Touch control ghost hand signal processing method, device and equipment, medium and vehicle
By using a touch recognition model in the vehicle system to identify and filter ghost hand signals, the problem of accidental touches on the vehicle screen has been solved, improving driving safety and user experience.
Patent Information
- Application Number
- CN202410538384.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-30
- Publication Date
- 2025-10-31
AI Technical Summary
The complex electromagnetic compatibility of in-vehicle screens leads to frequent ghost hand phenomena, resulting in a high risk of accidental touches, affecting user experience and potentially endangering driving safety.
By using a pre-set touch recognition model, the raw signal data uploaded by the touch chip is identified and judged, and ghost hand signals are identified and filtered. The system-on-a-chip is used to realize the judgment and filtering of signal types.
It effectively reduces accidental touches, improves driving safety, and enhances user experience.
Smart Images
Figure CN120872172A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus, device, medium, and vehicle for processing touch-sensitive ghost hand signals. Background Technology
[0002] In recent years, in-vehicle screens have become increasingly larger. Due to the complex electromagnetic compatibility (EMC) environment inside vehicles, static electricity issues frequently occur, leading to a certain probability of accidental touches, which we call "ghost touches." Ghost touches occurring on the central control screen can cause accidental door locking, trunk opening, app launches, and volume adjustments, severely impacting user experience and potentially jeopardizing driving safety in severe cases. Summary of the Invention
[0003] To address the aforementioned technical problems, this disclosure provides a method, apparatus, device, medium, and vehicle for processing touch-sensitive ghost hand signals.
[0004] In a first aspect, this disclosure provides a method for processing touch-sensitive ghost hand signals, including:
[0005] Receive raw signal data uploaded by the touch chip;
[0006] The original signal data is identified and judged by a pre-set touch recognition model to obtain the signal type of the original signal data.
[0007] If the signal type of the original signal data is a ghost hand signal, then the original signal data is filtered.
[0008] Secondly, this disclosure provides a touch-sensitive ghost hand signal processing device, comprising:
[0009] The data receiving module is used to receive the raw signal data uploaded by the touch chip;
[0010] The signal recognition module is used to perform signal recognition and judgment on the original signal data through a pre-set touch recognition model to obtain the signal type of the original signal data;
[0011] The data filtering module is used to filter the original signal data when the signal type of the original signal data is a ghost hand signal.
[0012] Thirdly, this disclosure provides a touch-sensitive ghost hand signal processing device, including:
[0013] processor;
[0014] Memory, used to store executable instructions;
[0015] The processor is used to read executable instructions from memory and execute the executable instructions to implement the touch ghost hand signal processing method of the first aspect.
[0016] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the touch ghost hand signal processing method of the first aspect.
[0017] Fifthly, this disclosure provides a vehicle including the touch-sensitive ghost hand signal processing device described above.
[0018] The technical solution provided in this disclosure has the following advantages compared with the prior art:
[0019] The touch-sensitive ghost hand signal processing method, apparatus, device, medium, and vehicle disclosed herein can receive raw signal data uploaded by a touch chip, then perform signal recognition and judgment on the raw signal data through a pre-set touch recognition model to obtain the signal type of the raw signal data, and finally filter the raw signal data if the signal type of the raw signal data is a ghost hand signal. Thus, the raw signal data can be directly identified and judged by the system-on-a-chip, and raw signal data with the signal type of ghost hand signal can be filtered, thereby reducing accidental touches and improving driving safety. Attached Figure Description
[0020] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.
[0021] Figure 1 A flowchart illustrating a touch-sensitive ghost hand signal processing method provided in an embodiment of this disclosure;
[0022] Figure 2 A flowchart illustrating another touch-sensitive ghost hand signal processing method provided in this embodiment of the present disclosure;
[0023] Figure 3 This is a schematic diagram of the structure of a touch-sensitive ghost hand signal processing device provided in an embodiment of the present disclosure;
[0024] Figure 4 This is a schematic diagram of the structure of a touch-sensitive ghost hand signal processing device provided in an embodiment of this disclosure. Detailed Implementation
[0025] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.
[0026] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.
[0027] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.
[0028] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.
[0029] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".
[0030] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
[0031] To address the aforementioned problems, this disclosure provides a method, apparatus, device, medium, and vehicle for processing touch-sensitive ghost hand signals. The following will first combine... Figures 1 to 2 The touch-sensitive ghost hand signal processing method provided in the embodiments of this disclosure will be described in detail.
[0032] Figure 1 A schematic flowchart of a touch-sensitive ghost hand signal processing method provided in an embodiment of this disclosure is shown.
[0033] In this embodiment of the disclosure, the touch-sensitive ghost hand signal processing method can be executed by an electronic device. Specifically, the electronic device may include, but is not limited to, mobile terminals such as computer devices, mobile phones, in-vehicle devices, vehicle controllers, tablet computers, and wearable devices.
[0034] like Figure 1 As shown, the touch ghost hand signal processing method may include the following steps.
[0035] S110: Receives raw signal data uploaded by the touch chip.
[0036] In this embodiment of the disclosure, the electronic device can receive raw signal data uploaded by the touch chip.
[0037] Alternatively, the raw signal data can be unprocessed signal data received by the electronic device.
[0038] Alternatively, a touch sensor can be used to collect data on user actions on the touch screen.
[0039] Alternatively, the target screen can be an in-vehicle screen in the vehicle.
[0040] Alternatively, a touch key IC is a circuit module that integrates various electronic components on a silicon substrate to achieve a specific function.
[0041] Alternatively, a system-on-chip (SOC) is an integrated circuit with a specific purpose.
[0042] Specifically, electronic devices can be equipped with in-vehicle screens, where users can perform various operations. Touch key ICs can collect raw signal data from user operations via touch sensors and upload this raw signal data to the electronic device. The electronic device can then receive the raw signal data uploaded by the touch key IC via a system-on-a-chip (SoC).
[0043] S120. The original signal data is identified and judged by a pre-set touch recognition model to obtain the signal type of the original signal data.
[0044] In this embodiment of the disclosure, the electronic device can use a pre-set touch recognition model to perform signal recognition and judgment on the original signal data to obtain the signal type of the original signal data.
[0045] Optionally, the pre-set touch recognition model can be a pre-trained model for recognizing touch signals. This touch recognition model can be trained using large-scale artificial intelligence (AI) models or other available models through collected data, or it can utilize existing models such as Convolutional Neural Networks (CNNs) or LeNets; no limitation is made here.
[0046] Optionally, the signal identification and judgment can be used to determine the signal type of the signal data.
[0047] Optionally, the signal type can be used to characterize the type to which the signal belongs.
[0048] Specifically, after acquiring the raw signal data, the electronic device can use a touch recognition model pre-set in the system-on-a-chip (SOC) to identify and judge the signal type of the raw signal data.
[0049] S130. If the signal type of the original signal data is a ghost hand signal, filter the original signal data.
[0050] In this embodiment of the disclosure, the electronic device can filter the original signal data if the signal type of the original signal data is a ghost hand signal.
[0051] Optionally, the ghost hand signal can be a signal used to characterize a signal as a false touch.
[0052] Specifically, after obtaining the signal type of the original signal data, if the signal type of the original signal data is a ghost hand signal, which is a signal of accidental touch, the electronic device can filter the original signal data.
[0053] Therefore, in this embodiment of the present disclosure, the system can receive raw signal data uploaded by the touch chip, then perform signal recognition and judgment on the raw signal data through a pre-set touch recognition model to obtain the signal type of the raw signal data, and finally filter the raw signal data if the signal type of the raw signal data is a ghost hand signal. Thus, the system-on-a-chip can directly perform signal recognition and judgment on the raw signal data and filter the raw signal data with the signal type of ghost hand signal, thereby reducing accidental touches and improving driving safety.
[0054] Optionally, S120 may specifically include: inputting the original signal data into the pre-set touch recognition model, comparing and recognizing the original signal data according to the pre-established ghost hand database through the touch recognition model, and determining the signal type of the original signal data, wherein the pre-established ghost hand database is used to store ghost hand data features corresponding to multiple ghost hand signals.
[0055] In this embodiment of the disclosure, the electronic device can input the original signal data into the pre-set touch recognition model, and the touch recognition model compares and identifies the original signal data according to the pre-established ghost hand database to determine the signal type of the original signal data.
[0056] Optionally, the ghost hand database can be a pre-built database used to store different ghost hand data characteristics.
[0057] Optionally, the ghost hand data features can be features used to characterize the attributes of the ghost hand.
[0058] Specifically, after obtaining the original signal data, the electronic device can perform signal recognition and judgment on the original signal data through the touch recognition model pre-set in the system-on-a-chip. That is, the original signal data is input into the pre-set touch recognition model, and the original signal data is compared and identified according to the pre-established ghost hand database to determine whether the original signal data is the same as the ghost hand data stored in the ghost hand database, thereby determining the signal type of the original signal data.
[0059] Optionally, the touch recognition model compares and identifies the original signal data according to a pre-established database to determine the signal type of the original signal data, including: comparing the data features of the original signal data with the ghost hand data features corresponding to multiple ghost hand signals stored in the ghost hand database, wherein the data features include at least one of operation habit features, click frequency features, signal quantity features, and swipe speed features; if the data features of the original signal data are the same as the ghost hand data features corresponding to the ghost hand signal, the signal type of the original signal data is determined to be the ghost hand signal; if the data features of the original signal data are different from the ghost hand data features corresponding to the ghost hand signal, the signal type of the original signal data is determined to be the normal signal.
[0060] In this embodiment of the disclosure, the electronic device can compare the data features of the original signal data with the data features of the ghost hand in the ghost hand database using the touch recognition model.
[0061] Optionally, data features can be various attributes of the data. These data features may include at least one of the following: operation habit features, click frequency features, semaphore features, and swipe speed features.
[0062] Optionally, the ghost hand database can be a pre-established database for storing ghost hand data features corresponding to multiple ghost hand signals.
[0063] Optionally, the ghost hand data features can be features used to characterize ghost hand attributes. For example, the swipe speed feature, which indicates a swipe speed that is too fast or too slow, and the click frequency feature, which indicates an excessive number of clicks, are all abnormal features of ghost hand data.
[0064] Specifically, after obtaining the raw signal data, the electronic device can input the raw signal data into a pre-set touch recognition model. The touch recognition model can then compare the data features of the raw signal data with the ghost hand data features in the ghost hand database.
[0065] In some embodiments of this disclosure, if the data characteristics of the original signal data are the same as the data characteristics of the ghost hand signal, the signal type of the original signal data is determined to be the ghost hand signal.
[0066] Specifically, after comparing the data characteristics of the original signal data with the ghost hand data characteristics in the ghost hand database, if the data characteristics are the same as the ghost hand data characteristics, the electronic device can determine that the signal type of the original signal data is the ghost hand signal.
[0067] In other embodiments of this disclosure, if the data characteristics of the original signal data are different from the data characteristics of the ghost hand signal, the signal type of the original signal data is determined to be the normal signal.
[0068] Specifically, after comparing the data characteristics of the original signal data with the data characteristics of the ghost hand in the ghost hand database, if the data characteristics are different from the ghost hand data characteristics, the electronic device can determine that the signal type of the original signal data is the normal signal.
[0069] Optionally, prior to S120, the touch ghost hand signal processing method may further include: acquiring training data, wherein the training data is composed of collected operation signal data of testers and corresponding operation signal types, the operation signal data including operation data features, the operation data features including at least one of operation habit features, click frequency features, signal quantity features and sliding speed features; dividing the training data into multiple training sample sets according to the operation data features, and using the training sample sets to train the touch recognition model to be trained to obtain a preliminary touch recognition model; and training the preliminary touch recognition model using the ghost hand database to obtain the touch recognition model, the touch recognition model being used to identify the signal type of the original signal data.
[0070] In this embodiment of the disclosure, the electronic device can acquire training data.
[0071] Optionally, the training data can be composed of the operation signal data collected from the test personnel and the corresponding operation signal types.
[0072] Optionally, the operation signal data includes operation data features, which include at least one of operation habit features, click frequency features, signal quantity features, and sliding speed features.
[0073] Specifically, electronic devices can collect operation signal data from testers, such as repeated clicks and swipes on the screen, and determine the corresponding operation signal type to generate training data for model training.
[0074] Furthermore, the electronic device can divide the training data into multiple training sample sets based on the operational data characteristics, and use the training sample sets to train the touch recognition model to be trained, thereby obtaining a preliminary touch recognition model.
[0075] Alternatively, multiple training sample sets can be determined based on different operational data characteristics.
[0076] Specifically, after obtaining the training data, the electronic device can divide the training data into multiple training sample sets according to the characteristics of the operation data, and use the training sample sets to train the touch recognition model to be trained, thereby obtaining a preliminary touch recognition model.
[0077] Optionally, after training the touch recognition model to be trained using the training data to obtain a preliminary touch recognition model, the method further includes: performing enhanced recognition training on the preliminary touch recognition model using the Ghost Hand database to obtain the touch recognition model, wherein the touch recognition model is used to identify the signal type of the original signal data.
[0078] In this embodiment of the disclosure, the electronic device can train the preliminary touch recognition model using the ghost hand database to obtain the touch recognition model, which is used to identify the signal type of the original signal data.
[0079] Specifically, after obtaining the initial touch recognition model, the electronic device can use the ghost hand database to enhance the recognition training of the initial touch recognition model, thereby obtaining a touch recognition model, which can be used to identify whether the signal data is a ghost hand signal.
[0080] Optionally, after determining that the signal type of the original signal data is the normal signal, the touch ghost hand signal processing method may further include: responding to the original signal data through the system-on-a-chip.
[0081] In this embodiment of the disclosure, the electronic device can respond to the raw signal data through the system-on-a-chip.
[0082] Specifically, after determining that the signal type of the original signal data is the normal signal, the electronic device can respond to the original signal data through a system-on-a-chip (SOC).
[0083] Optionally, after comparing the data features of the original signal data with the ghost hand data features in the ghost hand database using the touch recognition model, the touch ghost hand signal processing method may further include: constructing optimized data using the original signal data and the corresponding signal type; updating the ghost hand database using the optimized data; and optimizing the touch recognition model using the updated ghost hand database to obtain an optimized touch recognition model.
[0084] In this embodiment of the disclosure, the electronic device can construct optimized data using the original signal data and the corresponding signal type.
[0085] Alternatively, the optimization data can be data used for model optimization.
[0086] Furthermore, the electronic device can update the ghost hand database using the optimized data, and then use the updated ghost hand database to optimize and learn the touch recognition model, thereby obtaining an optimized touch recognition model.
[0087] Therefore, the model can be optimized based on user habits, thereby accelerating the recognition and judgment speed of the touch recognition model.
[0088] Figure 2 A flowchart illustrating another touch-sensitive ghost hand signal processing method provided in an embodiment of this disclosure is shown.
[0089] like Figure 2 As shown, the touch ghost hand signal processing method may include the following steps.
[0090] S210. Train the touch recognition model to be trained to obtain the touch recognition model.
[0091] In this embodiment of the disclosure, the electronic device can train the model of the touch recognition model to be trained to obtain the touch recognition model.
[0092] Specifically, electronic devices can collect data on the user's actions, such as repeated clicks and swipes on the screen, to generate training data for model training. This training data can then be used to train the touch recognition model to obtain a preliminary touch recognition model. After obtaining the preliminary touch recognition model, the electronic device can use a ghost hand database to train the preliminary touch recognition model, thereby obtaining a new touch recognition model. This touch recognition model can be used to identify whether signal data is a ghost hand signal.
[0093] S220: Collects raw signal data from the target screen via a touch sensor, and uploads the raw signal data to the system-on-a-chip via a touch chip.
[0094] In this embodiment of the disclosure, the electronic device can collect raw signal data of the target screen through a touch sensor and upload the raw signal data to the system-on-a-chip through a touch chip.
[0095] Specifically, electronic devices can use touch sensors in the target screen of a vehicle to collect raw signal data of user actions on the screen and send this raw signal data to a touch key IC. Furthermore, the electronic device can use the touch key IC to upload this raw signal data to a system-on-a-chip (SoC).
[0096] S230. The data features of the original signal data are compared with the data features of the ghost hand in the ghost hand database through the touch recognition model.
[0097] In this embodiment of the disclosure, the electronic device can compare the data features of the original signal data with the data features of the ghost hand in the ghost hand database using a touch recognition model.
[0098] After obtaining the raw signal data, the electronic device can input the raw signal data into a pre-set touch recognition model. The touch recognition model can then compare the data features of the raw signal data with the data features of the ghost hand in the ghost hand database.
[0099] S240. When the signal type of the original signal data is a normal signal, the original signal data is responded to by the system-on-a-chip.
[0100] In this embodiment of the disclosure, the electronic device can respond to the original signal data through a system-on-a-chip when the signal type of the original signal data is a normal signal.
[0101] After comparing the data characteristics of the original signal data with the data characteristics of the ghost hand in the ghost hand database, if the data characteristics are different from the ghost hand data characteristics, the electronic device can determine that the signal type of the original signal data is the normal signal.
[0102] S250. If the signal type of the original signal data is a ghost hand signal, filter the original signal data.
[0103] In this embodiment of the disclosure, the electronic device can filter the original signal data when the signal type of the original signal data is a ghost hand signal.
[0104] Specifically, after comparing the data characteristics of the original signal data with the ghost hand data characteristics in the ghost hand database, if the data characteristics are the same as the ghost hand data characteristics, the electronic device can determine that the signal type of the original signal data is the ghost hand signal.
[0105] Figure 3 A schematic diagram of a touch-sensitive ghost hand signal processing device provided in an embodiment of this disclosure is shown.
[0106] In some embodiments of this disclosure, Figure 3 The touch-sensitive ghost hand signal processing device shown can be installed in an electronic device. Specifically, the electronic device may include, but is not limited to, mobile terminals such as computer equipment, mobile phones, in-vehicle equipment, vehicle controllers, tablet computers, and wearable devices.
[0107] like Figure 3 As shown, the touch-sensitive ghost hand signal processing device 300 may include a data receiving module 310, a signal recognition module 320, and a data filtering module 330.
[0108] The data receiving module 310 can be used to receive raw signal data uploaded by the touch chip.
[0109] The signal recognition module 320 can be used to perform signal recognition and judgment on the original signal data through a pre-set touch recognition model to obtain the signal type of the original signal data.
[0110] The data filtering module 330 can be used to filter the original signal data when the signal type of the original signal data is a ghost hand signal.
[0111] Therefore, in this embodiment of the present disclosure, the system can receive raw signal data uploaded by the touch chip, then perform signal recognition and judgment on the raw signal data through a pre-set touch recognition model to obtain the signal type of the raw signal data, and finally filter the raw signal data if the signal type of the raw signal data is a ghost hand signal. Thus, the system-on-a-chip can directly perform signal recognition and judgment on the raw signal data and filter the raw signal data with the signal type of ghost hand signal, thereby reducing accidental touches and improving driving safety.
[0112] In some embodiments of this disclosure, the signal recognition module 320 may specifically include a signal recognition unit.
[0113] The signal recognition unit can be used to input the original signal data into the pre-set touch recognition model. The touch recognition model compares and identifies the original signal data according to the pre-established ghost hand database to determine the signal type of the original signal data. The pre-established ghost hand database is used to store ghost hand data features corresponding to multiple ghost hand signals.
[0114] In some embodiments of this disclosure, the signal recognition module 320 may further include a data comparison unit, a first determination unit, and a second determination unit.
[0115] The data comparison unit compares the data features of the original signal data with the data features of multiple ghost hand signals stored in the ghost hand database through the touch recognition model. The data features include at least one of operation habit features, click frequency features, signal quantity features, and swipe speed features.
[0116] The first determining unit can be used to determine the signal type of the original signal data as the ghost hand signal when the data characteristics of the original signal data are the same as the ghost hand data characteristics corresponding to the ghost hand signal.
[0117] The second determining unit can be used to determine the signal type of the original signal data as the normal signal when the data characteristics of the original signal data are different from the data characteristics of the ghost hand signal.
[0118] In some embodiments of this disclosure, the touch-sensitive ghost hand signal processing device 300 may further include a data acquisition module, a first training module, a second training module, and a third training module.
[0119] The data acquisition module can be used to acquire training data before the signal type of the original signal data is obtained by performing signal recognition and judgment on the original signal data through a pre-set touch recognition model. The training data is composed of the operation signal data of the collected test personnel and the corresponding operation signal type. The operation signal data includes operation data features, which include at least one of operation habit features, click frequency features, signal quantity features and swipe speed features.
[0120] The second training module can be used to perform signal recognition training on the touch recognition model to be trained using the training data to obtain a preliminary touch recognition model. After obtaining a preliminary touch recognition model, the training data is divided into multiple training sample sets according to the operation data characteristics, and the touch recognition model to be trained is trained using the training sample sets to obtain a preliminary touch recognition model.
[0121] The third training module can be used to enhance the initial touch recognition model through the ghost hand database to obtain the touch recognition model, which is used to identify the signal type of the original signal data.
[0122] In some embodiments of this disclosure, the touch-sensitive ghost hand signal processing device 300 may further include a data construction module and an optimization learning module.
[0123] This data construction module can be used to construct optimized data from the original signal data and the corresponding signal type.
[0124] The optimization learning module can be used to update the ghost hand database with the optimization data, and to optimize the touch recognition model with the updated ghost hand database to obtain an optimized touch recognition model.
[0125] It should be noted that, Figure 3 The touch-sensitive ghost hand signal processing device 300 shown can perform... Figures 1 to 2 The various steps in the method embodiment shown are implemented. Figures 1 to 2 The processes and effects in the method embodiments shown are not described in detail here.
[0126] Figure 4 A schematic diagram of the structure of a touch-sensitive ghost hand signal processing device provided in an embodiment of this disclosure is shown.
[0127] In some embodiments of this disclosure, Figure 4The touch-sensitive ghost hand signal processing device shown can be any electronic device that the user wants to process touch-sensitive ghost hand signals. Specifically, the electronic device can be, but is not limited to, mobile terminals such as mobile phones, in-vehicle devices, vehicle controllers, tablets, wearable devices, and smart home devices.
[0128] like Figure 4 As shown, the touch-screen signal processing device may include a processor 401 and a memory 402 storing computer program instructions.
[0129] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.
[0130] Memory 402 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway device. In a particular embodiment, memory 402 is a non-volatile solid-state memory. In a particular embodiment, memory 402 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.
[0131] The processor 401 reads and executes computer program instructions stored in the memory 402 to perform the steps of the touch ghost hand signal processing method provided in the embodiments of this disclosure.
[0132] In one example, the touch-sensitive ghost hand signal processing device may further include a transceiver 403 and a bus 404. Wherein, as... Figure 4As shown, the processor 401, memory 402 and transceiver 403 are connected via bus 404 and communicate with each other.
[0133] Bus 404 includes hardware, software, or both. For example, and not limitingly, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 404 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.
[0134] This disclosure also provides a computer-readable storage medium that can store a computer program. When the computer program is executed by a processor, the processor implements the touch ghost hand signal processing method provided in this disclosure.
[0135] The aforementioned storage medium may, for example, include a memory 402 containing computer program instructions, which can be executed by the processor 401 of the touch ghost hand signal processing device to complete the touch ghost hand signal processing method provided in this embodiment. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.
[0136] This disclosure also provides a vehicle including the touch-sensitive ghost hand signal processing device as described above. It is understood that the vehicle may also include a processor, a memory, and a computer program. The computer program is stored in the memory and configured to be executed by the processor to implement the touch-sensitive ghost hand signal processing method provided in this disclosure. The processor and memory are already... Figure 4 The parts of the illustrated embodiments will not be repeated here.
[0137] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.
[0138] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for processing touch-sensitive ghost hand signals, characterized in that, include: Receive raw signal data uploaded by the touch chip; The original signal data is identified and judged by a pre-set touch recognition model to obtain the signal type of the original signal data. If the signal type of the original signal data is a ghost hand signal, then the original signal data is filtered.
2. The method according to claim 1, characterized in that, The step of performing signal recognition and judgment on the original signal data through a pre-set touch recognition model to obtain the signal type of the original signal data includes: The original signal data is input into the pre-set touch recognition model. The touch recognition model compares and identifies the original signal data according to the pre-established ghost hand database to determine the signal type of the original signal data. The pre-established ghost hand database is used to store ghost hand data features corresponding to multiple ghost hand signals.
3. The method according to claim 2, characterized in that, The step of comparing and identifying the original signal data using the touch recognition model based on a pre-established ghost hand database to determine the signal type of the original signal data includes: The touch recognition model compares the data features of the original signal data with the data features of the ghost hand signals stored in the ghost hand database. The data features of the original signal data include at least one of operation habit features, click frequency features, signal quantity features, and swipe speed features. If the data characteristics of the original signal data are the same as the data characteristics of the ghost hand signal, the signal type of the original signal data is determined to be the ghost hand signal. If the data characteristics of the original signal data are different from the data characteristics of the ghost hand signal, the signal type of the original signal data is determined to be a normal signal.
4. The method according to claim 3, characterized in that, Before performing signal recognition and judgment on the original signal data using a pre-set touch recognition model to obtain the signal type of the original signal data, the method further includes: Acquire training data, which is composed of the operation signal data collected from the test personnel and the corresponding operation signal type. The operation signal data includes operation data features, which include at least one of operation habit features, click frequency features, signal quantity features, and sliding speed features. Based on the operational data characteristics, the training data is divided into multiple training sample sets, and the touch recognition model to be trained is trained by signal recognition using the training sample sets to obtain a preliminary touch recognition model. The initial touch recognition model is enhanced and trained using the Ghost Hand database to obtain the touch recognition model, which is used to identify the signal type of the original signal data.
5. The method according to claim 1, characterized in that, After filtering the original signal data, the method further includes: Optimized data is constructed using the original signal data and the corresponding signal type; The Ghost Hand database is updated using the optimized data.
6. A touch-sensitive ghost hand signal processing device, characterized in that, include: The data receiving module is used to receive the raw signal data uploaded by the touch chip; The signal recognition module is used to perform signal recognition and judgment on the original signal data through a pre-set touch recognition model to obtain the signal type of the original signal data; The data filtering module is used to filter the original signal data when the signal type of the original signal data is a ghost hand signal.
7. A touch-sensitive ghost hand signal processing device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the touch ghost hand signal processing method according to any one of claims 1-5.
8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the touch ghost hand signal processing method according to any one of claims 1-5.
9. A vehicle, characterized in that, Includes the touch ghost hand signal processing device as described in claim 6, the touch ghost hand signal processing apparatus as described in claim 7, or the computer-readable storage medium as described in claim 8.