An electric seat abnormality detection method and device

Abstract

The application relates to the technical field of automobile data processing, and discloses an electric seat abnormality detection method and device, which can monitor the starting state of an electric seat in real time; when the starting state of the electric seat is a heating state, the temperature rising speed within a first preset time is calculated; if the temperature rising speed is less than a preset temperature rising speed threshold, it is determined that the electric seat is abnormal in heating; when the starting state of the electric seat is a refrigeration state, the temperature falling speed within a second preset time and a wind speed noise correlation coefficient are calculated; if the temperature falling speed is less than a preset temperature falling speed threshold or the wind speed noise correlation coefficient is outside a preset correlation standard range, it is determined that the electric seat is abnormal in refrigeration. The application improves the real-time performance of data monitoring, improves the accuracy of monitoring data, and improves the riding comfort of passengers.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile data processing, in particular to an abnormality detection method and device for an electric seat. BACKGROUND

[0002] With the increasing demand for comfort in car interiors, ventilation and heating functions of car seats have become standard in high-end sedans and SUVs. Modern car seat ventilation systems usually use fans to extract air from the seat and circulate it to achieve rapid cooling. The heating system, on the other hand, heats the seat surface through resistance wires or thermoelectric materials, providing a warm seating experience. In recent years, car manufacturers have been exploring new technologies such as intelligent temperature control and personalized settings to further enhance the comfort of seats.

[0003] However, existing technologies often monitor the ventilation or heating effects of cars separately, lacking comprehensive consideration, resulting in imperfect data collection and feedback mechanisms. Moreover, when the ventilation function of the car seat is started, the fan will produce noise, and the existing technology does not have the ability to monitor and feedback the noise level in real time, affecting the user experience of the passengers. SUMMARY

[0004] The present application provides an abnormality detection method and device for an electric seat, which improves the real-time performance of data monitoring, improves the accuracy of monitoring data, and improves the comfort of passengers.

[0005] To solve the above technical problems, the present application provides an abnormality detection method for an electric seat, comprising:

[0006] real-time monitoring of the starting state of the electric seat;

[0007] when the starting state of the electric seat is a heating state, calculating the temperature rise speed within a first predetermined time;

[0008] if the temperature rise speed is less than a predetermined temperature rise speed threshold, the electric seat heating is determined to be abnormal;

[0009] when the starting state of the electric seat is a cooling state, calculating the cooling speed and the wind speed noise correlation coefficient within a second predetermined time;

[0010] if the cooling speed is less than a predetermined cooling speed threshold or the wind speed noise correlation coefficient is outside a predetermined correlation standard range, the electric seat cooling is determined to be abnormal.

[0011] The application can monitor the heating and cooling abnormality of the electric seat in real time, improve the real-time performance of data monitoring, use different indexes to judge whether the electric seat is abnormal according to different starting states of the electric seat, improve the accuracy of monitoring data, and further improve the riding comfort of passengers.

[0012] Further, when the starting state of the electric seat is the heating state, the temperature rising speed in the first preset time is calculated, specifically:

[0013] When the starting state of the electric seat is the heating state, the seat temperature and the environment temperature in the first preset time are collected.

[0014] Based on the seat temperature and the environment temperature in the first preset time, the temperature difference data in the first preset time is calculated.

[0015] Based on the time length of the first preset time, the temperature rising speed in the first preset time is calculated according to the temperature difference data in the first preset time.

[0016] When the starting state of the electric seat is the heating state, the temperature difference data is calculated by collecting the seat temperature and the environment temperature, and then the temperature rising speed of the electric seat is calculated based on the temperature difference data. When calculating the temperature rising speed of the electric seat, not only the seat temperature but also the environment temperature is collected, the temperature rising speed is calculated by considering the environmental factors, and the accuracy of the data can be improved.

[0017] Further, when the starting state of the electric seat is the cooling state, the temperature falling speed and the wind speed noise correlation coefficient in the second preset time are calculated, including:

[0018] When the starting state of the electric seat is the cooling state, the seat temperature and the environment temperature in the second preset time are collected.

[0019] Based on the seat temperature and the environment temperature in the second preset time, the temperature difference data in the second preset time is calculated.

[0020] Based on the time length of the second preset time, the temperature falling speed in the preset time is calculated according to the temperature difference data in the second preset time.

[0021] The application calculates the temperature difference data by collecting the seat temperature and the ambient temperature when the starting state of the electric seat is the refrigeration state, and then calculates the heating speed of the electric seat based on the temperature difference data.

[0022] Further, when the starting state of the electric seat is the refrigeration state, the cooling speed in the second preset time and the wind speed noise correlation coefficient are calculated, comprising:

[0023] When the starting state of the electric seat is the refrigeration state, the original wind speed data and the original noise data in the second preset time are synchronously collected;

[0024] The original wind speed data and the original noise data are respectively subjected to data standardization processing to form first wind speed data and first noise data;

[0025] The first wind speed data and the first noise data are subjected to correlation analysis to obtain the wind speed noise correlation coefficient.

[0026] When calculating the wind speed noise correlation coefficient, the original wind speed data and the original noise data are synchronously collected, and the original wind speed data and the original noise data are subjected to data standardization processing to generate the first wind speed data and the first noise data corresponding to each other, thereby improving the accuracy of the correlation analysis.

[0027] Further, the first wind speed data and the first noise data are subjected to correlation analysis to obtain the wind speed noise correlation coefficient, specifically:

[0028] The wind speed mean value is calculated based on the first wind speed data;

[0029] The noise mean value is calculated based on the first noise data;

[0030] The first wind speed data and the first noise data are subjected to data matching to form a plurality of groups of observation data;

[0031] Pearson correlation analysis is performed based on the wind speed mean value, the noise mean value and the plurality of groups of observation data to obtain the wind speed noise correlation coefficient.

[0032] Further, when the starting state of the electric seat is the refrigeration state, the original wind speed data and the original noise data in the second preset time are synchronously collected, and further comprising:

[0033] The air vent area of the electric seat is obtained;

[0034] calculate the ventilation amount in the second preset time based on the ventilation port area and original wind speed data in the second preset time;

[0035] If the ventilation amount in the second preset time is greater than the preset ventilation amount threshold, it is determined that the electric seat is abnormally cooled.

[0036] The ventilation port area of the electric seat is obtained, and the ventilation amount of the electric seat can be calculated based on the collected original wind speed data and the ventilation port area. The ventilation amount can also be used as a judgment condition for abnormal cooling of the electric seat, improving the real-time performance of seat abnormality detection, and further improving the riding comfort of the passenger.

[0037] Further, after determining that the electric seat is abnormally cooled, the method further comprises:

[0038] analyzing the original noise data to obtain noise level fluctuation;

[0039] determining the current vehicle state based on the noise level fluctuation based on a preset vehicle state noise library;

[0040] locating and analyzing the original noise data to determine a noise source;

[0041] performing frequency spectrum analysis on the original noise data to determine a main noise frequency band;

[0042] determining the current vehicle state, the noise source and the main noise frequency band as noise analysis data, and determining an abnormality processing measure based on the noise analysis data.

[0043] After determining that the electric seat is abnormally cooled, the current vehicle state, the noise source and the main noise frequency band can be obtained by analyzing the noise level fluctuation, the location analysis and the frequency spectrum analysis of the original noise data. According to these noise analysis data, subsequent abnormality processing measures can be determined, which can more accurately solve the abnormal problems of the electric seat and improve the efficiency of abnormality processing of the electric seat.

[0044] Further, the original wind speed data and the original noise data in the second preset time are synchronously collected, specifically:

[0045] The original wind speed data in the second preset time is collected by a wind speed sensor; the wind speed sensor is installed at the middle position of the ventilation port of the electric seat; the installation direction of the wind speed sensor is consistent with the airflow direction of the ventilation port of the electric seat;

[0046] The original noise data in the second preset time is collected by a sound level meter; the sound level meter is installed at the bottom of the electric seat.

[0047] The application collects original wind speed data of the electric seat by a wind speed sensor, which is installed at the middle position of the air vent of the electric seat, can ensure that the wind speed sensor is directly contacted with the airflow, and controls the installation direction of the wind speed sensor to be consistent with the airflow direction of the air vent of the electric seat, so as to improve the accuracy of data measurement; original noise data of the electric seat is collected by a sound level meter, which is installed at the bottom of the electric seat, can ensure that the noise signal is effectively captured.

[0048] Further, the seat temperature and the ambient temperature are collected based on a temperature sensor; the temperature sensor is installed on the surface of the electric seat.

[0049] The application collects the seat temperature and the ambient temperature by a temperature sensor, which is installed on the surface of the electric seat, can ensure the stability of data collection.

[0050] Correspondingly, the application provides an electric seat abnormality detection device, which comprises a monitoring module, a first calculation module, a first determination module, a second calculation module and a second determination module.

[0051] The monitoring module is used for monitoring the starting state of the electric seat in real time.

[0052] The first calculation module is used for calculating the temperature rising speed within a first preset time when the starting state of the electric seat is the heating state.

[0053] The first determination module is used for determining that the electric seat is abnormal in heating if the temperature rising speed is less than a preset temperature rising speed threshold.

[0054] The second calculation module is used for calculating the temperature falling speed within a second preset time and a wind speed noise correlation coefficient when the starting state of the electric seat is the cooling state.

[0055] The second determination module is used for determining that the electric seat is abnormal in cooling if the temperature falling speed is less than a preset temperature falling speed threshold or the wind speed noise correlation coefficient is out of a preset correlation standard range. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The flowchart of one embodiment of the electric seat abnormality detection method provided by the application is shown;

[0057] Figure 2 The structural schematic diagram of one embodiment of the electric seat provided by the application is shown;

[0058] Figure 3 The flowchart of another embodiment of the electric seat abnormality detection method provided by the application is shown;

[0059] Figure 4This is a schematic diagram of one embodiment of the electric seat abnormality detection device provided by the present invention. Detailed Implementation

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

[0061] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0062] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0063] Example 1

[0064] like Figure 1 The diagram shown is a flowchart of an embodiment of the electric seat anomaly detection method provided by the present invention. The method includes steps 101 to 105, and the specific steps are as follows:

[0065] Step 101: Monitor the activation status of the electric seat in real time.

[0066] In the first embodiment of the present invention, the electric seat has heating and cooling functions. When the electric seat is activated to the heating state, it will activate its heating function; when the electric seat is activated to the cooling state, it will activate its ventilation function. If the electric seat is in the heating state, the seat temperature will affect the occupant's comfort; if the electric seat is in the cooling state, the seat fan will be activated to perform the ventilation function, therefore, both fan noise and seat temperature will affect the occupant's comfort. Therefore, when performing anomaly detection on the electric seat, it is necessary to first determine the electric seat's activation state in order to collect corresponding data for anomaly determination and improve the accuracy of the detection.

[0067] Step 102: When the electric seat is in heating mode, calculate the heating rate within the first preset time.

[0068] Furthermore, in the first embodiment of the present invention, when the electric seat is in a heating state, the heating rate within a first preset time period is calculated, specifically as follows:

[0069] collecting seat temperature and ambient temperature within a first preset time when the starting state of the electric seat is a heating state;

[0070] calculating temperature difference data within the first preset time based on the seat temperature and the ambient temperature within the first preset time;

[0071] calculating a heating speed within the first preset time based on the temperature difference data within the first preset time according to the length of the first preset time.

[0072] In the first embodiment of the present application, when the starting state of the electric seat is a heating state, the seat temperature and the ambient temperature can be collected within a first preset time, the difference between the seat temperature and the ambient temperature is calculated, the temperature difference data within the first preset time is obtained, the length of the first preset time is obtained, and the heating speed within the first preset time is obtained by dividing the temperature difference data within the first preset time by the length of the first preset time. When calculating the heating speed of the electric seat, not only the seat temperature but also the ambient temperature is collected, the heating speed is calculated by considering environmental factors, and the accuracy of the data can be improved.

[0073] Step 103: If the heating speed is less than a preset heating speed threshold, it is determined that the electric seat heating is abnormal.

[0074] In the first embodiment of the present application, the historical temperature data of the electric seat and the passenger's ride comfort experience feedback are obtained, the heating speed threshold reflecting the ride comfort is obtained by analyzing the historical data, the heating speed of the electric seat calculated in real time is obtained, the heating speed is compared with the preset heating speed threshold, and if the heating speed is less than the preset heating speed threshold, it is determined that the heating speed cannot meet the ride comfort standard, and therefore it is determined that the electric seat heating is abnormal.

[0075] Step 104: When the starting state of the electric seat is a cooling state, the cooling speed within a second preset time and the wind speed noise correlation coefficient are calculated.

[0076] Further, in the first embodiment of the present application, when the starting state of the electric seat is a cooling state, the cooling speed within a second preset time and the wind speed noise correlation coefficient are calculated, including:

[0077] collecting seat temperature and ambient temperature within a second preset time when the starting state of the electric seat is a cooling state;

[0078] calculating temperature difference data within the second preset time based on the seat temperature and the ambient temperature within the second preset time;

[0079] calculating a cooling speed within the second preset time based on the temperature difference data within the second preset time according to the length of the second preset time.

[0080] In the first embodiment of the present application, when the starting state of the electric seat is the refrigeration state, the seat temperature and the ambient temperature can be collected within a second preset time, the difference between the seat temperature and the ambient temperature is calculated, the temperature difference data within the second preset time is obtained, the length of the second preset time is obtained, the temperature difference data within the second preset time is divided by the length of the second preset time, and the cooling speed within the second preset time is obtained. When calculating the cooling speed of the electric seat, not only the seat temperature is collected, but also the ambient temperature is collected. The cooling speed is calculated by considering environmental factors, which can improve the accuracy of the data.

[0081] Further, in the first embodiment of the present application, the seat temperature and the ambient temperature are collected based on a temperature sensor; the temperature sensor is installed on the surface of the electric seat.

[0082] As an example of the first embodiment of the present application, a temperature sensor with a measurement range of -40℃ to 125℃ and an accuracy of ±0.5℃ can be used to collect the seat temperature and the ambient temperature. The temperature sensor is installed on the surface of the electric seat by using a fixed adhesive tape, which can ensure the stability of data collection. When the electric seat is in the heating state or the refrigeration state, the temperature sensor records the seat temperature and the ambient temperature of the electric seat at the same time every second. The seat temperature and the ambient temperature of the same second are calculated by difference, and the temperature difference data is obtained. According to the data collection time, the formula R=△T / △t can be used to calculate the heating speed or the cooling speed, wherein R is the heating speed or the cooling speed, △T is the temperature difference data (℃), and △t is the data collection time (second).

[0083] Further, in the first embodiment of the present application, when the starting state of the electric seat is the refrigeration state, the cooling speed within the second preset time and the wind speed noise correlation coefficient are calculated, including:

[0084] When the starting state of the electric seat is the refrigeration state, the original wind speed data and the original noise data within the second preset time are collected synchronously;

[0085] The original wind speed data and the original noise data are respectively subjected to data standardization processing to form first wind speed data and first noise data;

[0086] The first wind speed data and the first noise data are subjected to correlation analysis to obtain the wind speed noise correlation coefficient.

[0087] In the first embodiment of the present application, when the starting state of the electric seat is the refrigeration state, the original wind speed data and the original noise data are collected simultaneously under the same condition within the second preset time, and the original wind speed data and the original noise data are subjected to data standardization processing to ensure that the data units of the wind speed data and the noise data are consistent, for example, the unit of the wind speed data is meter / second, and the unit of the noise data is decibel (dB), so as to ensure the accuracy of the correlation analysis. The wind speed noise correlation coefficient is calculated based on the first wind speed data and the first noise data after the data standardization processing, and the correlation of the wind speed data and the noise data can be obtained.

[0088] Further, in the first embodiment of the present application, the first wind speed data and the first noise data are subjected to correlation analysis to obtain the wind speed noise correlation coefficient, specifically:

[0089] The wind speed mean value is calculated based on the first wind speed data;

[0090] The noise mean value is calculated based on the first noise data;

[0091] The first wind speed data and the first noise data are matched to form a plurality of groups of observation data;

[0092] The wind speed noise correlation coefficient is obtained by performing Pearson correlation analysis based on the wind speed mean value, the noise mean value, and the plurality of groups of observation data.

[0093] In the first embodiment of the present application, the Pearson correlation analysis method can measure the linear correlation degree between the wind speed and the noise, so the wind speed mean value is calculated from the first wind speed data, and the noise mean value is calculated from the first noise data. Based on the calculated wind speed mean value and noise mean value, and the collected first wind speed data and first noise data, the Pearson correlation coefficient can be calculated. The Pearson correlation coefficient is calculated as follows:

[0094]

[0095] In the formula, x i is the wind speed data at time point i; y i is the noise data at time point i; is the noise mean value; is the noise mean value; and r is the Pearson correlation coefficient, which is between -1 and 1.

[0096] Step 105: If the cooling speed is less than the preset cooling speed threshold or the wind speed noise correlation coefficient is outside the preset correlation standard range, it is determined that the electric seat is abnormally refrigerated.

[0097] In the first embodiment of the present application, the historical temperature data of the electric seat and the ride comfort experience feedback of the occupant are acquired, and the cooling speed threshold reflecting the ride comfort is obtained by analyzing the historical data. After the real-time calculated cooling speed of the electric seat is acquired, the cooling speed is compared with the preset cooling speed threshold. If the cooling speed is less than the preset cooling speed threshold, it is determined that the cooling speed cannot reach the ride comfort standard, so it is determined that the electric seat cooling is abnormal.

[0098] In the first embodiment of the present application, based on the wind speed noise correlation coefficient, the linear relationship between the wind speed and the noise can be obtained. If the wind speed noise correlation coefficient is between 0 and 1, it indicates that there is a positive correlation between the wind speed and the noise, that is, when the wind speed increases, the noise level tends to increase; and the closer the wind speed noise correlation coefficient is to 1, the stronger the positive correlation between the wind speed and the noise. If the wind speed noise correlation coefficient is between -1 and 0, it indicates that there is a negative correlation between the wind speed and the noise, that is, when the wind speed increases, the noise level tends to decrease; and the closer the wind speed noise correlation coefficient is to -1, the stronger the negative correlation between the wind speed and the noise. If the wind speed noise correlation coefficient is 0, it indicates that there is no obvious linear relationship between the wind speed and the noise.

[0099] In the first embodiment of the present application, the historical wind speed data and the historical noise data of the electric seat and the ride comfort experience feedback of the occupant are acquired, and the correlation threshold reflecting the ride comfort is obtained by analyzing the historical data. Based on the correlation threshold, the correlation standard range is set. If the wind speed noise correlation coefficient obtained by real-time detection suddenly changes and exceeds the correlation standard range, it is determined that the electric seat fan is damaged or the control system of the electric seat is faulty, and then it is determined that the electric seat cooling is abnormal.

[0100] Further, if it is obtained by analyzing the historical wind speed data and the historical noise data of the electric seat and the ride comfort experience feedback of the occupant that there is a strong correlation between the wind speed and the noise of the electric seat, it can be considered to add a wind speed noise adjustment mechanism in the control of the electric seat to improve the comfort and reduce the noise of the seat.

[0101] Further, in the first embodiment of the present application, when the starting state of the electric seat is the cooling state, the original wind speed data and the original noise data within the second preset time are synchronously acquired, and further comprising:

[0102] Acquiring the vent area of the electric seat;

[0103] Based on the vent area and the original wind speed data within the second preset time, calculating the ventilation volume within the second preset time;

[0104] If the ventilation volume within the second preset time is greater than the preset ventilation volume threshold, it is determined that the electric seat cooling is abnormal.

[0105] In the first embodiment of the present application, a ventilation volume threshold reflecting the ride comfort can be obtained by analyzing the historical ventilation volume data of the electric seat and the ride comfort experience feedback of the occupant. The ventilation volume of the electric seat in the second preset time can be calculated by obtaining the ventilation opening area of the electric seat and combining the original wind speed data collected in the second preset time. The real-time collected ventilation volume is compared with the preset ventilation volume threshold. If the ventilation volume is greater than the ventilation volume threshold, it is determined that the ventilation volume cannot meet the ride comfort standard, so it is determined that the electric seat is abnormally refrigerated. The present application can use the ventilation volume as a judgment condition for the abnormal refrigeration of the electric seat, improve the real-time performance of the seat abnormal detection, and further improve the ride comfort of the occupant.

[0106] Further, in the first embodiment of the present application, the original wind speed data and the original noise data in the second preset time are synchronously collected, specifically:

[0107] The original wind speed data in the second preset time is collected by using a wind speed sensor; the wind speed sensor is installed at the middle position of the ventilation opening of the electric seat; the installation direction of the wind speed sensor is consistent with the airflow direction of the ventilation opening of the electric seat;

[0108] The original noise data in the second preset time is collected by using a sound level meter; the sound level meter is installed at the bottom of the electric seat.

[0109] As an example of the first embodiment of the present application, a wind speed sensor with a measurement range of 0.1 m / s to 10 m / s, an accuracy of 0.1 m / s, and a response time less than or equal to 1 second can be used to collect the original wind speed data of the electric seat. The wind speed sensor is fixed at the middle position of the ventilation opening of the electric seat to ensure that it is in the airflow path of the ventilation opening of the electric seat and directly contacts the airflow. The installation angle of the wind speed sensor is adjusted to be consistent with the airflow direction of the ventilation opening of the electric seat to improve the measurement accuracy. When the electric seat is in the refrigeration state, the sampling frequency of the wind speed sensor is set to 1 Hz. The wind speed data is read in real time in the second preset time, and the ventilation volume in the second preset time is calculated based on the formula Q=A x V, wherein Q is the ventilation volume (m 3 / s), A is the ventilation opening area (m 2 ), and V is the wind speed (m / s).

[0110] As an example of the first embodiment of the present application, the original noise data of the electric seat can be collected by a sound level meter with a measurement range of 30 dB to 130 dB, an accuracy of ±1 dB, and a frequency range of 20 Hz to 20 kHz. The sound level meter is fixed on the bottom of the seat to ensure that it can effectively capture the noise signal. According to the test environment, the sensitivity of the sound level meter is adjusted to adapt to the noise environment of the fan working of the electric seat. When the electric seat is in the refrigeration state, the sampling frequency of the sound level meter is set to 1 Hz, and the noise data is obtained in real time within the second preset time to form the original noise data.

[0111] Further, in the first embodiment of the present application, after determining that the electric seat is abnormally refrigerated, the method further comprises:

[0112] analyzing the original noise data to obtain noise level fluctuation;

[0113] determining the current vehicle state based on the noise level fluctuation based on a preset vehicle state noise library;

[0114] positioning and analyzing the original noise data to determine a noise source;

[0115] performing frequency spectrum analysis on the original noise data to determine a main noise frequency band;

[0116] determining the current vehicle state, the noise source, and the main noise frequency band as noise analysis data, and determining an abnormal processing measure based on the noise analysis data.

[0117] In the first embodiment of the present application, the noise level fluctuation is the change of the sound intensity with time, that is, the dynamic range and stability of the sound. If the noise level fluctuation is too large, it may indicate that the electric seat has unstable factors, such as fan vibration, component loosening, etc. Therefore, by analyzing the noise level fluctuation, the effectiveness of the subsequent abnormal processing measure can be improved. First, the noise data of the vehicle in different states such as the start, operation and shutdown of the ventilation system is recorded, and the noise level fluctuation of the vehicle in different states is obtained by analyzing various noise data, and recorded in the preset vehicle state noise library. When the electric seat is detected in real time, the sound level meter captures the sound signal in the environment through the built-in microphone and converts it into an electrical signal. Using fast (Fast) or slow (Slow) response time settings, the sound level meter can monitor the instantaneous level of the sound in real time. By weighting and averaging the continuous sound signal (such as A-weighting) and time weighting, the sound level meter can calculate the average noise level (such as Leq) in different time periods, display the fluctuation of the noise level, and determine the current vehicle state based on the preset vehicle state noise library.

[0118] In the first embodiment of the present application, the noise source refers to the specific location or device component that produces sound. By identifying the noise source, the specific component that produces abnormal sound can be located. In real-time detection of electric seats, noise data is collected by using multiple microphones (such as array microphones), and based on beamforming technology and sound source positioning algorithms, the time difference of sound wave propagation can be analyzed from the sound signals received by multiple microphones, so as to locate the position of the noise source.

[0119] In the first embodiment of the present application, the frequency characteristic refers to the distribution of sound signals at different frequencies, including the main frequency component and possible harmonics. Normal device operation usually has specific frequency characteristics. If the frequency characteristics change significantly, such as the appearance of new peaks or changes in the intensity of original peaks, it may indicate that changes have occurred inside the device, such as wear, damage or misalignment, which may indicate potential failures. In real-time detection of electric seats, noise data is collected in real time using a sound level meter, and the collected sound signals are converted to the frequency domain using Fast Fourier Transform (FFT), the energy distribution of each frequency component is analyzed, and the noise characteristics at specific frequencies are identified, such as resonance frequency or specific noise types (low frequency noise, high frequency noise, etc.).

[0120] In the first embodiment of the present application, the current vehicle state can be determined by analyzing the noise level fluctuations; the fault point can be located by analyzing the noise source, narrowing the scope of fault diagnosis; the detailed characteristics of the noise signal are determined by analyzing the frequency characteristics, which helps to identify and diagnose specific fault types. Therefore, by analyzing the noise data to obtain the current vehicle state, noise source and main frequency band of the noise, relevant personnel can specify more effective abnormal handling measures.

[0121] As an example of the first embodiment of the present application, refer to Figure 2 is a structural schematic diagram of an embodiment of the electric seat provided by the present application. The temperature sensor is installed on the surface of the electric seat to monitor the seat surface temperature and the ambient temperature. The air speed sensor (anemometer) is installed at the outlet of the cooling fan of the electric seat to monitor the cooling air volume of the electric seat. The sound level meter and the receiving microphone are fixed at the bottom of the electric seat to monitor the fan and ambient sound volume. The temperature sensor, air speed sensor and sound level meter collect analog data through the data board card, and after being converted into digital data by the data board card, they are connected to the host computer through the control USB cable, and the data is returned to the host computer data and feedback system software for recording. The host computer uses CAN network tools to connect with the controller of the electric seat through the CAN network, sends corresponding start message signals, and starts the seat ventilation and heating function.

[0122] As an example of the first embodiment of the present application, refer to Figure 3is a flowchart of another embodiment of the electric seat abnormality detection method provided by the application. When the operator starts the upper computer, the upper computer starts the seat function using the CAN network, including the ventilation function or the heating function of the electric seat. The data and feedback system software of the operator starts the upper computer, sends an enable instruction to the corresponding data board card through the USB cable, starts the data board card, and the data board card is connected with the temperature sensor, air speed sensor (anemometer) and sound level meter installed in the electric seat rack through the data line. After receiving the enable instruction, the data board card starts the temperature sensor, anemometer and sound level meter in the rack to monitor the electric seat. After the electric seat controller receives the CAN message corresponding to the ventilation or heating function sent by the upper computer through the CAN network, the ventilation function or the heating function is executed. The execution of the ventilation function will start the anemometer collection and the sound level meter collection, and the execution of the heating function will start the temperature sensor collection. After the anemometer, sound level meter and temperature sensor collect data to the data board card, the data is returned to the data and feedback system software of the upper computer through the USB cable. When the test is stopped, the data and feedback system software of the upper computer generates a corresponding test report to record the temperature data, wind speed data and noise data in this test.

[0123] In summary, the first embodiment of the application provides an electric seat abnormality detection method, which can monitor the starting state of the electric seat in real time, calculate the heating speed of the electric seat in real time when the starting state of the electric seat is the heating state, and judge whether the electric seat is abnormal according to the heating speed of the electric seat; when the starting state of the electric seat is the cooling state, the cooling speed of the electric seat and the wind speed noise correlation coefficient are calculated in real time, and whether the electric seat is abnormal is judged according to the cooling speed of the electric seat and the wind speed noise correlation coefficient. The application can monitor the heating abnormality and cooling abnormality of the electric seat in real time, improve the real-time performance of data monitoring, use different indicators to judge whether the electric seat is abnormal according to different starting states of the electric seat, improve the accuracy of monitoring data, and further improve the riding comfort of passengers.

[0124] Embodiment 2

[0125] Reference Figure 4 is a structure schematic diagram of an embodiment of the electric seat abnormality detection device provided by the application. The device comprises a monitoring module 201, a first calculation module 202, a first determination module 203, a second calculation module 204 and a second determination module 205;

[0126] The monitoring module 201 is used for monitoring the starting state of the electric seat in real time;

[0127] The first calculation module 202 is used for calculating the heating speed in the first preset time when the starting state of the electric seat is the heating state;

[0128] The first determining module 203 is configured to determine that the electric seat is abnormal in heating if the temperature rising speed is less than a preset temperature rising speed threshold value.

[0129] The second calculating module 204 is configured to calculate a temperature dropping speed and a wind speed-noise correlation coefficient within a second preset time when the starting state of the electric seat is in a cooling state.

[0130] The second determining module 205 is configured to determine that the electric seat is abnormal in cooling if the temperature dropping speed is less than a preset temperature dropping speed threshold value or the wind speed-noise correlation coefficient is out of a preset correlation standard range.

[0131] Further, in the second embodiment of the present application, when the starting state of the electric seat is in a heating state, a temperature rising speed within a first preset time is calculated, specifically:

[0132] When the starting state of the electric seat is in a heating state, the seat temperature and the ambient temperature within the first preset time are collected.

[0133] Based on the seat temperature and the ambient temperature within the first preset time, temperature difference data within the first preset time is calculated.

[0134] Based on the time length of the first preset time, the temperature rising speed within the first preset time is calculated according to the temperature difference data within the first preset time.

[0135] Further, in the second embodiment of the present application, when the starting state of the electric seat is in a cooling state, a temperature dropping speed and a wind speed-noise correlation coefficient within a second preset time are calculated, including:

[0136] When the starting state of the electric seat is in a cooling state, the seat temperature and the ambient temperature within the second preset time are collected.

[0137] Based on the seat temperature and the ambient temperature within the second preset time, temperature difference data within the second preset time is calculated.

[0138] Based on the time length of the second preset time, the temperature dropping speed within the preset time is calculated according to the temperature difference data within the second preset time.

[0139] Further, in the second embodiment of the present application, when the starting state of the electric seat is in a cooling state, a temperature dropping speed and a wind speed-noise correlation coefficient within a second preset time are calculated, including:

[0140] When the starting state of the electric seat is in a cooling state, the original wind speed data and the original noise data within the second preset time are collected synchronously.

[0141] The original wind speed data and the original noise data are respectively subjected to data standardization processing to form first wind speed data and first noise data.

[0142] Correlation analysis is performed on the first wind speed data and the first noise data to obtain a wind speed-noise correlation coefficient.

[0143] Further, in the second embodiment of the present application, correlation analysis is performed on the first wind speed data and the first noise data to obtain a wind speed-noise correlation coefficient, specifically:

[0144] A wind speed average is calculated based on the first wind speed data;

[0145] A noise average is calculated based on the first noise data;

[0146] The first wind speed data and the first noise data are matched to form a plurality of groups of observation data;

[0147] Pearson correlation analysis is performed based on the wind speed average, the noise average, and the plurality of groups of observation data to obtain a wind speed-noise correlation coefficient.

[0148] Further, in the second embodiment of the present application, when the starting state of the electric seat is a refrigeration state, the original wind speed data and the original noise data within a second preset time are synchronously collected, and further comprising:

[0149] An air vent area of the electric seat is obtained;

[0150] Based on the air vent area and the original wind speed data within the second preset time, a ventilation volume within the second preset time is calculated;

[0151] If the ventilation volume within the second preset time is greater than a preset ventilation volume threshold, it is determined that the electric seat is abnormally refrigerated.

[0152] Further, in the second embodiment of the present application, after determining that the electric seat is abnormally refrigerated, further comprising:

[0153] An analysis of the original noise data is performed to obtain a noise level fluctuation;

[0154] Based on a preset vehicle state noise library, a current vehicle state is determined according to the noise level fluctuation;

[0155] Positioning analysis is performed on the original noise data to determine a noise source;

[0156] Frequency spectrum analysis is performed on the original noise data to determine a noise main frequency band;

[0157] The current vehicle state, the noise source, and the noise main frequency band are determined as noise analysis data, and an abnormal processing measure is determined based on the noise analysis data.

[0158] Further, in the second embodiment of the present application, the original wind speed data and the original noise data within the second preset time are synchronously collected, specifically:

[0159] The original wind speed data within the second preset time is collected by using a wind speed sensor; the wind speed sensor is installed at the middle position of the air vent of the electric seat; the installation direction of the wind speed sensor is consistent with the air flow direction of the air vent of the electric seat;

[0160] The original noise data within the second preset time is collected by using a sound level meter; the sound level meter is installed at the bottom of the electric seat.

[0161] Further, in the second embodiment of the present application, the seat temperature and the ambient temperature are collected based on a temperature sensor; the temperature sensor is installed on the surface of the electric seat.

[0162] In summary, the second embodiment of the present application provides an electric seat abnormality detection device, which is based on the organic combination of modules, and can monitor the starting state of the electric seat in real time. When the starting state of the electric seat is the heating state, the heating speed of the electric seat is calculated in real time, and whether the electric seat is abnormal in heating is determined according to the heating speed of the electric seat. When the starting state of the electric seat is the cooling state, the cooling speed of the electric seat and the wind speed noise correlation coefficient are calculated in real time, and whether the electric seat is abnormal in cooling is determined according to the cooling speed of the electric seat and the wind speed noise correlation coefficient. The present application can monitor the heating abnormality and the cooling abnormality of the electric seat in real time, and improves the real-time performance of data monitoring. Different indicators are used to determine whether the electric seat is abnormal according to different starting states of the electric seat, which improves the accuracy of the monitoring data and further improves the riding comfort of the occupant.

[0163] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not used to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. An electric seat abnormality detection method characterized by comprising: The method comprises the following steps: monitoring the starting state of the electric seat in real time; when the starting state of the electric seat is a heating state, calculating the temperature rising speed within a first preset time; if the temperature rising speed is less than a preset temperature rising speed threshold, determining that the electric seat is abnormally heated; when the starting state of the electric seat is a cooling state, calculating the temperature falling speed and the wind speed-noise correlation coefficient within a second preset time; if the temperature falling speed is less than a preset temperature falling speed threshold or the wind speed-noise correlation coefficient is outside a preset correlation standard range, determining that the electric seat is abnormally cooled.

2. The electric seat abnormality detection method according to claim 1, characterized by, When the starting state of the electric seat is a heating state, the temperature rising speed within a first preset time is calculated, specifically: when the starting state of the electric seat is a heating state, collecting the seat temperature and the ambient temperature within a first preset time; based on the seat temperature and the ambient temperature within the first preset time, calculating the temperature difference data within the first preset time; based on the length of the first preset time, calculating the temperature rising speed within the first preset time according to the temperature difference data within the first preset time.

3. The electric seat abnormality detection method according to claim 1, characterized by, When the starting state of the electric seat is a cooling state, the temperature falling speed and the wind speed-noise correlation coefficient within a second preset time are calculated, including: when the starting state of the electric seat is a cooling state, collecting the seat temperature and the ambient temperature within a second preset time; based on the seat temperature and the ambient temperature within the second preset time, calculating the temperature difference data within the second preset time; based on the length of the second preset time, calculating the temperature falling speed within the preset time according to the temperature difference data within the second preset time.

4. The electric seat abnormality detection method according to claim 1, characterized by, When the starting state of the electric seat is a cooling state, the temperature falling speed and the wind speed-noise correlation coefficient within a second preset time are calculated, including: when the starting state of the electric seat is a cooling state, synchronously collecting the original wind speed data and the original noise data within a second preset time; respectively performing data standardization processing on the original wind speed data and the original noise data to form first wind speed data and first noise data; performing correlation analysis on the first wind speed data and the first noise data to obtain a wind speed-noise correlation coefficient.

5. The electric seat abnormality detection method according to claim 4, characterized by, The correlation analysis on the first wind speed data and the first noise data to obtain a wind speed-noise correlation coefficient is specifically: based on the first wind speed data, calculating a wind speed mean value; based on the first noise data, calculating a noise mean value; performing data matching on the first wind speed data and the first noise data to form a plurality of groups of observation data; based on the wind speed mean value, the noise mean value, and the plurality of groups of observation data, performing Pearson correlation analysis to obtain a wind speed-noise correlation coefficient.

6. The electric seat abnormality detection method according to claim 4, characterized by, When the starting state of the electric seat is a cooling state, synchronously collecting the original wind speed data and the original noise data within a second preset time, further comprising: obtaining the vent area of the electric seat; based on the vent area and the original wind speed data within the second preset time, calculating the ventilation volume within the second preset time; if the ventilation volume within the second preset time is greater than a preset ventilation volume threshold, determining that the electric seat is abnormally cooled.

7. The electric seat abnormality detection method according to claim 6, characterized by, After determining that the electric seat is abnormally cooled, further comprising: analyzing the original noise data to obtain noise level fluctuation; determining a current vehicle state according to the noise level fluctuation based on a preset vehicle state noise library; performing positioning analysis on the original noise data to determine a noise source; performing frequency spectrum analysis on the original noise data to determine a noise main frequency band; determining the current vehicle state, the noise source and the noise main frequency band as noise analysis data, and determining an abnormality processing measure based on the noise analysis data.

8. The electric seat abnormality detection method according to claim 4, characterized by, The original wind speed data and the original noise data within the second preset time are synchronously collected, specifically: an original wind speed data within a second preset time is collected by using a wind speed sensor; the wind speed sensor is installed at a middle position of a ventilation port of the electric seat; an installation direction of the wind speed sensor is consistent with an air flow direction of the ventilation port of the electric seat; an original noise data within the second preset time is collected by using a sound level meter; the sound level meter is installed at a bottom of the electric seat.

9. The electric seat abnormality detection method according to claim 2 or 3, characterized by, The seat temperature and the ambient temperature are collected based on a temperature sensor; the temperature sensor is installed on a surface of the electric seat.

10. An electric seat abnormality detection device characterized by comprising: comprising: a monitoring module, a first calculation module, a first determination module, a second calculation module and a second determination module; the monitoring module is used for monitoring a starting state of the electric seat in real time; the first calculation module is used for calculating a temperature rising speed within a first preset time when the starting state of the electric seat is a heating state; the first determination module is used for determining that the electric seat is abnormal in heating if the temperature rising speed is less than a preset temperature rising speed threshold value; the second calculation module is used for calculating a temperature falling speed within a second preset time and a wind speed noise correlation coefficient when the starting state of the electric seat is a cooling state; the second determination module is used for determining that the electric seat is abnormal in cooling if the temperature falling speed is less than a preset temperature falling speed threshold value or the wind speed noise correlation coefficient is out of a preset correlation standard range.

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