Dynamic frequency modulation playing control method and system for treating insomnia by chime music

Abstract

The invention discloses a dynamic frequency modulation playing control method and system for treating insomnia through chime music, and relates to the technical field of music, and the key points of the technical scheme comprise the following steps: obtaining historical insomnia treatment record data matched with a current target insomnia patient, constructing a first frequency modulation mapping model, a second frequency modulation mapping model and a chime music treatment effect evaluation model based on the historical insomnia treatment record data; playing initial chime music for the current target insomnia patient, and collecting real-time brain wave rhythm data of the current target insomnia patient in a preprocessing monitoring period of a preset brain wave rhythm monitoring interval period; inputting the real-time brain wave rhythm data into a chime music treatment effect evaluation model to obtain an initial treatment effect evaluation result; the traditional Chinese medicine composition can effectively improve the insomnia treatment effect.

Description

Technical Field

[0001] This invention relates to the field of music technology, and more specifically, to a dynamic frequency modulation playback control method and system for using chime bells to treat insomnia. Background Technology

[0002] Electroencephalogram (EEG) rhythm, as an important physiological indicator reflecting the human sleep state, has relatively mature monitoring technology, providing a basis for personalized treatment based on physiological data. Bianzhong (chime bells), a traditional Chinese musical form, possesses unique timbre and rhythm with soothing and calming effects. How to integrate Bianzhong music with individual EEG rhythm changes is a pressing issue. Traditional music therapy lacks a dynamic response to the patient's real-time physiological state and cannot adjust the music's frequency cycle in a timely manner according to changes in the patient's EEG, resulting in insufficient targeting and effectiveness of the treatment. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a dynamic frequency modulation playback control method and system for chime bell music therapy for insomnia.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for controlling the dynamic frequency modulation playback of chime bell music for treating insomnia, the method comprising the following steps: Acquire historical insomnia treatment records that are appropriate for the current target insomnia patients, and construct a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model based on the historical insomnia treatment records data; Initial chime music was played for the current target insomnia patient, and real-time EEG rhythm data of the current target insomnia patient was collected during the preprocessing monitoring period within the preset EEG rhythm monitoring interval. Real-time EEG rhythm data was input into the chime bell music therapy effect evaluation model to obtain initial treatment effect evaluation results; If the initial treatment effect assessment result determines that the treatment is effective, the real-time EEG rhythm data will be input into the first frequency modulation mapping model or the second frequency modulation mapping model to obtain the dynamic frequency modulation cycle parameters of the chime music. The playback frequency modulation period of the chime bell music is adjusted according to the dynamic frequency modulation period parameter of the chime bell music to achieve dynamic frequency modulation playback control for the insomnia treatment of the current target insomnia patient.

[0005] Preferably, the construction of a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model based on historical insomnia treatment record data specifically includes the following steps: Historical real-time adaptive data was extracted from historical insomnia treatment records, and the historical real-time adaptive data showed a real-time adaptation between the tuning cycle of the chime music and the brain wave rhythm data of insomnia patients. The time interval features, EEG rhythm term difference correlation features, and EEG rhythm corresponding data of adjacent frequency modulations are extracted from historical real-time adaptive data. The first frequency modulation mapping model is established based on the time interval features, EEG rhythm term difference correlation features, and EEG rhythm corresponding data. Historical stable fit data was extracted from historical insomnia treatment records. The historical stable fit data showed a stable fit between the frequency tuning cycle of the chime music and the EEG rhythm data of insomnia patients. The duration interval characteristics of each frequency tuning cycle and the real-time corresponding data of the EEG rhythm were extracted from the historical stable fit data. A second frequency modulation mapping model is established based on the time interval characteristics and real-time corresponding data; A model for evaluating the effectiveness of chime bell music therapy was established based on historical frequency cycles, corresponding EEG rhythm data, and historical treatment results from historical insomnia treatment records.

[0006] Preferably, the time interval features of adjacent frequency modulations, the correlation features of differences in EEG rhythm terms, and the corresponding data of EEG rhythms are extracted from historical real-time adaptive data. A first frequency modulation mapping model is then established based on these features, including the following steps: If the tuning cycle of the chime bell music and the EEG rhythm data are out of sync in the historical real-time adaptive data, the time interval of each adjustment of the tuning cycle of the chime bell music can be statistically analyzed to obtain the time interval change trend characteristics. The variation trend characteristics of the time interval between two adjacent adjustments in each frequency modulation cycle of the chime bell music are statistically analyzed to obtain the variation trend characteristics of the time interval. Based on the time interval variation trend characteristics, the corresponding data of the chime music frequency modulation cycle and the brain wave rhythm are extracted from the historical real-time adaptive data. The time interval variation trend characteristics and the corresponding data of the chime music frequency modulation cycle and the brain wave rhythm are combined into the first mapping training set. If there are changes in the number of multi-dimensional EEG rhythm items in the historical real-time adaptive data, then the trend characteristics of rhythm item changes are obtained by statistical analysis of the correlation of differences in the multi-dimensional EEG rhythm items between two adjacent adjustments. Based on the trend characteristics of rhythm changes, data corresponding to the frequency modulation cycle of chime music and the EEG rhythm were extracted from historical real-time adaptive data. The rhythmic variation trend characteristics, the frequency modulation period of the chime music and the corresponding data of the EEG rhythm were combined to form the second mapping training set; The first frequency modulation mapping model is established based on the first mapping training set and the second mapping training set.

[0007] Preferably, the real-time corresponding data of the duration interval characteristics of each frequency modulation cycle and the EEG rhythm are extracted from the historical stable adaptive data, specifically including the following steps: In historically stable adaptive data, the priority order of the multi-dimensional EEG rhythm items to which the data is adapted remains unchanged, and the multi-dimensional EEG rhythm items and the frequency modulation cycle are time-synchronized. In addition, the data adaptation with the same time interval between each two adjacent adjustments can extract the real-time corresponding data of the frequency modulation cycle of the chime music and the EEG rhythm from the historically stable adaptive data. The duration interval characteristics of each frequency modulation cycle of the chime music were analyzed in the historical stable adaptation data.

[0008] Preferably, real-time brainwave rhythm data of the current target insomnia patient is collected during a preprocessing monitoring period within a preset brainwave rhythm monitoring interval, specifically including the following steps: The interval for monitoring brainwave rhythm is preset based on the frequency modulation period parameters of the initial chime music; Extract the preprocessed monitoring period from the preset EEG rhythm monitoring interval; Real-time EEG rhythm data of the current target insomnia patient are obtained based on the pre-processing monitoring period.

[0009] Preferably, the real-time electroencephalogram (EEG) rhythm data is input into the chime bell music therapy effect evaluation model to obtain the initial treatment effect evaluation result, specifically including the following steps: Real-time EEG rhythm data is divided into multiple continuous monitoring segments according to sleep cycle stages, and the amplitude variation trend, rhythm synchronization characteristics and abnormal fluctuation frequency of EEG rhythm in each continuous monitoring segment are extracted. The matching degree is obtained by hierarchically matching the amplitude variation trend, rhythm synchronization characteristics, and frequency of abnormal fluctuations with the historical effective treatment feature database of the corresponding stage. If the matching degree meets the preset matching degree threshold, the initial treatment effect is determined to be effective. If the matching degree does not meet the preset matching degree threshold, the initial treatment effect is determined to be ineffective.

[0010] Preferably, the real-time EEG rhythm data is input into the first frequency modulation mapping model or the second frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music, specifically including the following steps: After analyzing real-time EEG rhythm data, transient change features of rhythm fluctuations and multi-band rhythm coupling degree are extracted; If the frequency of transient change characteristics exceeds the set value, or if the multi-band rhythm coupling degree shows non-periodic variation, the real-time EEG rhythm data is input into the first frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music. If the frequency of transient change characteristics is lower than the set value, or if the multi-band rhythm coupling degree maintains periodic variation, the real-time EEG rhythm data is input into the second frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music.

[0011] Preferably, adjusting the playback frequency modulation period of the chime bell music according to the dynamic frequency modulation period parameter of the chime bell music specifically includes the following steps: The frequency modulation cycle adjustment sequence is constructed based on the cycle base and correction coefficient of the dynamic frequency modulation cycle parameters of the chime bells music; the initial playback cycle is set based on the cycle base, and the cycle adjustment step size and direction are determined according to the correction coefficient. If the dynamic frequency modulation period parameter of the chime bell music contains a progressive rate feature, the playback period is gradually shortened or extended according to the progressive rate feature. If the dynamic frequency modulation cycle parameter of the chime bells music includes a step update rule, the stage is adjusted according to the step update rule. The stage transition time is obtained based on the change in the multi-band rhythm coupling degree, and the adaptability transformation of the chime bell pitch combination is triggered, so that the pitch combination automatically matches the current rhythm characteristics as the frequency modulation cycle is adjusted.

[0012] A dynamic frequency modulation playback control system for chime bell music therapy for insomnia includes: Construction Module: Obtain historical insomnia treatment record data that is suitable for the current target insomnia patient, and construct a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model based on the historical insomnia treatment record data; Acquisition module: Plays initial chime music for the current target insomnia patient, and collects real-time EEG rhythm data of the current target insomnia patient during the preprocessing monitoring period within the preset EEG rhythm monitoring interval; First processing module: Inputs real-time EEG rhythm data into the chime bell music therapy effect evaluation model to obtain initial treatment effect evaluation results; Second processing module: If the initial treatment effect assessment result determines that the treatment is effective, the real-time EEG rhythm data is input into the first frequency modulation mapping model or the second frequency modulation mapping model to obtain the dynamic frequency modulation cycle parameters of the chime music; Control module: Adjusts the playback frequency modulation period of the chime bell music according to the dynamic frequency modulation period parameter of the chime bell music, so as to realize dynamic frequency modulation playback control for the insomnia treatment of the current target insomnia patient.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a dynamic frequency modulation playback control method for chime bell music therapy for insomnia.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention constructs a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model by acquiring historical insomnia treatment records adapted to the current target insomnia patient. Different patients exhibit variations in their insomnia and responses to chime bell music; the model built based on historical data can capture these individual differences, providing a solid foundation for subsequent dynamic frequency modulation. Real-time EEG rhythm data of the current target insomnia patient is collected during a preprocessing monitoring period with preset EEG rhythm monitoring intervals, enabling real-time monitoring of the patient's physiological state changes during initial chime bell music therapy. The real-time EEG rhythm data is input into the chime bell music therapy effect evaluation model to obtain the initial treatment effect evaluation result, determining the effectiveness of the initial treatment and improving treatment efficiency and accuracy. If the initial treatment effect is effective, the real-time EEG rhythm data is input into either the first or second frequency modulation mapping model to obtain the dynamic frequency modulation cycle parameters of the chime bell music, achieving dynamic adjustment based on the patient's real-time physiological state. The first and second frequency modulation mapping models, respectively targeting different characteristics of brainwave rhythm changes, can provide the most suitable frequency modulation period parameters when different types of changes occur in the patient's brainwave rhythm. Adjusting the playback frequency modulation period of the chime bell music according to the dynamic frequency modulation period parameters achieves dynamic frequency modulation playback control for the current target insomnia patient, continuously optimizing the treatment effect. This dynamic adjustment method keeps the therapeutic effect of the chime bell music at its optimal state, continuously adjusting the playback period as the patient's sleep improves until the patient achieves good sleep. Therefore, this application can effectively improve the effect of insomnia treatment. Attached Figure Description

[0015] Figure 1 This invention provides a schematic diagram illustrating the steps of a dynamic frequency modulation playback control method for treating insomnia with chime bell music. Figure 2 This invention presents a schematic diagram of a dynamic frequency modulation playback control system for chime bell music therapy for insomnia. Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0016] 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-3 As shown.

[0021] The embodiments further illustrate the dynamic frequency modulation playback control method and system for chime bell music therapy for insomnia proposed in this invention.

[0022] A method for controlling the dynamic frequency modulation playback of chime bell music for treating insomnia, the method comprising the following steps: Acquire historical insomnia treatment records that are appropriate for the current target insomnia patients, and construct a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model based on the historical insomnia treatment records data; Initial chime music was played for the current target insomnia patient, and real-time EEG rhythm data of the current target insomnia patient was collected during the preprocessing monitoring period within the preset EEG rhythm monitoring interval. Real-time EEG rhythm data was input into the chime bell music therapy effect evaluation model to obtain initial treatment effect evaluation results; If the initial treatment effect assessment result determines that the treatment is effective, the real-time EEG rhythm data will be input into the first frequency modulation mapping model or the second frequency modulation mapping model to obtain the dynamic frequency modulation cycle parameters of the chime music. The playback frequency modulation period of the chime bell music is adjusted according to the dynamic frequency modulation period parameter of the chime bell music to achieve dynamic frequency modulation playback control for the insomnia treatment of the current target insomnia patient.

[0023] Based on historical insomnia treatment records, a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy efficacy evaluation model were constructed, specifically including the following steps: Historical real-time adaptive data was extracted from historical insomnia treatment records, and the historical real-time adaptive data showed a real-time adaptation between the tuning cycle of the chime music and the brain wave rhythm data of insomnia patients. The time interval features, EEG rhythm term difference correlation features, and EEG rhythm corresponding data of adjacent frequency modulations are extracted from historical real-time adaptive data. The first frequency modulation mapping model is established based on the time interval features, EEG rhythm term difference correlation features, and EEG rhythm corresponding data. Historical stable fit data was extracted from historical insomnia treatment records. The historical stable fit data showed a stable fit between the frequency tuning cycle of the chime music and the EEG rhythm data of insomnia patients. The duration interval characteristics of each frequency tuning cycle and the real-time corresponding data of the EEG rhythm were extracted from the historical stable fit data. A second frequency modulation mapping model is established based on the time interval characteristics and real-time corresponding data; A model for evaluating the effectiveness of chime bell music therapy was established based on historical frequency cycles, corresponding EEG rhythm data, and historical treatment results from historical insomnia treatment records.

[0024] The historical frequency modulation cycle of chime bell music therapy was extracted from historical insomnia treatment records. This refers to the time intervals during which the frequency of the chime bell music was adjusted in past treatments. For example, in one treatment session for a particular insomnia patient, the frequency modulation cycle of the chime bell music was adjusted every five minutes. Simultaneously, corresponding brainwave rhythm data was extracted, i.e., the rhythmic data of the patient's brainwaves during the chime bell music therapy at the corresponding frequency cycle, such as the frequency and amplitude of the brainwaves. Furthermore, historical treatment effect results were extracted, such as changes in the patient's insomnia symptoms after undergoing chime bell music therapy at that specific frequency cycle.

[0025] This model integrates historical frequency modulation cycles, corresponding EEG rhythm data, and historical treatment outcome results, using methods such as regression or classification models from machine learning, to establish an evaluation model for the effectiveness of chime bell music therapy. For example, it trains the model by inputting historical frequency modulation cycles, EEG rhythm data, and corresponding treatment outcomes from a large number of different patients, allowing the model to learn the correlation between frequency modulation cycles, EEG rhythm data, and treatment outcomes. When a new insomnia patient receives chime bell music therapy, their real-time EEG rhythm data during treatment, along with the corresponding chime bell music frequency modulation cycle, is input into the model. The model then evaluates the effectiveness of this particular chime bell music therapy based on the previously learned patterns, thereby determining whether the treatment was effective.

[0026] The time interval features of adjacent frequency modulations, the correlation features of differences in EEG rhythm terms, and the corresponding data of EEG rhythms are extracted from historical real-time adaptive data. Based on the time interval features, the correlation features of differences in EEG rhythm terms, and the corresponding data of EEG rhythms, a first frequency modulation mapping model is established, which specifically includes the following steps: If the tuning cycle of the chime bell music and the EEG rhythm data are out of sync in the historical real-time adaptive data, the time interval of each adjustment of the tuning cycle of the chime bell music can be statistically analyzed to obtain the time interval change trend characteristics. The variation trend characteristics of the time interval between two adjacent adjustments in each frequency modulation cycle of the chime bell music are statistically analyzed to obtain the variation trend characteristics of the time interval. Based on the time interval variation trend characteristics, the corresponding data of the chime music frequency modulation cycle and the brain wave rhythm are extracted from the historical real-time adaptive data. The time interval variation trend characteristics and the corresponding data of the chime music frequency modulation cycle and the brain wave rhythm are combined into the first mapping training set. If there are changes in the number of multi-dimensional EEG rhythm items in the historical real-time adaptive data, then the trend characteristics of rhythm item changes are obtained by statistical analysis of the correlation of differences in the multi-dimensional EEG rhythm items between two adjacent adjustments. Based on the trend characteristics of rhythm changes, data corresponding to the frequency modulation cycle of chime music and the EEG rhythm were extracted from historical real-time adaptive data. The rhythmic variation trend characteristics, the frequency modulation period of the chime music and the corresponding data of the EEG rhythm were combined to form the second mapping training set; The first frequency modulation mapping model is established based on the first mapping training set and the second mapping training set.

[0027] When there is a mismatch in the timing of the tuning cycle of the chime bell music and the EEG rhythm data in the historical real-time adaptive data, the time interval of each adjustment of the tuning cycle of the chime bell music is statistically analyzed to obtain the trend characteristics of the time interval changes. For example, if the time interval of the first tuning cycle adjustment of the chime bell music during a patient's treatment is 3 minutes, the second adjustment is 4 minutes, and the third adjustment is 5 minutes, the trend characteristics of the time interval changes are obtained by statistically analyzing the time intervals between these adjacent adjustments. Then, based on the trend characteristics of the time interval changes, the corresponding data of the tuning cycle of the chime bell music and the EEG rhythm are extracted from the historical real-time adaptive data, such as the frequency and amplitude data of the EEG rhythm corresponding to a certain time interval change. The trend characteristics of the time interval changes and these corresponding data are combined to form the first mapping training set.

[0028] If the number of multi-dimensional EEG rhythm items in the historical real-time adaptive data changes—for example, if the EEG rhythm items change from two dimensions (alpha and beta waves) to three dimensions (alpha, beta, and theta waves)—then statistical analysis of the correlation trend characteristics of the multi-dimensional EEG rhythm items between two adjacent adjustments is performed to obtain the rhythm item change trend characteristics. Based on the rhythm item change trend characteristics, data corresponding to the chime music frequency modulation period and EEG rhythms are extracted from the historical real-time adaptive data. The rhythm item change trend characteristics and these corresponding data are combined to form a second mapping training set. A first frequency modulation mapping model is established based on the first and second mapping training sets. This model maps the chime music frequency modulation period to EEG rhythm data based on changes in time intervals or rhythm items, providing a basis for subsequent dynamic frequency modulation.

[0029] The real-time corresponding data of the duration interval characteristics of each frequency modulation cycle and the EEG rhythm are extracted from the historical stable adaptive data. The specific steps include: In historically stable adaptive data, the priority order of the multi-dimensional EEG rhythm items to which the data is adapted remains unchanged, and the multi-dimensional EEG rhythm items and the frequency modulation cycle are time-synchronized. In addition, the data adaptation with the same time interval between each two adjacent adjustments can extract the real-time corresponding data of the frequency modulation cycle of the chime music and the EEG rhythm from the historically stable adaptive data. The duration interval characteristics of each frequency modulation cycle of the chime music were analyzed in the historical stable adaptation data.

[0030] In historically stable adaptive data, the priority order of the multi-dimensional EEG rhythm items remains unchanged, for example, always prioritizing alpha waves, theta waves, and delta waves. Simultaneously, the multi-dimensional EEG rhythm items and the frequency modulation cycle are time-synchronized. For instance, the changes in the frequency modulation cycle of the chime bell music and the changes in the EEG rhythm perfectly match in time, and the time interval between each two adjacent adjustments is the same, for example, each adjustment interval is 2 minutes. Under this data adaptation condition, real-time corresponding data of the chime bell music frequency modulation cycle and EEG rhythm is extracted from the historically stable adaptive data, such as the specific values ​​of each EEG rhythm corresponding to a certain frequency modulation cycle. Then, the duration interval characteristics of each frequency modulation cycle adjustment in the historically stable adaptive data are statistically analyzed, that is, the time interval of each frequency modulation cycle adjustment is statistically judged. For example, if the time interval of multiple adjustments is 2 minutes, the pattern of its duration interval is clarified.

[0031] Real-time EEG rhythm data of the target insomnia patient is collected during the preprocessing monitoring period within a preset EEG rhythm monitoring interval. The specific steps include: The interval for monitoring brainwave rhythm is preset based on the frequency modulation period parameters of the initial chime music; Extract the preprocessed monitoring period from the preset EEG rhythm monitoring interval; Real-time EEG rhythm data of the current target insomnia patient are obtained based on the pre-processing monitoring period.

[0032] The frequency modulation period of the initial chime music is used to preset the interval for monitoring brainwave rhythms. For example, if the initial chime music's frequency modulation period is adjusted every 4 minutes, then an interval of monitoring brainwave rhythms every 4 minutes is preset accordingly. A preprocessing monitoring period is extracted from the preset brainwave rhythm monitoring interval to select specific time periods suitable for data collection. Data is collected based on this preprocessing monitoring period to obtain real-time brainwave rhythm data for the target insomnia patient, such as the specific rhythmic information of the patient's brainwave frequency and amplitude within this period. This data will provide a basis for subsequent treatment effect evaluation and dynamic frequency modulation.

[0033] Real-time EEG rhythm data is input into the chime bell music therapy efficacy evaluation model to obtain initial efficacy evaluation results, specifically including the following steps: Real-time EEG rhythm data is divided into multiple continuous monitoring segments according to sleep cycle stages, and the amplitude variation trend, rhythm synchronization characteristics and abnormal fluctuation frequency of EEG rhythm in each continuous monitoring segment are extracted. The matching degree is obtained by hierarchically matching the amplitude variation trend, rhythm synchronization characteristics, and frequency of abnormal fluctuations with the historical effective treatment feature database of the corresponding stage. If the matching degree meets the preset matching degree threshold, the initial treatment effect is determined to be effective. If the matching degree does not meet the preset matching degree threshold, the initial treatment effect is determined to be ineffective.

[0034] Real-time EEG rhythm data is divided into multiple continuous monitoring segments according to sleep cycle stages, such as the sleep onset period, light sleep period, and deep sleep period. From each continuous monitoring segment, the amplitude variation trend of the EEG rhythm is extracted, such as whether the amplitude gradually increases or decreases; rhythm synchronicity characteristics are the degree of synchronization of the EEG rhythm in time or frequency; and the frequency of abnormal fluctuations is the number of times abnormal fluctuations occur in the EEG.

[0035] The amplitude variation trend, rhythm synchronicity characteristics, and frequency of abnormal fluctuations are hierarchically matched with a historical effective treatment feature database for the corresponding sleep stage to obtain the matching degree. The historical effective treatment feature database records features such as a slow decrease in amplitude, good rhythm synchronicity, and low frequency of abnormal fluctuations during the sleep onset period. If the current patient's features during the sleep onset period match the historical database to a high degree, a high matching degree is obtained.

[0036] If the matching degree meets the preset matching degree threshold, such as exceeding the preset matching degree threshold of 80%, the initial treatment effect is determined to be effective; if the matching degree does not meet the preset matching degree threshold, such as falling below the preset matching degree threshold of 80%, the initial treatment effect is determined to be ineffective.

[0037] The real-time EEG rhythm data is then input into the first or second frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music. This process includes the following steps: After analyzing real-time EEG rhythm data, transient change features of rhythm fluctuations and multi-band rhythm coupling degree are extracted; If the frequency of transient change characteristics exceeds the set value, or if the multi-band rhythm coupling degree shows non-periodic variation, the real-time EEG rhythm data is input into the first frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music. If the frequency of transient change characteristics is lower than the set value, or if the multi-band rhythm coupling degree maintains periodic variation, the real-time EEG rhythm data is input into the second frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music.

[0038] Real-time EEG rhythm data is analyzed to extract transient change features and multi-band rhythm coupling. For example, transient change features can be sudden increases or decreases in EEG waves within a short period of time, while multi-band rhythm coupling is the degree of correlation between different frequency bands of EEG waves. Based on the performance of these two features, either a first or second frequency modulation mapping model is selected. If the frequency of transient change features exceeds a set value, such as frequent sudden fluctuations in EEG waves over a period of time, or if the multi-band rhythm coupling exhibits non-periodic changes (i.e., the correlation between different frequency bands of EEG waves is irregular), then the real-time EEG rhythm data is input into the first frequency modulation mapping model to obtain the dynamic frequency modulation period parameters for the chime music.

[0039] If the frequency of transient changes is lower than the set value—for example, if there are no obvious sudden fluctuations in brain waves over a relatively long period, or if the multi-band rhythm coupling degree maintains periodic changes (i.e., the correlation between different frequency bands of brain waves exhibits a regular periodicity)—then the real-time brain wave rhythm data is input into the second frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music. In this way, based on the patient's real-time brain wave state, an appropriate model can be selected to determine the dynamic frequency modulation period of the chime music, thus achieving more precise insomnia treatment.

[0040] Adjusting the playback frequency modulation period of the chime bell music according to the dynamic frequency modulation period parameters includes the following steps: The frequency modulation cycle adjustment sequence is constructed based on the cycle base and correction coefficient of the dynamic frequency modulation cycle parameters of the chime bells music; the initial playback cycle is set based on the cycle base, and the cycle adjustment step size and direction are determined according to the correction coefficient. If the dynamic frequency modulation period parameter of the chime bell music contains a progressive rate feature, the playback period is gradually shortened or extended according to the progressive rate feature. If the dynamic frequency modulation cycle parameter of the chime bells music includes a step update rule, the stage is adjusted according to the step update rule. The stage transition time is obtained based on the change in the multi-band rhythm coupling degree, and the adaptability transformation of the chime bell pitch combination is triggered, so that the pitch combination automatically matches the current rhythm characteristics as the frequency modulation cycle is adjusted.

[0041] First, a frequency modulation cycle adjustment sequence is constructed based on the cycle base and correction coefficient of the chime bell music dynamic frequency modulation cycle parameters. The initial playback cycle is set based on the cycle base, and the step size and direction of the cycle adjustment are determined according to the correction coefficient. For example, if the cycle base is 5 minutes and the correction coefficient is positive, the initial playback cycle is 5 minutes, and the playback cycle is subsequently extended by the step size.

[0042] If the dynamic frequency modulation period parameter of the chime bells music includes a progressive rate feature, then the playback period will be gradually shortened or lengthened according to the progressive rate feature. For example, if the progressive rate feature is to shorten the playback period by 1 minute every two minutes, then the playback period will gradually shorten from the initial 5 minutes to 4 minutes.

[0043] If the parameter includes a step-up update rule, the stage is adjusted according to the step-up update rule. Simultaneously, the stage transition time is obtained based on the change in multi-band rhythm coupling, triggering an adaptive transformation of the chime bell pitch combination. This allows the pitch combination to automatically match the current rhythm characteristics as the playback frequency modulation cycle changes. For example, when the change in multi-band rhythm coupling reaches a certain level, the chime bell pitch combination changes from predominantly low frequencies to a combination of high and low frequencies, thus better matching the current patient's EEG rhythm characteristics and improving treatment effectiveness.

[0044] A dynamic frequency modulation playback control system for chime bell music therapy for insomnia includes: Construction Module: Obtain historical insomnia treatment record data that is suitable for the current target insomnia patient, and construct a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model based on the historical insomnia treatment record data; Acquisition module: Plays initial chime music for the current target insomnia patient, and collects real-time EEG rhythm data of the current target insomnia patient during the preprocessing monitoring period within the preset EEG rhythm monitoring interval; First processing module: Inputs real-time EEG rhythm data into the chime bell music therapy effect evaluation model to obtain initial treatment effect evaluation results; Second processing module: If the initial treatment effect assessment result determines that the treatment is effective, the real-time EEG rhythm data is input into the first frequency modulation mapping model or the second frequency modulation mapping model to obtain the dynamic frequency modulation cycle parameters of the chime music; Control module: Adjusts the playback frequency modulation period of the chime bell music according to the dynamic frequency modulation period parameter of the chime bell music, so as to realize dynamic frequency modulation playback control for the insomnia treatment of the current target insomnia patient.

[0045] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a dynamic frequency modulation playback control method for chime bell music therapy for insomnia.

[0046] like Figure 3 As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logic instructions in the memory 630 to execute a dynamic frequency modulation playback control method for treating insomnia with chime bell music.

[0047] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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 the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0048] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute a dynamic frequency modulation playback control method for chime music therapy for insomnia.

[0049] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a dynamic frequency modulation playback control method for chime music therapy for insomnia.

[0050] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0051] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic frequency modulation playback control of chime bell music for treating insomnia, characterized in that, The method includes the following steps: Acquire historical insomnia treatment records that are appropriate for the current target insomnia patients, and construct a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model based on the historical insomnia treatment records data; Initial chime music was played for the current target insomnia patient, and real-time EEG rhythm data of the current target insomnia patient was collected during the preprocessing monitoring period within the preset EEG rhythm monitoring interval. Real-time EEG rhythm data was input into the chime bell music therapy effect evaluation model to obtain initial treatment effect evaluation results; If the initial treatment effect assessment result determines that the treatment is effective, the real-time EEG rhythm data will be input into the first frequency modulation mapping model or the second frequency modulation mapping model to obtain the dynamic frequency modulation cycle parameters of the chime music. The playback frequency modulation period of the chime bell music is adjusted according to the dynamic frequency modulation period parameter of the chime bell music to achieve dynamic frequency modulation playback control for the insomnia treatment of the current target insomnia patient.

2. The method for dynamic frequency modulation playback control of chime bell music for treating insomnia according to claim 1, characterized in that, Based on historical insomnia treatment records, a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy efficacy evaluation model were constructed, specifically including the following steps: Historical real-time adaptive data was extracted from historical insomnia treatment records, and the historical real-time adaptive data showed a real-time adaptation between the tuning cycle of the chime music and the brain wave rhythm data of insomnia patients. The time interval features, EEG rhythm term difference correlation features, and EEG rhythm corresponding data of adjacent frequency modulations are extracted from historical real-time adaptive data. The first frequency modulation mapping model is established based on the time interval features, EEG rhythm term difference correlation features, and EEG rhythm corresponding data. Historical stable fit data was extracted from historical insomnia treatment records. The historical stable fit data showed a stable fit between the frequency tuning cycle of the chime music and the EEG rhythm data of insomnia patients. The duration interval characteristics of each frequency tuning cycle and the real-time corresponding data of the EEG rhythm were extracted from the historical stable fit data. A second frequency modulation mapping model is established based on the time interval characteristics and real-time corresponding data; A model for evaluating the effectiveness of chime bell music therapy was established based on historical frequency cycles, corresponding EEG rhythm data, and historical treatment results from historical insomnia treatment records.

3. The method for dynamic frequency modulation playback control of chime bell music for treating insomnia according to claim 2, characterized in that, The time interval features of adjacent frequency modulations, the correlation features of differences in EEG rhythm terms, and the corresponding data of EEG rhythms are extracted from historical real-time adaptive data. Based on the time interval features, the correlation features of differences in EEG rhythm terms, and the corresponding data of EEG rhythms, a first frequency modulation mapping model is established, which specifically includes the following steps: If the tuning cycle of the chime bell music and the EEG rhythm data are out of sync in the historical real-time adaptive data, the time interval of each adjustment of the tuning cycle of the chime bell music can be statistically analyzed to obtain the time interval change trend characteristics. The variation trend characteristics of the time interval between two adjacent adjustments in each frequency modulation cycle of the chime bell music are statistically analyzed to obtain the variation trend characteristics of the time interval. Based on the time interval variation trend characteristics, the corresponding data of the chime music frequency modulation cycle and the brain wave rhythm are extracted from the historical real-time adaptive data. The time interval variation trend characteristics and the corresponding data of the chime music frequency modulation cycle and the brain wave rhythm are combined into the first mapping training set. If there are changes in the number of multi-dimensional EEG rhythm items in the historical real-time adaptive data, then the trend characteristics of rhythm item changes are obtained by statistical analysis of the correlation of differences in the multi-dimensional EEG rhythm items between two adjacent adjustments. Based on the trend characteristics of rhythm changes, data corresponding to the frequency modulation cycle of chime music and the EEG rhythm were extracted from historical real-time adaptive data. The rhythmic variation trend characteristics, the frequency modulation period of the chime music and the corresponding data of the EEG rhythm were combined to form the second mapping training set; The first frequency modulation mapping model is established based on the first mapping training set and the second mapping training set.

4. The method for dynamic frequency modulation playback control of chime bell music for treating insomnia according to claim 2, characterized in that, The real-time corresponding data of the duration interval characteristics of each frequency modulation cycle and the EEG rhythm are extracted from the historical stable adaptive data. The specific steps include: In historically stable adaptive data, the priority order of the multi-dimensional EEG rhythm items to which the data is adapted remains unchanged, and the multi-dimensional EEG rhythm items and the frequency modulation cycle are time-synchronized. In addition, the data adaptation with the same time interval between each two adjacent adjustments can extract the real-time corresponding data of the frequency modulation cycle of the chime music and the EEG rhythm from the historically stable adaptive data. The duration interval characteristics of each frequency modulation cycle of the chime music were analyzed in the historical stable adaptation data.

5. The method for dynamic frequency modulation playback control of chime bell music for treating insomnia according to claim 4, characterized in that, Real-time EEG rhythm data of the target insomnia patient is collected during the preprocessing monitoring period within a preset EEG rhythm monitoring interval. The specific steps include: The interval for monitoring brainwave rhythm is preset based on the frequency modulation period parameters of the initial chime music; Extract the preprocessed monitoring period from the preset EEG rhythm monitoring interval; Real-time EEG rhythm data of the current target insomnia patient are obtained based on the pre-processing monitoring period.

6. The method for dynamic frequency modulation playback control of chime bell music for treating insomnia according to claim 5, characterized in that, Real-time EEG rhythm data is input into the chime bell music therapy efficacy evaluation model to obtain initial efficacy evaluation results, specifically including the following steps: Real-time EEG rhythm data is divided into multiple continuous monitoring segments according to sleep cycle stages, and the amplitude variation trend, rhythm synchronization characteristics and abnormal fluctuation frequency of EEG rhythm in each continuous monitoring segment are extracted. The matching degree is obtained by hierarchically matching the amplitude variation trend, rhythm synchronization characteristics, and frequency of abnormal fluctuations with the historical effective treatment feature database of the corresponding stage. If the matching degree meets the preset matching degree threshold, the initial treatment effect is determined to be effective. If the matching degree does not meet the preset matching degree threshold, the initial treatment effect is determined to be ineffective.

7. The method for dynamic frequency modulation playback control of chime bell music for treating insomnia according to claim 6, characterized in that, The real-time EEG rhythm data is then input into the first or second frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music. This process includes the following steps: After analyzing real-time EEG rhythm data, transient change features of rhythm fluctuations and multi-band rhythm coupling degree are extracted; If the frequency of transient change characteristics exceeds the set value, or if the multi-band rhythm coupling degree shows non-periodic variation, the real-time EEG rhythm data is input into the first frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music. If the frequency of transient change characteristics is lower than the set value, or if the multi-band rhythm coupling degree maintains periodic variation, the real-time EEG rhythm data is input into the second frequency modulation mapping model to obtain the dynamic frequency modulation period parameters of the chime music.

8. The method for dynamic frequency modulation playback control of chime bell music for treating insomnia according to claim 7, characterized in that, Adjusting the playback frequency modulation period of the chime bell music according to the dynamic frequency modulation period parameters includes the following steps: The frequency modulation cycle adjustment sequence is constructed based on the cycle base and correction coefficient of the dynamic frequency modulation cycle parameters of the chime bells music; the initial playback cycle is set based on the cycle base, and the cycle adjustment step size and direction are determined according to the correction coefficient. If the dynamic frequency modulation period parameter of the chime bells music contains a progressive rate feature, the playback period is gradually shortened or extended according to the progressive rate feature. If the dynamic frequency modulation cycle parameter of the chime bells music includes a step update rule, the stage is adjusted according to the step update rule. The stage transition time is obtained based on the change in the multi-band rhythm coupling degree, and the adaptability transformation of the chime bell pitch combination is triggered, so that the pitch combination automatically matches the current rhythm characteristics as the frequency modulation cycle is adjusted.

9. A dynamic frequency modulation playback control system for chime bell music therapy for insomnia, applied to the dynamic frequency modulation playback control method for chime bell music therapy for insomnia as described in any one of claims 1 to 8, characterized in that, include: Construction Module: Obtain historical insomnia treatment record data that is suitable for the current target insomnia patient, and construct a first frequency modulation mapping model, a second frequency modulation mapping model, and a chime bell music therapy effect evaluation model based on the historical insomnia treatment record data; Acquisition module: Plays initial chime music for the current target insomnia patient, and collects real-time EEG rhythm data of the current target insomnia patient during the preprocessing monitoring period within the preset EEG rhythm monitoring interval; First processing module: Inputs real-time EEG rhythm data into the chime bell music therapy effect evaluation model to obtain initial treatment effect evaluation results; Second processing module: If the initial treatment effect assessment result determines that the treatment is effective, the real-time EEG rhythm data is input into the first frequency modulation mapping model or the second frequency modulation mapping model to obtain the dynamic frequency modulation cycle parameters of the chime music; Control module: Adjusts the playback frequency modulation period of the chime bell music according to the dynamic frequency modulation period parameter of the chime bell music, so as to realize dynamic frequency modulation playback control for the insomnia treatment of the current target insomnia patient.

10. 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 dynamic frequency modulation playback control method for chime music therapy for insomnia as described in any one of claims 1 to 8.

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