CN116909433A - Touch event detection method, device and system and computer equipment - Google Patents
Touch event detection method, device and system and computer equipment Download PDFInfo
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Abstract
The application relates to a touch event detection method, a touch event detection device, a touch event detection system and computer equipment. The method comprises the following steps: acquiring data to be detected, wherein the data to be detected comprises current detection values of at least two touch detection modules; screening the data to be detected based on preset conditions, and removing abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events; determining a touch reference value based on the target data and a sample set, wherein the sample set comprises touch detection values of each touch detection module when no touch event occurs and the touch detection modules are in different environmental conditions; and determining whether a touch event occurs or not based on the data to be detected and the touch reference value. The touch event detection method can effectively avoid the influence of the rapid change of the environmental condition on the reference value and effectively improve the accuracy of touch event detection.
Description
Technical Field
The present application relates to the field of touch technologies, and in particular, to a method, an apparatus, a system, and a computer device for detecting a touch event.
Background
At present, the capacitive screen is used as a mainstream screen form and is widely applied to the field of mobile terminals such as vehicle-mounted terminals, mobile phones and tablet computers. The capacitive touch keys are widely applied to the scenes such as vehicle-mounted man-machine interaction, intelligent home and the like.
In the conventional art, a capacitive touch detection system adapted to a capacitive screen or a touch panel determines whether a touch event occurs by detecting a change in capacitance. Specifically, in the conventional technology, a reference value is dynamically updated based on an original value obtained by detecting a capacitive screen, and then whether a touch event occurs is determined by comparing the original value with the reference value.
However, to maintain stability of touch event detection, the reference value is typically set at a lower update frequency. Therefore, under the condition that the environmental condition changes rapidly, the update speed of the reference value lags behind the change speed of the environmental condition, so that misjudgment of the touch detection event is caused, and the detection accuracy of the touch event is reduced.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a touch event detection method, apparatus, system, and computer device that can improve the accuracy of touch event detection.
In a first aspect, the present application provides a touch event detection method. The method comprises the following steps:
acquiring data to be detected, wherein the data to be detected comprises current detection values of at least two touch detection modules;
screening the data to be detected based on preset conditions, and removing abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events;
determining a touch reference value based on the target data and a sample set, wherein the sample set comprises touch detection values of each touch detection module when no touch event occurs and the touch detection modules are in different environmental conditions;
and determining whether a touch event occurs or not based on the data to be detected and the touch reference value.
In one embodiment, the determining the touch reference value based on the target data and the sample set includes:
determining a first difference between the target data and a plurality of reference samples, wherein the reference samples comprise touch detection values of the touch detection module when no touch event occurs, and the plurality of reference samples are under the same environmental condition;
and determining the corresponding reference sample as the touch reference value under the condition that the sum of the absolute values of the first difference values is minimum.
In one embodiment, the screening the data to be detected based on the preset condition, removing the abnormal data, and obtaining the target data includes:
determining a second difference value of each current detection value and the reference sample;
and determining that all corresponding current detection values of which the second difference values are smaller than a preset threshold value are the target data.
In one embodiment, the screening the data to be detected based on the preset condition, removing the abnormal data, and obtaining the target data includes:
normalizing the sample set to obtain preset parameters, wherein the preset parameters comprise a preset average value and a preset standard deviation;
normalizing the data to be detected based on the preset parameters to obtain a normalized value to be detected;
and eliminating discrete data in the normalized value to be detected, and determining the average value of the residual data as the target data.
In one embodiment, the determining the touch reference value based on the target data and the sample set includes:
and performing inverse normalization processing on the target data based on the preset parameters to obtain a touch reference value of each touch detection module.
In one embodiment, determining the touch reference value based on the target data and the sample set includes:
Inputting the target data into a reference value generation model, and outputting to obtain the touch reference value, wherein the target data comprises target detection data of each touch detection module and corresponding target weight;
the reference value generation model is obtained through training in the following way:
superposing random interference noise on the sample set to obtain a first training set, and setting a first weight for each sample data in the first training set, wherein the sum of the first weights of the superposed interference noise in the first training set is smaller than the sum of the first weights of the non-superposed interference noise;
and training the first initial model based on the sample set, the first training set and the first weight to obtain a benchmark value generation model.
In one embodiment, screening the data to be detected based on a preset condition, removing abnormal data, and obtaining target data includes:
inputting the data to be detected into a target detection model, and outputting to obtain target data, wherein the target data comprises target detection data of each touch detection module and corresponding target weights;
the target detection model is obtained through training in the following way:
superposing random interference noise on the sample set to obtain a second training set, and setting a second weight for each sample data in the second training set, wherein the sum of the second weights of the superposed interference noise in the second training set is smaller than the sum of the second weights of the non-superposed interference noise;
And training the second initial model based on the second training set and the second weight to obtain a target detection model.
In a second aspect, the application further provides a touch event detection device. The device comprises:
the data acquisition module is used for acquiring data to be detected, wherein the data to be detected comprises current detection values of at least two touch detection modules;
the data processing module is used for screening the data to be detected based on preset conditions, removing abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events;
a reference value generation module for determining a touch reference value based on the target data and a sample set including touch detection values of each of the touch detection modules when no touch event occurs and at different environmental conditions;
and the touch event judging module is used for determining whether a touch event occurs or not based on the data to be detected and the touch reference value.
In a third aspect, the present application further provides a touch event detection system, where the system includes an intelligent surface, at least two touch detection modules, and the touch event detection device according to the second aspect, where the at least two touch detection modules are respectively connected to the intelligent surface and the touch detection device, and the at least two touch detection modules are respectively matched with at least two touch areas of the intelligent surface.
In a fourth aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of any of the touch event detection methods of the first aspect described above when the processor executes the computer program.
According to the touch event detection method, the device, the system and the computer equipment, the data to be detected are obtained, and the data to be detected comprise the current detection values of at least two touch detection modules; screening the data to be detected based on preset conditions, and removing abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events; determining a touch reference value based on the target data and a sample set, wherein the sample set comprises touch detection values of each touch detection module when no touch event occurs and the touch detection modules are in different environmental conditions; and determining whether a touch event occurs or not based on the data to be detected and the touch reference value. According to the touch event detection method, the target data can be obtained after screening the data to be detected based on the preset condition, and the data interference caused by the suspected touch event in the data to be detected is eliminated. And determining a touch reference value based on the target data and a sample set, wherein the sample set comprises touch detection values of each touch detection module when no touch event occurs and the touch detection modules are in different environmental conditions. The touch event detection method can effectively avoid the influence of the rapid change of the environmental condition on the reference value when determining whether the touch event occurs or not, and effectively improve the accuracy of the touch event detection.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is an application environment diagram of a touch event detection method in one embodiment;
FIG. 2 is a flow chart of a touch event detection method in one embodiment;
FIG. 3 is a schematic diagram of a preset environmental condition setting in one embodiment;
FIG. 4 is a schematic diagram of touch detection values of the touch detection module under different environmental conditions in one embodiment;
FIG. 5 is a schematic diagram of a touch detection value smoothing process in one embodiment;
FIG. 6 is a schematic diagram of a smoothed data normalization process in one embodiment;
FIG. 7 is a schematic diagram of a reference value generation model in one embodiment;
FIG. 8 is a schematic diagram of a target detection model structure in one embodiment;
FIG. 9 is a block diagram of a touch event detection device in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Unless defined otherwise, technical or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," "these" and similar terms in this application are not intended to be limiting in number, but may be singular or plural. The terms "comprising," "including," "having," and any variations thereof, as used herein, are intended to encompass non-exclusive inclusion; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (units) is not limited to the list of steps or modules (units), but may include other steps or modules (units) not listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this disclosure are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. Typically, the character "/" indicates that the associated object is an "or" relationship. The terms "first," "second," "third," and the like, as referred to in this disclosure, merely distinguish similar objects and do not represent a particular ordering for objects.
The terms "module," "unit," and the like are used below as a combination of software and/or hardware that can perform a predetermined function. While the means described in the following embodiments are preferably implemented in hardware, implementations of software, or a combination of software and hardware, are also possible and contemplated.
The touch event detection method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The terminal screen 102 is connected to the processor 104, and the data storage system may store data that the processor 104 needs to process. The data storage system may be integrated into the processor 104 or may be provided separately and coupled to the processor 104. The terminal screen 102 obtains data to be detected, and then sends the data to be detected to the processor, where the data to be detected includes current detection values of at least two touch detection modules. The processor 104 screens the data to be detected based on preset conditions, eliminates abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events, determines a touch reference value based on the target data and a sample set, comprises touch detection values of each touch detection module when no touch event occurs and the touch detection modules are under different environmental conditions, and determines whether the touch event occurs based on the data to be detected and the touch reference value. The terminal screen 102 may include, but is not limited to, various vehicle-mounted terminal smart surfaces, personal computer smart surfaces, notebook computer smart surfaces, smart phone smart surfaces, and the like.
In the embodiment of the present application, as shown in fig. 2, a touch event detection method is provided, and an application scenario of the method applied to fig. 1 is illustrated as an example, and the method includes the following steps:
s201: and obtaining data to be detected, wherein the data to be detected comprises current detection values of at least two touch detection modules.
In the embodiment of the application, the touch detection module can be connected with the intelligent surface and used for collecting the signal value based on the intelligent surface, and in other embodiments, the touch detection module can also be integrally arranged in the intelligent surface. The intelligent surface may include at least two touch areas, and the at least two touch detection modules are respectively matched with the at least two touch areas of the intelligent surface. A smart surface is an injection molded surface that adds electronic functionality, and is typically interactive. The smart surface may include a capacitive touch panel and a capacitive interactive screen. For example, as a traditional injection molding product of an automobile interior and exterior trim part, for aesthetic and fashion, a plastic film and plastic are usually adopted for injection molding or bonding together, various exquisite patterns are printed on the plastic film, and electronic layers are added in the film so as to realize the functions of touch, photoelectric display, vibration feedback during touch and the like. This technology of seamlessly integrating information display, intelligent control, atmosphere lighting, etc. functions into a unified surface is collectively referred to as intelligent surface technology. The smart surfacing technology changed simple plastic trim, glass, or other traditional functional products into smart technology products. Along with the rapid development of technology and the continuous maturity of automatic driving technology, the cockpit gradually turns to intelligent cabin, and traditional automobile decoration has been given the new function of "intelligent interaction", such as portable center console, luminous decoration, thermal-insulated preceding windshield of sound insulation, welcome function luggage rack etc.. The automobile decoration is the most important carrier for the future intelligent interaction technology, and brings more comfortable and interesting driving experience for passengers. In the international electronic consumer exhibition in recent years, various car manufacturers widely apply intelligent surface technology on their conceptual vehicles to replace the traditional mechanical keys, and even the whole decoration surface becomes a display or touch interface. At the same time, industry enterprises are also focusing on the research of intelligent materials and intelligent surface technology. Automobile decoration gradually changes from traditional decoration functions to intelligent interactive development directions.
In some embodiments, acquiring the data to be detected may include acquiring capacitance values based on the capacitive smart surface by at least two touch detection modules under the current environmental conditions, and may also include acquiring voltage values based on the resistive smart surface. The application is not limited to the specific representation mode of the current detection value.
S203: screening the data to be detected based on preset conditions, and removing abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events.
In the embodiment of the application, under the condition that the environmental conditions are the same or similar, the current detection value of the touch detection module when the touch event occurs is obviously changed compared with other untouched current detection values. Therefore, in order to more accurately determine the reference value of each touch detection module matched with the current environmental condition, the data to be detected can be screened based on the preset condition, and abnormal data can be removed to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events. It can be understood that the data corresponding to the suspected touch event in the data to be detected is regarded as abnormal data, and after the abnormal data in the data to be detected is removed, the rest data are determined to be target data. The data corresponding to the suspected touch event may be the data corresponding to the touch event, or may be the current detection value collected by the touch detection module under the strong interference, or may be the abnormal detection value collected by the touch detection module fault or the screen fault.
In the embodiment of the present application, screening the data to be detected based on the preset condition may include making a difference between the data to be detected and a touch detection value of the touch detection module when no touch event occurs, and rejecting a corresponding current detection value if the difference is greater than a preset threshold. In other embodiments, the screening the data to be detected based on the preset condition may also include inputting the data to be detected into a target detection model, and outputting to obtain target data, where the target detection model is used for rejecting abnormal data. In other embodiments, the screening the data to be detected based on the preset condition may further include selecting a preset number of larger values from all the current detection values as abnormal data rejection, where the preset number is not greater than the total number of the touch detection modules.
S205: a touch benchmark value is determined based on the target data and a sample set that includes touch detection values for each of the touch detection modules when no touch event has occurred and at different environmental conditions.
In an embodiment of the present application, the sample data in the sample set may include touch detection values of each touch detection module when no touch event occurs and the touch detection modules are under different environmental conditions, where the environmental conditions may include temperature, humidity, electromagnetic interference, and the like. The sample data in the sample set may include single data or a data sequence, for example, one of the single data may be a touch detection value of the touch detection module a under a certain temperature condition when no touch event occurs, or the like; one of the data sequences may be a sequence of touch detection values of all the touch detection modules under certain temperature and humidity conditions when no touch event occurs, and the like.
In some embodiments, the sample set may be obtained in the following manner. The touch detection module is arranged in the constant temperature and humidity test box, the environmental condition change of the test box is controlled based on the preset environmental condition, such as any one of the environmental condition changes of set temperature, humidity and the like, and the touch detection value of the touch detection module under different environmental conditions is collected as initial data. In one embodiment, the preset environmental conditions may be as shown in FIG. 3, with temperature and humidity cycling over time. In a specific embodiment, taking the total number of touch detection modules 12 as an example, initial data of 12 touch detection modules is shown in fig. 4. In fig. 4, the vertical axis represents a touch detection value, and the horizontal axis represents time (seconds), and since the touch detection module is disposed in the constant temperature and humidity test chamber, the time change in fig. 4 may represent a temperature change. As can be seen from fig. 4, all touch detection values decrease with increasing temperature. Although they have similar variation tendencies, their variation magnitudes are different. For example, the trend of the touch detection of the number 5 touch detection module along with the change of temperature is not obvious, while the original value of the number 6 touch detection module along with the change of the temperature is as high as more than 400. In some embodiments, smoothing may be performed on sample data in the sample set, and the initial data may be smoothed to obtain smoothed data by sliding a preset time window along a time axis, and obtaining an average value of the data in the preset time window. When determining the average data, the number of data is reduced accordingly, which is determined based on the sliding characteristics of the preset time window. For example, if the time sequence corresponds to 20 data, the preset time window is set to 10, the preset time window is averaged between 1 and 10 for the first time, and is averaged between 2 and 11 for the second time … … until the last time is averaged between 11 and 20, so that 11 data can be obtained. By intercepting part of the data in the initial data, the data can be matched with the average data quantity.
In a specific embodiment, as shown in fig. 5, the smoothed data obtained after smoothing the initial data of the 12 touch detection modules is regarded as the real influence of the environmental condition on the touch detection value of the touch detection module at a certain moment in the smoothed data.
S207: and determining whether a touch event occurs or not based on the data to be detected and the touch reference value.
In the embodiment of the application, whether a touch event occurs can be determined based on whether the difference value between the data to be detected and the touch reference value is larger than the preset judging threshold value. In other embodiments, the data to be detected and the touch reference value may be input into the touch detection model, and the determination result of whether the touch event occurs may be output through the touch detection model. Of course, reference may be made to other specific methods of determining whether a touch event occurs based on the data to be detected and the touch reference value in the prior art, which the present application is not limited to.
According to the touch event detection method, the target data can be obtained after screening the data to be detected based on the preset condition, and the data interference caused by the suspected touch event in the data to be detected is eliminated. And determining a touch reference value based on the target data and a sample set, wherein the sample set comprises touch detection values of each touch detection module when no touch event occurs and the touch detection modules are in different environmental conditions. The touch event detection method can effectively avoid the influence of the rapid change of the environmental condition on the reference value when determining whether the touch event occurs or not, and effectively improve the accuracy of the touch event detection.
On the other hand, the current reference value in the conventional art is determined depending on the historical reference value. Thus, in the device start-up phase, in the absence of the historical reference value, it will be difficult to accurately determine the current reference value, resulting in a failure or erroneous judgment of touch detection in a short time after start-up. However, based on the touch detection algorithm in the application, the touch event can still be accurately detected under the condition that the history reference value is not needed, and the touch event detection accuracy is higher in the equipment starting stage.
The steps of screening the data to be detected to obtain target data and determining the touch reference value are described below through the embodiment of the application. In some embodiments, the determining a touch reference value based on the target data and the sample set includes:
s301: determining a first difference between the target data and a plurality of reference samples, wherein the reference samples comprise touch detection values of the touch detection module when no touch event occurs, and the plurality of reference samples are under the same environmental condition.
S303: and determining the corresponding reference sample as the touch reference value under the condition that the sum of the absolute values of the first difference values is minimum.
In the embodiment of the present application, the reference samples are subsets of the sample set, for example, the plurality of reference samples may include a detection value sequence formed by touch detection values of each touch detection module when no touch event occurs under the conditions of the first temperature and the second temperature … … nth temperature, and if the total number of the touch detection modules is 12, the detection value sequence at each temperature includes 12 touch detection values. Correspondingly, determining the first difference between the target data and the plurality of reference samples may include determining a difference sequence formed by the difference between the target data of each touch detection module and the reference samples under the nth temperature conditions of the first temperature and the second temperature … …, if the total number of the touch detection modules is 12, the difference sequence at each temperature includes 12 differences, and it is understood that the removed abnormal data may be subjected to zero setting processing when subjected to difference making, and the obtained difference is the difference between 0 and the reference samples. In the embodiment of the present application, determining the first difference between the target data and the plurality of reference samples may include subtracting the target data from the plurality of reference samples one by one to determine the first difference, or may include subtracting the target data from the plurality of reference samples simultaneously to determine the first difference.
In the embodiment of the present application, in the case that the sum of the absolute values of the first differences is minimum, determining that the corresponding reference sample is the touch reference value may include determining the sum of the absolute values of the first differences under each environmental condition, determining the minimum value among the sums of the absolute values of the plurality of environmental conditions, and taking the reference sample under the environmental condition as the touch reference value. It can be understood that, in the target data after the abnormal data is removed, all the current detection values can be considered as detection values when the current environmental condition is not touched, and the smaller the sum of the absolute values of the first differences under the certain environmental condition is, the higher the similarity degree between the target data and the touch detection values of the environmental condition in the sample set, namely, the closest environmental condition to the current environmental condition is. Based on this, the corresponding reference sample with the smallest sum of absolute values in the sample set can be determined as the touch reference value under the current environmental condition.
In some embodiments, the screening the data to be detected based on the preset condition, removing the abnormal data, and obtaining the target data includes:
s401: a second difference is determined for each current detection value and the reference sample.
S403: and determining that all corresponding current detection values of which the second difference values are smaller than a preset threshold value are the target data.
In the embodiment of the application, a second difference value of each current detection value and a reference sample is determined, if the second difference value is larger than a preset threshold value, the data corresponding to the suspected touch event of the current detection value is considered, the current detection value is determined to be abnormal data and is removed, the rest current detection values in the data to be detected are taken as target data, namely, all corresponding current detection values with the second difference value smaller than the preset threshold value are determined to be the target data.
In one particular embodiment, the target data may also be determined and the touch reference value may also be determined by the following steps. If the total number of the touch detection modules is N and the touch detection modules are numbered 1-N in sequence, a preset threshold value is set to be M, a number threshold value is set to be K, and K is smaller than N-1. The data to be detected is a current detection value sequence of numbers 1-N, and the plurality of reference samples comprise touch detection values of numbers 1-N under a plurality of environmental conditions when no touch event occurs. Subtracting the data to be detected from a plurality of reference samples one by one to obtain a plurality of difference sequences, wherein the difference sequences comprise differences with numbers 1-N. If any difference value in a certain difference value sequence is smaller than a negative preset threshold value, an abnormal sample exists in the sample set, and the difference value sequence is removed. Judging the difference value number of the difference value sequences, wherein the difference value number is larger than a preset threshold value, if the difference value number is larger than a number threshold value K, the fact that most suspected touch detection modules in the data to be detected are touched is indicated, the similarity value is difficult to match in a sample set, then a reference value is determined, and the touch detection flow is ended. If the difference value greater than the preset threshold value M exists in the difference value sequence, the fact that the corresponding touch detection module is suspected of generating a touch event is indicated, and the difference value is set to be zero. And taking the sum of all the absolute values of the differences in each difference sequence, determining the difference sequence when the sum of the absolute values is minimum, and taking the reference sample corresponding to the difference sequence as a touch reference value.
In the embodiment of the present application, the screening the data to be detected based on the preset condition, removing the abnormal data, and obtaining the target data includes:
s501: and carrying out normalization processing on the sample set to obtain preset parameters, wherein the preset parameters comprise a preset average value and a preset standard deviation.
S503: and carrying out normalization processing on the data to be detected based on the preset parameters to obtain a normalization value to be detected.
S505: and eliminating discrete data in the normalized value to be detected, and determining the average value of the residual data as the target data.
In the embodiment of the application, the normalization processing of the sample set may include taking an average value of touch detection values of each touch detection module in the sample set when no touch event occurs as a preset average value, subtracting the preset average value from the touch detection values, and dividing the touch detection values by a preset standard deviation to obtain normalized data. The preset standard deviation may refer to a method for determining the standard deviation in the prior art, which is not described herein. In one embodiment, normalized data normalized to the data shown in FIG. 5 is shown in FIG. 6. As can be seen from fig. 6, the different touch detection modules have almost the same trend of change with temperature change.
In the embodiment of the application, based on preset parameters obtained by normalizing the sample set, normalizing the data to be detected to obtain the normalized value to be detected. Removing the discrete data in the normalized value to be detected and determining the average value of the residual data as the target data may include determining the discrete data in the normalized value to be detected and removing the discrete data by a RANSAC regression algorithm and determining the average value of the residual data as the target data; the method can also comprise the step of directly calculating a normalization value to be detected after the influence of outlier data is reduced based on a Huber regression algorithm; and selecting a preset number of larger values from all the current detection data as abnormal data to be rejected, and determining the average value of the rest data as the target data, wherein the preset number is not greater than the total number of the touch detection modules.
In some embodiments, the determining a touch reference value based on the target data and the sample set includes:
s601: and performing inverse normalization processing on the target data based on the preset parameters to obtain a touch reference value of each touch detection module.
In the embodiment of the application, performing inverse normalization processing on the target data based on the preset parameters may include multiplying each current detection value in the target data by a preset standard deviation and adding a preset average value to obtain a touch reference value of each touch detection module.
In the embodiment of the application, the touch reference value and the target data can be determined based on the neural network. In some embodiments, determining the touch reference value based on the target data and the sample set includes:
s701: and inputting the target data into a reference value generation model, and outputting to obtain the touch reference value, wherein the target data comprises target detection data of each touch detection module and corresponding target weight.
S703: the reference value generation model is obtained through training in the following way:
and superposing random interference noise on the sample set to obtain a first training set, and setting a first weight for each sample data in the first training set, wherein the sum of the first weights of the superposed interference noise in the first training set is smaller than the sum of the first weights of the non-superposed interference noise.
S705: and training the first initial model based on the sample set, the first training set and the first weight to obtain a benchmark value generation model.
In the embodiment of the application, the target data may include target detection data of each touch detection module and corresponding target weights, and the target detection data and the corresponding target weights may represent target data obtained by screening the data to be detected and removing abnormal data. For example, if the total number of the touch detection modules is 12, and the data to be detected is the current detection value of numbers 1 to 12, the corresponding target weight of the abnormal data in the 12 current detection values may be set to 0 or a lower value, and the corresponding target weight of the non-abnormal data may be set to a higher value. In some embodiments, all target weights are non-negative and add to 1, and at least two of the target weights are non-0.
In the embodiment of the application, a sample set is defined as a training sample setI t ={i t1 ,i t2 ,…,i tk }. The training sample set may be processed in the following mannerI t Random interference noise is superimposed. Determining touch detection values of all touch detection modules in the same environmental condition in the sample set as a detection value sequencei tk Randomly selecting r touch detection values from each detection value sequence, and overlapping random noise amplitude values to obtain an overlapped detection value sequencea tk Wherein r is a random positive integer between 0 and the total number of touch detection modules. In some embodiments, the random noise amplitude may be any value between 200-1000 for simulating a touch detection value when a touch event occurs. A first training set can be obtained after the random interference noise is superimposedA t ={a t1 ,a t2 , …,a tk }. Setting a first weight for each sample data in the first training set may include, at each superimposed sequence of detection valuesa tk Respectively, weights are set for each element, it can be understood that due to the randomness of superposition, the sequence of the detection values after superpositiona tk The touch detection values with the random noise amplitude being overlapped may be included, the touch detection values without the random noise amplitude being overlapped may be included, different first weights are respectively set for the touch detection values, and the sum of the first weights of the overlapped interference noise is smaller than the sum of the first weights of the non-overlapped interference noise.
In the embodiment of the application, training the first initial model based on the sample set, the first training set and the first weight may include inputting the first training set and the first weight into the first initial model, and outputting to obtain the predicted touch reference value. And comparing the predicted touch reference value with a corresponding sample in the training sample set, and adjusting parameters of the first initial model based on the comparison result until the comparison result meets the preset requirement. It can be understood that comparing the predicted touch reference value with the corresponding sample in the training sample set and adjusting the parameters, the corresponding sample can be used as a training target, and the output of the reference value approaches the training target by adjusting the parameters until the output of the reference value generation model meets the preset requirement. The loss function of the first initial model may use a mean square error function, and the training process of the first initial model may be performed based on an Adam optimizer. In one specific embodiment, the total number of touch detection modules is 12, and the reference value generation model is shown in fig. 7.
In the embodiment of the present application, screening the data to be detected based on preset conditions, removing abnormal data, and obtaining target data includes:
S801: and inputting the data to be detected into a target detection model, and outputting to obtain the target data, wherein the target data comprises target detection data of each touch detection module and corresponding target weight.
S803: the target detection model is obtained through training in the following way:
and superposing random interference noise on the sample set to obtain a second training set, and setting a second weight for each sample data in the second training set, wherein the sum of the second weights of the superposed interference noise in the second training set is smaller than the sum of the second weights of the non-superposed interference noise.
S805: and training the second initial model based on the second training set and the second weight to obtain a target detection model.
In the embodiment of the present application, the step of overlapping random interference noise with the sample set to obtain the second training set may refer to the step of overlapping random interference noise with the sample set to obtain the first training set in the above steps, which is not described herein again. In some embodiments, training the second initial model based on the second training set and the second weight, where obtaining the target detection model may include inputting the second training set into the second initial model, outputting to obtain the predicted target detection data and the corresponding predicted target weight, comparing the predicted target detection data and the corresponding predicted target weight with the corresponding sample in the sample training set and the second weight, and adjusting parameters of the second initial model based on the comparison result until the comparison result meets a preset requirement. The loss function of the second initial model can use a binary cross entropy loss function, and the training process of the first initial model can be performed based on an Adam optimizer.
The training process of the object detection model is described below with reference to fig. 8. In a specific embodiment, the total number of the touch detection modules is 12, the target detection model is shown in fig. 8, and valid or invalid labels can be set correspondingly by overlapping detection value sequences after random interference noise is overlapped on each of the second training sets, if the ratio of the number of detection values of the overlapped random interference noise to the number of detection values of the non-overlapped random interference noise in the detection value sequences is greater than a preset ratio, most of detection values in the detection value sequences can be considered to be detection values when the detection values are touched, and the labels of the detection value sequences after the overlapped random interference noise are set to be invalid labels; otherwise, the label of the detection value sequence with the ratio not larger than the preset ratio after the random interference noise is overlapped is an effective label. And a second training set provided with valid or invalid labels is used for training an initial model, sample data to be detected is input into a target detection model, the target detection data is output after invalid data are filtered, and corresponding valid verification marks and target weights are output. The valid verification mark is used for indicating a prediction result of whether the sample data to be detected is valid or not, which is determined after the target detection model predicts, the prediction result is compared with a valid label and an invalid label of the sample data to be detected, and if the prediction result is inconsistent with the valid label and the invalid label, the parameters of the target detection model are adjusted until the prediction result is consistent with the set valid label or the set invalid label. Sigmoid is a mapping function in fig. 8 for mapping the output of the linear layer to the (0, 1) interval. The linear layer (12, 1) represents that the linear layer has 12 pieces of input data and 1 piece of output data, and the output data of the linear layer (12, 1) is mapped to the (0, 1) interval by a sigmoid function and then used as a valid verification mark, so that the probability that the target detection data is valid can be represented. The linear layer (12, 12) represents that the linear layer has 12 input data and 12 output data, and the 12 output data of the linear layer (12, 12) are mapped to the (0, 1) interval through a sigmoid function respectively and then used as target weights, so that the probability that each touch detection value corresponding to 12 touch detection modules in the target detection data is a detection value in a non-touch state can be represented. By predicting the validity of the sample data to be detected, the target detection data output by the target detection model can be valid data. In addition, the valid verification flag is used as a flag for whether the target detection data is valid or not for the training stage of the target detection model, so that the valid verification flag output of the target detection model can be canceled after the training is completed. That is, in the application stage of the target detection model, after the data to be detected is input into the target detection model, only the target detection data and the corresponding target weight may be output, or the target detection data and the corresponding valid verification flag and the target weight may be output.
It should be noted that, the "abnormal data" and the "invalid data" described in the present application should be distinguished, where the abnormal data includes data corresponding to a suspected touch event, and the invalid data includes a detection value sequence in which most of detection values are detection values when touched in a training process of the target detection model.
In a specific embodiment, the root mean square error is determined based on the input data and the output data of steps S301-S303, S601, and S701-S705, respectively, and the root mean square errors are 4.11, 16.28, and 9.31, respectively, and the parameter amounts are 768, 24, and 586, respectively, which are all smaller than or close to the measurement errors of the initial data. The smaller the root mean square error, the higher the detection accuracy, and the higher the accuracy of determining whether a touch event occurs based on the method for determining the reference value in the above three embodiments can be seen.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a touch event detection device for realizing the touch event detection method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the touch event detection device or devices provided below may be referred to the limitation of the touch event detection method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 9, there is provided a touch event detection apparatus 900, comprising:
the data acquisition module 901 is configured to acquire data to be detected, where the data to be detected includes current detection values of at least two touch detection modules;
the data processing module 902 is configured to screen the data to be detected based on a preset condition, and reject abnormal data to obtain target data, where the abnormal data includes data corresponding to a suspected touch event;
a reference value generation module 903, configured to determine a touch reference value based on the target data and a sample set, where the sample set includes a touch detection value of each of the touch detection modules when no touch event occurs and under different environmental conditions;
And the touch event judging module 904 is used for determining whether a touch event occurs or not based on the data to be detected and the touch reference value.
The various modules in the touch event detection apparatus 900 described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a touch event detection system is provided, the system includes a smart surface, at least two touch detection modules, and the touch event detection device 900 according to the above embodiment, where the at least two touch detection modules are respectively connected to the smart surface and the touch detection device, and the at least two touch detection modules are respectively matched to at least two touch areas of the smart surface.
In one embodiment, a computer device is provided, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 10. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a touch event detection method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the touch event detection method of any of the embodiments described above when the computer program is executed.
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 of the touch event detection method of any of the embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the touch event detection method of any of the embodiments described above.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as Static Random access memory (Static Random access memory AccessMemory, SRAM) or dynamic Random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method of touch event detection, the method comprising:
acquiring data to be detected, wherein the data to be detected comprises current detection values of at least two touch detection modules;
screening the data to be detected based on preset conditions, and removing abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events;
Determining a touch reference value based on the target data and a sample set, wherein the sample set comprises touch detection values of each touch detection module when no touch event occurs and the touch detection modules are in different environmental conditions;
and determining whether a touch event occurs or not based on the data to be detected and the touch reference value.
2. The touch event detection method according to claim 1, wherein the determining a touch reference value based on the target data and a sample set includes:
determining a first difference between the target data and a plurality of reference samples, wherein the reference samples comprise touch detection values of the touch detection module when no touch event occurs, and the plurality of reference samples are under the same environmental condition;
and determining the corresponding reference sample as the touch reference value under the condition that the sum of the absolute values of the first difference values is minimum.
3. The method for detecting a touch event according to claim 2, wherein the screening the data to be detected based on the preset condition, removing abnormal data, and obtaining target data includes:
determining a second difference value of each current detection value and the reference sample;
and determining that all corresponding current detection values of which the second difference values are smaller than a preset threshold value are the target data.
4. The method for detecting a touch event according to claim 1, wherein the screening the data to be detected based on the preset condition, removing abnormal data, and obtaining target data includes:
normalizing the sample set to obtain preset parameters, wherein the preset parameters comprise a preset average value and a preset standard deviation;
normalizing the data to be detected based on the preset parameters to obtain a normalized value to be detected;
and eliminating discrete data in the normalized value to be detected, and determining the average value of the residual data as the target data.
5. The touch event detection method of claim 4, wherein the determining a touch reference value based on the target data and a sample set comprises:
and performing inverse normalization processing on the target data based on the preset parameters to obtain a touch reference value of each touch detection module.
6. The touch event detection method of claim 1, wherein determining a touch reference value based on the target data and a sample set comprises:
inputting the target data into a reference value generation model, and outputting to obtain the touch reference value, wherein the target data comprises target detection data of each touch detection module and corresponding target weight;
The reference value generation model is obtained through training in the following way:
superposing random interference noise on the sample set to obtain a first training set, and setting a first weight for each sample data in the first training set, wherein the sum of the first weights of the superposed interference noise in the first training set is smaller than the sum of the first weights of the non-superposed interference noise;
and training the first initial model based on the sample set, the first training set and the first weight to obtain a benchmark value generation model.
7. The method for detecting a touch event according to claim 1, wherein the screening the data to be detected based on the preset condition, removing the abnormal data, and obtaining the target data comprises:
inputting the data to be detected into a target detection model, and outputting to obtain target data, wherein the target data comprises target detection data of each touch detection module and corresponding target weights;
the target detection model is obtained through training in the following way:
superposing random interference noise on the sample set to obtain a second training set, and setting a second weight for each sample data in the second training set, wherein the sum of the second weights of the superposed interference noise in the second training set is smaller than the sum of the second weights of the non-superposed interference noise;
And training the second initial model based on the second training set and the second weight to obtain a target detection model.
8. A touch event detection device, the device comprising:
the data acquisition module is used for acquiring data to be detected, wherein the data to be detected comprises current detection values of at least two touch detection modules;
the data processing module is used for screening the data to be detected based on preset conditions, removing abnormal data to obtain target data, wherein the abnormal data comprises data corresponding to suspected touch events;
a reference value generation module for determining a touch reference value based on the target data and a sample set including touch detection values of each of the touch detection modules when no touch event occurs and at different environmental conditions;
and the touch event judging module is used for determining whether a touch event occurs or not based on the data to be detected and the touch reference value.
9. A touch event detection system comprising a smart surface, at least two touch detection modules, and a touch event detection device as recited in claim 8, wherein the at least two touch detection modules are respectively coupled to the smart surface and the touch detection device, and wherein the at least two touch detection modules are respectively matched to at least two touch areas of the smart surface.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
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