressure impact data in the vehicle cabin under the adjusted state of the sampling object parameter, performing multiple linear regression analysis, and constructing a regression equation, which includes:

[0102] The multiple linear regression analysis is performed with the set changed sampling object and the corresponding parameter item parameter value as the independent variable, and the pressure data at the set sampling position in the vehicle cabin as the dependent variable, and the regression equation is constructed.

[0103] Specifically, the algorithm for constructing the rapid prediction model is not limited to the multiple linear regression algorithm, and neural network algorithm or deep learning algorithm can also be used.

[0104] In the embodiment, the multiple linear regression analysis is performed with the set changed sampling object and the corresponding parameter item parameter value as the independent variable, and the pressure data at the set sampling position in the vehicle cabin as the dependent variable, and the regression equation is constructed, which includes:

[0105] According to the screening and definition of the key data, the actual pressure relief area X1 of the pressure relief valve, the air tightness X2 of the whole vehicle, the door hole Y projection area / seat volume X3, and the door closing speed X4 are taken as the independent variables, and the pressure peak value at the set sampling position in the vehicle cabin is taken as the dependent variable, and the multiple linear regression analysis is performed to construct the regression equation.

[0106] The regression equation includes: pressure peak value = 75.67-0.012X1+0.204X2+532.1X3+47.3X4.

[0107] wherein, according to the body sheet metal airflow passage hole area, the interior trim grille opening area and the number of pressure relief valve arrangements, the actual pressure relief area X1 of the pressure relief valve is generated;

[0108] According to the door area and the cabin volume, the door hole Y-direction projection area / cabin volume X3 is generated.

[0109] Specifically, the adjustment mode of X1 is to block part of the pressure relief channel, and the parameter variable of X1 includes 3002mm² and 9294mm². The adjustment mode of X2 is to block the leakage points of the commercial vehicle door, door handle and other body parts, and the parameter variable of X2 includes 201SCFM@125Pa, 176SCFM@125Pa and 120SCFM@125Pa. The adjustment mode of X3 is to fill a closed box as a placeholder in the cabin of the commercial vehicle, and the parameter variable of X3 includes 0.2043m⁻¹, 0.1858m⁻¹ and 0.1748m⁻¹. The adjustment mode of X4 is to directly adjust the door closing speed, and the parameter variable of X4 includes 1.1m / s, 1.2m / s, 1.3m / s and 1.4m / s.

[0110] Figure 2 is a structural diagram of a vehicle cabin air pressure impact control system provided by one or more embodiments of the present application.

[0111] As Figure 2 shown, the vehicle cabin air pressure impact control system comprises a key parameter acquisition and definition module, a data acquisition and database establishment module, a human ear perception equivalent model construction module, a rapid prediction model establishment module and a result verification and application module.

[0112] The key parameter acquisition and definition module is used to acquire vehicle cabin air pressure transmission associated sampling object and parameter item information.

[0113] The data acquisition and database establishment module is used to set an experimental process and acquire vehicle cabin air pressure impact data under a sampling object parameter adjustment state.

[0114] The human ear perception equivalent model construction module is used to set a simulation model in the vehicle cabin and construct a human ear perception equivalent model.

[0115] The rapid prediction model establishment module is used to perform multiple linear regression analysis and construct a regression equation according to the vehicle cabin air pressure impact data under the sampling object parameter adjustment state.

[0116] The result verification and application module is used to identify the state of the vehicle cabin air pressure impact in the vehicle door closing process based on the constructed regression equation and the constructed human ear perception equivalent model.

[0117] It is worth noting that although the system / apparatus only discloses the modules of multi-modal perception data acquisition and preprocessing, multi-modal data fusion and feature extraction, intelligent emotion recognition and demand inference, personalized recommended content generation, multi-modal interaction response, and adaptive recommendation optimization, it does not mean that the apparatus is limited to the above basic functional modules. On the contrary, the meaning expressed by the present application is that on the basis of the above basic functional modules, those skilled in the art can add one or more functional modules to form infinite embodiments or technical solutions in combination with existing technologies. That is to say, the system / apparatus is open rather than closed, and it cannot be considered that the protection scope of the present application is limited to the above disclosed basic functional modules just because the present embodiment discloses only individual basic functional modules.

[0118] Figure 7 is a structure block diagram of an electronic device provided by one or more embodiments of the present application.

[0119] As shown in Figure 7 The present application provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus.

[0120] The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the vehicle cabin air pressure impact control method.

[0121] The present application also provides a computer readable storage medium storing a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the vehicle cabin air pressure impact control method.

[0122] The present application also provides a cockpit system, comprising:

[0123] An electronic device is used to implement the steps of the vehicle cabin air pressure impact control method.

[0124] A processor runs a program, and when the program runs, the data output from the electronic device executes the steps of the vehicle cabin air pressure impact control method.

[0125] A storage medium is used to store a program, and when the program runs, the data output from the electronic device executes the steps of the vehicle cabin air pressure impact control method.

[0126] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0127] The electronic device includes a hardware layer, an operating system layer running above the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and a memory. The operating system can be any one or more computer operating systems that implement control of the electronic device through a process, such as a Linux operating system, a Unix operating system, an Android operating system, an iOS operating system, or a windows operating system. In embodiments of the present application, the electronic device can be a handheld device such as a smartphone or a tablet computer, or an electronic device such as a desktop computer or a portable computer, and is not particularly limited in embodiments of the present application.

[0128] The execution subject of the electronic device control in embodiments of the present application can be the electronic device, or a functional module capable of calling and executing a program in the electronic device. The electronic device can obtain a firmware corresponding to the storage medium, and the firmware corresponding to the storage medium is provided by a vendor. The firmware corresponding to different storage media can be the same or different, and is not limited herein. After the electronic device obtains the firmware corresponding to the storage medium, the electronic device can write the firmware corresponding to the storage medium into the storage medium, specifically, burn the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented by using existing technology, and is not described in detail in embodiments of the present application.

[0129] The electronic device can also obtain a reset command corresponding to the storage medium, and the reset command corresponding to the storage medium is provided by a vendor. The reset command corresponding to different storage media can be the same or different, and is not limited herein.

[0130] At this time, the storage medium of the electronic device is a storage medium into which the corresponding firmware is written, and the electronic device can respond to the reset command corresponding to the storage medium in the storage medium into which the corresponding firmware is written, so that the electronic device resets the storage medium into which the corresponding firmware is written according to the reset command corresponding to the storage medium. The process of resetting the storage medium according to the reset command can be implemented by using existing technology, and is not described in detail in embodiments of the present application.

[0131] For the convenience of description, the above apparatus is described in various units, modules, and the like in terms of functions. Of course, the functions of the units and modules can be implemented in one or more software and / or hardware in implementing the present application.

[0132] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in a general dictionary should be interpreted as having a meaning consistent with the meaning in the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0133] For the convenience of description, the above apparatus is described in various units, modules, and the like in terms of functions. Of course, the functions of the units and modules can be implemented in one or more software and / or hardware in implementing the present application.

[0134] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and the necessary universal hardware platforms. Based on such an understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of the present application.

[0135] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for 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 application.

Claims

1. A method for controlling air pressure shock in a vehicle cabin, characterized in that, The vehicle cabin air pressure shock control method includes the following steps: key parameter acquisition and definition, data acquisition and database establishment, construction of human ear perception equivalent model, establishment of rapid prediction model, and result verification and application. The steps for acquiring and defining key parameters include acquiring information on the sampling objects and parameter items associated with the transmission of air pressure inside the vehicle compartment; The steps for data acquisition and database establishment include setting up the experimental procedure and collecting air pressure impact data inside the vehicle cabin under the condition of parameter adjustment of the sampling object. The steps for constructing an equivalent model of human ear perception include setting up a simulation model inside the vehicle cabin and constructing an equivalent model of human ear perception. The steps to establish a rapid prediction model include: conducting multiple linear regression analysis based on the vehicle cabin air pressure impact data under the parameter adjustment state of the collected sampling object, and constructing a regression equation; The steps for result verification and application include identifying the state of air pressure impact inside the vehicle cabin during the vehicle door closing process, based on the construction of regression equations and the construction of an equivalent model of human ear perception.

2. The vehicle cabin air pressure shock control method according to claim 1, characterized in that, The steps for acquiring and defining the key parameters include acquiring information on the sampling objects and parameter items associated with the transmission of air pressure inside the vehicle compartment, including: Based on the vehicle cabin air pressure transmission model, sampling objects related to air pressure transmission in the vehicle cabin were selected, including sampling objects such as doors, holes, valves, and volumes. The parameters of the sampled objects include: door closing speed, door area, cabin volume, airflow channel perforation area of ​​the body sheet metal, airflow channel direction, interior grille opening area, pressure relief valve performance, number of pressure relief valves, pressure relief valve placement, and overall vehicle airtightness.

3. The vehicle cabin air pressure shock control method according to claim 1, characterized in that, The data acquisition and database establishment steps include setting up the experimental procedure and collecting vehicle cabin air pressure impact data under the adjusted parameter state of the sampling object, including: Sampling locations and pressure threshold ranges were set within the vehicle compartment; Pre-selected sampling objects and their corresponding initial parameter values; Set up experimental groups that change the sampling objects and the corresponding preset parameter values; According to the experimental group, pressure data were collected at designated sampling locations inside the vehicle compartment. The experimental groups were classified and recorded based on the pressure data of the sampling locations set in the vehicle compartment and the pressure threshold range of the sampling locations set in the vehicle compartment. Based on the classification and recording of experimental groups, key data were selected and defined.

4. The vehicle cabin air pressure shock control method according to claim 1, characterized in that, The steps for constructing the equivalent model of human ear perception include setting up a simulation model inside the vehicle cabin, and constructing the equivalent model of human ear perception includes: Based on the fact that the sampling location inside the vehicle cabin is the human ear and the pressure threshold range is the human ear pressure range, a door speed meter and a pressure sensor are set up to construct a simulation model of the air pressure impact on the human ear under the operation of the vehicle door opening and closing. Based on the door speed meter controlling the door opening and closing status, air pressure impact data from the pressure sensor is collected to construct an equivalent model of human ear perception.

5. The vehicle cabin air pressure shock control method according to claim 1, characterized in that, The steps for establishing a rapid prediction model include performing multiple linear regression analysis based on the vehicle cabin pressure impact data under the parameter adjustment state of the collected sampling object, and constructing the regression equation, including: Using the set change sampling object and corresponding parameter values ​​as independent variables, and the pressure data at the set sampling location in the vehicle compartment as the dependent variable, a multiple linear regression analysis was performed to construct a regression equation.

6. The vehicle cabin air pressure shock control method according to claim 5, characterized in that, The method involves using the set change sampling object and corresponding parameter values ​​as independent variables, and the pressure data at the set sampling location inside the vehicle compartment as the dependent variable, to perform a multiple linear regression analysis and construct the regression equation, including: Based on the selection and definition of key data, multiple linear regression analysis was conducted with the independent variables X1 (actual pressure relief area of ​​the pressure relief valve), X2 (air tightness of the whole vehicle), X3 (Y-direction projection area of ​​the door opening / cabin volume), and X4 (door closing speed) as the independent variables, and the measured peak pressure at the sampling position set in the vehicle cabin as the dependent variable, to construct the regression equation. The regression equation is constructed as follows: peak pressure = 75.67 - 0.012X1 + 0.204X2 + 532.1X3 + 47.3X4; Among them, the actual pressure relief area X1 of the pressure relief valve is generated based on the through area of ​​the airflow channel of the body sheet metal, the opening area of ​​the interior grille, and the number of pressure relief valves. Based on the door area and cabin volume, generate the door opening Y-direction projection area / cabin volume X3.

7. A vehicle cabin air pressure shock control system, characterized in that, The vehicle cabin air pressure shock control system includes: a module for acquiring and defining key parameters, a module for data acquisition and database establishment, a module for constructing an equivalent model of human ear perception, a module for establishing a rapid prediction model, and a module for result verification and application. The module for acquiring and defining key parameters is used to acquire information on sampling objects and parameter items related to air pressure transmission within the vehicle cabin; The data acquisition and database establishment module is used to set up the experimental process and collect air pressure impact data inside the vehicle cabin under the condition of parameter adjustment of the sampling object. The module for constructing an equivalent model of human ear perception is used to set up a simulation model inside the vehicle cabin and construct an equivalent model of human ear perception. The module for building a rapid prediction model is used to perform multiple linear regression analysis and construct regression equations based on the pressure impact data of the vehicle cabin under the parameter adjustment state of the collected sampling object. The results verification and application module is used to identify the state of air pressure impact inside the vehicle cabin during the vehicle door closing process, based on the construction of regression equations and the construction of an equivalent model of human ear perception.

8. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory stores a computer program that, when executed by a processor, causes the processor to perform the steps of the vehicle cabin pressure shock control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The device stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the vehicle cabin pressure shock control method as described in any one of claims 1 to 6.

10. A cockpit testing platform, characterized in that, include: An electronic device for implementing the steps of the vehicle cabin pressure shock control method as described in any one of claims 1 to 6; A processor that runs a program that, when the program is running, executes the steps of the vehicle cabin pressure shock control method as described in any one of claims 1 to 6 from data output by an electronic device. A storage medium for storing a program that, when running, performs the steps of the vehicle cabin pressure shock control method as described in any one of claims 1 to 6 on data output from an electronic device.

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