Data processing method and device based on vehicle-mounted equipment

Abstract

The embodiment of the present application discloses a data processing method based on an on-board device and a device thereof, which is applied to the on-board device, wherein the on-board device includes a steering wheel, and the gripping part of the steering wheel is provided with an electrocardiogram collector, wherein the method includes: when the target object holds the gripping part, collecting the first electrocardiogram data of the first preset time period through the electrocardiogram collector; extracting the R wave position in the first electrocardiogram data to obtain an R wave position sequence; determining the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series; determining the target mental load parameter according to the time series; and determining the target mental state type of the target object corresponding to the target mental load parameter. The use of the embodiment of the present application can improve the efficiency and accuracy of mental load evaluation.

Description

Technical Field

[0001] The present application relates to the field of vehicle networking technology or sensor technology, and specifically to a data processing method and device based on vehicle-mounted equipment. Background Art

[0002] In practical applications, the methods of mental workload evaluation mainly include subjective evaluation and physiological evaluation. The subjective evaluation method is the most widely used. It evaluates the driver's subjective feelings and mainly uses scales and questionnaires. Currently, the most widely used ones are NASA-TLX, SWAT, and Cooper-Harper scales.

[0003] Among them, the relevant professional knowledge of the subjective evaluation method, differences in observation ability and communication ability will affect the evaluation results. At the same time, there are large individual differences among drivers. The delay between the time of receiving the evaluation and the time of executing the driving task will also affect the evaluation results. It cannot be evaluated in real time during the driving process. Therefore, how to improve the efficiency and accuracy of mental workload evaluation needs to be solved urgently. Summary of the invention

[0004] The embodiment of the present application provides a data processing method and device based on vehicle-mounted equipment, which helps to improve the efficiency and accuracy of mental workload evaluation.

[0005] In a first aspect, an embodiment of the present application provides a data processing method based on an in-vehicle device, which is applied to the in-vehicle device, wherein the in-vehicle device includes a steering wheel, and a grip portion of the steering wheel is provided with an electrocardiograph collector, and the method includes:

[0006] When the target object holds the holding part, collecting first electrocardiogram data of a first preset time period through the electrocardiogram collector;

[0007] Extracting the R wave position in the first electrocardiogram data to obtain an R wave position sequence;

[0008] Determine the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series;

[0009] determining a target mental workload parameter based on the time series;

[0010] A target mental state type of the target object corresponding to the target mental load parameter is determined.

[0011] In a second aspect, an embodiment of the present application provides a data processing device based on an on-board device, which is applied to the on-board device. The on-board device includes a steering wheel, and a grip portion of the steering wheel is provided with an electrocardiograph collector. The device includes: an electrocardiograph collector unit and a processor unit, wherein:

[0012] The electrocardiogram collector unit is used to collect first electrocardiogram data of a first preset time period through the electrocardiogram collector when the target object grasps the grasping part;

[0013] The processor unit is used to extract the R wave position in the first electrocardiogram data to obtain an R wave position sequence; determine the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series; determine the target mental load parameter according to the time series; and determine the target mental state type of the target object corresponding to the target mental load parameter.

[0014] In a third aspect, an embodiment of the present application provides a vehicle-mounted device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0017] The implementation of the embodiments of the present application has the following beneficial effects:

[0018] It can be seen that the data processing method based on the vehicle-mounted device and the device thereof described in the embodiment of the present application are applied to the vehicle-mounted device, and the vehicle-mounted device includes a steering wheel, and the gripping part of the steering wheel is provided with an electrocardiograph collector. When the target object grips the gripping part, the first electrocardiograph data of the first preset time period is collected by the electrocardiograph collector, and the R wave position in the first electrocardiograph data is extracted to obtain an R wave position sequence, and the time difference between two adjacent R waves is determined according to the R wave position sequence to obtain a set of time series, and the target mental load parameters are determined according to the time series, and the target mental state type of the target object corresponding to the target mental load parameters is determined. In this way, the electrocardiograph data of the user can be collected, and the corresponding time series can be determined based on the corresponding R wave sequence, and the time series can be used to analyze the user's mental load parameters and mental state type, thereby helping to improve the efficiency and accuracy of mental load evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flowchart of a data processing method based on a vehicle-mounted device provided in an embodiment of the present application;

[0021] Figure 2 is a structural schematic diagram of a steering wheel provided in an embodiment of the present application;

[0022] Figure 3 This is a schematic diagram of a scene demonstration of a steering wheel provided in an embodiment of the present application;

[0023] Figure 4 It is a structural schematic diagram of a vehicle-mounted device provided in an embodiment of the present application;

[0024] Figure 5 It is a block diagram of the functional units of a data processing device based on a vehicle-mounted device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0027] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] In the related art, the physiological evaluation method is an objective method to evaluate the level of mental load by detecting the physiological characteristics of the driver. This method is based on the fact that the driving task will cause changes in human physiological signals. When the driver perceives and makes decisions about the surrounding environment during driving, the interaction of the central nervous system will be transmitted to the peripheral nervous system, causing changes in physiological characteristics such as electrical activity in the cerebral cortex and changes in cardiac potential. Existing physiological measurement methods do not allow the driver to have obvious operational changes, making it difficult to monitor in real time during driving. In the embodiments of the present application, the real-time monitoring of mental load can be carried out in real time, feedback can be provided, and the driving task can be adjusted dynamically to avoid excessive or low mental load states and improve driving efficiency and safety.

[0029] The following is a detailed description of the embodiments of the present application.

[0030] See also Figure 1 , Figure 1 : is a flow chart of a data processing method based on a vehicle-mounted device provided in an embodiment of the present application, which is applied to a vehicle-mounted device, wherein the vehicle-mounted device includes a steering wheel, and an electrocardiograph is provided at a gripping portion of the steering wheel. As shown in the figure, the data processing method based on the vehicle-mounted device includes:

[0031] 101. When the target object holds the holding part, the electrocardiogram data of a first preset time period is collected by the electrocardiogram collector.

[0032] In an embodiment of the present application, the vehicle-mounted device may include a steering wheel, or it may be other equipment for controlling the steering wheel. The other equipment may include at least one of the following: a vehicle-mounted refrigerator, a vehicle-mounted audio system, a vehicle-mounted air conditioner, a vehicle-mounted battery, a vehicle-mounted controller, etc., which are not limited here.

[0033] In the embodiment of the present application, the vehicle-mounted device may include a steering wheel, and an ECG collector is provided at the gripping part of the steering wheel, that is, the ECG collector may be installed on the steering wheel, and the ECG data of the user may be collected through the ECG collector. Figure 2 As shown, the ECG collector may include ECG electrodes, which may be disposed on the steering wheel. Figure 3 As shown, when both hands are on the steering wheel, the electrocardiogram signal (electrocardiogram data) can be collected through the electrocardiogram electrodes.

[0034] The first preset time period can be preset or set by the system default. The target object can be a driver, or can be any person holding the holding part. In a specific implementation, when the target object holds the holding part, the first electrocardiogram data of the first preset time period is collected by the electrocardiogram collector. For example, 30 seconds of electrocardiogram data can be extracted, and its electrocardiogram data sequence (first electrocardiogram data) is expressed as X(n)={x1,x2,...,x n}, n represents the number of ECG data.

[0035] 102. Extract the R wave position in the first electrocardiogram data to obtain an R wave position sequence.

[0036] In the embodiment of the present application, in a specific implementation, the R wave position of the electrocardiogram data can be detected first, that is, the R wave position in the first electrocardiogram data is extracted to obtain an R wave position sequence, that is, the R wave position sequence is composed of multiple R wave positions.

[0037] Optionally, the above step 102, extracting the R wave position in the first electrocardiogram data to obtain the R wave position sequence, may include the following steps:

[0038] 21. Determine the extreme value points of the first electrocardiogram data to obtain an extreme value point sequence;

[0039] 22. Determine the R wave position according to the extreme point sequence to obtain the R wave position sequence.

[0040] In the embodiment of the present application, the extreme value points of the first electrocardiogram data can be determined to obtain an extreme value point sequence, that is, a sequence composed of multiple extreme value points, to obtain an extreme value point sequence, and then the R wave position is determined according to the extreme value point sequence to obtain an R wave position sequence. The extreme value points may include maximum value points and / or minimum value points.

[0041] In the specific implementation, we can find the extreme points in the ECG data, that is, traverse the ECG data sequence X(n), if x i-1 <x i >x i+1 , then x i is an extreme point, and the extreme point sequence is represented by Q(m)={q1,q2,...,q m}, m represents the number of extreme points.

[0042] Further, optionally, the above step 22, determining the R wave position according to the extreme point sequence to obtain the R wave position sequence, may include the following steps:

[0043] 221. Acquire second electrocardiogram data of an extreme value point a in a second preset time period, wherein the extreme value point a is any extreme value point in the extreme value point sequence, and the second preset time period includes a time point corresponding to the extreme value point a;

[0044] 222. Determine the cumulative amount of the second electrocardiogram data;

[0045] 223. When the cumulative amount is greater than a preset threshold, confirm that the extreme point a is the apex of the R wave, and the apex of the R wave is the R wave position;

[0046] 224. When the cumulative amount is less than or equal to the preset threshold, confirm that the extreme point a is not the apex of the R wave.

[0047] The above-mentioned preset threshold value can be preset or set by system default. The second preset time period can be preset or set by system default.

[0048] In the embodiment of the present application, taking extreme point a as an example, extreme point a is any extreme point in the extreme point sequence, and the second preset time period includes the time point corresponding to the extreme point a. Then, the second electrocardiogram data of the extreme point a in the second preset time period can be obtained, and the accumulation amount of the second electrocardiogram data can be determined. When the accumulation amount is greater than the preset threshold, it is confirmed that the extreme point a is the apex of the R wave, and the apex of the R wave is the R wave position. On the contrary, when the accumulation amount is less than or equal to the preset threshold, it is confirmed that the extreme point a is not the apex of the R wave.

[0049] Optionally, the above step 222, determining the cumulative amount of the second electrocardiogram data, can be implemented as follows:

[0050] The cumulative amount is determined according to the following formula:

[0051]

[0052] Wherein, SE1 represents the cumulative amount; k represents the number of electrocardiogram data in the second electrocardiogram data; y(n) represents the nth electrocardiogram data; j is an imaginary unit, i.e., j 2 =-1, e -j2πl / N =cos(2πk / n)+jsin(2πk / n).

[0053] In the specific implementation, the R wave can be found in the extreme point sequence, and the R wave position sequence R(N) = {r1, r2, ..., r N For example, the ECG data of 0.2 seconds before and after the extreme point can be taken to calculate the cumulative amount of data SE1. If SE1 is greater than the threshold TH, the extreme point is the apex of the R wave.

[0054] For example, the sampling frequency is 1000 Hz, the number of ECG data to be calculated is 400, and the data sequence is y(k) = {y1, y2, ..., y k}, k = 400. The formula for calculating the cumulative amount is as follows:

[0055]

[0056] Among them, j is the imaginary unit, that is, j 2 =-1, e -j2πn / k =cos(2πn / k)+jsin(2πn / k).

[0057] 103. Determine the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series.

[0058] In a specific implementation, the time difference between two adjacent R waves can be calculated to obtain a set of time series.

[0059] Optionally, the above step 103, determining the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series, can be implemented as follows:

[0060] Determine each element in the time series according to the following formula:

[0061] t i =(r i+1 -r i )÷rea

[0062] Among them, t i is the i-th element in the time series; r i is the position of the ith R wave; r i+1 is the position of the i+1th R wave; rea is the sampling frequency.

[0063] In the specific implementation, the time difference between two adjacent R waves can be calculated to obtain a set of time series T(j)={t1,t2,...,t J}. The calculation formula is as follows:

[0064] t i =(r i+1 -r i )÷rea

[0065] Among them, r i is the position of the R wave, and rea is the sampling frequency.

[0066] 104. Determine a target mental workload parameter according to the time series.

[0067] In the embodiment of the present application, the time series can be analyzed to obtain the mental load parameters, and then, the mental load parameters can be judged to determine whether the mental load is relaxed, normal or tense.

[0068] Optionally, the above step 104, determining the target mental workload parameter according to the time series, may include the following steps:

[0069] 41. Determine a time increment parameter according to the time series;

[0070] 42. Determine a time deviation parameter according to the time series;

[0071] 43. Determine the target mental workload parameter according to the time increment parameter and the time deviation parameter.

[0072] In a specific implementation, a time increment parameter can be determined according to a time series, and then a time deviation parameter can be determined according to the time series, and the target mental workload parameter can be determined according to the time increment parameter and the time deviation parameter. For example, the product of the time increment parameter and the time deviation parameter can be used as the target mental workload parameter.

[0073] Further, optionally, if the time series is T(j)={t1, t2, ..., t J}, the above step 41, determining the time increment parameter according to the time series, can be implemented as follows:

[0074] The time increment parameter is determined according to the following formula:

[0075]

[0076] Among them, t i is the i-th element in the time series; t i+1 is the i+1th element in the time series; represents the time fluctuation parameter; LI represents the time increment parameter; N represents the number of R wave positions in the R wave position sequence.

[0077] In a specific implementation, the time increment parameter may be calculated based on the above formula.

[0078] Further, optionally, the above step 42, determining the time deviation parameter according to the time series, can be implemented in the following manner:

[0079] The time deviation parameter is determined according to the following formula:

[0080]

[0081] Among them, t iis the i-th element in the time series; t i+1 is the (i + 1)-th element in the time series; represents the time difference parameter; AI represents the time deviation parameter; N represents the number of R-wave positions in the R-wave position sequence.

[0082] In specific implementation, the time deviation parameter can be calculated based on the above formula.

[0083] Then, the target mental load parameter can be calculated according to the following formula:

[0084] SE2 = LI × AI

[0085] where SE2 represents the target mental load parameter.

[0086] 105. Determine the target mental state type of the target object corresponding to the target mental load parameter.

[0087] In specific implementation, the mapping relationship between the preset mental load parameter and the mental state type can be stored in advance. The mental state type can include at least one of the following: relaxed, normal, tense, etc., which is not limited here. Furthermore, the target mental state type of the target object corresponding to the target mental load parameter can be determined based on this mapping relationship.

[0088] For example, two thresholds th1 and th2 can also be set. When SE2 < th1, the mental load is small and it is in a relaxed state; when th1 < SE2 < th2, the mental load is normal; when th2 < SE2, the mental load is large and it is in a tense state.

[0089] In the embodiments of the present application, the real-time monitoring of mental load can be carried out for real-time monitoring and feedback, and the driving task can be dynamically adjusted to avoid excessive or too low mental load states, improving driving efficiency and safety.

[0090] In specific implementation, the mapping relationship between the preset mental state type and the control parameter can also be set in advance. Furthermore, the target control parameter corresponding to the target mental state type can be determined based on this mapping relationship, and then the vehicle can be controlled to move based on the target control parameter. Furthermore, the control corresponding to the mental state type can be realized for the vehicle, which helps to improve the driving safety of the vehicle. The control parameter can include at least one of the following: speed limit, limit turning angle, limit driving duration, vehicle mode adjustment (massage mode, locking mode), etc., which is not limited here.

[0091] In the specific implementation, let's take an example. In actual application, hold the steering wheel with both hands, and the ECG collector in the device collects ECG data. Extract 30 seconds of ECG data, first detect the R wave position of the ECG data, then calculate the time difference between two adjacent R waves, obtain a set of time series, analyze the time series to obtain the mental load parameters. Finally, through the judgment of the mental load parameters, determine whether the mental load is relaxed, normal or tense.

[0092] In the embodiment of the present application, a physiological signal collection and analysis device is integrated on the steering wheel. The driver holds the steering wheel with both hands to start collecting electrocardiogram signals, and analyzes the physiological signals to obtain the driver's mental workload.

[0093] The vehicle-mounted device in the embodiment of the present application may not only include the method part (algorithm) of the present invention, but also an ECG collector unit, a processor unit, a storage unit, a wireless transmission unit, and a power supply unit. Among them, the ECG collection unit is used to collect the driver's ECG signal; the processor unit is used to analyze the ECG signal to obtain the mental load; the wireless transmission unit transmits the mental load to the mobile terminal or PC for display. In a specific implementation, the value of the mental load can be obtained by analyzing the signal collected by the ECG collection module.

[0094] It can be seen that the data processing method based on the vehicle-mounted device described in the embodiment of the present application is applied to the vehicle-mounted device, and the vehicle-mounted device includes a steering wheel, and the gripping part of the steering wheel is provided with an ECG collector. When the target object grips the gripping part, the first ECG data of the first preset time period is collected by the ECG collector, and the R wave position in the first ECG data is extracted to obtain an R wave position sequence, and the time difference between two adjacent R waves is determined according to the R wave position sequence to obtain a set of time series, and the target mental load parameters are determined according to the time series, and the target mental state type of the target object corresponding to the target mental load parameters is determined. In this way, the user's ECG data can be collected, and the corresponding time series can be determined based on the corresponding R wave sequence, and the user's mental load parameters and mental state type can be analyzed using the time series, thereby helping to improve the efficiency and accuracy of mental load evaluation.

[0095] In the specific implementation, the mental load can be monitored in real time, and real-time monitoring and feedback can be carried out to dynamically adjust the driving task to avoid excessive or low mental load states and improve driving efficiency and safety.

[0096] In accordance with the above embodiment, please refer to Figure 4 , Figure 4: is a structural schematic diagram of a vehicle-mounted device provided in an embodiment of the present application. As shown in the figure, the vehicle-mounted device includes a processor, a memory, a communication interface, and one or more programs, which are applied to the vehicle-mounted device. The vehicle-mounted device includes a steering wheel, and an electrocardiograph is provided at the gripping part of the steering wheel. The one or more programs are stored in the memory and are configured to be executed by the processor. In the embodiment of the present application, the program includes instructions for executing the following steps:

[0097] When the target object holds the holding part, collecting first electrocardiogram data of a first preset time period through the electrocardiogram collector;

[0098] Extracting the R wave position in the first electrocardiogram data to obtain an R wave position sequence;

[0099] Determine the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series;

[0100] determining a target mental workload parameter based on the time series;

[0101] A target mental state type of the target object corresponding to the target mental load parameter is determined.

[0102] Optionally, in the aspect of extracting the R wave position in the first electrocardiogram data to obtain the R wave position sequence, the program includes instructions for executing the following steps:

[0103] Determine the extreme value points of the first electrocardiogram data to obtain an extreme value point sequence;

[0104] The R wave position is determined according to the extreme point sequence to obtain the R wave position sequence.

[0105] Further, optionally, in determining the R wave position according to the extreme point sequence to obtain the R wave position sequence, the above program includes instructions for executing the following steps:

[0106] Acquire second electrocardiogram data of an extreme point a in a second preset time period, wherein the extreme point a is any extreme point in the extreme point sequence, and the second preset time period includes a time point corresponding to the extreme point a;

[0107] determining a cumulative amount of the second electrocardiogram data;

[0108] When the accumulated amount is greater than a preset threshold, the extreme point a is confirmed to be the apex of the R wave, and the apex of the R wave is the R wave position;

[0109] When the accumulated amount is less than or equal to the preset threshold, it is confirmed that the extreme point a is not the apex of the R wave.

[0110] Further, optionally, in determining the cumulative amount of the second electrocardiogram data, the program includes instructions for performing the following steps:

[0111] The cumulative amount is determined according to the following formula:

[0112]

[0113] Wherein, SE1 represents the cumulative amount; k represents the number of electrocardiogram data in the second electrocardiogram data; y(n) represents the nth electrocardiogram data; j is an imaginary unit, i.e., j 2 =-1, e -j2πn / k =cos(2πn / k)+jsin(2πn / k).

[0114] Further, optionally, in the aspect of determining the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series, the program includes instructions for executing the following steps:

[0115] Determine each element in the time series according to the following formula:

[0116] t i =(r i+1 -r i )÷rea

[0117] Among them, t i is the i-th element in the time series; r i is the position of the ith R wave; r i+1 is the position of the i+1th R wave; rea is the sampling frequency.

[0118] Further, optionally, in determining the target mental workload parameter according to the time series, the program includes instructions for executing the following steps:

[0119] determining a time increment parameter according to the time series;

[0120] determining a time deviation parameter based on the time series;

[0121] The target mental workload parameter is determined according to the time increment parameter and the time deviation parameter.

[0122] Further, optionally, if the time series is T(j)={t1, t2, ..., t J In determining the time increment parameter according to the time series, the program includes instructions for performing the following steps:

[0123] The time increment parameter is determined according to the following formula:

[0124]

[0125] Among them, t i is the i-th element in the time series; t i+1 is the i+1th element in the time series; represents the time fluctuation parameter; LI represents the time increment parameter; N represents the number of R wave positions in the R wave position sequence.

[0126] Further, optionally, in the aspect of determining the time deviation parameter according to the time series, the program includes instructions for executing the following steps:

[0127] The time deviation parameter is determined according to the following formula:

[0128]

[0129] Among them, t i is the i-th element in the time series; t i+1 is the i+1th element in the time series; represents the time difference parameter; AI represents the time deviation parameter; N represents the number of R wave positions in the R wave position sequence.

[0130] It can be seen that the vehicle-mounted device described in the embodiment of the present application includes a steering wheel, and the gripping part of the steering wheel is provided with an ECG collector. When the target object grips the gripping part, the first ECG data of the first preset time period is collected by the ECG collector, and the R wave position in the first ECG data is extracted to obtain an R wave position sequence, and the time difference between two adjacent R waves is determined according to the R wave position sequence to obtain a set of time series, and the target mental load parameters are determined according to the time series, and the target mental state type of the target object corresponding to the target mental load parameters is determined. In this way, the user's ECG data can be collected, and the corresponding time series can be determined based on the corresponding R wave sequence, and the user's mental load parameters and mental state type can be analyzed using the time series, thereby helping to improve the efficiency and accuracy of mental load evaluation.

[0131] Figure 5 This is a functional unit block diagram of a data processing device 500 based on a vehicle-mounted device involved in an embodiment of the present application. The data processing device 500 based on a vehicle-mounted device is applied to a vehicle-mounted device, the vehicle-mounted device includes a steering wheel, and the grip part of the steering wheel is provided with an electrocardiograph collector. The device includes: an electrocardiograph collector unit 501, a processor unit 502, wherein:

[0132] The electrocardiogram collector unit 501 is used to collect first electrocardiogram data of a first preset time period through the electrocardiogram collector when the target object holds the holding part;

[0133] The processor unit 502 is used to extract the R wave position in the first electrocardiogram data to obtain an R wave position sequence; determine the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series; determine the target mental load parameter according to the time series; and determine the target mental state type of the target object corresponding to the target mental load parameter.

[0134] Optionally, in extracting the R wave position in the first electrocardiogram data to obtain an R wave position sequence, the method includes:

[0135] Determine the extreme value points of the first electrocardiogram data to obtain an extreme value point sequence;

[0136] The R wave position is determined according to the extreme point sequence to obtain the R wave position sequence.

[0137] Further, optionally, in determining the R wave position according to the extreme point sequence to obtain the R wave position sequence, the method includes:

[0138] Acquire second electrocardiogram data of an extreme point a in a second preset time period, wherein the extreme point a is any extreme point in the extreme point sequence, and the second preset time period includes a time point corresponding to the extreme point a;

[0139] determining a cumulative amount of the second electrocardiogram data;

[0140] When the accumulated amount is greater than a preset threshold, the extreme point a is confirmed to be the apex of the R wave, and the apex of the R wave is the R wave position;

[0141] When the accumulated amount is less than or equal to the preset threshold, it is confirmed that the extreme point a is not the apex of the R wave.

[0142] Further, optionally, in determining the cumulative amount of the second electrocardiogram data, the method includes:

[0143] The cumulative amount is determined according to the following formula:

[0144]

[0145] Wherein, SE1 represents the cumulative amount; k represents the number of electrocardiogram data in the second electrocardiogram data; y(n) represents the nth electrocardiogram data; j is an imaginary unit, i.e., j 2 =-1, e -j2πn / k =cos(2πn / k)+jsin(2πn / k).

[0146] Further, optionally, in determining the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series, the method includes:

[0147] Determine each element in the time series according to the following formula:

[0148] t i =(r i+1 -r i )÷rea

[0149] Among them, t i is the i-th element in the time series; r i is the position of the ith R wave; r i+1 is the position of the i+1th R wave; rea is the sampling frequency.

[0150] Further, optionally, in determining the target mental workload parameter according to the time series, the method includes:

[0151] determining a time increment parameter according to the time series;

[0152] determining a time deviation parameter based on the time series;

[0153] The target mental workload parameter is determined according to the time increment parameter and the time deviation parameter.

[0154] Further, optionally, if the time series is T(j)={t1, t2, ..., t J}, in determining the time increment parameter according to the time series, comprising:

[0155] The time increment parameter is determined according to the following formula:

[0156]

[0157] Among them, t i is the i-th element in the time series; t i+1 is the i+1th element in the time series; represents the time fluctuation parameter; LI represents the time increment parameter; N represents the number of R wave positions in the R wave position sequence.

[0158] Further, optionally, in determining the time deviation parameter according to the time series, the method includes:

[0159] The time deviation parameter is determined according to the following formula:

[0160]

[0161] Among them, t i is the i-th element in the time series; t i+1 is the i+1th element in the time series; represents the time difference parameter; AI represents the time deviation parameter; N represents the number of R wave positions in the R wave position sequence.

[0162] It can be seen that the data processing device based on the vehicle-mounted equipment described in the embodiment of the present application is applied to the vehicle-mounted equipment, and the vehicle-mounted equipment includes a steering wheel, and the gripping part of the steering wheel is provided with an electrocardiograph collector. When the target object grips the gripping part, the first electrocardiograph data of the first preset time period is collected by the electrocardiograph collector, and the R wave position in the first electrocardiograph data is extracted to obtain an R wave position sequence, and the time difference between two adjacent R waves is determined according to the R wave position sequence to obtain a set of time series, and the target mental load parameters are determined according to the time series, and the target mental state type of the target object corresponding to the target mental load parameters is determined. In this way, the electrocardiograph data of the user can be collected, and the corresponding time series can be determined based on the corresponding R wave sequence, and the time series can be used to analyze the user's mental load parameters and mental state type, thereby helping to improve the efficiency and accuracy of mental load evaluation.

[0163] It can be understood that the functions of each program module of the data processing device based on the vehicle-mounted equipment in this embodiment can be specifically implemented according to the method in the above method embodiment, and its specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0164] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments.

[0165] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the above method embodiment. The computer program product may be a software installation package.

[0166] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0167] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0168] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0169] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0171] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0172] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0173] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data processing method based on vehicle-mounted equipment, characterized in that: Applied to an in-vehicle device, the in-vehicle device includes a steering wheel, and a grip portion of the steering wheel is provided with an electrocardiogram collector, the method includes: When the target object holds the holding part, collecting first electrocardiogram data of a first preset time period through the electrocardiogram collector; Extracting the R wave position in the first electrocardiogram data to obtain an R wave position sequence; Determine the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series; determining a target mental workload parameter based on the time series; determining a target mental state type of the target object corresponding to the target mental load parameter; Wherein, determining the target mental workload parameter according to the time series includes: determining a time increment parameter according to the time series; determining a time deviation parameter based on the time series; Determining the target mental workload parameter according to the time increment parameter and the time deviation parameter; Wherein, if the time series is T(j)={t1,t2,...,t J }, the determining of the time increment parameter according to the time series comprises: The time increment parameter is determined according to the following formula: Among them, t i is the i-th element in the time series; t i+1 is the i+1th element in the time series; represents the time fluctuation parameter; LI represents the time increment parameter; N represents the number of R wave positions in the R wave position sequence; Wherein, determining the time deviation parameter according to the time series includes: The time deviation parameter is determined according to the following formula: Among them, t i is the i-th element in the time series; t i+1 is the i+1th element in the time series; represents the time difference parameter; AI represents the time deviation parameter; N represents the number of R wave positions in the R wave position sequence; The step of determining the target mental workload parameter according to the time increment parameter and the time deviation parameter is as follows: The target mental workload parameter is calculated according to the following formula: SE2=LI×AI Among them, SE2 represents the target mental load parameter.

2. The method according to claim 1, characterized in that The step of extracting the R wave position in the first electrocardiogram data to obtain an R wave position sequence includes: Determine the extreme value points of the first electrocardiogram data to obtain an extreme value point sequence; The R wave position is determined according to the extreme point sequence to obtain the R wave position sequence.

3. The method according to claim 2, characterized in that Determining the R wave position according to the extreme point sequence to obtain the R wave position sequence includes: Acquire second electrocardiogram data of an extreme point a in a second preset time period, wherein the extreme point a is any extreme point in the extreme point sequence, and the second preset time period includes a time point corresponding to the extreme point a; determining a cumulative amount of the second electrocardiogram data; When the accumulated amount is greater than a preset threshold, the extreme point a is confirmed to be the apex of the R wave, and the apex of the R wave is the R wave position; When the accumulated amount is less than or equal to the preset threshold, it is confirmed that the extreme point a is not the apex of the R wave.

4. The method according to claim 3, characterized in that The step of determining the accumulated amount of the second electrocardiogram data comprises: The cumulative amount is determined according to the following formula: Wherein, SE1 represents the cumulative amount; k represents the number of electrocardiogram data in the second electrocardiogram data; y(n) represents the nth electrocardiogram data; j is an imaginary unit, i.e., j 2 =-1, e -j2πn / k =cos(2πn / k)+jsin(2πn / k).

5. The method according to claim 4, characterized in that The step of determining the time difference between two adjacent R waves according to the R wave position sequence to obtain a set of time series includes: Determine each element in the time series according to the following formula: t i =(r i+1 -r i )÷rea Among them, t i is the i-th element in the time series; r i is the position of the ith R wave; r i+1 is the position of the i+1th R wave; rea is the sampling frequency.

6. A vehicle-mounted device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the programs include instructions for executing the steps in the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 5.

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