An out-of-vehicle handle intelligent control system and method for preventing false triggering

Abstract

The application discloses an intelligent control system and method of an out-of-vehicle handle capable of preventing false triggering, and relates to the technical field of automobile electronics. In the application, an electrode array is arranged in a detection area of an out-of-vehicle handle, and self-capacitance and mutual-capacitance detection are alternately performed to collect capacitance signals. The capacitance signals are subjected to median filtering and IIR filtering processing, and then normalized by using a dynamic reference signal to obtain normalized self-capacitance and mutual-capacitance signals. The amplitude, change rate trend and fluctuation rate of the signals are comprehensively analyzed, and compared with preset threshold values, so as to accurately distinguish between human body touch and external interference, and perform corresponding operations. A clustering analysis mechanism is introduced to learn historical judgment results, automatically optimize the distinguishing threshold value, and form a closed-loop optimization. The application effectively overcomes the defects of false judgment of conductive media such as water in the traditional scheme, and significantly improves the accuracy and environmental adaptability of touch control recognition.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automotive electronics, in particular to an intelligent control system and method for preventing false triggering of an exterior handle. BACKGROUND

[0002] In the wave of the development of automotive intelligence, the interaction mode of the exterior handle is gradually evolving from the traditional mechanical switch to the electronic touch switch. Among them, the capacitive touch switch has become the preferred solution for many vehicle models due to its convenient operation, aesthetic appearance, and durability. This technology triggers instructions by detecting the change in capacitance caused by human touch, improving the user's driving experience and the technological feel of the vehicle.

[0003] However, the capacitive touch switch technology traditionally applied to the exterior handle has obvious defects: the detection scheme based on self-capacitance principle is simple in structure, but cannot effectively distinguish the presence of human touch and conductive media such as water and ice, leading to system misjudgment as touch; some schemes use mutual capacitance detection, but traditional mutual capacitance detection works in the low frequency band, its ability to distinguish water interference is still limited, and it is sensitive to the thickness and uniformity of the plastic shell covering it; therefore, there is an urgent need for a water splash false triggering prevention scheme based on the frequency characteristics of the medium itself. SUMMARY

[0004] The purpose of the present application is to provide an intelligent control system and method for preventing false triggering of an exterior handle to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an intelligent control method for preventing false triggering of an exterior handle, the method comprising the following steps:

[0006] Step S1, deploying an electrode array in a detection area, the electrode array is used for self-capacitance detection and mutual-capacitance detection;

[0007] Further, step S1 comprises:

[0008] The detection area is an exterior handle; the electrode array is deployed in a planar or point-like manner;

[0009] In the planar deployment, the electrodes are arranged in 2 rows and N columns on the circuit board in the detection area, and N is an even positive integer; when mutual-capacitance detection is performed, two electrodes on the same side of the adjacent two columns are connected to form an emitter and a receiver, respectively, and the emitter and the receiver are alternately distributed; when self-capacitance detection is performed, each electrode acts as a separate self-capacitance sensor to sense the change in the capacitance to ground;

[0010] In the point arrangement, electrodes in a group are an inner electrode and an outer electrode surrounding the inner electrode; when mutual-capacitance detection, the inner electrode and the outer electrode in a group are a transmitting electrode and a receiving electrode; when self-capacitance detection, the inner electrode and the outer electrode in a group are both self-capacitance sensors; and a plurality of groups of electrodes are arranged in the detection area.

[0011] In step S2, self-capacitance signals and mutual-capacitance signals are collected through self-capacitance detection and mutual-capacitance detection respectively, and the collected signals are processed to obtain a judgment signal.

[0012] Further, step S2 includes:

[0013] The judgment signal includes self-capacitance signals and mutual-capacitance signals, and the self-capacitance signals and the mutual-capacitance signals each include a plurality of sampling points and a capacitance value corresponding to each sampling point.

[0014] A preset detection period is divided into a self-capacitance detection period, a mutual-capacitance detection period, and a waiting period, and the waiting period is used to separate the self-capacitance detection period and the mutual-capacitance detection period; in the self-capacitance detection period, the electrode array performs self-capacitance detection; and in the mutual-capacitance detection period, the electrode array performs mutual-capacitance detection.

[0015] When entering the self-capacitance detection period, raw self-capacitance signals of each electrode in the capacitance array are continuously collected, and an average value of the raw self-capacitance signals is calculated as an initial self-capacitance value; in the self-capacitance detection period, the electrodes in the electrode array are charged to make the voltage of the electrodes reach a preset voltage value; a preset voltage threshold is used to discharge the electrodes and record the time for the preset voltage value to change to the voltage threshold as a change time, and a current self-capacitance value of the electrode is calculated according to the change time:

[0016] ;

[0017] Wherein represents the current self-capacitance value of the electrode, t represents the change time, and R represents the resistance value of the electrode, represents the preset voltage value, represents the voltage threshold.

[0018] The absolute value of the difference between the initial self-capacitance value and the current self-capacitance value is taken as the self-capacitance signal.

[0019] When entering the mutual-capacitance detection period, a signal generator generates a signal of a preset frequency for driving the transmitting electrode to generate an alternating electric field; the receiving electrode is connected to a signal conditioning circuit for sensing the alternating electric field and generating a voltage signal; and in the mutual-capacitance detection period, the voltage signal is sampled to obtain a mutual-capacitance signal.

[0020] Step S3, filtering the judgment signal to obtain a filtered judgment signal; obtaining an amplitude, a change rate trend and a fluctuation rate of the filtered judgment signal, and distinguishing the to-be-detected object to obtain a distinguishing result, and storing the distinguishing result in a database;

[0021] Further, step S3 includes:

[0022] Step S3-1, the filtered judgment signal includes a filtered self-capacitance signal and a filtered mutual-capacitance signal;

[0023] Taking a median value of the capacitance values of the self-capacitance signals in the M sampling points as a self-capacitance median signal, and taking a median value of the capacitance values of the mutual-capacitance signals in the N sampling points as a mutual-capacitance median signal; respectively performing IIR filtering on the self-capacitance median signal and the mutual-capacitance median signal, which can adopt a second-order IIR low-pass filter, and presetting a cutoff frequency and a filter coefficient, and respectively inputting the self-capacitance median signal and the mutual-capacitance median signal into the second-order IIR low-pass filter to obtain the filtered self-capacitance signal and the filtered mutual-capacitance signal;

[0024] Step S3-2, presetting a reference signal, the reference signal including a reference self-capacitance signal and a reference mutual-capacitance signal; obtaining a dynamic reference signal according to the filtered judgment signal and the reference signal:

[0025] ;

[0026] Wherein represents the dynamic reference signal, represents the filtered judgment signal corresponding to the dynamic reference signal, represents the reference signal corresponding to the dynamic reference signal, represents a preset weight, used to control the amplitude of the reference change;

[0027] According to the dynamic reference signal, the filtered judgment signal is normalized to obtain a normalized signal:

[0028] ;

[0029] Wherein represents the normalized signal, the normalized signal including a normalized self-capacitance signal and a normalized mutual-capacitance signal ;

[0030] Step S3-3, presetting a judgment period, obtaining an amplitude and a change rate trend of the normalized signal in the judgment period, the change rate trend being a set of instantaneous change rates of the normalized signal calculated according to a preset interval in the judgment period; presetting a stable time, recording a maximum change rate of the normalized signal relative to the amplitude within the stable time after the normalized signal reaches the amplitude as a fluctuation rate;

[0031] The amplitude includes a self-capacitance signal amplitude and a mutual-capacitance signal amplitude, the rate trend includes a self-capacitance signal rate trend and a mutual-capacitance signal rate trend, and the fluctuation rate includes a self-capacitance signal fluctuation rate and a mutual-capacitance signal fluctuation rate;

[0032] The preset amplitude threshold, the rate threshold, and the fluctuation rate threshold; the amplitude threshold includes a self-capacitance amplitude threshold and a mutual-capacitance amplitude threshold, the rate threshold includes a self-capacitance rate threshold and a mutual-capacitance rate threshold, and the fluctuation rate threshold includes a self-capacitance fluctuation rate threshold and a mutual-capacitance fluctuation rate threshold;

[0033] The object to be detected includes human touch and interference;

[0034] If the amplitudes are all greater than the corresponding amplitude thresholds, the amplitude condition is considered to be met;

[0035] In the rate trend, if the preset percentage of instantaneous rates are all greater than the corresponding rate thresholds, the rate condition is considered to be met;

[0036] If the fluctuation rate is less than the corresponding fluctuation rate threshold, the fluctuation rate condition is considered to be met;

[0037] When the amplitude condition, the rate condition, and the fluctuation rate condition are all met, the discrimination result is considered to be human touch, and a preset operation is performed; otherwise, the discrimination result is considered to be interference, and no operation is performed;

[0038] The discrimination result and the corresponding judgment parameters are associated and stored in the database, the judgment parameters include the amplitude, the rate trend, and the fluctuation rate, and the rate trend is stored in the database in the form of a vector.

[0039] Step S4, clustering analysis is performed on the discrimination results in the database, and the process of discrimination is optimized;

[0040] Further, step S4 includes:

[0041] Step S4-1, a preset optimization period is set, when the data update period reaches the optimization period, all samples in the database in the optimization period are extracted, the samples include the discrimination results and the corresponding judgment parameters; two groups of judgment parameters with the discrimination results as human touch and interference are selected as initial clustering centers; all samples in the optimization period are traversed, the distance between each sample and the two initial clustering centers is calculated through the Euclidean distance, the sample is attributed to the initial clustering center of the discrimination result with closer distance, and two initial clustering clusters are obtained, including an initial human touch clustering cluster and an initial interference clustering cluster;

[0042] Step S4-2, respectively calculate the average value of the judgment parameters in the two groups of initial clustering clusters, take the average value of the judgment parameters as the new clustering center of the initial clustering cluster, calculate the distance between each sample and the two new clustering centers through the Euclidean distance, and attribute the sample to the division result of the new clustering center with closer distance; a preset termination threshold, when the average value change rate of the corresponding two groups of judgment parameters before and after one attribution is not greater than the termination threshold, it is considered that the clustering is completed, and the human touch clustering cluster and the interference clustering cluster are obtained;

[0043] Step S4-3, take the smallest amplitude in the human touch clustering cluster, and the largest amplitude in the interference clustering cluster, the smallest amplitude includes the smallest self-capacitance signal amplitude and the smallest mutual-capacitance signal amplitude, and the largest amplitude includes the largest self-capacitance signal amplitude and the largest mutual-capacitance signal amplitude;

[0044] Take the intermediate value of the smallest amplitude and the corresponding largest amplitude as a new amplitude threshold;

[0045] Respectively calculate the effective value of the change rate trend in the human touch clustering cluster and the effective value of the change rate trend in the interference clustering cluster, the effective value is the average value of the instantaneous change rate greater than the corresponding change rate threshold in the change rate trend, including the self-capacitance effective value and the mutual-capacitance effective value;

[0046] Take the intermediate value of the effective value of the change rate trend in the human touch clustering cluster and the effective value of the change rate trend in the interference clustering cluster as a new change rate threshold;

[0047] Take the largest fluctuation rate in the human touch clustering cluster and the smallest fluctuation rate in the interference clustering cluster, the largest fluctuation rate includes the largest self-capacitance signal fluctuation rate and the largest mutual-capacitance signal fluctuation rate, and the smallest fluctuation rate includes the smallest self-capacitance signal fluctuation rate and the smallest mutual-capacitance signal fluctuation rate;

[0048] Take the intermediate value of the largest fluctuation rate and the corresponding smallest fluctuation rate as a new fluctuation rate threshold.

[0049] A vehicle exterior handle intelligent control system for preventing false triggering, the system comprises an electrode array deployment module, a capacitive signal processing module, an object differentiation storage module and a clustering analysis optimization module;

[0050] The electrode array deployment module is used for deploying an electrode array inside a detection area;

[0051] The capacitive signal processing module is used for collecting capacitive signals and processing the collected capacitive signals to obtain judgment signals;

[0052] The object differentiation storage module is used for filtering the judgment signals to obtain filtered judgment signals, differentiating a to-be-detected object to obtain a differentiation result, and storing the differentiation result in a database.

[0053] The clustering analysis optimization module is used for clustering analysis on the distinguishing results in the database and optimization on the process of distinguishing.

[0054] The output end of the electrode array deployment module is connected with the input end of the capacitive signal processing module; the output end of the capacitive signal processing module is connected with the input end of the object distinguishing storage module; and the output end of the object distinguishing storage module is connected with the input end of the clustering analysis optimization module.

[0055] The electrode array deployment module comprises a planar electrode deployment unit and a point electrode deployment unit.

[0056] The planar electrode deployment unit is used for deploying the electrode array in a planar deployment mode.

[0057] The point electrode deployment unit is used for deploying the electrode array in a point deployment mode.

[0058] The capacitive signal processing module comprises a self-capacitance signal acquisition unit and a mutual-capacitance signal acquisition unit.

[0059] The self-capacitance signal acquisition unit is used for acquiring the self-capacitance signal.

[0060] The mutual-capacitance signal acquisition unit is used for acquiring the mutual-capacitance signal.

[0061] The object distinguishing storage module comprises a signal filtering unit and an object distinguishing storage unit.

[0062] The signal filtering unit is used for filtering the judgment signal to obtain a filtered judgment signal, and performing normalization processing on the filtered judgment signal to obtain a normalized signal, and obtaining the amplitude, the change rate and the fluctuation rate of the normalized signal.

[0063] The object distinguishing storage unit is used for presetting an amplitude threshold value, a change rate threshold value and a fluctuation rate threshold value, distinguishing the to-be-detected object to obtain a distinguishing result, and storing the distinguishing result into a database.

[0064] The clustering analysis optimization module comprises a result clustering analysis unit and a distinguishing threshold value optimization unit.

[0065] The result clustering analysis unit is used for clustering analysis on the distinguishing results in the database.

[0066] The distinguishing threshold value optimization unit is used for optimization on the process of distinguishing.

[0067] Compared with the prior art, the present application has the following beneficial effects:

[0068] 1、The present application realizes efficient collection and processing of capacitive signals by deploying electrode arrays inside the exterior handle of the vehicle, using planar or point-like deployment methods, through the combination of self-capacitance detection and mutual-capacitance detection. Self-capacitance detection can sensitively perceive changes in ground capacitance, while mutual-capacitance detection effectively distinguishes the interference of conductive media such as water through the alternating electric field induction of the transmitting and receiving electrodes. By presetting the detection period, alternating self-capacitance and mutual-capacitance detection, and utilizing signal processing techniques such as IIR filtering and dynamic reference normalization, the signal-to-noise ratio is significantly improved. This method overcomes the limitations of traditional single detection mode, enabling the system to accurately identify human touch and avoid false triggering caused by environmental factors such as rain and frost, thereby improving the reliability and accuracy of the exterior handle intelligent control system, reducing false alarm rates, and enhancing user experience.

[0069] 2、The present application optimizes the clustering analysis mechanism to intelligently learn and adjust the differentiation results in the database. The system periodically extracts stored sample data, including amplitude, change rate trend, and volatility rate as judgment parameters, and uses Euclidean distance for clustering analysis to automatically separate touch and interference patterns. Through iterative calculation of new cluster centers and dynamic updating of amplitude threshold, change rate threshold, and volatility rate threshold, the system can adapt to environmental changes and user usage habits. This self-optimization process ensures the scientificity and real-time nature of the differentiation threshold, avoids false positives caused by fixed thresholds, significantly improves the robustness and long-term stability of the system in complex environments, reduces maintenance requirements, and reduces the risk of misoperation caused by improper threshold settings.

[0070] 3、The present application integrates electrode array deployment, capacitive signal processing, object differentiation storage, and clustering analysis optimization modules to build a complete intelligent control system. The system realizes accurate differentiation of touch events by real-time acquisition and processing of capacitive signals combined with multi-condition judgment. The results are stored in the database for subsequent optimization, forming a closed-loop control. This integrated design not only improves response speed but also ensures efficient operation of the system under various working conditions, effectively preventing false triggering, enhancing user experience and the technological feel of automotive intelligence, reducing energy waste or safety hazards caused by misoperation, and enhancing the practicality and universality of the system. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 A flowchart of the intelligent control method of the exterior handle of the present application to prevent false triggering;

[0072] Figure 2 A structural diagram of the intelligent control system of the exterior handle of the present application to prevent false triggering;

[0073] Figure 3 A planar deployment method of the electrode array in the present application;

[0074] Figure 4 The point-like arrangement of the electrode array in the present application. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0076] Embodiment one: as shown in the figure, the present application provides a technical solution, an intelligent control method for preventing false triggering of an out-of-vehicle handle, the intelligent control method for preventing false triggering of an out-of-vehicle handle comprising: Figure 1

[0077] Step S1, deploying an electrode array in a detection area, the electrode array being used for self-capacitance detection and mutual-capacitance detection;

[0078] Step S1 comprises:

[0079] The detection area is an out-of-vehicle handle; the electrode array is arranged in a planar arrangement or a point-like arrangement;

[0080] In the planar arrangement, the electrodes are arranged in 2 rows and N columns on a circuit board in the detection area, the N being an even positive integer; when mutual-capacitance detection is performed, two electrodes on the same side in two adjacent columns are connected to form an emitter and a receiver respectively, the emitter and the receiver being alternately distributed; when self-capacitance detection is performed, each electrode serves as a separate self-capacitance sensor to sense the change in the capacitance to ground;

[0081] In the point-like arrangement, an internal electrode located in a central area and an external electrode surrounding the internal electrode form a group of electrodes; when mutual-capacitance detection is performed, the internal electrode and the external electrode in a group of electrodes serve as an emitter and a receiver respectively; when self-capacitance detection is performed, the internal electrode and the external electrode in a group of electrodes both serve as self-capacitance sensors; a plurality of groups of electrodes are arranged in the detection area.

[0082] Step S2, collecting capacitance signals through self-capacitance detection and mutual-capacitance detection respectively, and processing the collected capacitance signals to obtain a judgment signal;

[0083] Step S2 comprises:

[0084] The judgment signal comprises a self-capacitance signal and a mutual-capacitance signal, the self-capacitance signal and the mutual-capacitance signal each comprising a plurality of sampling points and capacitance values corresponding to the sampling points;

[0085] ​A preset detection period is divided into a self-capacitance detection period, a mutual-capacitance detection period and a waiting period, the waiting period is used to separate the self-capacitance detection period and the mutual-capacitance detection period; in the self-capacitance detection period, the electrode array performs self-capacitance detection; in the mutual-capacitance detection period, the electrode array performs mutual-capacitance detection;

[0086] When entering the self-capacitance detection period, the original self-capacitance signals of each electrode in the capacitance array are continuously collected, and the average value of the original self-capacitance signals is calculated as an initial self-capacitance value; in the self-capacitance detection period, the electrodes in the electrode array are charged, so that the voltage of the electrodes reaches a preset voltage value; a preset voltage threshold is set, the electrodes are discharged, and the time for the preset voltage value to change to the voltage threshold is recorded as a change time, and the current self-capacitance value of the electrode is calculated according to the change time:

[0087] ;

[0088] Wherein represents the current self-capacitance value of the electrode, t represents the change time, and R represents the resistance value of the electrode, represents the preset voltage value, represents the voltage threshold;

[0089] The absolute value of the difference between the initial self-capacitance value and the current self-capacitance value is taken as the self-capacitance signal.

[0090] When entering the mutual-capacitance detection period, a signal generator generates a signal of a preset frequency, which is used to drive the emitter to generate an alternating electric field; the receiver is connected to a signal conditioning circuit, which is used to sense the alternating electric field and generate a voltage signal; in the mutual-capacitance detection period, the voltage signal is sampled to obtain a mutual-capacitance signal.

[0091] Step S3, filtering the judgment signal to obtain a filtered judgment signal; obtaining the amplitude, change rate trend and fluctuation rate of the filtered judgment signal, and distinguishing the to-be-detected object to obtain a distinction result, and storing the distinction result in a database;

[0092] Step S3 includes:

[0093] Step S3-1, the filtered judgment signal includes a filtered self-capacitance signal and a filtered mutual-capacitance signal;

[0094] Taking the median value of the capacitance values of the self-capacitance signals in M sampling points as a self-capacitance median signal, and taking the median value of the capacitance values of the mutual-capacitance signals in N sampling points as a mutual-capacitance median signal; the self-capacitance median signal and the mutual-capacitance median signal are respectively subjected to IIR filtering, a second-order IIR low-pass filter can be used, a preset cutoff frequency and a filter coefficient are set, and the self-capacitance median signal and the mutual-capacitance median signal are respectively input into the second-order IIR low-pass filter to obtain a filtered self-capacitance signal and a filtered mutual-capacitance signal;

[0095] Step S3-2, presetting a reference signal, the reference signal including a reference self-capacitance signal and a reference mutual-capacitance signal; obtaining a dynamic reference signal according to the filtered judgment signal and the reference signal:

[0096] ;

[0097] wherein represents the dynamic reference signal, represents the filtered judgment signal corresponding to the dynamic reference signal, represents the reference signal corresponding to the dynamic reference signal, represents a preset weight, used for controlling the amplitude of the reference change;

[0098] performing normalization processing on the filtered judgment signal according to the dynamic reference signal to obtain a normalized signal:

[0099] ;

[0100] wherein represents the normalized signal, the normalized signal including a normalized self-capacitance signal and a normalized mutual-capacitance signal ;

[0101] Step S3-3, presetting a judgment period, obtaining the amplitude and the change rate trend of the normalized signal in the judgment period, the change rate trend being a set of instantaneous change rates of the normalized signal calculated at preset intervals in the judgment period; presetting a stable time, recording the maximum change rate of the normalized signal relative to the amplitude in the stable time after the normalized signal reaches the amplitude as a fluctuation rate;

[0102] The amplitude includes a self-capacitance signal amplitude and a mutual-capacitance signal amplitude, the change rate trend includes a self-capacitance signal change rate trend and a mutual-capacitance signal change rate trend, and the fluctuation rate includes a self-capacitance signal fluctuation rate and a mutual-capacitance signal fluctuation rate;

[0103] Presetting an amplitude threshold, a change rate threshold and a fluctuation rate threshold; the amplitude threshold includes a self-capacitance amplitude threshold and a mutual-capacitance amplitude threshold, the change rate threshold includes a self-capacitance change rate threshold and a mutual-capacitance change rate threshold, and the fluctuation rate threshold includes a self-capacitance fluctuation rate threshold and a mutual-capacitance fluctuation rate threshold;

[0104] The to-be-detected object includes a human touch and an interference;

[0105] If the amplitudes are all greater than the corresponding amplitude thresholds, it is considered that the amplitude condition is met;

[0106] In the change rate trend, if a preset percentage of the instantaneous change rates are all greater than the corresponding change rate thresholds, it is considered that the change rate condition is met;

[0107] if the fluctuation rate is less than a corresponding fluctuation rate threshold, the fluctuation rate condition is deemed to be satisfied;

[0108] when the amplitude condition, the change rate condition and the fluctuation rate condition are all satisfied, the discrimination result is deemed to be human touch, and a preset operation is performed; otherwise, the discrimination result is deemed to be interference, and no operation is performed;

[0109] the discrimination result and the corresponding judgment parameters are associated and stored in the database, the judgment parameters including the amplitude, the change rate trend and the fluctuation rate, wherein the change rate trend is stored in the database in the form of a vector.

[0110] Step S4, cluster analysis is performed on the discrimination results in the database, and the discrimination process is optimized;

[0111] Step S4 includes:

[0112] Step S4-1, a preset optimization period is set, when the data update period reaches the optimization period, all samples in the database within the optimization period are extracted, the samples including the discrimination results and the corresponding judgment parameters; two groups of judgment parameters with the discrimination results being human touch and interference are selected as initial cluster centers respectively; all samples within the optimization period are traversed, the distances between each sample and the two initial cluster centers are calculated by the Euclidean distance, the samples are attributed to the discrimination result of the initial cluster center with a closer distance, and two initial cluster clusters are obtained, including an initial human touch cluster and an initial interference cluster;

[0113] Step S4-2, the average values of the judgment parameters in the two initial cluster clusters are calculated respectively, the average values of the judgment parameters are taken as the new cluster centers of the initial cluster clusters, the distances between each sample and the two new cluster centers are calculated by the Euclidean distance, and the samples are attributed to the discrimination result of the new cluster center with a closer distance; a preset termination threshold is set, when the average value change rates of the corresponding two groups of judgment parameters before and after the attribution are not greater than the termination threshold, the clustering is deemed to be completed, and the human touch cluster and the interference cluster are obtained;

[0114] Step S4-3, the smallest amplitude in the human touch cluster and the largest amplitude in the interference cluster are taken, the smallest amplitude including the smallest self-capacitance signal amplitude and the smallest mutual-capacitance signal amplitude, and the largest amplitude including the largest self-capacitance signal amplitude and the largest mutual-capacitance signal amplitude;

[0115] the intermediate value between the smallest amplitude and the corresponding largest amplitude is taken as a new amplitude threshold;

[0116] the effective values of the change rate trends in the human touch cluster and the interference cluster are calculated respectively, the effective value being the average value of the instantaneous change rates greater than the corresponding change rate threshold in the change rate trend, including the self-capacitance effective value and the mutual-capacitance effective value;

[0117] Taking the intermediate value of the effective value of the change rate trend in the human touch clustering cluster and the effective value of the change rate trend in the interference clustering cluster as a new change rate threshold value;

[0118] Taking the maximum fluctuation rate in the human touch clustering cluster and the minimum fluctuation rate in the interference clustering cluster as a new fluctuation rate threshold value, wherein the maximum fluctuation rate includes the maximum self-capacitance signal fluctuation rate and the maximum mutual-capacitance signal fluctuation rate, and the minimum fluctuation rate includes the minimum self-capacitance signal fluctuation rate and the minimum mutual-capacitance signal fluctuation rate;

[0119] Taking the intermediate value of the maximum fluctuation rate and the corresponding minimum fluctuation rate as a new fluctuation rate threshold value.

[0120] For example:

[0121] An electrode array is arranged in the internal detection area of the external handle, and a planar arrangement is adopted. Three groups of electrodes are arranged, each group including a circular internal electrode (5 mm in diameter) and a ring-shaped external electrode (6 mm in inner diameter and 10 mm in outer diameter). The electrode material is copper, which is printed on a circuit board. The electrode array covers the handle surface, ensuring that the detection area has no blind area;

[0122] The circuit board with electrodes is fixed to the inside of the plastic shell of the external handle by an adhesive process. After assembly, the cavity is filled with potting adhesive to form a waterproof structure, ensuring that the detection electrodes are tightly attached to the shell without gaps, and eliminating adverse environmental factors such as air gaps that affect electric field conduction;

[0123] The preset detection period is 100 milliseconds, which is divided into self-capacitance detection period, mutual-capacitance detection period and waiting period. The waiting period is used for isolation detection to reduce interference.

[0124] During the self-capacitance detection period, the original self-capacitance signals of each electrode are continuously collected.

[0125] For example, the self-capacitance values of 10 sampling points are collected, and the initial self-capacitance value is calculated as 150 pF. Then the electrode is charged to a preset voltage value of 5 V, discharged to a voltage threshold value of 1 V, and the change time t = 2 μs is recorded. According to the discharge formula, the current self-capacitance value is calculated as 160 pF. The absolute difference between the initial value and the current value of the self-capacitance signal is 10 pF, i.e. |160-150|=10 pF. The self-capacitance signal contains multiple sampling points, with values of 10 pF, 11 pF, 9 pF, 12 pF, 10 pF, and so on.

[0126] During the mutual-capacitance detection period, the signal generator generates a 1 MHz frequency signal to drive the emitter, and the receiver generates a voltage signal through the signal conditioning circuit. The mutual-capacitance signal is sampled, for example, with a value of 20 pF. The mutual-capacitance signal also contains 10 sampling points, with values of 20 pF, 21 pF, 19 pF, 22 pF, 20 pF, and so on.

[0127] The processed judgment signals include self-capacitance signals and mutual-capacitance signals, each signal being composed of a sampling point and a capacitance value thereof;

[0128] The median value of the capacitance values of the self-capacitance signals in M=5 sampling points is taken as a self-capacitance median signal, for example, the sampling values [10 pF, 11 pF, 9 pF, 12 pF, 10 pF], and the median value is 10 pF; similarly, the median value of the mutual-capacitance signals in N=5 sampling points is taken as a mutual-capacitance median signal;

[0129] A second-order IIR low-pass filter with a cutoff frequency of 10 Hz is applied for filtering, and the filtered self-capacitance signal is 10.5 pF, and the filtered mutual-capacitance signal is 20.5 pF;

[0130] The preset reference signals are normalized: the reference self-capacitance signal is 10 pF, and the reference mutual-capacitance signal is 20 pF;

[0131] The preset weight α=0.1 is used to control the reference change amplitude;

[0132] For the self-capacitance signal, the filtered judgment signal is 10.5 pF, the reference signal is 10 pF, and the dynamic reference signal is α×10.5+(1-α)×10=10.05 pF;

[0133] For the mutual-capacitance signal, the similar calculation gives 20.05 pF;

[0134] The filtered judgment signals are normalized:

[0135] The self-capacitance normalized signal =10.5 / 10.05≈1.044;

[0136] The mutual-capacitance normalized signal =20.5 / 20.05≈1.022.

[0137] The preset judgment period is 50 milliseconds, and the amplitude, change rate trend and fluctuation rate of the normalized signals are obtained:

[0138] The amplitude: the self-capacitance signal amplitude is 1.044, and the mutual-capacitance signal amplitude is 1.022;

[0139] The change rate trend: in the judgment period, the instantaneous change rate is calculated every 10 milliseconds, the self-capacitance signal change rate trend is [0.05, 0.06, 0.04, 0.05, 0.06], and the mutual-capacitance signal change rate trend is [0.03, 0.04, 0.02, 0.03, 0.04];

[0140] The fluctuation rate: the preset stable time is 20 milliseconds, the maximum change rate after the amplitude is recorded, the self-capacitance signal fluctuation rate is 0.02, and the mutual-capacitance signal fluctuation rate is 0.01;

[0141] The preset threshold is used to distinguish the conditions:

[0142] The self-capacitance amplitude threshold is 1.0, and the mutual-capacitance amplitude threshold is 1.0;

[0143] The self-capacitance change rate threshold is 0.03, and the mutual-capacitance change rate threshold is 0.02;

[0144] The self-capacitance fluctuation rate threshold is 0.05, and the mutual-capacitance fluctuation rate threshold is 0.05.

[0145] Check the conditions:

[0146] The amplitude condition is met;

[0147] The preset 80% instantaneous change rate is greater than the threshold, all values in the self-capacitance trend are greater than 0.03, and all values in the mutual-capacitance trend are greater than 0.02, which meets the condition;

[0148] The fluctuation rate condition is met;

[0149] Therefore, the distinguishing result is human touch, the preset operation is executed, and the distinguishing result is associated with the judgment parameter and stored in the database;

[0150] The preset optimization period is 24 hours, and when the data update period reaches the optimization period, all samples in the database are extracted, for example, 100 samples, including human touch and interference;

[0151] For two n-dimensional vectors X=(x1,x2,...,x n ) and Y=(y1,y2,...,y n ), the Euclidean distance between X and Y is:

[0152] ;

[0153] Where D represents the Euclidean distance between X and Y;

[0154] The samples are spliced into feature vectors, and the initial cluster centers are selected:

[0155] The human touch center feature vector is (1.04, 1.02, 0.02, 0.01);

[0156] The interference center is (0.8, 0.9, 0.1, 0.08);

[0157] Traverse the samples, for example, a sample feature vector is (1.05, 1.03, 0.03, 0.02), and calculate the Euclidean distance:

[0158] The distance from the human touch center is:

[0159] ;

[0160] Similarly, the distance from the center of interference: ;because Less than This sample belongs to the human touch cluster. Similar to processing all samples, the initial cluster is obtained.

[0161] Iteratively optimize the cluster centers by calculating the average value of the feature vectors in each cluster. The new centers for the human touch cluster are (1.05, 1.03, 0.025, 0.015), and the new centers for the interference cluster are (0.85, 0.92, 0.09, 0.07). Recalculate the sample distances until the rate of change of the centers is less than a termination threshold, such as 0.01, at which point the clustering is complete.

[0162] Take the smallest self-capacity amplitude in the human touch cluster, such as 1.04, and the largest self-capacity amplitude in the interference cluster, such as 0.95. The new self-capacity amplitude threshold is (1.04+0.95) / 2=0.995.

[0163] Similarly, the new mutual capacitance amplitude threshold is (1.02+0.93) / 2=0.975;

[0164] For the rate of change threshold, the effective value of the rate of change trend is calculated: the effective value of the self-capacity of the human touch cluster is 0.05, the effective value of the interference cluster is 0.02, and the new self-capacity rate of change threshold is (0.05+0.02) / 2=0.035;

[0165] For the volatility threshold, the maximum self-capacity volatility of the human touch cluster is set to 0.03, and the minimum self-capacity volatility of the interference cluster is set to 0.06. The new self-capacity volatility threshold is (0.03+0.06) / 2=0.045. The new threshold is used to update the differentiation process and improve accuracy.

[0166] Example 2: Figure 2 As shown, the present invention provides an intelligent control system for preventing accidental triggering of vehicle exterior handles. The system includes an electrode array deployment module, a capacitor signal processing module, an object differentiation and storage module, and a clustering analysis and optimization module.

[0167] The electrode array deployment module is used to deploy an electrode array within the detection area;

[0168] The capacitor signal processing module is used to acquire capacitor signals and process the acquired capacitor signals to obtain a judgment signal.

[0169] The object differentiation and storage module is used to filter the judgment signal to obtain a filtered judgment signal, differentiate the objects to be detected to obtain a differentiation result, and store the differentiation result in the database.

[0170] The clustering analysis optimization module is configured to perform clustering analysis on the differentiation results in the database and optimize the process of differentiation.

[0171] An output end of the electrode array deployment module is connected to an input end of the capacitive signal processing module; an output end of the capacitive signal processing module is connected to an input end of the object differentiation storage module; and an output end of the object differentiation storage module is connected to an input end of the clustering analysis optimization module.

[0172] The electrode array deployment module comprises a planar electrode deployment unit and a point electrode deployment unit.

[0173] The planar electrode deployment unit is configured to deploy the electrode array in a planar manner.

[0174] The point electrode deployment unit is configured to deploy the electrode array in a point manner.

[0175] The capacitive signal processing module comprises a self-capacitance signal acquisition unit and a mutual-capacitance signal acquisition unit.

[0176] The self-capacitance signal acquisition unit is configured to acquire the self-capacitance signal.

[0177] The mutual-capacitance signal acquisition unit is configured to acquire the mutual-capacitance signal.

[0178] The object differentiation storage module comprises a signal filtering unit and an object differentiation storage unit.

[0179] The signal filtering unit is configured to filter the judgment signal to obtain a filtered judgment signal, normalize the filtered judgment signal to obtain a normalized signal, and obtain the amplitude, the change rate and the fluctuation rate of the normalized signal.

[0180] The object differentiation storage unit is configured to preset an amplitude threshold, a change rate threshold and a fluctuation rate threshold, differentiate the to-be-detected object to obtain differentiation results, and store the differentiation results in a database.

[0181] The clustering analysis optimization module comprises a result clustering analysis unit and a differentiation threshold optimization unit.

[0182] The result clustering analysis unit is configured to perform clustering analysis on the differentiation results in the database.

[0183] The differentiation threshold optimization unit is configured to optimize the process of differentiation.

[0184] It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments and that the present application can be implemented in other particular forms without departing from the spirit or essential characteristics of the present application. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference signs in the claims shall be construed as limiting the scope of the claims.

Claims

1. A method for preventing false triggering of an out-of-vehicle handle smart control, characterized in that: The method comprises the following steps: Step S1, deploying an electrode array in a detection area, the electrode array being used for self-capacitance detection and mutual-capacitance detection; Step S2, collecting capacitance signals through self-capacitance detection and mutual-capacitance detection respectively, and processing the collected capacitance signals to obtain a judgment signal; Step S3, filtering the judgment signal to obtain a filtered judgment signal, obtaining an amplitude, a rate trend and a fluctuation rate of the filtered judgment signal, and distinguishing the object to be detected to obtain a distinguishing result, and storing the distinguishing result in a database; Step S4, performing cluster analysis on the distinguishing result in the database to optimize the distinguishing process. The step S1 comprises: The detection area is an external handle of a vehicle; the electrode array is deployed in a planar manner or a point-like manner; In the planar deployment, the electrodes are arranged in 2 rows and N columns on a circuit board in the detection area, and N is an even positive integer; when mutual-capacitance detection is performed, two electrodes on the same side in adjacent two columns are connected to form a transmitting electrode and a receiving electrode respectively, and the transmitting electrode and the receiving electrode are alternately distributed; when self-capacitance detection is performed, each electrode serves as a separate self-capacitance sensor to sense the change of the capacitance to ground; In the point-like deployment, an internal electrode located in a central area and an external electrode surrounding the internal electrode form a group of electrodes; when mutual-capacitance detection is performed, the internal electrode and the external electrode in a group of electrodes serve as a transmitting electrode and a receiving electrode respectively; when self-capacitance detection is performed, the internal electrode and the external electrode in a group of electrodes both serve as self-capacitance sensors; a plurality of groups of electrodes are deployed in the detection area; The step S2 comprises: The judgment signal comprises a self-capacitance signal and a mutual-capacitance signal, and the self-capacitance signal and the mutual-capacitance signal each comprise a plurality of sampling points and capacitance values corresponding to the sampling points; A detection period is preset, and the detection period is divided into a self-capacitance detection period, a mutual-capacitance detection period and a waiting period, the waiting period being used to separate the self-capacitance detection period and the mutual-capacitance detection period; in the self-capacitance detection period, the electrode array performs self-capacitance detection; in the mutual-capacitance detection period, the electrode array performs mutual-capacitance detection; When the self-capacitance detection period is entered, the original self-capacitance signals of the electrodes of the capacitance array are continuously collected, and the average value of the original self-capacitance signals is calculated as an initial self-capacitance value; in the self-capacitance detection period, the electrodes in the electrode array are charged, so that the voltage of the electrodes reaches a preset voltage value; a preset voltage threshold is set, the electrodes are discharged, and the time when the preset voltage value changes to the voltage threshold is recorded as a change time, the current self-capacitance value of the electrode is calculated according to the change time, and the absolute value of the difference between the initial self-capacitance value and the current self-capacitance value is taken as the self-capacitance signal; When the mutual-capacitance detection period is entered, a signal generator generates a signal of a preset frequency, which is used to drive the transmitting electrode to generate an alternating electric field; the receiving electrode is connected to a signal conditioning circuit, which is used to sense the alternating electric field and generate a voltage signal; in the mutual-capacitance detection period, the voltage signal is sampled to obtain a mutual-capacitance signal.

2. The intelligent control method for preventing false triggering of an out-of-vehicle handle according to claim 1, characterized in that: The step S3 comprises: Step S3-1, the filtered judgment signal comprises a filtered self-capacitance signal and a filtered mutual-capacitance signal; Taking the median of the capacitance values of the self-capacitance signals in M sampling points as a self-capacitance median signal, and taking the median of the capacitance values of the mutual-capacitance signals in N sampling points as a mutual-capacitance median signal; performing IIR filtering on the self-capacitance median signal and the mutual-capacitance median signal respectively to obtain a filtered self-capacitance signal and a filtered mutual-capacitance signal; Step S3-2, a preset reference signal is provided, the reference signal includes a reference self-capacitance signal and a reference mutual-capacitance signal; a dynamic reference signal is obtained according to the filtered judgment signal and the reference signal: ； wherein a dynamic reference signal, a filtered decision signal corresponding to the dynamic reference signal, a reference signal corresponding to the dynamic reference signal, a preset weight for controlling a magnitude of the reference variation; According to the dynamic reference signal, a normalized signal is obtained by normalizing the filtered judgment signal: ； wherein represents a normalized signal comprising a normalized self-capacitance signal and a normalized mutual-capacitance signal ; Step S3-3, a preset judgment period is provided, the amplitude and the change rate trend of the normalized signal in the judgment period are obtained, the change rate trend is a set of instantaneous change rates of the normalized signal calculated at a preset interval in the judgment period; a preset stable time is provided, and the maximum change rate of the normalized signal relative to the amplitude in the stable time after the amplitude is reached is recorded as the volatility rate; The amplitude includes a self-capacitance signal amplitude and a mutual-capacitance signal amplitude, the change rate trend includes a self-capacitance signal change rate trend and a mutual-capacitance signal change rate trend, and the volatility rate includes a self-capacitance signal volatility rate and a mutual-capacitance signal volatility rate; A preset amplitude threshold, a change rate threshold and a volatility rate threshold are provided; the amplitude threshold includes a self-capacitance amplitude threshold and a mutual-capacitance amplitude threshold, the change rate threshold includes a self-capacitance change rate threshold and a mutual-capacitance change rate threshold, and the volatility rate threshold includes a self-capacitance volatility rate threshold and a mutual-capacitance volatility rate threshold; The object to be detected includes human touch and interference; If the amplitudes are all greater than the corresponding amplitude thresholds, it is considered that the amplitude condition is met; In the change rate trend, if a preset percentage of the instantaneous change rates are all greater than the corresponding change rate thresholds, it is considered that the change rate condition is met; If the volatility rate is less than the corresponding volatility rate threshold, it is considered that the volatility rate condition is met; When the amplitude condition, the change rate condition and the volatility rate condition are all met, the discrimination result is considered as human touch, and a preset operation is performed; otherwise, the discrimination result is considered as interference, and no operation is performed; The discrimination result and the corresponding judgment parameters are associated and stored in the database, the judgment parameters include the amplitude, the change rate trend and the volatility rate, and the change rate trend is stored in the database in the form of a vector.

3. The intelligent control method for preventing false triggering of an out-of-vehicle handle according to claim 2, characterized in that: The step S4 includes: Step S4-1, a preset optimization period is provided, when the data update period reaches the optimization period, all samples in the database in the optimization period are extracted, the samples include the discrimination result and the corresponding judgment parameters; two groups of judgment parameters with the discrimination result as human touch and interference are selected as initial clustering centers; all samples in the optimization period are traversed, the distances between each sample and the two initial clustering centers are calculated by the Euclidean distance, the samples are attributed to the discrimination result of the initial clustering center with closer distance, and two initial clustering clusters are obtained, including an initial human touch clustering cluster and an initial interference clustering cluster; Step S4-2, average values of the judgment parameters in the two groups of initial clustering clusters are respectively calculated, the average values of the judgment parameters are taken as new clustering centers of the initial clustering clusters, distances between each sample and the two new clustering centers are calculated by using the Euclidean distance, and the samples are attributed to the new clustering center of the division result in which the distance is closer; a preset termination threshold is set, when the change rate of the average values of the corresponding two groups of judgment parameters before and after one attribution is not greater than the termination threshold, it is considered that the clustering is completed, and the human touch clustering cluster and the interference clustering cluster are obtained; Step S4-3, the smallest amplitude in the human touch clustering cluster and the largest amplitude in the interference clustering cluster are taken, the smallest amplitude includes the smallest self-capacitance signal amplitude and the smallest mutual-capacitance signal amplitude, and the largest amplitude includes the largest self-capacitance signal amplitude and the largest mutual-capacitance signal amplitude; The intermediate value of the smallest amplitude and the corresponding largest amplitude is taken as a new amplitude threshold value; Effective values of the change rate trend in the human touch clustering cluster and the change rate trend in the interference clustering cluster are respectively calculated, the effective value is the average value of the instantaneous change rate greater than the corresponding change rate threshold in the change rate trend, and includes a self-capacitance effective value and a mutual-capacitance effective value; The intermediate value of the effective value of the change rate trend in the human touch clustering cluster and the effective value of the change rate trend in the interference clustering cluster is taken as a new change rate threshold value; The largest fluctuation rate in the human touch clustering cluster and the smallest fluctuation rate in the interference clustering cluster are taken, the largest fluctuation rate includes the largest self-capacitance signal fluctuation rate and the largest mutual-capacitance signal fluctuation rate, and the smallest fluctuation rate includes the smallest self-capacitance signal fluctuation rate and the smallest mutual-capacitance signal fluctuation rate; The intermediate value of the largest fluctuation rate and the corresponding smallest fluctuation rate is taken as a new fluctuation rate threshold value.

4. An intelligent control system for preventing false triggering of an exterior handle, applied to the intelligent control method for preventing false triggering of an exterior handle according to any one of claims 1-3, characterized in that: The system comprises an electrode array deployment module, a capacitive signal processing module, an object division storage module and a clustering analysis optimization module; The electrode array deployment module is used for deploying an electrode array in a detection area; The capacitive signal processing module is used for collecting capacitive signals and processing the collected capacitive signals to obtain judgment signals; The object division storage module is used for filtering the judgment signals to obtain filtered judgment signals, dividing a to-be-detected object to obtain a division result, and storing the division result in a database; The clustering analysis optimization module is used for performing clustering analysis on the division result in the database and optimizing the division process; An output end of the electrode array deployment module is connected to an input end of the capacitive signal processing module; an output end of the capacitive signal processing module is connected to an input end of the object division storage module; and an output end of the object division storage module is connected to an input end of the clustering analysis optimization module.

5. The intelligent control system for an out-of-vehicle handle to prevent false triggering according to claim 4, characterized in that: The electrode array deployment module comprises a planar electrode deployment unit and a point electrode deployment unit; The planar electrode deployment unit is used for deploying an electrode array in a planar manner; The point electrode deployment unit is used for deploying an electrode array in a point manner.

6. The intelligent control system for an out-of-vehicle handle to prevent false triggering according to claim 4, characterized in that: The capacitive signal processing module comprises a self-capacitance signal acquisition unit and a mutual-capacitance signal acquisition unit; The self-capacitance signal acquisition unit is used for collecting self-capacitance signals; The mutual capacitance signal acquisition unit is configured to acquire the mutual capacitance signal.

7. The intelligent control system for an out-of-vehicle handle to prevent false triggering according to claim 4, characterized in that: The object distinguishing storage module comprises a signal filtering unit and an object distinguishing storage unit. The signal filtering unit is configured to filter the judgment signal to obtain a filtered judgment signal, and perform normalization processing on the filtered judgment signal to obtain a normalized signal, and obtain an amplitude, a change rate and a fluctuation rate of the normalized signal. The object distinguishing storage unit is configured to preset an amplitude threshold, a change rate threshold and a fluctuation rate threshold, distinguish the to-be-detected object to obtain a distinguishing result, and store the distinguishing result in a database.

8. The intelligent control system for an out-of-vehicle handle to prevent false triggering according to claim 4, characterized in that: The clustering analysis optimization module comprises a result clustering analysis unit and a distinguishing threshold optimization unit. The result clustering analysis unit is configured to perform clustering analysis on the distinguishing result in the database. The distinguishing threshold optimization unit is configured to optimize the distinguishing process.

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