A meditation training method and device based on a virtual digital person and a storage medium

Abstract

The application discloses a meditation training method and device based on a virtual digital person and a storage medium, and the method comprises the following steps: collecting electroencephalogram signals of a user when the user is performing mindfulness meditation training; evaluating the concentration degree of the user's mindfulness meditation training according to the electroencephalogram signals; loading a meditation scene for feeding back the user's mindfulness meditation training; loading a virtual digital person in the meditation scene; generating meditation voice data for guiding the user's mindfulness meditation training in the meditation scene according to the concentration degree when the meditation scene is controlled according to the concentration degree; controlling the facial expression and / or body movement of the virtual digital person according to the concentration degree, and controlling the virtual digital person to play the meditation voice data so as to guide the user's mindfulness meditation training in the meditation scene. The user's mindfulness meditation training is guided in vision and hearing, the mindfulness meditation can be effectively improved in pertinence and professionalism, and the efficiency of the user's mindfulness meditation training can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mindfulness meditation, and in particular to a meditation training method based on a virtual digital person, a device and a storage medium. BACKGROUND

[0002] With the acceleration of modern life pace, more and more people practice mindfulness meditation to relieve the pressure of work and life and improve physical and mental health.

[0003] At present, mindfulness meditation is mainly guided by professionals, and self-silence training is carried out in leisure time. In the self-silence training, the mindfulness meditation mainly relies on the cognition of the user, resulting in low efficiency of mindfulness meditation. SUMMARY

[0004] Therefore, the present application provides a meditation training method based on a virtual digital person, a device and a storage medium to improve the efficiency of the user practicing mindfulness meditation.

[0005] The first aspect of the present application provides a meditation training method based on a virtual digital person, comprising:

[0006] acquiring the brain electrical signals of the user during mindfulness meditation training of the user;

[0007] evaluating the attention concentration degree of the user during mindfulness meditation training according to the brain electrical signals;

[0008] loading a meditation scene for feedback on the mindfulness meditation training of the user;

[0009] loading a virtual digital person in the meditation scene;

[0010] generating meditation voice data for guiding the mindfulness meditation training of the user in the meditation scene according to the attention concentration degree while controlling the change of the meditation scene according to the attention concentration degree;

[0011] controlling the change of facial expressions and / or body movements of the virtual digital person according to the attention concentration degree, and controlling the virtual digital person to play the meditation voice data to guide the mindfulness meditation training of the user in the meditation scene.

[0012] The second aspect of the present application provides a meditation training device based on a virtual digital person, comprising:

[0013] a brain electrical signal acquisition module for acquiring the brain electrical signals of the user during mindfulness meditation training of the user;

[0014] an attention evaluation module for evaluating the attention concentration degree of the user during mindfulness meditation training according to the brain electrical signals;

[0015] a meditation scene loading module configured to load a meditation scene for feeding back the mindfulness meditation training of the user;

[0016] a virtual digital human loading module configured to load a virtual digital human in the meditation scene;

[0017] a meditation voice data generating module configured to generate meditation voice data for guiding the mindfulness meditation training of the user in the meditation scene according to the degree of concentration of attention when controlling the change of the meditation scene according to the degree of concentration of attention;

[0018] a meditation guiding module configured to control the change of facial expression and / or body movement of the virtual digital human according to the degree of concentration of attention, and control the virtual digital human to play the meditation voice data for guiding the mindfulness meditation training of the user in the meditation scene.

[0019] A third aspect of the present application provides an electronic device, comprising:

[0020] at least one processor; and

[0021] a memory connected with the at least one processor in communication; wherein,

[0022] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the mindfulness meditation training method based on a virtual digital human according to the first aspect.

[0023] A fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the mindfulness meditation training method based on a virtual digital human according to the first aspect.

[0024] A fifth aspect of the present application provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the mindfulness meditation training method based on a virtual digital human according to the first aspect.

[0025] In the embodiment, when the user is in mindfulness meditation training, the electroencephalogram of the user is collected; the degree of concentration of the user in mindfulness meditation training is evaluated according to the electroencephalogram; a meditation scene for feedback of the mindfulness meditation training of the user is loaded; a virtual digital person is loaded in the meditation scene; when the meditation scene is controlled to change according to the degree of concentration, meditation voice data for guiding the mindfulness meditation training of the user in the meditation scene is generated according to the degree of concentration; the facial expression and / or body movement of the virtual digital person are controlled to change according to the degree of concentration, and the virtual digital person is controlled to play the meditation voice data to guide the mindfulness meditation training of the user in the meditation scene. When the user is in self-silence mindfulness meditation training, the facial expression and / or body movement of the virtual digital person are adaptively controlled according to the degree of concentration, the meditation voice data is adaptively generated and controlled to be played by the virtual digital person according to the meditation scene and the degree of concentration, and the mindfulness meditation training of the user is guided in vision and hearing, and the guiding manner is dynamically changed to adapt to the state of the user in the mindfulness meditation training, so that the guiding manner is flexible and suitable for the actual state of the user, the mindfulness meditation can be effectively improved in pertinence and professionalism, the cognitive requirement of the user for the mindfulness meditation is reduced, and the efficiency of the mindfulness meditation training of the user is improved.

[0026] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0028] Figure 1 is a flowchart of a meditation training method based on a virtual digital person provided by the first embodiment of the present application.

[0029] Figure 2 is an example diagram of a sad expression of a virtual digital person provided by the first embodiment of the present application.

[0030] Figure 3 is an example diagram of a calm expression of a virtual digital person provided by the first embodiment of the present application.

[0031] Figure 4 is an example diagram of a happy expression of a virtual digital person provided by the first embodiment of the present application.

[0032] Figure 5is a flow chart of a meditation training method based on a virtual digital person provided by the second embodiment of the present application.

[0033] Figure 6 is a structural schematic diagram of a meditation training device based on a virtual digital person provided by the third embodiment of the present application.

[0034] Figure 7 is a structural schematic diagram of an electronic device provided by the fourth embodiment of the present application. DETAILED DESCRIPTION

[0035] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can encompass the order of implementation other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0037] Embodiment one

[0038] Referring to Figure 1 , a flow chart of a meditation training method based on a virtual digital person provided by the first embodiment of the present application is shown, which can be executed by a meditation training device based on a virtual digital person, which can be realized in the form of hardware and / or software, and can be configured in an electronic device.

[0039] In one case, the electronic device is an integrated device, especially a head-mounted device such as a smart helmet, smart glasses, etc., and the virtual reality device is provided with components such as brain-computer interface, speaker and screen for feedback on mindfulness meditation. The speaker and screen can be fixed in the electronic device or can be a mobile terminal such as a user's mobile phone.

[0040] In another case, the electronic device may also be a physically separable or detachable device. In this case, the virtual reality device includes a headband and external auxiliary devices that are independent of each other. The headband and the auxiliary devices can be connected by wired or wireless means, such as Wi-Fi (Wireless Fidelity), Bluetooth, ZigBee, etc. The headband is provided with a brain-computer interface, and the auxiliary device is equipped with components such as a screen, speakers, and lights for providing feedback on mindfulness meditation. The auxiliary device can be an independent device that matches the headband, or it can be a non-independent device such as a multiplexed TV.

[0041] like Figure 1 As shown, the method includes:

[0042] Step 101: When a user is performing mindfulness meditation training, collect the user's EEG signals.

[0043] In this embodiment, the user wears an electronic device for mindfulness meditation training. During this process, the brain-computer interface in the electronic device can be called to collect single-channel or multi-channel EEG signals from the user at a preset frequency.

[0044] EEG (electroencephalogram) signals are electrical signals generated by the activity of neurons in the brain. Neurons connect to each other through synapses, forming complex neural networks. When neurons activate, they generate bioelectrical phenomena, which can be captured using electrodes placed on the scalp.

[0045] Taking a single-channel EEG signal as an example, the brain-computer interface includes electrodes, a signal processor, and an A / D (Analogue to Digital conversion) converter.

[0046] Among them, the electrodes include a reference electrode, a ground electrode, and an EEG electrode for a signal channel; the ground electrode is used to determine the zero potential of the EEG signal, and the reference electrode and ground electrode are placed at the temples on both sides of the user's head; the EEG electrode is placed at the Fp2 position defined in the 10-20 system.

[0047] The signal processor is used to amplify the collected EEG signal through an amplifier with a sampling frequency of 250 Hz, and to filter out the 50 Hz power frequency noise in the EEG signal through a notch filter, and then use a 0.1-50 Hz bandpass filter to filter out the DC component and high-frequency noise.

[0048] The A / D converter converts the amplified and filtered EEG signals from analog signals to digital signals through a 24-bit resolution digital-to-analog conversion chip.

[0049] Step 102: Evaluate the user's concentration level during mindfulness meditation training based on the EEG signal.

[0050] In the embodiment, the meditation detection model can be constructed in advance, and electroencephalogram signals of a plurality of users in a relaxed state (or resting state) and a meditation state are collected as samples to train the meditation detection model, so that the meditation detection model can evaluate the attention concentration degree of the user in the mindfulness meditation training according to the electroencephalogram signals, and realize the meditation state evaluation within and across subjects and over time.

[0051] The attention concentration degree can be provided to the user as a score (for example, the value range is [0, 100]) quantifying the effect of mindfulness meditation, so that the user can intuitively feel the state of his / her mindfulness meditation.

[0052] The meditation detection model can be a machine learning model, for example, a support vector machine (SVM), a decision tree, a random forest, etc.

[0053] In addition, the meditation detection model can also be a deep learning model, for example, a convolutional neural network (CNN), a long short-term memory (LSTM), a Transformer (a neural network architecture based on self-attention mechanism), etc.

[0054] For the deep learning model, the structure of the meditation detection model is not limited to the artificially designed neural network, but can also be a neural network optimized by model quantization method, a neural network searched for the characteristics of the electroencephalogram signals of the user in the mindfulness meditation training by NAS (Neural Architecture Search), etc., which are not limited in the embodiment.

[0055] In actual application, the electroencephalogram signal can be divided into a plurality of time equal (for example, 10 seconds) segment signals, and a band-pass filtering process is performed on each segment signal, for example, Chebyshev filtering, Butterworth band-pass filtering, etc., so as to improve the quality of the segment signal.

[0056] For each segment signal, time domain and / or frequency domain analysis can be performed to obtain the characteristics in the time domain and / or frequency domain.

[0057] The time domain analysis can include event-related potential (ERP), statistical analysis, etc. The event-related potential is used to observe the waveform change of the electroencephalogram signal under a specific task, and the statistical analysis is used to compare the electroencephalogram waveform difference under different conditions.

[0058] Frequency domain analysis includes Fourier transform (FFT), power spectral density (PSD), etc. Among them, Fourier transform is used to analyze the frequency components of EEG signals, and power spectral density is used to analyze the energy distribution of EEG signals.

[0059] Analysis of the time domain and frequency domain is also called time-frequency analysis, which includes short-time Fourier transform (STFT), wavelet transform, etc. Among them, short-time Fourier transform is used to analyze the frequency components of EEG signals that change over time, and wavelet transform is used to provide better time-frequency resolution.

[0060] The features of each segment signal in the time domain and / or frequency domain are input into the meditation detection model for processing to obtain the user's concentration level during mindfulness meditation training.

[0061] Step 103: Load a meditation scene that provides feedback on the user's mindfulness meditation training.

[0062] In this embodiment, meditation scenes can be recommended to users based on user preferences or business rules (such as randomness, popularity, etc.), or users can select meditation scenes themselves and load the meditation scenes into the auxiliary device.

[0063] The meditation scene is a business scene with multi-dimensional information. The meditation scene can provide users with at least one sensory feedback from visual feedback, auditory feedback, olfactory feedback, tactile feedback, and electrical stimulation feedback during mindfulness meditation training.

[0064] For example, visual feedback can include animations such as the sky, clouds, bonfires, waves, and forests; auditory feedback can include sounds such as pink noise, raindrops, flowing water, flames, and music; olfactory feedback can include gases such as aromatherapy; tactile feedback can include vibration of portable devices, seat massage, etc.; electrical stimulation feedback can include direct current stimulation, etc.

[0065] Step 104: Load the virtual digital human into the meditation scene.

[0066] A virtual engine, such as Unreal Engine 5 (UE5), is configured in the electronic device. When the user is practicing mindfulness meditation, the virtual engine can be called to generate a two-dimensional or three-dimensional virtual digital human in the meditation scene. The style of the virtual digital human includes cartoon, realism, etc., which can be default, matched with the meditation scene, or selected by the user.

[0067] For example, the following can be loaded in the meditation scene: Figure 2 、 Figure 3 and Figure 4 The virtual digital human shown in the hyper-realistic style.

[0068] In step 105, meditation voice data for guiding the user in the meditation scene is generated according to the degree of concentration.

[0069] In this embodiment, the trend of the degree of concentration can be evaluated every interval (for example, 2 seconds), and the meditation scene is controlled to change in the expressiveness of various feedback according to the trend of the degree of concentration, so that the trend of the expressiveness of the meditation scene in various feedback is the same as the trend of the degree of concentration, thereby feeding back to the user's mindfulness meditation training.

[0070] The control of the expressiveness of the meditation scene includes changes in the clarity, angle of view, etc. of the visual feedback, changes in the volume, tone, sound type, etc. of the auditory feedback, changes in the strength, etc. of the tactile feedback, changes in the strength, etc. of the electrical stimulation feedback, etc.

[0071] For example, the trend of the degree of concentration of the user is evaluated every interval (for example, 2 seconds), and if the degree of concentration at the current time is higher than that at the last time, i.e. the degree of concentration is increased, it indicates that the user's meditation level is deepened and the effect is improved, and the picture in the meditation scene is controlled to be clear and beautiful; if the degree of concentration at the current time is lower than that at the last time, i.e. the degree of concentration is decreased, it indicates that the user's meditation level is shallowed and the effect is deteriorated, and there may be a situation of distraction, and the picture in the meditation scene is controlled to be blurred and poor.

[0072] In the process of controlling the change of the meditation scene according to the degree of concentration, the virtual digital person can be controlled to simulate professional persons to give professional guidance to the user's mindfulness meditation training.

[0073] At this time, meditation voice data for guiding the user in the meditation scene can be generated according to the trend of the degree of concentration.

[0074] In one embodiment of the present application, step 105 can include the following steps:

[0075] In step 1051, the degree of concentration is mapped to the meditation state of the user in the mindfulness meditation training.

[0076] In actual application, the signal-to-noise ratio of the electroencephalogram is low and is easily affected by environmental noise, and the electroencephalograms of different users have certain differences, which limits the accuracy of detecting the degree of concentration of the user in the mindfulness meditation training. In order to reduce the influence of these differences, clustering, piecewise function, etc. can be used to linearly or nonlinearly map the degree of concentration to the meditation state of the user in the mindfulness meditation training.

[0077] Exemplarily, the first interval, the second interval and the third interval can be determined; any value in the first interval is greater than any value in the second interval, and any value in the second interval is greater than any value in the third interval; for example, the third interval is [0, 30), the second interval is [30, 70), and the first interval is [70, 100].

[0078] The attention concentration degree is compared with the first interval, the second interval and the third interval respectively.

[0079] When the attention concentration degree is in the first interval, it is determined that the meditation state of the user during the mindfulness meditation training is a good state.

[0080] When the attention concentration degree is in the second interval, it is determined that the meditation state of the user during the mindfulness meditation training is a stable state.

[0081] When the attention concentration degree is in the third interval, it is determined that the meditation state of the user during the mindfulness meditation training is a bad state.

[0082] Of course, the above meditation state is only an example, and other meditation states can be set according to actual conditions during implementation of the embodiment, and the embodiment does not limit this. In addition, in addition to the above meditation state, other meditation states can be used according to actual needs by those skilled in the art, and the embodiment does not limit this.

[0083] Step 1052, extracting a theme word from the meditation scene when the meditation state is the preset target state.

[0084] In the embodiment, part of the meditation states can be selected as the target state from all meditation states, and the virtual digital person can guide the user in the target state to train mindfulness meditation.

[0085] Exemplarily, when the meditation state includes a good state, a stable state and a bad state, the target state includes a good state and a bad state.

[0086] When it is detected that the meditation state is the preset target state, the user can be guided to train mindfulness meditation, at this time, a theme word can be extracted from the meditation scene, for example, bonfire, sea waves, forest, etc.

[0087] Step 1053, constructing a first guidance text information for feedback of the meditation state in the meditation scene using the theme word.

[0088] In the embodiment, based on the meditation scene, a subject word is used to construct a prompt for guiding the user to feedback the meditation state (especially the target state), denoted as first guiding text information. The prompt is text used to guide the language model (LM) to generate content of a specific type, theme or format.

[0089] In a specific implementation, the corpus for training the language model (especially the large language model (LLM)) has certain limitations. The corpus generally includes universal knowledge, common sense knowledge and professional knowledge in various fields, such as encyclopedias, news and novels, so that the language model has certain limitations in processing knowledge representation and application in specific fields.

[0090] In the embodiment, a knowledge base can be loaded. The knowledge base includes scene text information for describing various meditation scenes and scene text vectors converted from the scene text information.

[0091] For example, part of the information in the knowledge base includes introduction of digital robots, definition of mindfulness, content of mindfulness meditation, guidelines for users to meditate in various meditation scenes, and encouraging words, etc.

[0092] The subject word and the meditation state are used to construct a question text information for feedback on the meditation state in the meditation scene based on a question template.

[0093] The question text information is converted into a question text vector by calling an Embedding text vector model.

[0094] The question text vector is input into a preset vector retrieval service (such as DashVector), and a scene text vector similar to the question text vector is retrieved in the knowledge base as a target text vector.

[0095] The scene text information corresponding to the target text vector is extracted from the knowledge base as target text information.

[0096] The target text information and the question text information are used to construct first guiding text information for feedback on the meditation state in the meditation scene.

[0097] For example, in the meditation scene with visual feedback of bonfire, if the user's attention concentration level continues to be below 30 points, the first guidance text information can be constructed as "My meditation score is always below 30 points, give me a piece of mindfulness meditation guidance related to bonfire, remind me to improve the low meditation score, and require clear theme and detailed content." Or, "My score in bonfire meditation is in the interval of 0 to 30 points, and has been in this score interval for a long time, indicating that I am in a poor meditation state. You need to tell me how the current state is and give me direct suggestions to concentrate."

[0098] In the meditation scene with visual feedback of bonfire, if the user's attention concentration level continues to be above 70 points, the first guidance text information can be constructed as "My score in bonfire meditation is in the interval of 70 to 100 points, and has been in this score interval for a long time, indicating that I am in a good meditation state. You need to tell me how the current state is and give me some positive feedback to encourage me, with a maximum of 100 words in the reply."

[0099] In this way, the language model (especially LLM) can understand and acquire the domain knowledge of mindfulness meditation that exists outside its training knowledge range, and through the construction of specific first guidance text information Prompt, the language model (especially LLM) can be prompted to understand the intent and make answers based on the injected domain knowledge of mindfulness meditation when answering domain questions of mindfulness meditation.

[0100] Step 1054, input the first guidance text information into the preset language model to generate meditation text information.

[0101] In this embodiment, the first guidance text information Prompt is input into the preset language model (especially LLM), and the language model generates meditation text information that guides the user in the current target state to practice mindfulness meditation training in the current meditation scene according to the guidance of the first guidance text information Prompt.

[0102] Generally, the language model (especially LLM) can be a third-party pre-trained language model (especially LLM), or a language model (especially LLM) fine-tuned using Lora, Fine-tuning, etc. using corpus related to mindfulness meditation, which is not limited in this embodiment.

[0103] In one case, when the meditation state is an excellent state, the first guidance text information can be input into the preset language model (especially LLM) to generate meditation text information with an encouraging emotion, guiding the user to persist in the current physical and mental state for mindfulness meditation training.

[0104] Exemplarily, the language model (especially the LLM) can generate the meditation text information with an encouraging sentiment: "It is detected that your current meditation score has been above 70 points all the time. I hope you continue to persist and enjoy the peace and tranquility of each meditation. Your concentration and inner calm will gradually increase, bringing you more benefits. Believe in your ability and the power of meditation, you are on a positive and upward path, continue to work hard, and you will achieve greater success!", or "It is detected that your current meditation score has been above 70 points all the time. Congratulations, your meditation state is good, your concentration and inner calm are constantly improving, this calm and concentration will help you better handle stress and difficulties in daily life, continue to maintain concentration and calm, and enjoy this peaceful moment."

[0105] In another case, when the meditation state is a poor state, the first guidance text information is input into the preset language model to generate meditation text information with a reminding sentiment, encouraging the user to adjust the physical and mental state to optimize the mindfulness meditation training.

[0106] Exemplarily, the language model (especially the LLM) can generate the meditation text information with a reminding sentiment: "It is detected that your current meditation score has been below 30 points all the time. Maybe you realize how easily you can be distracted, and how easily sounds can make you daydream. If you find yourself thinking or judging, try to let them go. Return to the sound itself and allow it to present itself as it is.", or "It is detected that your current meditation score has been below 30 points all the time. Focus on your breath and slowly gather your attention, let your thoughts calm down. It is important to be patient and not too demanding of yourself, meditation is a process, and slow improvement is also possible, please maintain persistence and continuous practice."

[0107] Step 1055, count the duration of the target state.

[0108] When it is detected that the meditation state is the preset target state, the duration of the user in the same target state can be counted, i.e., the duration of the same target state is counted.

[0109] Step 1056, if the duration exceeds the time threshold configured for the target state, the meditation text information is converted into meditation voice data for guiding the user in the target state to practice mindfulness meditation in the meditation scene.

[0110] In the embodiment, if the duration of the same target state exceeds the time threshold configured for the target state, it indicates that the user stays in the same target state for a long time, and the target state is relatively stable. Therefore, the meditation text information can be subjected to TTS (Text To Speech) processing to convert the meditation text information into meditation voice data for guiding the user in the meditation scene to perform mindfulness meditation training.

[0111] In step 106, the facial expression and / or body movement of the virtual digital person are controlled according to the degree of concentration, and the virtual digital person is controlled to play the meditation voice data to guide the user in the meditation scene to perform mindfulness meditation training.

[0112] In the process of controlling the virtual digital person to simulate the professional person to give professional guidance to the user in the meditation scene, on the one hand, the facial expression and / or body movement of the virtual digital person can be controlled in the virtual engine according to the degree of concentration, so that the change trend of the facial expression and / or body movement of the virtual digital person is the same as the change trend of the degree of concentration, thereby feeding back to the user in the meditation scene. On the other hand, the meditation voice data is input into the virtual engine to control the virtual digital person to play the meditation voice data, simulating the professional person to give professional opinions to the user in the meditation scene. The combination of the two can guide the user in the meditation scene to perform mindfulness meditation training.

[0113] In the process of controlling the facial expression and / or body movement of the virtual digital person, the controller configured for each part (such as eyes, nose, mouth, joints, etc.) on the face and / or body of the virtual digital person can be queried in the virtual engine, and each part is configured with a controller.

[0114] For each controller, the parameter amplitude configured for each controller can be queried, wherein the parameter amplitude is a continuously variable variable.

[0115] The degree of concentration is mapped to each parameter amplitude in a proportional manner to obtain each parameter value.

[0116] Each parameter value is input into the corresponding controller to adjust the part, so that the facial expression and / or body movement of the virtual digital person changes from negative emotion to positive emotion when the degree of concentration changes from low to high, that is, the lower the degree of concentration, the more negative the facial expression and / or body movement of the virtual digital person, and vice versa. The higher the degree of concentration, the more positive the facial expression and / or body movement of the virtual digital person.

[0117] For example, as shown in FIG. 1, when the degree of concentration is 0, the virtual digital person presents a sad facial expression, as shown in FIG. 2. Figure 2 Figure 3 ​As shown, when the degree of concentration of attention is 50, the virtual digital person presents a calm facial expression, as Figure 4 As shown, when the degree of concentration of attention is 100, the virtual digital person presents a happy facial expression.

[0118] In this embodiment, when the user is meditating, the brain electrical signals of the user are collected; the degree of concentration of attention of the user during meditation is evaluated according to the brain electrical signals; a meditation scene for feedback to the user during meditation is loaded; a virtual digital person is loaded in the meditation scene; when the meditation scene is controlled according to the degree of concentration of attention, meditation voice data for guiding the user during meditation is generated according to the degree of concentration of attention; the facial expression and / or body movement of the virtual digital person are controlled according to the degree of concentration of attention, and the virtual digital person plays the meditation voice data to guide the user during meditation. When the user is meditating in silence, the facial expression and / or body movement of the virtual digital person are adaptively controlled according to the degree of concentration of attention, and the virtual digital person adaptively generates and plays the meditation voice data according to the meditation scene and the degree of concentration of attention, guiding the user during meditation in a visual and auditory manner. The guiding manner dynamically changes according to the state of the user during meditation, making the guiding manner flexible and suitable for the actual state of the user, effectively improving the pertinence and professionalism of meditation, reducing the cognitive requirements of the user for meditation, and thus improving the efficiency of meditation training of the user.

[0119] Embodiment Two

[0120] Referring to Figure 5 , a flowchart of a meditation training method based on a virtual digital person is shown, which is provided in Embodiment Two of the present application. The present embodiment adds the operation of voice interaction on the basis of the foregoing embodiments. As shown in Figure 5 , the method comprises the following steps.

[0121] Step 501, when the user is meditating, the brain electrical signals of the user are collected.

[0122] Step 502, the degree of concentration of attention of the user during meditation is evaluated according to the brain electrical signals.

[0123] Step 503, a meditation scene for feedback to the user during meditation is loaded.

[0124] Step 504, a virtual digital person is loaded in the meditation scene.

[0125] Step 505, when the meditation scene is controlled according to the degree of concentration of attention, meditation voice data for guiding the user during meditation is generated according to the degree of concentration of attention.

[0126] Step 506, controlling the facial expression and / or body movement change of the virtual digital person according to the degree of attention concentration, and controlling the virtual digital person to play the meditation voice data to guide the user to practice mindfulness meditation in the meditation scene.

[0127] Step 507, collecting the first interaction voice data spoken by the user.

[0128] In this embodiment, in addition to actively simulating the professional person giving professional guidance to the user's mindfulness meditation training, the virtual digital person can also passively communicate with the user in other aspects.

[0129] Then, in the process of the user's mindfulness meditation training, the microphone can be started to collect the voice data spoken by the user, denoted as the first interaction voice data. For example, "Who are you? What do you do?"

[0130] Step 508, generating second interaction voice data in reply to the first interaction voice data.

[0131] In this embodiment, the first interaction voice data can be subjected to natural language processing to understand the semantics of the first interaction voice data, and based on the semantics, the second interaction voice data in reply to the first interaction voice data can be generated. In particular, the virtual digital person can simulate a professional person to actively give the user guidance throughout the meditation scene.

[0132] In a specific implementation, speech recognition can be performed on the first interaction voice data to convert the first interaction voice data into first interaction text information, and the first interaction text information can be used to construct second guidance text information Prompt.

[0133] The second guidance text information Prompt is input into a pre-set language model (especially LLM) to generate second interaction text information in reply to the first interaction text information.

[0134] TTS processing is performed on the second interaction text information to convert the second interaction text information into second interaction voice data in reply to the first interaction voice data.

[0135] Step 509, controlling the virtual digital person to play the second interaction voice data to interact with the user in the meditation scene.

[0136] The second interaction voice data is input into the virtual engine to control the virtual digital person to play the second interaction voice data, so that the virtual digital person can interact with the user in multiple aspects in the meditation scene and provide more information support for the user's mindfulness meditation training.

[0137] Embodiment Three

[0138] Reference Figure 6Fig. 3 shows a structural schematic diagram of a meditation training device based on a virtual digital person according to Embodiment Three of the present application. As shown in Fig. 3, the device comprises: Figure 6

[0139] a brain electrical signal collection module 601, configured to collect brain electrical signals of a user when the user is performing mindfulness meditation training;

[0140] an attention evaluation module 602, configured to evaluate an attention concentration degree of the user when performing mindfulness meditation training according to the brain electrical signals;

[0141] a meditation scene loading module 603, configured to load a meditation scene for feeding back the mindfulness meditation training of the user;

[0142] a virtual digital person loading module 604, configured to load a virtual digital person in the meditation scene;

[0143] a meditation voice data generation module 605, configured to generate meditation voice data for guiding the mindfulness meditation training of the user in the meditation scene according to the attention concentration degree when the meditation scene is controlled according to the attention concentration degree;

[0144] a meditation guiding module 606, configured to control changes in facial expressions and / or body movements of the virtual digital person according to the attention concentration degree, and control the virtual digital person to play the meditation voice data, so as to guide the mindfulness meditation training of the user in the meditation scene.

[0145] In an embodiment of the present application, the meditation voice data generation module 605 comprises:

[0146] a meditation state mapping module, configured to map the attention concentration degree to a meditation state of the user when performing mindfulness meditation training;

[0147] a theme word extraction module, configured to extract a theme word from the meditation scene when the meditation state is a preset target state;

[0148] a first guidance text information construction module, configured to construct first guidance text information for feeding back the meditation state in the meditation scene by using the theme word;

[0149] a meditation text information generation module, configured to input the first guidance text information into a preset language model to generate meditation text information;

[0150] ​A duration statistics module is configured to count a duration of the target state; and a meditation voice data conversion module is configured to convert the meditation text information into meditation voice data for guiding the user in the target state to perform mindfulness meditation training in the meditation scene if the duration exceeds a time threshold configured for the target state.

[0151] In an embodiment of the present application, the meditation state mapping module comprises:

[0152] An interval determination module is configured to determine a first interval, a second interval and a third interval; a value in the first interval is greater than a value in the second interval, and the value in the second interval is greater than a value in the third interval;

[0153] An excellent state determination module is configured to determine that the meditation state of the user in the mindfulness meditation training is an excellent state if the attention concentration degree is located in the first interval;

[0154] A stable state determination module is configured to determine that the meditation state of the user in the mindfulness meditation training is a stable state if the attention concentration degree is located in the second interval;

[0155] A poor state determination module is configured to determine that the meditation state of the user in the mindfulness meditation training is a poor state if the attention concentration degree is located in the third interval.

[0156] In an embodiment of the present application, the target state comprises the excellent state and the poor state; and the meditation text information generation module comprises:

[0157] An encouragement text generation module is configured to generate meditation text information with an emotion of encouragement by inputting the first guidance text information into a preset language model if the meditation state is the excellent state;

[0158] A reminder text generation module is configured to generate meditation text information with an emotion of reminder by inputting the first guidance text information into a preset language model if the meditation state is the poor state.

[0159] In an embodiment of the present application, the first guidance text information construction module comprises:

[0160] A knowledge base loading module is configured to load a knowledge base; the knowledge base comprises scene text information for describing each meditation scene and a scene text vector converted from the scene text information;

[0161] A question text information construction module is configured to construct question text information for feedback on the meditation state in the meditation scene by using the theme word and the meditation state;

[0162] The question text vector conversion module is configured to convert the question text information into a question text vector.

[0163] The target text vector retrieval module is configured to input the question text vector into a preset vector retrieval service, and retrieve a scenario text vector similar to the question text vector from the knowledge base as a target text vector.

[0164] The target text information extraction module is configured to extract the scenario text information corresponding to the target text vector from the knowledge base as target text information.

[0165] The text information fusion construction module is configured to construct first guidance text information for feedback on the meditation state in the meditation scenario using the target text information and the question text information.

[0166] In an embodiment of the present application, the meditation guidance module 606 comprises:

[0167] The controller query module is configured to query a controller configured on a part of the face and / or limbs of the virtual digital person.

[0168] The parameter amplitude query module is configured to query a parameter amplitude configured for the controller.

[0169] The attention mapping module is configured to map the attention integration degree onto the parameter amplitude to obtain a parameter value.

[0170] The part adjustment module is configured to input the parameter value into the controller to adjust the part, so as to control the facial expression and / or limb movement of the virtual digital person to change from a negative emotion to a positive emotion when the attention integration degree changes from low to high.

[0171] In an embodiment of the present application, the meditation guidance module 606 further comprises:

[0172] The first interactive voice data acquisition module is configured to acquire first interactive voice data spoken by the user.

[0173] The second interactive voice data generation module is configured to generate second interactive voice data for replying to the first interactive voice data.

[0174] The second interactive voice data playing module is configured to control the virtual digital person to play the second interactive voice data, so as to interact with the user in the meditation scenario.

[0175] In an embodiment of the present application, the second interactive voice data generation module comprises:

[0176] The first interactive text information conversion module is configured to convert the first interactive voice data into first interactive text information.

[0177] The second guidance text information construction module is configured to construct second guidance text information by using the first interactive text information.

[0178] The second interactive text information generation module is configured to input the second guidance text information into a preset language model, and generate second interactive text information for replying to the first interactive text information.

[0179] The second interactive voice data conversion module is configured to convert the second interactive text information into second interactive voice data for replying to the first interactive voice data.

[0180] The virtual digital person-based meditation training device provided in the embodiments of the present application can execute the virtual digital person-based meditation training method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of executing the virtual digital person-based meditation training method.

[0181] Embodiment four

[0182] Referring to Figure 7 , a structural schematic diagram of an electronic device provided by an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0183] As Figure 7 shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0184] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a speaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0185] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the meditation training method based on a virtual digital human.

[0186] In some embodiments, the meditation training method based on a virtual digital human can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the meditation training method based on a virtual digital human described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the meditation training method based on a virtual digital human by any other appropriate means, such as by means of firmware.

[0187] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0188] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a remote machine or entirely on a remote machine or server.

[0189] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0190] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0191] The systems and techniques described here can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0192] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0193] Embodiment five

[0194] The embodiment of the present application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to realize the meditation training method based on a virtual digital person according to any embodiment of the present application.

[0195] The computer program product, in the implementation process, can be written in one or more programming languages or combinations thereof to implement the computer program code for performing the operations of the present application, the programming languages including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" language or similar programming languages. The program code can be executed entirely on a user computer, partially on a user computer, as an independent software package, partially on a user computer and partially on a remote computer, or entirely on a remote computer or server. In the case involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, connected through the Internet by using an Internet service provider).

[0196] It should be understood that the various forms of flow shown above can be used to reorder, add, or remove steps. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0197] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A meditation training method based on virtual digital human, characterized in that: include: When the user is practicing mindfulness meditation, collecting the user's electroencephalogram (EEG) signal; evaluating the user's concentration level during mindfulness meditation training based on the EEG signal; Loading a meditation scene that provides feedback on the user's mindfulness meditation training; loading a virtual digital human into the meditation scene; When controlling the change of the meditation scene according to the concentration level, mapping the concentration level to the meditation state of the user during mindfulness meditation training; When the meditation state is a preset target state, extracting a subject word from the meditation scene; constructing first guidance text information for providing feedback on the meditation state in the meditation scene using the keyword; Inputting the first guiding text information into a preset language model to generate meditation text information; Counting the duration of the target state; If the duration exceeds a time threshold configured for the target state, converting the meditation text information into meditation voice data for guiding the user in the target state to engage in mindfulness meditation training in the meditation scene; Querying controllers configured for parts of the face and / or limbs of the virtual digital human; Querying the parameter amplitude configured for the controller; Mapping the attention concentration degree to the parameter amplitude to obtain a parameter value; Inputting the parameter value into the controller to adjust the part, so as to control the facial expression and / or body movement of the virtual digital person to change from negative emotion to positive emotion when the concentration level changes from low to high; and The virtual digital human is controlled to play the meditation voice data to guide the user in mindfulness meditation training in the meditation scene.

2. The method according to claim 1, characterized in that Mapping the concentration level to the meditation state of the user during mindfulness meditation training includes: Determine a first interval, a second interval, and a third interval; the value in the first interval is greater than the value in the second interval, and the value in the second interval is greater than the value in the third interval; When the concentration level is within the first interval, determining that the meditation state of the user during the mindfulness meditation training is an excellent state; When the concentration level is within the second interval, determining that the meditation state of the user during the mindfulness meditation training is a stable state; When the concentration level is within the third interval, it is determined that the meditation state of the user during the mindfulness meditation training is a poor state.

3. The method according to claim 2, characterized in that The target state includes the good state and the bad state; and inputting the first guidance text information into a preset language model to generate meditation text information includes: When the meditation state is the excellent state, inputting the first guidance text information into a preset language model to generate meditation text information with an encouraging emotion; When the meditation state is the unfavorable state, the first guidance text information is input into a preset language model to generate meditation text information with the emotion of reminder.

4. The method according to claim 1, wherein The first guiding text information for providing feedback on the meditation state in the meditation scene using the keyword includes: Loading a knowledge base; the knowledge base contains scene text information for describing each meditation scene and scene text vectors converted from the scene text information; Using the subject words and the meditation state to construct question text information for providing feedback on the meditation state in the meditation scene; Converting the question text information into a question text vector; Inputting the question text vector into a preset vector search service, and searching the knowledge base for the scene text vector similar to the question text vector as a target text vector; Extracting the scene text information corresponding to the target text vector from the knowledge base as target text information; The target text information and the question text information are used to construct first guidance text information for providing feedback on the meditation state in the meditation scene.

5. The method according to any one of claims 1 to 4, characterized in that Also includes: collecting first interactive voice data spoken by the user; generating second interactive voice data in response to the first interactive voice data; The virtual digital human is controlled to play the second interactive voice data to interact with the user in the meditation scene.

6. The method according to claim 5, characterized in that The generating of the second interactive voice data in reply to the first interactive voice data includes: Converting the first interactive voice data into first interactive text information; constructing second guidance text information using the first interactive text information; inputting the second guidance text information into a preset language model to generate a second interactive text message in reply to the first interactive text message; The second interactive text information is converted into second interactive voice data that responds to the first interactive voice data.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the meditation training method based on a virtual digital human according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the meditation training method based on a virtual digital human according to any one of claims 1 to 6 is implemented.

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