A control method and system of a cinema massage chair based on sound perception

Abstract

This invention relates to a control method and system for a movie theater massage chair based on sound perception. The method includes the following steps: acquiring raw sound during movie projection and preprocessing it; extracting features from the preprocessed raw sound to form a sound feature sequence, and inputting it into a sound recognition model for scene recognition, outputting the scene category and corresponding confidence score; matching a basic massage mode from a massage mode library based on the scene category; adaptively adjusting the basic massage mode based on the confidence score to generate a final massage mode; and controlling the massage chair to perform massage actions based on the final massage mode. This invention can identify the movie projection status and control the massage chair without relying on external interfaces, achieving a natural linkage between the massage rhythm and the movie rhythm, enhancing the immersive movie-watching experience.

Description

Technical Field

[0001] This invention relates to a control method and system for a cinema massage chair based on sound perception, belonging to the field of massage chair control technology. Background Technology

[0002] With the rapid development of the cultural and entertainment industry, the integration of massage chairs with cinema seats has become an important way to enhance the comfort and experience of moviegoers. Currently, cinema massage chairs are controlled by pre-setting several massage programs, meaning viewers can only select one program throughout the entire movie. This prevents dynamic adjustments to the massage movements based on the film's plot and rhythm, easily leading to a disconnect between the massage rhythm and the film's scenes.

[0003] Chinese invention patent application CN109288649A discloses an intelligent voice-controlled massage chair. The technical solution includes a massage chair body, and a voice input module, a voice processing module, and an intelligent execution module disposed on the massage chair body. The massage chair itself houses a voice input module, a voice processing module, and an intelligent execution module, and operates under the control of the intelligent execution module. The voice input module receives voice commands from the user and transmits them to the voice processing module. The voice processing module processes the received voice commands and sends the processed action commands to the intelligent execution module. The intelligent execution module receives the action commands and controls the massage chair to operate accordingly. However, this technical solution requires the user to actively issue voice commands to control the massage chair, which interrupts the user's viewing rhythm, distracts attention, and fails to guarantee an immersive viewing experience. Furthermore, this solution can only execute preset voice commands, failing to adequately consider background noise interference in the cinema environment. The massage modes are fixed and disconnected from the movie plot, resulting in low accuracy in recognizing movie scenes and an inability to dynamically adjust massage movements according to different scenes such as calm dialogue and tense moments. In addition, this solution lacks offline learning capabilities, requiring manual reconfiguration of massage mode parameters for films with different styles and audio characteristics, leading to high maintenance costs and poor flexibility.

[0004] Therefore, developing a cinema massage chair control method that can automatically identify scenes based on movie sound characteristics, dynamically adjust massage modes, and has collaborative control capabilities has significant practical significance and market value. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a control method and system for a cinema massage chair based on sound perception.

[0006] The technical solution of the present invention is as follows: The original sound during the movie screening was collected using the sound acquisition device of the massage chair and preprocessed; features were extracted from the preprocessed original sound to form a sound feature sequence. The sound feature sequence is input into a sound recognition model based on the fusion of multi-head self-attention mechanism and temporal convolutional network for scene recognition, and the scene category and corresponding confidence score are output. A basic massage pattern is obtained by matching the massage pattern library based on the scene category; the basic massage pattern is adaptively adjusted based on the confidence level to generate the final massage pattern; The massage chair is controlled to perform massage actions based on the final massage mode.

[0007] Preferably, the preprocessing includes pre-emphasis, framing, and windowing, wherein: The pre-emphasis specifically refers to filtering the original sound using a preset pre-emphasis signal; The framing specifically involves dividing the pre-emphasized original sound into multiple short frames according to a preset time sequence. The windowing process specifically involves multiplying each short-time frame by a window function to reduce spectral leakage.

[0008] Preferably, the sound feature sequence includes time-domain features, frequency-domain features, and Mel-frequency cepstral coefficient features.

[0009] Preferably, the sound recognition model includes a feature encoding layer, a temporal convolutional network layer, a multi-head self-attention layer, a temporal convolutional network layer, and a scene recognition layer, wherein: The feature encoding layer uses a one-dimensional convolutional network to extract local features from the sound feature sequence, obtain local features, and transmit them to the temporal convolutional network layer; The temporal convolutional network layer consists of multiple layers of bidirectional gated recurrent units, used to extract long-term dependencies in local features and output the hidden state at each time step; The multi-head self-attention layer performs weighted aggregation of the hidden states at each time step to obtain aggregated features; The temporal convolutional network layer employs a dilated convolutional structure to capture multi-scale features of aggregated features; Input multi-scale features into the scene recognition layer and output the probability of each scene category; The scene category with the highest probability is selected as the identified scene category, and its corresponding probability is used as the confidence level.

[0010] Preferably, the scene categories include: silent opening sequence, opening screening, calm dialogue, tense plot, climactic action, and end credits.

[0011] Preferably, each massage mode in the preset massage mode library corresponds to a scene category, and the parameters of the massage mode include a basic intensity curve, frequency modulation parameters, region activation vector, massage style label, and upper and lower bounds for parameter adjustment; Basic massage patterns are obtained by matching the massage pattern library based on the scene category.

[0012] Preferably, the basic massage mode is adaptively adjusted based on confidence level, and the specific steps are as follows: If the confidence level is greater than or equal to the high confidence threshold, then the parameters of the basic massage mode are used as the parameters of the final massage mode. If the confidence level is less than the high confidence threshold but greater than or equal to the low confidence threshold, then the certainty factor is calculated based on the confidence level, and the calculation method is as follows: ; In the formula, Indicates the deterministic factor; Indicates the first Confidence level corresponding to each scene category; This represents a preset high-confidence threshold; This indicates a preset low confidence threshold; The parameters of the basic massage mode are interpolated and adjusted based on a deterministic factor to generate the parameters of the final massage mode. If the confidence level is less than or equal to the low confidence threshold, then the preset massage mode is used as the final massage mode.

[0013] On the other hand, the present invention also provides a control system for a cinema massage chair based on sound perception, the system including a sound acquisition module, a scene recognition module, a pattern matching module, and a massage execution module; The sound acquisition module collects the original sound during the movie screening through the sound acquisition device of the massage chair and performs preprocessing; it then extracts features from the preprocessed original sound to form a sound feature sequence. The scene recognition module inputs the sound feature sequence into a sound recognition model based on the fusion of multi-head self-attention mechanism and temporal convolutional network to perform scene recognition, and outputs the scene category and the corresponding confidence score. The pattern matching module matches basic massage patterns from the massage pattern library based on scene category; it then adaptively adjusts the basic massage patterns based on confidence level to generate the final massage pattern. The massage execution module controls the massage chair to perform massage actions based on the final massage mode.

[0014] In another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the present invention.

[0015] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the present invention.

[0016] The present invention has the following beneficial effects: 1. This invention employs a pre-emphasis, frame-by-frame windowing, and adaptive frame length audio preprocessing workflow, combined with a multi-head self-attention mechanism and a temporal convolutional network-fused audio recognition model, to accurately distinguish movie scenes such as silent opening sequences and tense scenes; it improves the accuracy and anti-interference capability of scene recognition, reduces the impact of environmental noise on recognition results, enhances the matching accuracy between massage modes and movie scenes, and avoids the disconnect between massage actions and scenes. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0020] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0022] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0023] See Figure 1 In some embodiments, a control method for a cinema massage chair based on sound perception is proposed, including the following steps: The original sound during the movie screening was collected using the sound acquisition device of the massage chair and preprocessed; features were extracted from the preprocessed original sound to form a sound feature sequence. The sound feature sequence is input into a sound recognition model based on the fusion of multi-head self-attention mechanism and temporal convolutional network for scene recognition, and the scene category and corresponding confidence score are output. A basic massage pattern is obtained by matching the massage pattern library based on the scene category; the basic massage pattern is adaptively adjusted based on the confidence level to generate the final massage pattern; The massage chair is controlled to perform massage actions based on the final massage mode.

[0024] In some embodiments, the preprocessing includes pre-emphasis, framing, and windowing, wherein: The pre-emphasis specifically involves filtering the original sound using a preset pre-emphasis signal, calculated as follows: ; In the formula, This indicates the original sound after pre-emphasis; Represents the original sound; This represents the pre-emphasis factor, with a value ranging from 0.9 to 0.97. It is used to enhance high-frequency components and compensate for high-frequency attenuation of the sound signal during transmission. The framing specifically involves dividing the pre-emphasized original sound into segments according to a preset time sequence. Several overlapping short frames, with frame lengths ranging from 20 to 40 milliseconds and frame shifts of 50% of the frame length, are used to accommodate the short-term stationary characteristics of the audio signal. The windowing process specifically involves multiplying each short-time frame by a window function to reduce spectral leakage and improve the accuracy of subsequent spectral analysis. The window function is either a Hamming window or a Hanning window, and its calculation method is as follows: ; ; In the formula, Indicates the first A window function for a short time frame; Indicates frame length; This indicates the original sound after windowing processing; Indicates the first A short time frame.

[0025] In some embodiments, a noise suppression algorithm based on spectral subtraction can be used before pre-emphasis to eliminate background noise from the collected sound signal according to the spectral characteristics of the cinema environment noise.

[0026] In some embodiments, after frame division, automatic gain control is performed on each frame signal to normalize its amplitude range to a preset range in order to cope with the large dynamic range of movie sound.

[0027] In some embodiments, the sound feature sequence includes time-domain features, frequency-domain features, and Mel-frequency cepstral coefficient features.

[0028] In one specific embodiment, the time-domain features include short-time energy and zero-crossing rate; The frequency domain features include the spectral centroid, spectral bandwidth, spectral roll-off point, and spectral flatness. The power spectrum of each frame of preprocessed raw sound is calculated and mapped to a Mel-scale filter bank to obtain a Mel spectrogram. The characteristics of the Mel frequency cepstral coefficients are obtained by taking the logarithm of the Mel spectrum and performing a discrete cosine transform.

[0029] In some embodiments, the sound recognition model includes a feature encoding layer, a temporal convolutional network layer, a multi-head self-attention layer, a temporal convolutional network layer, and a scene recognition layer, wherein: The feature encoding layer uses a one-dimensional convolutional network to extract local features from the sound feature sequence, obtains local features, and transmits them to the temporal convolutional network layer. The calculation method is as follows: ; In the formula, Indicates local features; express function; This represents a one-dimensional convolution, where the kernel size is set to 3 and the stride is 1. Represents a sequence of sound features; The temporal convolutional network layer consists of multiple layers of bidirectional gated recurrent units, used to extract long-term dependencies in local features and output the hidden state at each time step to fully capture the contextual information of the sound sequence. The calculation method is as follows: ; ; ; In the formula, Indicates time step The forward-hidden state; Indicates a bidirectional gated loop unit; Indicates time step The hidden state; Indicates time step Local features; Indicates time step The hidden state; Indicates time step The hidden state of ; The multi-head self-attention layer performs weighted aggregation of the hidden states at each time step to obtain aggregated features, calculated as follows: ; ; ; ; ; In the formula, Indicates time step Multiple head and tail characteristics; express function; Indicates time step The query matrix; Indicates time step The key matrix; Indicates time step The value matrix; Indicates the preset projection dimension; Indicates the transpose operation; This indicates the preset query weight; Indicates the preset key weight; This indicates the preset value weight; Indicates the concatenation function; This indicates the preset aggregation weight; Indicates time step Multiple head and tail characteristics; Indicates aggregation features; The temporal convolutional network layer uses a dilated convolutional structure to capture multi-scale features of aggregated features, and the calculation method is as follows: ; In the formula, Indicates time step Multiscale features; Indicates the first The weights of each convolutional kernel; Indicates time step The next Aggregated features of each convolutional kernel; This represents the expansion coefficient, which increases with the number of layers. This layer can expand the receptive field without significantly increasing the parameters, effectively modeling long-range dependencies. Multi-scale features are input into the scene recognition layer, which outputs the probability of each scene category. The scene recognition layer consists of global average pooling, a fully connected layer, and Softmax, and the calculation method is as follows: ; ; In the formula, Indicates pooling characteristics; Indicates global average pooling; This represents the preset weights of the fully connected layer; This indicates the preset bias of the fully connected layer; Indicates the probability of the current scene category; The scene category with the highest probability is selected as the identified scene category, and its corresponding probability is used as the confidence level.

[0030] In some embodiments, the scene categories include: opening silence, opening screening, calm dialogue, tense plot, climax action, and end credits.

[0031] In one specific embodiment, the opening silent audio is presented as a low-volume ambient noise or a completely silent segment lasting several seconds to tens of seconds before the start of the main feature of the movie. The judgment characteristics include short-term energy continuously below the low decibel threshold, low and stable zero-crossing rate, high spectral flatness (close to 1), indicating that it is close to the noise spectrum, and continuous silence time greater than or equal to the preset time threshold. The opening screening audio is represented by the film company logo sound, the opening theme music, or the first strong sound effect; The distinguishing features include a short-term energy increase difference greater than a preset range after the silent period, and the spectral centroid being in the mid-to-high frequency decibel range (e.g., 800-3000Hz). The smooth dialogue audio is characterized by dialogue between characters, with soft or absent background music, and a calm mood. The distinguishing features include moderate short-term energy with small fluctuations, a spectral roll-off point located within the main speech frequency band (200-4000Hz), and a narrow spectral bandwidth. The tense scenes are represented by background music that gradually intensifies and accelerates in pace. The distinguishing features include a slow upward trend in short-term energy, a gradual shift of the spectral centroid towards the mid-frequency, and a decrease in spectral flatness. The climax audio is characterized by concentrated sections of strong sound effects such as explosions, fighting, and chases, with the highest volume and strongest dynamics. The identifying characteristics include short-term energy approaching or reaching a preset maximum value, and a significant increase in spectral bandwidth. The audio for the end credits is presented as the end of the main feature and the end credits music begins, usually a full song or soothing instrumental music, with a moderate and relatively stable volume. The distinguishing features include a significant decrease in energy after the climax and a tendency to stabilize, and a stable spectral centroid in the mid-frequency range.

[0032] In one specific embodiment, if the current scene category is the opening screening, the massage chair is triggered to enter the experience mode, and the experience mode continues for a preset duration before entering the pending payment state.

[0033] In some embodiments, each massage mode in the preset massage mode library Each corresponds to a scene category The parameters of the massage mode include a basic intensity curve. This indicates the standard force variation and frequency modulation parameters in this scenario. The reference frequency and region activation vector represent the massage motion. Indicates the activation intensity and massage style label of the massage chair. Used to describe the massage type, such as "soothing," "rhythmic," "wave," etc., and to adjust the upper and lower limits of parameters. Used to set the allowable adjustment range for each key parameter; Basic massage patterns are obtained by matching the massage pattern library based on the scene category.

[0034] In some embodiments, the basic massage pattern is adaptively adjusted based on confidence level to generate the final massage pattern. The specific steps are as follows: If the confidence level is greater than or equal to the high confidence threshold, then the parameters of the basic massage mode are used as the parameters of the final massage mode. If the confidence level is less than the high confidence threshold but greater than or equal to the low confidence threshold, then the certainty factor is calculated based on the confidence level, and the calculation method is as follows: ; In the formula, Indicates the deterministic factor; Indicates the first Confidence level corresponding to each scene category; This indicates a preset high confidence threshold, such as 0.8; This indicates a preset low confidence threshold, such as 0.5; The parameters of the basic massage mode are then interpolated and adjusted based on a deterministic factor to generate the parameters of the final massage mode. Specifically, the parameters of the massage mode are interpolated and adjusted based on a deterministic factor, using the region activation vector... For example, let its base value be... The adjusted region activation vector is ,in Indicates the first The lower limit of area activation for each massage mode; If the confidence level is less than or equal to the low confidence threshold, then the preset massage mode is used as the final massage mode.

[0035] In some embodiments, constraints are also imposed on all parameters to ensure that the output is within the range of physical and physiological safety.

[0036] In some embodiments, a sound-perception-based control system for a cinema massage chair is proposed, the system including a sound acquisition module, a scene recognition module, a pattern matching module, and a massage execution module; The sound acquisition module collects the original sound during the movie screening through the sound acquisition device of the massage chair and performs preprocessing; it then extracts features from the preprocessed original sound to form a sound feature sequence. The scene recognition module inputs the sound feature sequence into a sound recognition model based on the fusion of multi-head self-attention mechanism and temporal convolutional network to perform scene recognition, and outputs the scene category and the corresponding confidence score. The pattern matching module matches basic massage patterns from the massage pattern library based on scene category; it then adaptively adjusts the basic massage patterns based on confidence level to generate the final massage pattern. The massage execution module controls the massage chair to perform massage actions based on the final massage mode.

[0037] In some embodiments, the system further includes a user preference learning module for recording user adjustment behaviors of massage modes in various scenarios.

[0038] In some embodiments, the system has offline learning capabilities, enabling it to update the sound scene recognition model based on newly acquired movie sounds without connecting to the cinema's central system, in order to adapt to the audio styles of different films.

[0039] In some embodiments, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.

[0040] In some embodiments, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the method as described in any embodiment of the present invention.

[0041] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. 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 of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and 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.

[0042] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0043] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0044] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, 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 a 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 several 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0045] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A control method for a cinema massage chair based on sound perception, characterized in that, Includes the following steps: The original sound from the movie screening was collected using the sound acquisition device of the massage chair and then preprocessed. Feature extraction is performed on the preprocessed original sound to form a sound feature sequence; The sound feature sequence is input into a sound recognition model based on the fusion of multi-head self-attention mechanism and temporal convolutional network for scene recognition, and the scene category and corresponding confidence score are output. Basic massage patterns are obtained by matching from the massage pattern library based on scene categories; The basic massage pattern is adaptively adjusted based on confidence level to generate the final massage pattern. The massage chair is controlled to perform massage actions based on the final massage mode.

2. The control method for a cinema massage chair based on sound perception according to claim 1, characterized in that, The preprocessing includes pre-emphasis, framing, and windowing, wherein: The pre-emphasis specifically refers to filtering the original sound using a preset pre-emphasis signal; The framing specifically involves dividing the pre-emphasized original sound into multiple short frames according to a preset time sequence. The windowing process specifically involves multiplying each short-time frame by a window function to reduce spectral leakage.

3. The control method for a cinema massage chair based on sound perception according to claim 2, characterized in that, The sound feature sequence includes time-domain features, frequency-domain features, and Mel-frequency cepstral coefficient features.

4. The control method for a cinema massage chair based on sound perception according to claim 3, characterized in that, The sound recognition model includes a feature encoding layer, a temporal convolutional network layer, a multi-head self-attention layer, a temporal convolutional network layer, and a scene recognition layer. in: The feature encoding layer uses a one-dimensional convolutional network to extract local features from the sound feature sequence, obtain local features, and transmit them to the temporal convolutional network layer; The temporal convolutional network layer consists of multiple layers of bidirectional gated recurrent units, used to extract long-term dependencies in local features and output the hidden state at each time step; The multi-head self-attention layer performs weighted aggregation of the hidden states at each time step to obtain aggregated features; The temporal convolutional network layer employs a dilated convolutional structure to capture multi-scale features of aggregated features; Input multi-scale features into the scene recognition layer and output the probability of each scene category; The scene category with the highest probability is selected as the identified scene category, and its corresponding probability is used as the confidence level.

5. The control method for a cinema massage chair based on sound perception according to claim 4, characterized in that, The scene categories include: silent opening sequence, opening screening, calm dialogue, tense plot, climactic action, and end credits.

6. The control method for a cinema massage chair based on sound perception according to claim 5, characterized in that, Each massage mode in the preset massage mode library corresponds to a scene category. The parameters of the massage mode include a basic intensity curve, frequency modulation parameters, region activation vector, massage style label, and upper and lower bounds for parameter adjustment.

7. The control method for a cinema massage chair based on sound perception according to claim 6, characterized in that, The basic massage mode is adaptively adjusted based on confidence level. The specific steps are as follows: If the confidence level is greater than or equal to the high confidence threshold, then the parameters of the basic massage mode are used as the parameters of the final massage mode. If the confidence level is less than the high confidence threshold but greater than or equal to the low confidence threshold, then the certainty factor is calculated based on the confidence level, and the calculation method is as follows: ； In the formula, Indicates the deterministic factor; Indicates the first Confidence level corresponding to each scene category; This represents a preset high-confidence threshold; This indicates a preset low confidence threshold; The parameters of the basic massage mode are interpolated and adjusted based on a deterministic factor to generate the parameters of the final massage mode. If the confidence level is less than or equal to the low confidence threshold, then the preset massage mode is used as the final massage mode.

8. A control system for a cinema massage chair based on sound perception, characterized in that, The system includes a sound acquisition module, a scene recognition module, a pattern matching module, and a massage execution module; The sound acquisition module collects the original sound during the movie screening through the massage chair's sound acquisition device and performs preprocessing. Feature extraction is performed on the preprocessed original sound to form a sound feature sequence; The scene recognition module inputs the sound feature sequence into a sound recognition model based on the fusion of multi-head self-attention mechanism and temporal convolutional network to perform scene recognition, and outputs the scene category and the corresponding confidence score. The pattern matching module matches basic massage patterns from the massage pattern library based on scene categories; The basic massage pattern is adaptively adjusted based on confidence level to generate the final massage pattern. The massage execution module controls the massage chair to perform massage actions based on the final massage mode.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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