Mobile control system, mobile control method, device, apparatus, and storage medium

Abstract

The embodiment of the application provides a mobile control system, a mobile control method, a device, equipment and a storage medium, and belongs to the technical field of ultrasonic control. The mobile control method comprises a signal acquisition device, a signal processing device and an ultrasonic control device. The target electroencephalogram signal is acquired through the signal acquisition device, and the signal processing device is used for electroencephalogram feature processing on the target electroencephalogram signal. The electroencephalogram feature processing is specifically as follows: a pre-trained mobile direction recognition model is used to classify the target electroencephalogram feature data in the target electroencephalogram signal to obtain mobile direction data, and a target electroencephalogram feature data is used to obtain a mobile step length, and a mobile control signal is generated according to the mobile direction data and the mobile step length. The ultrasonic control device controls the target object to move in the target direction according to the mobile control signal, so that the non-contact ultrasonic control based on the electroencephalogram control is realized, and the universality of the ultrasonic control is improved.

Description

Technical Field

[0001] This application relates to the field of ultrasonic control technology, and in particular to a motion control system, motion control method, device, equipment and storage medium. Background Technology

[0002] Currently, it is possible to control the movement of target objects using the sound field generated by ultrasonic control equipment.

[0003] In related technologies, operators directly control ultrasonic control equipment via manual methods such as keyboard input and program invocation. However, when the program control logic of the ultrasonic control equipment is too complex, or when operators cannot manually control it in real time, there is a problem that operators cannot control the ultrasonic control equipment through these manual methods. In other words, the aforementioned manual operation methods lack versatility in controlling ultrasonic control equipment. Therefore, improving the versatility of ultrasonic control equipment control has become an urgent technical problem to be solved. Summary of the Invention

[0004] The main objective of this application is to provide a motion control system, motion control method, device, equipment, and storage medium, aiming to improve the universality of control over ultrasonic control equipment.

[0005] To achieve the above objectives, a first aspect of this application provides a mobile control system, the system comprising:

[0006] A signal acquisition device, wherein the signal acquisition device is used to acquire target electroencephalogram (EEG) signals;

[0007] A signal processing device, which is electrically connected to the signal acquisition device, is used to perform EEG feature processing on the target EEG signal to obtain a movement control signal;

[0008] An ultrasonic control device is electrically connected to the signal processing device. The ultrasonic control device is used to generate a target sound field based on the movement control signal. The target sound field is used to control the movement of a target object in the target direction.

[0009] In some embodiments, the ultrasonic control device includes:

[0010] An ultrasonic transducer, which is electrically connected to the signal processing device, is used to generate target sound waves according to the motion control signal.

[0011] A reflector is disposed opposite to the ultrasonic transducer to form a moving region. The reflector is used to reflect the target sound wave to form the target sound field within the moving region. The target sound field is used to control the target object to move along the target direction within the moving region.

[0012] In some embodiments, the mobility control system further includes:

[0013] A motion detection device, wherein the motion detection device is used to detect the movement trajectory of the target object and generate a motion detection signal based on the movement trajectory;

[0014] A motion analysis device is electrically connected to a motion detection device, a signal processing device, and an external display device. The motion analysis device is used to generate EEG feedback information based on the motion detection signal. The external display device is used to perform display operations based on the EEG feedback information.

[0015] In some embodiments, the signal acquisition device includes:

[0016] A signal acquisition unit, wherein the signal acquisition unit is used to acquire initial electroencephalogram (EEG) signals;

[0017] A signal filtering unit is electrically connected to the signal acquisition unit. The signal filtering unit is used to filter the initial EEG signal to obtain the target EEG signal.

[0018] To achieve the above objectives, a second aspect of this application provides a motion control method, which is applied to the motion control system described in the first aspect; the method includes:

[0019] EEG feature extraction is performed on the target EEG signal to obtain target EEG feature data;

[0020] The target EEG feature data is classified according to the movement direction based on a pre-trained target movement direction recognition model to obtain movement direction data.

[0021] The movement step length is generated based on the target EEG feature data;

[0022] A movement control signal is generated based on the movement direction data and the movement step size; wherein, the ultrasonic control device is used to control the target object to move along the target direction according to the movement control signal.

[0023] In some embodiments, the step of extracting EEG features from the target EEG signal to obtain target EEG feature data includes:

[0024] Frequency band analysis is performed on the target EEG signal to obtain signal frequency band data;

[0025] The target EEG feature data is calculated based on the signal frequency band data.

[0026] In some embodiments, before classifying the target EEG feature data according to a pre-trained target movement direction recognition model to obtain movement direction data, the method further includes training the target movement direction recognition model, specifically including:

[0027] Acquire sample EEG feature data and sample EEG tags for the sample EEG feature data; wherein, the sample EEG tags are used to characterize the direction of movement of the target object controlled according to the sample EEG feature data;

[0028] The sample EEG feature data is input into a preset original movement direction recognition model for recognition to obtain an original EEG label; wherein, the original EEG label is used to characterize the direction of movement of the target object controlled by the sample EEG feature data;

[0029] The original movement direction recognition model is adjusted by means of the sample EEG tags and the original EEG tags to obtain the target movement direction recognition model.

[0030] To achieve the above objectives, a third aspect of this application provides a motion control device, the motion control device comprising:

[0031] The EEG feature calculation module is used to extract EEG features from the target EEG signal to obtain target EEG feature data.

[0032] The movement direction classification module is used to classify the target EEG feature data according to the pre-trained target movement direction recognition model to obtain movement direction data.

[0033] A movement step generation module is used to generate a movement step based on the target EEG feature data;

[0034] A control signal generation module is used to generate a movement control signal based on the movement direction data and the movement step size; wherein, the ultrasonic control device is used to control the target object to move along the target direction based on the movement control signal.

[0035] To achieve the above objectives, a fourth aspect of the present application provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0036] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0037] The mobile control system, mobile control method, device, equipment, and storage medium proposed in this application acquire target EEG signals through a signal acquisition device, and then perform EEG feature processing on the target EEG signals through a signal processing device. The ultrasound control device controls the target object to move in the target direction according to the mobile control signal, thereby realizing non-contact ultrasound manipulation based on EEG control, and thus improving the control universality of the ultrasound control device. Attached Figure Description

[0038] Figure 1 This is a block diagram of a mobile control system according to a specific embodiment of this application;

[0039] Figure 2 This is a flowchart of the motion control method provided in the embodiments of this application;

[0040] Figure 3 yes Figure 2 The flowchart of step S201 in the text;

[0041] Figure 4 This is a flowchart of the training target movement direction recognition model according to an embodiment of this application;

[0042] Figure 5 This is a first schematic diagram illustrating the movement of the target object according to an embodiment of this application;

[0043] Figure 6 This is a second schematic diagram illustrating the movement of the target object in an embodiment of this application;

[0044] Figure 7 This is a schematic diagram of an ultrasonic control device according to a specific embodiment of this application;

[0045] Figure 8 This is a block diagram of a mobile control system according to another specific embodiment of this application;

[0046] Figure 9 This is a schematic diagram of the structure of the mobile control device provided in the embodiments of this application;

[0047] Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application.

[0048] Figure label:

[0049] Signal acquisition device 110, signal processing device 120, ultrasonic control device 130, ultrasonic transducer 131, reflector 132, motion detection device 140, motion analysis device 150, EEG feature calculation module 910, motion direction classification module 920, motion step length generation module 930, control signal generation module 940, processor 101, memory 102, input / output interface 103, communication interface 104, bus 105. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0052] Unless otherwise defined, all technical and 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 terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0053] First, let's analyze some of the terms used in this application:

[0054] Brain-Computer Interface (BCI) is a novel control and interaction method that directly connects human brain signals to computers or other external devices. It allows humans to directly control computers or other external devices using electroencephalogram (EEG), magnetoencephalogram (MEG), or other physiological signals. In BCI, the main signal acquisition methods are divided into invasive and non-invasive approaches. Invasive BCI requires surgery to implant electrodes into the brain to collect EEG signals, while non-invasive BCI can collect EEG signals by wearing a head-mounted device or a patch-like device.

[0055] Non-contact control refers to controlling the directional movement of an object on demand without physical contact. It involves generating a specified field in the medium surrounding the controlled object and using the force exerted on the object within that field to manipulate its movement. Compared to traditional contact-based control methods, non-contact control has the significant advantage of no mechanical contact with the controlled object, avoiding cross-contact and better adapting to increasingly complex control environments. Currently, non-contact control technologies primarily utilize optical control, magnetic control, and voice control.

[0056] Neurofeedback training is a biofeedback technique based on electroencephalography (EEG) used to help people monitor and regulate their own brain activity. In EEG training, the operator wears an EEG probe to record the brain's electrical activity. This activity is then converted into visual feedback signals, typically displayed on a computer screen. Participants learn how to regulate and change their own brain activity by observing these signals. They can control their brain activity through relaxation, focus, concentration, or other specific strategies, and observe the success of their efforts through real-time feedback. The goal of EEG training is to help people change patterns of brain activity to improve specific cognitive, emotional, or behavioral performance.

[0057] Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0058] Machine learning is the study of how computers can simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence. In machine learning, deep learning (DL) learns the inherent patterns and hierarchical representations of sample data. The information gained during this learning process greatly helps in interpreting data such as text, images, and sound. Its ultimate goal is to enable machines to have analytical and learning capabilities like humans, and to recognize data such as text, images, and sound. Deep learning is a complex machine learning algorithm that has achieved results in speech and image recognition far exceeding previous related technologies.

[0059] Currently, it is possible to control the movement of target objects using the sound field generated by ultrasonic control equipment.

[0060] In related technologies, operators directly control ultrasonic control equipment via manual methods such as keyboard input and program invocation. However, when the program control logic of the ultrasonic control equipment is too complex, or when operators cannot manually control it in real time (e.g., when the operator is physically disabled or needs to operate other equipment), there is a problem that operators cannot control the ultrasonic control equipment through these manual methods. In other words, the aforementioned manual operation methods lack versatility in controlling ultrasonic control equipment. Therefore, improving the versatility of ultrasonic control equipment control has become an urgent technical problem to be solved.

[0061] Based on this, embodiments of this application provide a motion control system, motion control method, apparatus, device, and storage medium, aiming to improve the universality of ultrasonic control.

[0062] The mobile control method provided in this application relates to the field of artificial intelligence technology. The mobile control method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a tablet computer, a laptop computer, a desktop computer, etc.; the server can be configured as an independent physical server, a server cluster consisting of multiple physical servers, or a distributed system, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the mobile control method, but is not limited to the above forms.

[0063] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0064] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user EEG information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to a confirmation page. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the normal operation of embodiments of this application obtained.

[0065] The mobile control system, mobile control method, device, equipment, and storage medium provided in the embodiments of this application are specifically described through the following embodiments. First, the mobile control system in the embodiments of this application is described.

[0066] Figure 1 This is an optional block diagram of a mobile control system provided in an embodiment of this application. The mobile control system includes: a signal acquisition device 110, a signal processing device 120, and an ultrasound control device 130. The signal acquisition device 110 is used to acquire target EEG signals; the signal processing device 120 is electrically connected to the signal acquisition device 110 and is used to perform EEG feature processing on the target EEG signals to obtain a mobile control signal; the ultrasound control device 130 is electrically connected to the signal processing device 120 and is used to generate a target sound field based on the mobile control signal. The target sound field is used to control the target object to move in the target direction.

[0067] The mobile control system illustrated in this application acquires the target EEG signal through the signal acquisition device 110, then performs EEG feature processing on the target EEG signal by the signal processing device 120 to generate a mobile control signal, and finally the ultrasound control device 130 controls the target object to move in the target direction according to the mobile control signal, thereby realizing non-contact ultrasound control based on EEG control. When the program control logic of the ultrasound control device is too complex, or the operator cannot manually control it in real time, the operator can still achieve non-contact ultrasound control through EEG control, thereby improving the universality of ultrasound control.

[0068] In some embodiments of the mobile control system, the signal acquisition device 110 can be a brain electroencephalography (BCI) device, which can be either invasive or non-invasive. Invasive BCI devices have higher accuracy in acquiring EEG signals compared to non-invasive BCI devices, while non-invasive BCI devices offer wider operability and higher safety compared to invasive BCI devices. Invasive BCI devices include planar electrodes, point electrodes, and flexible electrode caps, which are surgically implanted into the brain to acquire target EEG signals. Non-invasive BCI devices include head-mounted devices or patch devices, which can acquire target EEG signals by being worn on the brain. After acquiring the target EEG signal, the signal acquisition device 110 sends the target EEG signal to the signal processing device 120.

[0069] After receiving the target EEG signal, the signal processing device 120 performs EEG feature processing on the target EEG signal to obtain a movement control signal, and sends the movement control signal to the ultrasound control device 130. The ultrasound control device 130 generates a target sound field based on the movement control signal, and controls the target object to move non-contactly within the target sound field. The target object can be an object that can move according to changes in the sound field, such as sound-controlled particles or droplets.

[0070] The following section details how to generate control signals to control the target object.

[0071] Figure 2 This is an optional flowchart of a motion control method provided in an embodiment of this application, which is applied to a signal processing device 120 in a motion control system. Figure 2 The method may include, but is not limited to, steps S201 to S204, which will be described in detail below.

[0072] Step S201: Extract brainwave features from the target EEG signal to obtain target EEG feature data;

[0073] Step S202: Classify the target EEG feature data according to the movement direction based on the pre-trained target movement direction recognition model to obtain movement direction data;

[0074] Step S203: Generate the movement step length based on the target EEG feature data;

[0075] Step S204: Generate a movement control signal based on the movement direction data and the movement step size; wherein, the ultrasonic control device is used to control the target object to move along the target direction according to the movement control signal.

[0076] Steps S201 to S204, as illustrated in this embodiment, determine the movement direction using a target movement direction recognition model and obtain the movement step length based on the target's EEG feature data. A movement control signal is then generated based on the movement direction data and the movement step length, enabling the movement control signal to accurately reflect the EEG features of the target's EEG signal. The movement control signal obtained from the target movement direction recognition model can more accurately reflect the direction in which the operator intends to control the target object's movement. Furthermore, the target movement direction recognition model can adapt to the target EEG feature data of different operators, effectively alleviating the problem of low accuracy in movement direction recognition due to individual differences among operators.

[0077] In step S201 of some embodiments, EEG feature extraction refers to extracting target EEG feature data from the target EEG signal. This target EEG feature data represents characteristic data indicating brain electrical activity. For example, the target EEG feature data may include data such as focus level, relaxation level, and fatigue level. Focus level reflects the brain's concentration state; the higher the level of concentration, the greater the focus; conversely, the lower the level of concentration, the lower the focus. Operators can regulate focus by altering the level of concentration, such as by furrowing or relaxing their brows.

[0078] In the following embodiments, attention level is used as an example of target EEG feature data for illustration. It is understood that other feature data that can represent brain electrical activity can also be applied to the motion control method of this application embodiment, and therefore should also fall within the protection scope of this application embodiment.

[0079] like Figure 3 As shown, in some embodiments of the present invention, step S201 includes, but is not limited to, steps S301 and S302, which will be described in detail below.

[0080] Step S301: Perform frequency band analysis on the target EEG signal to obtain signal frequency band data;

[0081] Step S302: Calculate the target EEG feature data based on the signal frequency band data.

[0082] In step S301 of some embodiments, the frequency band analysis operation refers to performing a Fourier transform on the acquired target EEG signal and then analyzing a specific frequency band of the transformed target EEG signal to obtain signal frequency band data. This specific frequency band analysis includes spectral feature analysis, energy feature analysis, and time-frequency feature analysis. The signal frequency band data obtained after analysis represents the power spectral density or band energy of the target EEG signal in the aforementioned specific frequency band. For example, the specific frequency band can be selected as the alpha wave band, and the signal frequency band data can be the band energy of the target EEG signal in the alpha wave band. Alpha waves are one of the basic EEG waves and can reflect a person's mental concentration state.

[0083] In step S302 of some embodiments, the target EEG characteristic data can be obtained by calculating using the analyzed signal frequency band data. For example, the signal frequency band data is the band energy of the alpha wave band, and the target EEG characteristic data is the level of concentration. The band energy is positively correlated with the level of concentration. When the band energy increases, it indicates that the brain is in a more focused state, and the value of concentration will also increase accordingly. Conversely, the value of concentration decreases.

[0084] Through the above steps S301 and S302, the target EEG feature data can be accurately extracted from the target EEG signal.

[0085] In step S202 of some embodiments, the target movement direction recognition model can identify the movement direction data represented by the operator's brain activity from the target EEG feature data. The movement direction can be classified into directions in three-dimensional space such as up, down, left, right, front, and back, based on the level of attention. The target movement direction recognition model can be selected from machine learning classification models such as decision tree models, support vector machine models, and Naive Bayes models. Decision tree models can classify target EEG feature data by constructing a tree structure, support vector machine models can classify target EEG feature data by constructing a classification hyperplane, and Naive Bayes models can classify target EEG feature data by calculating conditional probabilities based on Bayes' theorem. It is understood that the specific classification model used in the target movement direction recognition model can be adjusted according to actual needs.

[0086] Because different operators generate target EEG signals with different EEG parameters, the target EEG feature data of different operators will vary. Therefore, it is necessary to train the target movement direction recognition model in advance. The specific methods for training the target movement direction recognition model are described in detail below.

[0087] like Figure 4As shown, in some embodiments of the present invention, before step S202, the motion control method further includes training a target motion direction recognition model, specifically including but not limited to steps S401 to S403. These three steps will be described in detail below.

[0088] Step S401: Obtain sample EEG feature data and sample EEG tags for the sample EEG feature data; wherein, the sample EEG tags are used to characterize the direction of movement of the target object controlled according to the sample EEG feature data.

[0089] Step S402: Input the sample EEG feature data into the preset original movement direction recognition model for recognition to obtain the original EEG label; wherein, the original EEG label is used to characterize the direction of control of the target object's movement based on the sample EEG feature data;

[0090] Step S403: Adjust the parameters of the original movement direction recognition model based on the sample EEG tags and the original EEG tags to obtain the target movement direction recognition model.

[0091] In step S401 of some embodiments, the sample EEG feature data is sample feature data representing brain electrical activity, which may include data such as focus level, relaxation level, and fatigue value. The method of acquiring the sample EEG feature data is the same as that of the target EEG feature data, and both can be collected by the signal acquisition device 110. In the following description of embodiments, focus level is used as an example of sample EEG feature data, just like the target EEG feature data.

[0092] The sample EEG tag is a pre-configured tag for the sample EEG feature data. The sample EEG tag represents the direction of movement that the operator's brain activity intends to indicate. This direction of movement includes directions in three-dimensional space such as up, down, left, right, forward, and backward.

[0093] In step S402 of some embodiments, the original movement direction recognition model can be selected as a machine learning classification model such as a decision tree model, support vector machine model, or Naive Bayes model. Sample EEG feature data is input as a training set into the original movement direction recognition model, which then identifies and classifies the movement direction represented by the sample EEG feature data to output an original EEG label. This original EEG label represents the movement direction represented by the sample EEG feature data as identified by the original movement direction recognition model.

[0094] In step S403 of some embodiments, a loss calculation is performed on the sample EEG tags and the original EEG tags according to a preset loss function to obtain a loss value. The loss value is used to represent the error of the original movement direction recognition model in recognizing and classifying the movement direction. The parameters of the original movement direction recognition model are adjusted according to the calculated loss value to obtain the target movement direction recognition model.

[0095] Through steps 401 to 403 above, the accuracy of the target movement direction recognition model for different classifications can be improved, and the model can adapt to the target EEG feature data of different operators, thus effectively alleviating the problem of low accuracy in movement direction recognition due to individual differences among operators. It is understood that the sample EEG feature data and the target EEG feature data can come from the same operator; in this case, the target movement direction recognition model can improve the accuracy of recognizing the EEG features of that operator. Alternatively, the sample EEG feature data and the target EEG feature data can also come from different users, i.e., more EEG feature samples can be obtained; in this case, the target movement direction recognition model can improve the accuracy of recognizing the EEG features of different individual operators.

[0096] In step S203 of some embodiments, the movement step length represents the distance the operator's brain activity intends to represent. A corresponding movement step length can be generated based on the numerical value of the target EEG feature data. For example, the numerical value of focus is positively correlated with the movement step length; the higher the focus, the longer the movement step length, and vice versa.

[0097] In step S204 of some embodiments, the target direction is the direction represented by the movement direction data. The obtained movement direction data and movement step size are used to generate a corresponding movement control signal, which is then sent to the ultrasonic control device 130 so that the ultrasonic control device 130 can control the target object to move in the target direction in three-dimensional space.

[0098] In one specific embodiment Figure 5 , Figure 6 This is a diagram illustrating the movement of the target object; the white dot in the diagram represents the target object. First, refer to... Figure 5 When the focus level is 50, the target object is stationary on the baseline; this focus level is the baseline focus level. When the operator increases the focus level to 60 by means such as frowning, the focus level is greater than the baseline focus level, and the target movement direction recognition model identifies the movement direction as upward, meaning the target object moves upward. When the operator increases the focus level to 70, the target object also moves upward, but compared to the case of a focus level of 60, the upward movement step of the target object is longer.

[0099] Secondly refer to Figure 6 When the focus level is 50, the target object remains stationary on the baseline. When the operator reduces the focus level to 40 by means such as relaxing their brow, the focus level is less than the baseline focus level, and the target movement direction recognition model identifies the movement direction as downward, meaning the target object moves downward. When the operator reduces the focus level to 30, the target object also moves downward, but compared to the case where the focus level is 40, the downward movement step of the target object is longer.

[0100] Therefore, the movement step length of the target object corresponding to the level of focus is positively correlated with the absolute value of the difference between the actual level of focus and the baseline level of focus. Due to individual differences among operators, different operators have different baseline and actual levels of focus. Therefore, it is necessary to train a target movement direction recognition model using the methods described in steps 401 to 403 above to alleviate the problem of low accuracy in movement direction recognition caused by individual differences among operators.

[0101] It is understood that, in other specific embodiments, the target movement direction recognition model can set multiple baseline focuses, or combine focuses, to distinguish more movement directions in three-dimensional space.

[0102] In some embodiments of the motion control system of this application, the ultrasonic control device 130 includes an ultrasonic transducer 131 and a reflector 132. The ultrasonic transducer 131 is electrically connected to the signal processing device 120 and is used to generate target sound waves according to the motion control signal. The reflector 132 is disposed opposite to the ultrasonic transducer 131 to form a motion area. The reflector 132 is used to reflect the target sound waves to form a target sound field within the motion area. The target object is disposed within the motion area.

[0103] In some embodiments of the ultrasonic control device 130, multiple ultrasonic transducers 131 are provided, and the ultrasonic transducers 131 are arranged in an array. The array distribution of the ultrasonic transducers 131 can be planar, curved, or spherical, etc. It is understood that the specific array distribution of the ultrasonic transducers 131 can be adjusted according to the area where the target object to be controlled is to move.

[0104] The ultrasonic transducer 131 can be selected from capacitive micromechanical ultrasonic transducers (CMUT), piezoelectric micromechanical ultrasonic transducers (PMUT), etc. The frequency specification of the ultrasonic transducer 131 can be 25KHz, 40KHz, etc., and the diameter of the ultrasonic transducer 131 can be 10mm, 16mm, 20mm, etc.

[0105] Figure 7This is an optional schematic diagram of the ultrasonic control device 130 of this application. After receiving a movement control signal, the ultrasonic transducer 131 generates a first target sound wave A and sends it to a reflector 132 positioned opposite it. Upon receiving the first target sound wave A, the reflector 132 reflects the sound wave back to the ultrasonic transducer 131, resulting in a second target sound wave B. The first target sound wave A and the second target sound wave B form a target sound field within the movement area between the ultrasonic transducer 131 and the reflector 132. This target sound field is a focused sound field. Due to the intersection of the first target sound wave A and the second target sound wave B, the target sound field has multiple sound wave intersection nodes, and the target object X is located at any of these intersection nodes. The ultrasonic transducer 131 adjusts the phase or amplitude of the generated target sound wave according to the movement control signal, thereby changing the position of the sound wave intersection node in the movement area, thus causing the target object X to move in the target direction. The focusing diameter of the sound wave intersection node is less than millimeters, which allows for more stable confinement of the target object X at the sound wave intersection node.

[0106] It is understandable that, in addition to the focused sound field mentioned above, the target sound field can also be selected as a vortex sound field, collimated sound field, etc., and can be adapted according to actual needs.

[0107] like Figure 8 As shown, in some embodiments of the mobile control system of this application, the mobile control system further includes: a motion detection device 140 and a motion analysis device 150. The motion detection device 140 is used to detect the movement trajectory of the target object and generate a motion detection signal based on the movement trajectory; the motion analysis device 150 is electrically connected to the motion detection device 140, the signal processing device 120, and the external display device, respectively, and is used to generate EEG feedback information based on the motion detection signal; wherein, the external display device is used to perform display operations based on the EEG feedback information.

[0108] In some embodiments of the motion control system, the motion detection device 140 can acquire and record the movement trajectory of a target object using a photographic device such as a camera, thereby generating a motion detection signal. The motion analysis device 150 receives the motion detection signal from the motion detection device 140 and the target EEG feature data extracted from the target EEG signal by the signal processing device 120. Combining the motion detection signal and the target EEG feature data, it generates EEG feedback information that links concentration level to the target object's movement trajectory. An external display device displays the EEG feedback information, allowing the operator to see the relationship between the current EEG feature data and the target object's movement trajectory in real time, thus facilitating adjustments to the operator's concentration level as needed.

[0109] In some embodiments of the mobile control system of this application, the signal acquisition device 110 includes: a signal acquisition unit and a signal filtering unit. The signal acquisition unit is used to acquire an initial EEG signal; the signal filtering unit is electrically connected to the signal acquisition unit and is used to filter the initial EEG signal to obtain a target EEG signal.

[0110] In some embodiments of the signal acquisition device 110, the signal acquisition unit may employ an invasive BCI (Brain Interaction) EEG signal acquisition device, such as a planar electrode, a point electrode, or an elastic electrode cap. Alternatively, the signal acquisition unit may employ a non-invasive BCI EEG signal acquisition device, such as a head-mounted device or a patch device. After the signal acquisition device 110 acquires the initial EEG signal, the signal filtering unit performs signal filtering on the initial EEG signal to remove noise interference, thereby obtaining the target EEG signal. Through the filtering operation of the signal filtering unit, the signal-to-noise ratio of the target EEG signal can be improved.

[0111] In one specific embodiment of this application, the ultrasound control device 130 can also be electrically connected to a program configuration device. The program configuration device pre-stores a motion control program for controlling the movement of a target object. The program configuration device generates corresponding program control signals based on this motion control program, so that the ultrasound control device 130 controls the target object to move according to the program control signals. Specifically, the method by which the ultrasound control device 130 controls the target object according to the motion control program is the same as the method by which the ultrasound control device 130 controls the target object according to the motion control signals described above; please refer to the detailed description above for further details. It is understood that the operator can switch the ultrasound control device 130 between preset program control and EEG control according to actual needs.

[0112] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0113] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0114] Please see Figure 9This application also provides a motion control device that can implement the above-described motion control method. The device includes:

[0115] The EEG feature calculation module 910 is used to extract EEG features from the target EEG signal to obtain target EEG feature data.

[0116] The movement direction classification module 920 is used to classify the movement direction of the target EEG feature data according to the pre-trained target movement direction recognition model to obtain movement direction data.

[0117] The movement step length generation module 930 is used to generate movement step length based on target EEG feature data;

[0118] The control signal generation module 940 is used to generate a movement control signal based on the movement direction data and the movement step size; wherein, the ultrasonic control device is used to control the target object to move along the target direction based on the movement control signal.

[0119] It is evident that the content of the above-described mobile control method embodiments is applicable to this mobile control device embodiment. The specific functions implemented by this mobile control device embodiment are the same as those of the above-described mobile control method embodiments, and the beneficial effects achieved are also the same as those achieved by the above-described mobile control method embodiments.

[0120] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described mobile control method. This electronic device can be any smart terminal, including tablet computers, desktop computers, etc.

[0121] Please see Figure 10 , Figure 10 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0122] The processor 101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0123] The memory 102 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 102 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 102 and is called and executed by the processor 101 using the mobile control method of the embodiments of this application.

[0124] Input / output interface 103 is used to implement information input and output;

[0125] The communication interface 104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0126] Bus 105 transmits information between various components of the device (e.g., processor 101, memory 102, input / output interface 103, and communication interface 104);

[0127] The processor 101, memory 102, input / output interface 103 and communication interface 104 are connected to each other within the device via bus 105.

[0128] It is evident that the content of the above-described mobile control method embodiments is applicable to this electronic device embodiment. The specific functions implemented by this electronic device embodiment are the same as those of the above-described mobile control method embodiments, and the beneficial effects achieved are also the same as those achieved by the above-described mobile control method embodiments.

[0129] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described motion control method.

[0130] It is evident that the content of the above-described mobile control method embodiments is applicable to the present computer-readable storage medium embodiments. The specific functions implemented by the present computer-readable storage medium embodiments are the same as those of the above-described mobile control method embodiments, and the beneficial effects achieved are also the same as those achieved by the above-described mobile control method embodiments.

[0131] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0132] The mobile control system, mobile control method, device, equipment, and storage medium provided in this application acquire target EEG signals through a signal acquisition device, and then perform EEG feature processing on the target EEG signals through a signal processing device. Specifically, the EEG feature processing involves classifying the target EEG feature data in the target EEG signal according to the movement direction using a pre-trained movement direction recognition model to obtain movement direction data. Simultaneously, the movement step length is obtained using the target EEG feature data, and a movement control signal is generated based on the movement direction data and the movement step length. The ultrasound control device controls the target object to move in the target direction according to the movement control signal, thereby realizing non-contact ultrasound manipulation based on EEG control and improving the universality of ultrasound control.

[0133] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0134] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0135] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0137] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0138] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0140] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0142] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0143] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A mobile control system, characterized in that, The system includes: A signal acquisition device, wherein the signal acquisition device is used to acquire target electroencephalogram (EEG) signals; A signal processing device, electrically connected to a signal acquisition device, is used to extract EEG features from the target EEG signal to obtain target EEG feature data; classify the target EEG feature data according to a pre-trained target movement direction recognition model to obtain movement direction data; generate a movement step size based on the concentration value in the target EEG feature data, wherein the concentration value is positively correlated with the movement step size; and generate a movement control signal based on the movement direction data and the movement step size. The original movement direction recognition model identifies and classifies the movement direction represented by the sample EEG feature data to output an original EEG label, and performs loss calculation on the sample EEG label and the original EEG label of the sample EEG feature data to obtain a loss value. The parameters of the original movement direction recognition model are adjusted based on the loss value to obtain the target movement direction recognition model. The original movement direction recognition model is a machine learning classification model, including a decision tree model, a support vector machine model, and a Naive Bayes model. An ultrasonic control device is electrically connected to the signal processing device. The ultrasonic control device is used to generate a target sound field based on the movement control signal. The target sound field is used to control the target object to move along the target direction.

2. The mobile control system according to claim 1, characterized in that, The ultrasonic control device includes: An ultrasonic transducer, which is electrically connected to the signal processing device, is used to generate target sound waves according to the motion control signal. A reflector is disposed opposite to the ultrasonic transducer to form a moving region. The reflector is used to reflect the target sound wave to form the target sound field within the moving region. The target sound field is used to control the target object to move along the target direction within the moving region.

3. The mobile control system according to claim 1, characterized in that, The system also includes: A motion detection device, wherein the motion detection device is used to detect the movement trajectory of the target object and generate a motion detection signal based on the movement trajectory; A motion analysis device is electrically connected to a motion detection device, a signal processing device, and an external display device. The motion analysis device is used to generate EEG feedback information based on the motion detection signal. The external display device is used to perform display operations based on the EEG feedback information.

4. The mobile control system according to any one of claims 1 to 3, characterized in that, The signal acquisition device includes: A signal acquisition unit, wherein the signal acquisition unit is used to acquire initial electroencephalogram (EEG) signals; A signal filtering unit is electrically connected to the signal acquisition unit. The signal filtering unit is used to filter the initial EEG signal to obtain the target EEG signal.

5. A motion control method, characterized in that, Applied to the mobile control system as described in any one of claims 1 to 4; the method comprises: EEG feature extraction is performed on the target EEG signal to obtain target EEG feature data; The target EEG feature data is classified according to the movement direction based on a pre-trained target movement direction recognition model to obtain movement direction data. Specifically, the movement direction represented by the sample EEG feature data is identified and classified using an original movement direction recognition model to output original EEG labels. Loss calculations are performed on the sample EEG labels and the original EEG labels of the sample EEG feature data to obtain a loss value. Furthermore, the parameters of the original movement direction recognition model are adjusted based on the loss value to obtain the target movement direction recognition model. The original movement direction recognition model is a machine learning classification model, including decision tree models, support vector machine models, and Naive Bayes models. The movement step length is generated based on the target EEG feature data; A movement control signal is generated based on the movement direction data and the movement step size; wherein, the ultrasonic control device is used to control the target object to move along the target direction according to the movement control signal.

6. The motion control method according to claim 5, characterized in that, The step of extracting brainwave features from the target brainwave signal to obtain target brainwave feature data includes: Frequency band analysis is performed on the target EEG signal to obtain signal frequency band data; The target EEG feature data is calculated based on the signal frequency band data.

7. A mobile control device, characterized in that, Applied to the mobile control system as described in any one of claims 1 to 4; comprising: The EEG feature calculation module is used to extract EEG features from the target EEG signal to obtain target EEG feature data. A movement direction classification module is used to classify the movement direction of the target EEG feature data according to a pre-trained target movement direction recognition model to obtain movement direction data. Specifically, the module identifies and classifies the movement direction represented by the sample EEG feature data using an original movement direction recognition model to output original EEG labels. It then calculates a loss value by performing loss calculations on the sample EEG labels and the original EEG labels of the sample EEG feature data. Finally, it adjusts the parameters of the original movement direction recognition model based on the loss value to obtain the target movement direction recognition model. The original movement direction recognition model is a machine learning classification model, including decision tree models, support vector machine models, and Naive Bayes models. A movement step generation module is used to generate a movement step based on the target EEG feature data; A control signal generation module is used to generate a movement control signal based on the movement direction data and the movement step size; wherein, the ultrasonic control device is used to control the target object to move along the target direction based on the movement control signal.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the mobile control method according to claim 5 or 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the motion control method as described in claim 5 or 6.

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