Sensor signal processing method and apparatus, computer device, readable storage medium and program product

Abstract

The application relates to a sensor signal processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: obtaining an original sampling signal of a capacitive sensor arranged on a steering wheel, an ambient temperature signal collected by a temperature sensor and a heating function state of the steering wheel; inputting the ambient temperature signal into an adaptive filter to output a filtering result signal; determining an error signal between the filtering result signal and the original sampling signal to obtain a target sampling signal of the capacitive sensor; wherein the updating mode of the adaptive filter comprises the following steps: determining a contact state of the steering wheel according to the error signal; and updating a weight coefficient of the adaptive filter according to the ambient temperature signal and the error signal in the case that the heating function state of the steering wheel is an open state and the contact state is a hand-off state. The method can improve the accuracy of touch detection under the condition of ambient temperature noise interference.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a sensor signal processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the development of automotive safety technology, capacitive touch detection is now commonly found on car steering wheels to determine whether the driver's hands are on the wheel. If the system detects that the driver's hands have been off the steering wheel for an extended period while the vehicle is in motion, it will issue a warning sound to remind the driver to drive safely.

[0003] In traditional technologies, capacitive touch detection typically employs an amplitude threshold method. Specifically, a capacitive sensor collects the capacitance signal of the steering wheel in real time. When the driver touches the steering wheel, the amplitude of the capacitance signal increases accordingly. This capacitance signal amplitude is then compared with a preset touch threshold. If the threshold is exceeded, the system is determined to be in a touched state; otherwise, it is determined to be in a released state.

[0004] However, the addition of a heating function to the steering wheel in some current car models can easily cause the touch detection algorithm in traditional technology to fail. Summary of the Invention

[0005] Therefore, it is necessary to provide a sensor signal processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of touch detection under conditions of ambient temperature and noise interference, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a sensor signal processing method, including:

[0007] Acquire the original sampling signal from the capacitive sensor located on the steering wheel, the ambient temperature signal collected by the temperature sensor, and the heating function status of the steering wheel;

[0008] The ambient temperature signal is input into an adaptive filter, and the filtered result signal is output; wherein, the current weight coefficients of the adaptive filter are determined by the update of the previous time step, and the filtered result signal is used to characterize the estimated value of the noise caused by the ambient temperature signal;

[0009] Determine the error signal between the filtered result signal and the original sampled signal to obtain the target sampled signal of the capacitance sensor;

[0010] The adaptive filter is updated in the following ways:

[0011] The contact state of the steering wheel is determined based on the error signal;

[0012] When the steering wheel heating function is on and the contact state is off, the weight coefficients of the adaptive filter are updated according to the ambient temperature signal and the error signal.

[0013] In one embodiment, determining the contact state of the steering wheel based on the error signal includes:

[0014] The contact state of the steering wheel is determined by comparing at least one of the absolute value of the amplitude, the relative value of the change, and the rate of change of the error signal with the corresponding preset threshold.

[0015] In one embodiment, updating the weight coefficients of the adaptive filter based on the ambient temperature signal and the error signal includes:

[0016] The weight coefficients of the adaptive filter are updated using the normalized least mean square algorithm based on the ambient temperature signal and the error signal.

[0017] In one embodiment, updating the weight coefficients of the adaptive filter using a normalized least mean square algorithm based on the ambient temperature signal and the error signal includes:

[0018] The current ambient temperature signal sample values ​​and several historical time points are used to form the input vector;

[0019] Obtain the error signal and the current weight coefficient vector at the current moment;

[0020] The sum of the squares of each element in the input vector is calculated to obtain the instantaneous power estimate of the input vector;

[0021] The sum of the instantaneous power estimate and the preset regularization parameter is calculated to obtain the normalized denominator value;

[0022] The weight coefficient increment is calculated based on the preset step size factor, the normalized denominator value, the error signal, and the input vector.

[0023] The current weight coefficient vector is added to the weight coefficient increment to obtain the weight coefficient vector at the next time step.

[0024] In one embodiment, a heating wire is provided on the steering wheel, and the acquisition of the ambient temperature signal collected by the temperature sensor includes:

[0025] The ambient temperature signal is acquired by a temperature sensor set within a preset distance range of the heating wire.

[0026] In one embodiment, after determining the contact state of the steering wheel based on the error signal, the process includes:

[0027] When the heating function is off or the contact state is touched, the weight coefficients of the adaptive filter remain unchanged.

[0028] Secondly, this application also provides a sensor signal processing device, comprising:

[0029] The acquisition module is used to acquire the original sampling signal of the capacitive sensor set on the steering wheel, the ambient temperature signal collected by the temperature sensor, and the heating function status of the steering wheel.

[0030] A filtering module is used to input the ambient temperature signal into an adaptive filter and output a filtered result signal; wherein, the current weight coefficients of the adaptive filter are determined by the update of the previous time step, and the filtered result signal is used to characterize the estimated value of the noise caused by the ambient temperature signal;

[0031] The determination module is used to determine the error signal between the filtered result signal and the original sampled signal, and to obtain the target sampled signal of the capacitance sensor;

[0032] An update module is used to determine the contact state of the steering wheel based on the error signal; when the steering wheel heating function is on and the contact state is off-hand, the module updates the weight coefficients of the adaptive filter based on the ambient temperature signal and the error signal.

[0033] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above-mentioned embodiments.

[0034] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above claims.

[0036] The aforementioned sensor signal processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product, by adding an adaptive filter to process the original sampling signal of the capacitive sensor, suppress noise caused by the ambient temperature signal, and denoise the original sampling signal based on the error signal between the original sampling signal and the filtered result signal, and the adaptive filter is updated based on the ambient temperature signal and the error signal when the heating function is on and the steering wheel is off-hand, can realize the dynamic adjustment of the filter weight coefficient by combining dual-talk judgment, thereby achieving adaptive suppression of noise, and ultimately improving the accuracy of touch detection under ambient temperature noise interference. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating a sensor signal processing method in one embodiment;

[0039] Figure 2 This is a flowchart illustrating the steps of updating the weight coefficients of an adaptive filter based on ambient temperature and error signals in one embodiment.

[0040] Figure 3 This is a structural block diagram of a sensor signal processing device in one embodiment;

[0041] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] It should be noted that the terms "comprising" and "having," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusion. The term "multiple" as used in this application refers to two or more. The term "and / or" as used in this application refers to one of the solutions, or any combination of multiple solutions.

[0044] Because of the compact wiring layout of the steering wheel, the capacitive sensor is close to the heating wire. When the heating function is activated, the temperature near the capacitive sensor can reach over 60 degrees Celsius. This temperature increase causes a significant shift in the capacitive detection signal, exceeding the amplitude increment caused by human touch. The amplitude threshold judgment method cannot distinguish whether the signal change is caused by temperature or touch.

[0045] Based on this, this application provides a sensor signal processing method. This embodiment illustrates the application of this method to a terminal, such as... Figure 1 As shown, the method includes the following steps:

[0046] S110: Acquire the original sampling signal from the capacitive sensor located on the steering wheel, the ambient temperature signal collected by the temperature sensor, and the heating function status of the steering wheel.

[0047] The raw sampling signal from the capacitive sensor can be a mixed sampling signal, including effective touch components and temperature noise components, collected by the capacitive sensor on the steering wheel. The ambient temperature signal collected by the temperature sensor can be used to characterize the temperature changes around the capacitive sensor. The steering wheel heating function status can be used to characterize whether the steering wheel heating function is on or off.

[0048] For example, the output signal of the capacitive sensor on the steering wheel can be acquired in real time by the vehicle signal acquisition component to obtain the original sampling signal, the output signal of the temperature sensor around the capacitive sensor can be acquired to obtain the ambient temperature signal, and the output signal of the steering wheel heating control component can be read to obtain the heating function status.

[0049] S120 inputs the ambient temperature signal into the adaptive filter and outputs the filtered result signal; wherein, the current weight coefficients of the adaptive filter are determined by the update of the previous time step, and the filtered result signal is used to characterize the estimated value of the noise caused by the ambient temperature signal.

[0050] The adaptive filter is a digital filter that dynamically adjusts the weighting coefficients based on the input signal and error feedback to approximate the estimation of temperature noise. The filtered signal is the estimated signal output by the adaptive filter, used to fit the temperature noise component in the original sampled signal. For example, the adaptive filter can be a Normalized Least Mean Square (NLMS) adaptive filter.

[0051] For example, the time-series sampled values ​​of the ambient temperature signal can be used to construct a filter input vector, which is then input into an adaptive filter. The adaptive filter performs calculations on the input vector based on the weight coefficients updated in the previous time step, outputting a filtered signal that approximates the temperature noise component. The weight coefficients of the adaptive filter can be normalized and adjusted based on the power characteristics of the input signal.

[0052] S130, determine the error signal between the filtered result signal and the original sampled signal to obtain the target sampled signal of the capacitance sensor; wherein, the updating method of the adaptive filter includes: determining the contact state of the steering wheel according to the error signal; when the heating function of the steering wheel is on and the contact state is off-hand, updating the weight coefficients of the adaptive filter according to the ambient temperature signal and the error signal.

[0053] The error signal is obtained by subtracting the original sampled signal from the filtered signal. The target sampled signal is the effective touch detection signal after removing temperature noise. The steering wheel contact state characterizes whether the steering wheel is touched or removed by a hand.

[0054] For example, the difference between the original sampled signal and the filtered result signal can be calculated to obtain an error signal, which can then be used as the target sampled signal. The contact state of the steering wheel can be determined based on the characteristic parameters of the error signal. When the heating function is on and the contact state is off, the weight coefficients of the adaptive filter can be updated based on the ambient temperature signal and the error signal. In this way, it can be ensured that the effective touch signal is not falsely suppressed, and the filter weight coefficients can be accurately iterated only in pure temperature noise scenarios.

[0055] In the aforementioned sensor signal processing method, an adaptive filter is added to process the original sampling signal of the capacitive sensor, suppressing noise caused by the ambient temperature signal. Based on the error signal between the original sampling signal and the filtered result signal, the original sampling signal is denoised. Furthermore, the adaptive filter is updated based on the ambient temperature signal and the error signal when the heating function is on and the steering wheel is off-hand. This enables dynamic adjustment of the filter weight coefficients by combining dual-talk judgment, thereby achieving adaptive suppression of noise and ultimately improving the accuracy of touch detection under ambient temperature noise interference.

[0056] In an exemplary embodiment, the step of determining the contact state of the steering wheel based on the error signal may include: determining the contact state of the steering wheel based on a comparison result of at least one of the absolute value of the amplitude, the relative value of the change, and the rate of change of the error signal with a corresponding preset threshold.

[0057] For example, the error signal e(n) can be calculated to obtain the absolute value of the error signal amplitude, the relative value of the error signal change from the previous time to the current time, and the rate of change of the error signal per unit time. The absolute value of the error signal amplitude is compared with a preset amplitude threshold, the relative value of the error signal change is compared with a preset relative value threshold, and the rate of change of the error signal change is compared with a preset rate of change threshold. Based on the comparison results, the contact state of the steering wheel is determined to be either a touched state or a hands-free state. Specifically, when the absolute value of the error signal amplitude is greater than the preset amplitude threshold, or the relative value of the error signal change is greater than the preset relative value threshold, or the rate of change of the error signal change is greater than the preset rate of change threshold, the contact state of the steering wheel is determined to be a touched state. When the absolute value of the error signal amplitude is less than or equal to the preset amplitude threshold, and the relative value of the error signal change is less than or equal to the preset relative value threshold, and the rate of change of the error signal change is less than or equal to the preset rate of change threshold, the contact state of the steering wheel is determined to be a hands-free state.

[0058] In this embodiment, the steering wheel contact state is determined by comparing at least one of the absolute value of the amplitude, the relative value of the change, and the rate of change of the error signal with a preset threshold. This enables accurate differentiation between the steering wheel touch / remove state, avoids misjudgment of the contact state caused by noise interference, and improves the stability and accuracy of state recognition.

[0059] In an exemplary embodiment, the step of updating the weight coefficients of the adaptive filter based on the ambient temperature signal and the error signal may include: updating the weight coefficients of the adaptive filter using a normalized least mean square algorithm based on the ambient temperature signal and the error signal.

[0060] For example, the ambient temperature signal can be first smoothed in time to obtain a filter reference signal. Then, the adjustment direction and magnitude of the weight coefficients can be determined based on the real-time feedback of the error signal. The normalized least mean square algorithm is then used for iteration. By combining the filter reference signal and the error signal, the weight coefficients of the adaptive filter are updated in real time to complete the adaptive correction of the weight coefficients. The normalized least mean square algorithm adopts the principle of constraint optimization, which satisfies the filter output constraint while minimizing the change in the increment of the weight vector. The constraint optimization problem is solved by the Lagrange multiplier method to obtain the optimal update rule of the filter weight coefficients.

[0061] Optionally, such as Figure 2 As shown, the steps described above for updating the weight coefficients of the adaptive filter based on the ambient temperature signal and the error signal may include:

[0062] A1 is an input vector composed of the ambient temperature signal samples from the current moment and several historical moments.

[0063] For example, the current ambient temperature signal sample value x(n), the ambient temperature signal sample values ​​x(n-1), x(n-2), ..., x(n-L+1) from the previous L-1 historical moments can be used to form an input vector X(n) of length L. The input vector can be represented as X(n) = [x(n), x(n-1), ..., x(n-L+1)]. T , where L is the number of taps in the adaptive filter.

[0064] A2, obtain the error signal and the current weight coefficient vector at the current time.

[0065] For example, the denoised error signal e(n) at the current time and the weight coefficient vector W(n) of the adaptive filter at the current time can be obtained, where the weight coefficient vector W(n) = [w1, w2, ..., w L ].

[0066] A3 calculates the sum of the squares of all elements in the input vector to obtain the instantaneous power estimate of the input vector.

[0067] For example, the input vector X(n) can be transposed and multiplied by the original vector to calculate the sum of squares of each element of the input vector, thus obtaining the instantaneous power estimate of the input vector ||X(n)||. 2 , where ||X(n)|| 2 =X(n) T X(n).

[0068] A4 calculates the sum of the instantaneous power estimate and the preset regularization parameter to obtain the normalized denominator value.

[0069] The preset regularization parameter is a small positive number δ, which is used to prevent the denominator from being zero when the input power is too small.

[0070] For example, the instantaneous power estimate ||X(n)|| can be used. 2 Adding this to the preset regularization parameter δ yields the normalized denominator value ||X(n)||. 2 +δ.

[0071] A5 calculates the weight coefficient increment based on the preset step size factor, normalized denominator value, error signal, and input vector.

[0072] The preset step size factor μ is a value greater than 0 and less than 2, used to control the convergence speed and steady-state imbalance of the algorithm. The larger μ is, the faster the algorithm converges but the greater the steady-state imbalance; the smaller μ is, the slower the convergence speed but the smaller the steady-state imbalance.

[0073] For example, it can be done according to the formula μ / (||X(n)|| 2+δ)*X(n)*e(n) calculates the increment of the weight coefficients, where μ is the preset step size factor, ||X(n)|| 2 +δ is the normalized denominator value, X(n) is the input vector, and e(n) is the error signal at the current time.

[0074] A6 adds the current weight coefficient vector to the weight coefficient increment to obtain the weight coefficient vector at the next time step.

[0075] For example, the current weight coefficient vector W(n) can be added to the calculated weight coefficient increment, according to the formula W(n+1)=W(n)+μ / (||X(n)|| 2 +δ)*X(n)*e(n) yields the weight coefficient vector W(n+1) for the next time step.

[0076] In this embodiment, compared to the traditional LMS algorithm, the Normalized Least Mean Square algorithm addresses the gradient noise amplification problem by normalizing the input vector power, resulting in faster convergence and better stability. Furthermore, by constructing the input vector based on time-series temperature samples and combining instantaneous power estimates, regularization parameters, and step size factors to calculate the iterative filter weight coefficients, more accurate weight coefficient updates and more stable numerical calculations are achieved, ensuring the reliability and consistency of noise suppression.

[0077] In an exemplary embodiment, the steering wheel is provided with a heating wire, and the step of acquiring the ambient temperature signal collected by the temperature sensor may include: acquiring the ambient temperature signal collected by the temperature sensor set within a preset distance range of the heating wire.

[0078] For example, the temperature sensor can be set within a preset distance range adjacent to the steering wheel heating wire to collect the ambient temperature signal x(n) around the capacitive sensor in real time when the heating wire is working, so as to obtain a temperature sampling signal that is consistent with the temperature at which the capacitive sensor is disturbed.

[0079] In this embodiment, by setting the temperature sensor within a preset distance range of the heating wire to collect the ambient temperature signal, it is possible to ensure that the collected temperature reference signal accurately matches the actual disturbed temperature of the capacitive sensor, thereby improving the accuracy of temperature noise fitting.

[0080] In one exemplary embodiment, the above-described sensor signal processing method further includes:

[0081] S140, when the heating function is off or the contact state is touched, the weight coefficients of the adaptive filter remain unchanged.

[0082] For example, when the steering wheel heating function is detected to be off, the current weight coefficient vector W(n) of the adaptive filter can be left unchanged without iterative updates. When the steering wheel heating function is detected to be on and the steering wheel is in a touched state, it can be determined that the current state is a dual-talk state, the iterative calculation of the adaptive filter weight coefficients is stopped, and the value of the weight coefficient vector W(n) remains unchanged. Here, the dual-talk state refers to the state where the steering wheel heating function is on and a human hand is touching the steering wheel. If this state is not processed, the valid touch signal, along with the temperature noise signal, will be suppressed by the adaptive filter.

[0083] In this embodiment, by keeping the adaptive filter weight coefficients unchanged when the heating function is off or the steering wheel is touched, it is possible to avoid the effective touch signal being falsely suppressed and to avoid the touch signal being filtered out along with temperature noise in a two-way scenario.

[0084] In one possible implementation, the adaptive filter weight coefficient update is based on the minimum mean square error criterion, and the optimality and numerical stability of the weight coefficient update are ensured through constrained optimization. Let the original sampled signal of the capacitive sensor be d(n), which consists of the effective touch signal s(n) and additive noise v(n) introduced by the ambient temperature (including temperature caused by heating): d(n) = s(n) + v(n). An ambient temperature signal x(n) is collected near the heating wire and the capacitive sensor, and an output y(n) is generated by the adaptive filter, approximating v(n) under the minimum mean square error criterion. The error signal e(n) obtained by subtracting y(n) from d(n) is then the denoised touch signal. Specifically, the system can collect the current moment's raw capacitance sensor signal d(n) as the desired signal, the ambient temperature signal x(n) as the reference signal, and the heating function's on / off status. x(n) is stored in a buffer to form a reference vector X, the length of which is the same as the number of taps in the filter weight coefficient vector W. The filter output y(n) = WX is calculated, and the error signal e(n) = d(n) - y(n) is calculated. The absolute value, relative value, and rate of change of the error signal e(n) are used to determine whether the steering wheel is in a touch / remote state. This is then combined with the heating function's on / off status to determine if the system is in dual-talk mode. When the heating function is on and the system is not in dual-talk mode, the reference signal power value P(n) = X is calculated. T X, and update the filter weight coefficients W(n+1)=W(n)+(μ / (P(n)+δ))*X*e(n); when the heating function is off or in dual-talk mode, keep the filter weight coefficients W unchanged; output the error signal e(n) for subsequent touch detection or other components.

[0085] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0086] Based on the same inventive concept, this application also provides a sensor signal processing apparatus for implementing the sensor signal processing method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more sensor signal processing apparatus embodiments provided below can be found in the limitations of the sensor signal processing method described above, and will not be repeated here.

[0087] In one exemplary embodiment, such as Figure 3 As shown, a sensor signal processing device 300 is provided, including: an acquisition module 301, a filtering module 302, a determination module 303, and an update module 304, wherein:

[0088] The acquisition module 301 is used to acquire the original sampling signal of the capacitive sensor set on the steering wheel, the ambient temperature signal collected by the temperature sensor, and the heating function status of the steering wheel.

[0089] The filtering module 302 is used to input the ambient temperature signal into the adaptive filter and output the filtered result signal; wherein, the current weight coefficients of the adaptive filter are determined by the update of the previous moment, and the filtered result signal is used to characterize the estimated value of the noise caused by the ambient temperature signal.

[0090] The determination module 303 is used to determine the error signal between the filtered result signal and the original sampled signal, and to obtain the target sampled signal of the capacitance sensor.

[0091] The update module 304 is used to determine the contact state of the steering wheel based on the error signal; when the steering wheel heating function is on and the contact state is off-hand, the weight coefficients of the adaptive filter are updated based on the ambient temperature signal and the error signal.

[0092] In one embodiment, the update module 304 is further configured to:

[0093] The contact state of the steering wheel is determined by comparing at least one of the absolute value of the amplitude, the relative value of the change, and the rate of change of the error signal with the corresponding preset threshold.

[0094] In one embodiment, the update module 304 is further configured to:

[0095] The weight coefficients of the adaptive filter are updated using the normalized least mean square algorithm based on the ambient temperature signal and the error signal.

[0096] In one embodiment, the update module 304 is further configured to:

[0097] The current ambient temperature signal sample values ​​and several historical time points are used to form the input vector;

[0098] Obtain the error signal and the current weight coefficient vector at the current moment;

[0099] The sum of the squares of each element in the input vector is calculated to obtain the instantaneous power estimate of the input vector.

[0100] The sum of the instantaneous power estimate and the preset regularization parameter is calculated to obtain the normalized denominator value;

[0101] The increment of the weight coefficients is calculated based on the preset step size factor, normalized denominator value, error signal and input vector;

[0102] Add the current weight coefficient vector to the weight coefficient increment to obtain the weight coefficient vector at the next time step.

[0103] In one embodiment, a heating wire is provided on the steering wheel, and the acquisition module 301 is further configured to:

[0104] Acquire the ambient temperature signal collected by a temperature sensor set within a preset distance range of the heating wire.

[0105] In one embodiment, the update module 304 is further configured to:

[0106] When the heating function is off or the contact state is touched, the weight coefficients of the adaptive filter remain unchanged.

[0107] Each module in the aforementioned sensor signal processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0108] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a sensor signal processing method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0109] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0110] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0111] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0112] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0113] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0115] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A sensor signal processing method, characterized in that, The method includes: Acquire the original sampling signal from the capacitive sensor located on the steering wheel, the ambient temperature signal collected by the temperature sensor, and the heating function status of the steering wheel; The ambient temperature signal is input into an adaptive filter, and the filtered result signal is output; wherein, the current weight coefficients of the adaptive filter are determined by the update of the previous time step, and the filtered result signal is used to characterize the estimated value of the noise caused by the ambient temperature signal; Determine the error signal between the filtered result signal and the original sampled signal to obtain the target sampled signal of the capacitance sensor; The adaptive filter is updated in the following ways: The contact state of the steering wheel is determined based on the error signal; When the steering wheel heating function is on and the contact state is off, the weight coefficients of the adaptive filter are updated according to the ambient temperature signal and the error signal.

2. The method according to claim 1, characterized in that, Determining the contact state of the steering wheel based on the error signal includes: The contact state of the steering wheel is determined by comparing at least one of the absolute value of the amplitude, the relative value of the change, and the rate of change of the error signal with the corresponding preset threshold.

3. The method according to claim 1, characterized in that, The step of updating the weight coefficients of the adaptive filter based on the ambient temperature signal and the error signal includes: The weight coefficients of the adaptive filter are updated using the normalized least mean square algorithm based on the ambient temperature signal and the error signal.

4. The method according to claim 3, characterized in that, The step of updating the weight coefficients of the adaptive filter using the normalized least mean square algorithm based on the ambient temperature signal and the error signal includes: The current ambient temperature signal sample values ​​and several historical time points are used to form the input vector; Obtain the error signal and the current weight coefficient vector at the current moment; The sum of the squares of each element in the input vector is calculated to obtain the instantaneous power estimate of the input vector; The sum of the instantaneous power estimate and the preset regularization parameter is calculated to obtain the normalized denominator value; The weight coefficient increment is calculated based on the preset step size factor, the normalized denominator value, the error signal, and the input vector. The current weight coefficient vector is added to the weight coefficient increment to obtain the weight coefficient vector at the next time step.

5. The method according to claim 1, characterized in that, The steering wheel is equipped with a heating wire, and the acquisition of the ambient temperature signal collected by the temperature sensor includes: The ambient temperature signal is acquired by a temperature sensor set within a preset distance range of the heating wire.

6. The method according to claim 1, characterized in that, After determining the contact state of the steering wheel based on the error signal, the process includes: When the heating function is off or the contact state is touched, the weight coefficients of the adaptive filter remain unchanged.

7. A sensor signal processing device, characterized in that, The device includes: The acquisition module is used to acquire the original sampling signal of the capacitive sensor set on the steering wheel, the ambient temperature signal collected by the temperature sensor, and the heating function status of the steering wheel. A filtering module is used to input the ambient temperature signal into an adaptive filter and output a filtered result signal; wherein, the current weight coefficients of the adaptive filter are determined by the update of the previous time step, and the filtered result signal is used to characterize the estimated value of the noise caused by the ambient temperature signal; The determination module is used to determine the error signal between the filtered result signal and the original sampled signal, and to obtain the target sampled signal of the capacitance sensor; An update module is used to determine the contact state of the steering wheel based on the error signal; when the steering wheel heating function is on and the contact state is off-hand, the module updates the weight coefficients of the adaptive filter based on the ambient temperature signal and the error signal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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