Acupuncture data monitoring system based on brain-computer interface

Abstract

The invention discloses an acupuncture data monitoring system based on a brain-computer interface, which belongs to the field of acupuncture data monitoring and comprises a data acquisition module, an acupuncture difference analysis module, a brain wave judgment module and an acupuncture scheme dynamic adjustment and feedback module. The data acquisition module is used for acquiring WorldClim temperature data, Ketz climate classification data, brain wave basic value data of people of different regions and ages, real-time electroencephalogram signals of patients and acupuncture and moxibustion parameters; according to the acupuncture data monitoring system based on the brain-computer interface, acupuncture sensitivity degrees, brain wave threshold values and acupuncture suggestions of patients under different resident climates can be rapidly obtained, accurate matching of personalized treatment schemes is achieved, pertinence and effectiveness of acupuncture treatment are improved, meanwhile, doctors are assisted in dynamically adjusting strategies according to physiological responses of the patients, and the accuracy of acupuncture treatment is improved. And the treatment process is simplified.

Description

Technical Field

[0001] The present invention belongs to the technical field of acupuncture data monitoring, and specifically relates to an acupuncture data monitoring system based on a brain-computer interface. Background Art

[0002] Acupuncture treatment is a traditional Chinese medical therapy based on traditional Chinese medicine theory. It inserts special filiform needles into the patient's body at certain acupoints, and uses acupuncture techniques such as twirling and lifting-thrusting to stimulate the human meridians and acupoints, or burns and warms the body surface acupoints with moxa wool, etc., so as to achieve the purpose of dredging the meridians, regulating qi and blood, and balancing yin and yang; In the field of traditional acupuncture treatment, the formulation of treatment plans mostly relies on doctors' experience, and it is difficult to fully utilize environmental and physiological data to achieve precision medicine. On the one hand, factors such as climate environment and patient age have a significant impact on acupuncture sensitivity and brain wave data, but the existing technology lacks research on the environment and acupuncture treatment, resulting in treatment plans being difficult to adapt to the individual differences of patients in different regions, reducing the pertinence of acupuncture treatment; on the other hand, in the link of patient emotion monitoring, the traditional method lacks refined analysis of the patient's electroencephalogram data, and does not combine EEGbase electroencephalogram data with the electroencephalogram characteristics of the local healthy population to establish normal emotion electroencephalogram characteristics. Therefore, it is easy to cause misjudgment of the emotional state and interfere with the acupuncture treatment effect. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. For this reason, the present invention proposes an acupuncture data monitoring system based on a brain-computer interface, enabling medical staff to conveniently handle problems such as insufficient personalization of acupuncture treatment plans, inaccurate monitoring of patient emotional states, and inability to dynamically adjust parameters during the treatment process.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: An acupuncture data monitoring system based on a brain-computer interface, including a data acquisition module, an acupuncture difference analysis module, a brain wave judgment module, a dynamic adjustment of acupuncture plan, and a feedback module; The data acquisition module is used to collect WorldClim temperature data, Köppen climate classification data, basic brain wave value data of different regions and age groups, real-time electroencephalogram signals of patients, and acupuncture parameters; The acupuncture difference analysis module studies the relationship between known brain wave data and WorldClim temperature data, Köppen climate classification data, and constructs a query library. Inputting the patient's age, gender, and usual residence, it provides the patient's brain wave threshold, acupuncture sensitivity, and acupuncture suggestions; The brain wave judgment module judges the patient's emotional state according to the obtained brain wave threshold. When the emotion is abnormal, it conducts artificial counseling and continuous monitoring until the brain wave returns to normal; The acupuncture scheme dynamic adjustment module receives data from the brain wave judgment module, sets the basic stimulation threshold according to the patient's brain wave threshold, and continuously monitors the patient's brain wave data to dynamically adjust the acupuncture parameters; The feedback module outputs the status of the patient during acupuncture and performs status feedback.

[0005] Furthermore, the data acquisition module is used to collect WorldClim temperature data, Köppen climate classification data, brain wave basic value data of different regions and age groups, patients' real-time brain wave signals and acupuncture parameter processing process as follows: Collect 1km resolution annual average temperature and seasonal fluctuation raster data from the WorldClim platform; collect vector data of Köppen climate classification; Collect basic EEG data of different regions and age groups. The data sources include EEG data published by hospitals or research institutions and EEG data from the EEGbase platform. The brain wave data and acupuncture parameters of patients during acupuncture treatment are collected, and the brain wave data are aligned with the acupuncture parameters according to the timestamp. The acupuncture parameters include acupoint name, needle insertion depth, manipulation, and electroacupuncture parameters.

[0006] Aligning brain wave data with acupuncture parameters based on timestamps can establish a precise correspondence between EEG changes and acupuncture operations.

[0007] Furthermore, the acupuncture difference analysis module studies the relationship between known brain wave data and WorldClim temperature data and Köppen climate classification data, and constructs a query library to input the patient's age, gender, and permanent residence, and provides the patient's brain wave threshold, acupuncture sensitivity, and acupuncture recommendations: The EEG data was cleaned to remove abnormal values beyond the normal physiological range. At the same time, the corresponding individual climate data and temperature data were matched according to the geographical coordinates of the sampling points. Finally, the EEG data was divided into groups by age, specifically into developmental period, adulthood, and old age. The developmental period was 0-18 years old, and was further divided into developmental period at intervals of 2 years. The adult period was 19-60 years old, and was further divided into intervals of 5 years. The old age was over 60 years old, and was further divided into intervals of 10 years. Standardize WorldClim temperature data and Köppen climate classification data; A relationship model between known brain wave data and gender, age, climate, and temperature is established, and a query library is constructed. The patient's gender, age, and permanent residence are input to obtain the patient's brain wave threshold. At the same time, based on the obtained brain wave threshold, the patient's sensitivity to acupuncture and acupuncture recommendation data are further obtained.

[0008] Further, the process of establishing the relationship model between known brain wave data and gender, age, climate, and temperature, constructing a query library, inputting the gender, age, and usual residence of the patient to obtain the brain wave threshold of the patient, and further obtaining the acupuncture sensitivity of the patient and acupuncture advice data based on the obtained brain wave threshold is as follows: The relationship model between brain wave data and gender, age, climate, and temperature is: ; where Y is the brain wave threshold, a is the global scaling factor of the brain wave threshold, b is the age steepness, specifically controlling the slope of the age curve, is the age power exponent, used to adjust the age responsiveness, o is the actual age, is the gender coefficient, g is the gender code, taking the value of 0 for males and 1 for females, c is the temperature influence intensity coefficient, is the temperature damping, specifically the extreme temperature correction factor, t is the centralized temperature, T(C) is the climate function, and ; where K is the total number of climate types, taking the value of 5, is the Koppen climate serial number of the k-th category, is the weight coefficient of climate type k, is the indicator function, is the seasonal fluctuation sensitivity coefficient, reflecting the sensitivity of brain waves to temperature fluctuations, is the seasonal temperature difference, used to quantify climate instability; Integrate the brain wave parameters obtained from training, construct a query library, input the gender, age, and usual residence of the patient to obtain the brain wave threshold of the patient, and finally, based on the obtained brain wave threshold, further obtain the acupuncture sensitivity of the patient and acupuncture advice data.

[0009] Further, the process of integrating the brain wave parameters obtained from training, constructing a query library, inputting the gender, age, and usual residence of the patient to obtain the brain wave threshold is as follows: Convert the usual residence of the patient into a standard geographical location, and obtain climate information from WorldClim and Koppen climate classification data, including the temperature index of this location and obtain the Koppen climate classification data of this location; perform age group matching according to the age and gender input by the patient; Combine the age group data and temperature climate data, load the brain wave parameters, perform brain wave threshold calculation, and finally output the calculated brain wave threshold.

[0010] Further, the process of further obtaining the acupuncture sensitivity of the patient and acupuncture advice data based on the obtained brain wave threshold is as follows: Based on the obtained brain wave threshold data, first analyze the brain wave threshold offset. Compare the brain wave threshold data with the standard neural active range. The standard range of α wave is [8.0, 10.5] μV², the standard range of β wave is [12.0, 15.0] μV², and the standard range of θ wave is [4.0, 6.5] μV²; Calculate the climate sensitivity, and the specific formula is: ; Among them, 0.35 is the global adjustment coefficient, is the seasonal temperature difference, 15 is the temperature span, is the climate function; Combine the threshold offset with the climate sensitivity to comprehensively evaluate the acupuncture sensitivity level. When any brain wave exceeds the low-sensitivity threshold boundary: α < 7.5 or α > 11.0, β < 11.0 or β > 16.0, θ < 3.5 or θ > 7.0, it is classified as the high-sensitivity benchmark; when the brain wave is in the high-sensitivity transition interval: α wave in [7.5, 8.0) ∪ (10.5, 11.0] or β wave in [11.0, 12.0) ∪ (15.0, 16.0] or θ wave in [3.5, 4.0) ∪ (6.5, 7.0], it is classified as the medium-sensitivity benchmark; if all brain waves are within the normal range, it is the low-sensitivity benchmark, and then combine the climate sensitivity for dynamic correction: if > 0.4, then implement a one-level upgrade of the sensitivity level; Furthermore, the process of matching the acupuncture suggestions in the acupuncture rule library according to the acupuncture sensitivity data is as follows: Pre-set the acupuncture rule library. The content of the acupuncture rule library is: when the acupuncture sensitivity is high-sensitivity, it is recommended to use shallow needling, rapid needling, reducing method, and short needle retention time; when the acupuncture sensitivity is low-sensitivity, it is recommended to use deep needling, warm needling, reinforcing method, and long needle retention time. When the acupuncture sensitivity is medium-sensitivity, it is recommended to use medium needling, balanced reinforcing-reducing method; Match the acupuncture suggestions according to the sensitivity level and output the acupuncture suggestions.

[0011] Furthermore, the brain wave judgment module judges the patient's emotional state according to the obtained brain wave threshold. When the emotion is abnormal, conduct artificial counseling and continuous monitoring until the brain wave returns to normal. The processing process is as follows: Compare the patient's real-time brain wave data with the brain wave threshold. When the power of β wave > the threshold, and the θ / β ratio < 1.5, the power of α wave < the threshold, and LI < -0.2, the power of θ wave < the threshold, and the θ / β ratio < 1.8, all are judged as abnormal; among them, LI = the power of α wave in the left prefrontal lobe / the power of α wave in the right prefrontal lobe, that is, the α wave lateralization index; When the brain wave is normal, implement acupuncture in combination with the acupuncture suggestions. When the brain wave is abnormal, conduct artificial psychological counseling and continuously monitor the patient's brain wave data. When the brain wave data is continuously abnormal, transmit the abnormality to the acupuncture plan dynamic adjustment module.

[0012] Further, the acupuncture treatment plan dynamic adjustment module receives the data from the brain wave judgment module, sets the basic stimulation threshold according to the brain wave threshold of the patient, and continuously monitors the brain wave data of the patient. The process of dynamically adjusting the acupuncture parameters is as follows: Receive the data from the brain wave judgment module. When the patient's emotional state remains abnormal, dynamically adjust the acupuncture treatment plan. Divide the treatment time of the acupuncture treatment plan into the initial stage, the middle stage, and the later stage. In the initial stage, relieve the patient's emotions. In the middle stage, perform transitional acupuncture. In the later stage, acupuncture the patient's original symptoms. During acupuncture, continuously monitor the brain wave data of the patient and dynamically adjust the acupuncture parameters; When the patient's emotions are normal, continuously monitor the brain wave data of the patient and dynamically adjust the acupuncture parameters according to the brain wave data.

[0013] Further, when the patient's emotions are normal, the process of continuously monitoring the brain wave data of the patient and dynamically adjusting the acupuncture parameters according to the brain wave data is as follows: According to the continuously obtained brain wave data, set the current brain wave index to be calculated every 5 seconds. Compare the brain wave index with the brain wave threshold. If the deviation degree > 15%, it is a mild deviation. If the deviation degree > 30%, it is a significant deviation. Judge the acupuncture state of the patient according to the degree of brain wave deviation and adjust the acupuncture parameters.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. By setting up the acupuncture recommendation module, it is possible to quickly obtain the brain wave threshold, acupuncture sensitivity, and acupuncture suggestions of patients in different resident climates. Since the acupuncture sensitivity and brain wave threshold of patients are different in different climate environments, by integrating temperature, climate, age, and gender data, the environment where the patient is located can be accurately matched with the brain wave threshold. Thus, not only can the personalized treatment plan be accurately matched by using climate environmental factors to improve the pertinence and effectiveness of acupuncture treatment, but also different sensitivities and brain wave thresholds can be obtained to continuously monitor the physiological reactions of patients during the treatment process to assist doctors in dynamically adjusting the treatment strategy. At the same time, since a query library is constructed, when performing treatment, only the patient's permanent residence and age information need to be input to obtain the brain wave threshold, acupuncture sensitivity, and acupuncture suggestions, simplifying the treatment process.

[0015] 2. By setting up a brain wave judgment module, it is possible to determine whether the patient has abnormal emotions before acupuncture treatment. When the patient has abnormal emotions, the brain wave data will interfere with each other with the brain wave data during acupuncture treatment. Therefore, it is necessary to adjust the acupuncture plan according to the patient's emotional state. When the patient has abnormal emotions, the patient's emotions will be calmed through manual counseling and acupuncture treatment. After the calming is completed, acupuncture will be performed on the patient's original symptoms, which can reduce the difficulty and pain of needle insertion, ensure accurate acupoint selection and stimulation conduction, make the patient's body reach a sensitized state more receptive to acupuncture treatment, stimulate the meridian qi, enhance the conduction of acupuncture sensation, and significantly improve the effect of subsequent treatment of the original symptoms. 3. By setting up the acupuncture plan dynamic adjustment module, by real-time monitoring and calculating brain wave indicators every 5 seconds, it is possible to keenly capture the patient's immediate reaction to acupuncture stimulation and timely detect changes in the acupuncture state. In addition, calculating the deviation degree can further optimize the acupuncture treatment steps. When the deviation is mild, the parameters are fine-tuned to optimize the treatment effect. When the deviation is significant, it is quickly adjusted to avoid over-stimulation or under-treatment, which not only ensures the safety of the treatment, prevents adverse reactions caused by improper parameters, but also can achieve personalized treatment according to the individual differences of the patient, improve the effectiveness of acupuncture, ensure that the treatment plan always fits the patient's current physical state, and improve the overall treatment quality. Brief Description of the Drawings

[0016] Figure 1 It is a block diagram of an acupuncture data monitoring system based on a brain-computer interface of the present invention.

[0017] Figure 2 It is a flowchart for calculating the brain wave threshold of the present invention. Detailed Embodiments

[0018] Next, the technical solutions of the present invention will be described clearly and completely in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present invention.

[0019] As Figure 1 - Figure 2 shown, an acupuncture data monitoring system based on a brain-computer interface includes a data acquisition module, an acupuncture difference analysis module, a brain wave judgment module, dynamic adjustment of the acupuncture plan, and a feedback module; The data acquisition module is used to collect WorldClim temperature data, Köppen climate classification data, brain wave baseline value data of different regions and age groups, real-time brain electrical signals of patients, and acupuncture parameters; In this embodiment, the data acquisition module is used to collect WorldClim temperature data, Köppen climate classification data, brain wave basic value data of different regions and age groups, patients' real-time brain wave signals and acupuncture parameter processing process is as follows: Collect 1km resolution annual average temperature and seasonal fluctuation raster data from the WorldClim platform; collect vector data of Köppen climate classification; Collect basic EEG data of different regions and age groups. The data sources include EEG data published by hospitals or research institutions and EEG data from the EEGbase platform. The brain wave data and acupuncture parameters of patients during acupuncture treatment are collected, and the brain wave data are aligned with the acupuncture parameters according to the timestamp. The acupuncture parameters include acupoint name, needle insertion depth, manipulation, and electroacupuncture parameters.

[0020] It should be noted that by analyzing acupuncture parameters such as acupoint names, needle insertion depth, manipulation and electroacupuncture parameters, we can intuitively understand the effects of different acupuncture intervention methods on EEG signals, which is helpful to explore the intrinsic relationship between acupuncture treatment and EEG activity, and provide objective data support for exploring the mechanism of acupuncture regulation and improvement of brain function, thereby optimizing acupuncture treatment plans and improving treatment effects and targeting.

[0021] Acupuncture difference analysis module, which studies the relationship between known brain wave data and WorldClim temperature data and Köppen climate classification data, and builds a query library. It inputs the patient's age, gender, and permanent residence, and provides the patient's brain wave threshold, acupuncture sensitivity, and acupuncture recommendations; In this embodiment, the acupuncture difference analysis module studies the relationship between known brain wave data and WorldClim temperature data and Köppen climate classification data, and builds a query library. The patient's age, gender, and permanent residence are input to provide the patient's brain wave threshold, acupuncture sensitivity, and acupuncture recommendations: The EEG data was cleaned to remove abnormal values beyond the normal physiological range. At the same time, the corresponding individual climate data and temperature data were matched according to the geographical coordinates of the sampling points. Finally, the EEG data was divided into groups by age, specifically into developmental period, adulthood, and old age. The developmental period was 0-18 years old, and was further divided into developmental period at intervals of 2 years. The adult period was 19-60 years old, and was further divided into intervals of 5 years. The old age was over 60 years old, and was further divided into intervals of 10 years. Standardize WorldClim temperature data and Köppen climate classification data; Establish a relationship model between known brain wave data and gender, age, climate, and temperature, and build a query library to input the patient's gender, age, and permanent residence to obtain the patient's brain wave threshold. At the same time, based on the obtained brain wave threshold, further obtain the patient's sensitivity to acupuncture and acupuncture recommendation data; It should be noted that by combining climate and temperature data with age and gender, the personalization and precision of acupuncture treatment can be achieved, providing more practical treatment suggestions for patients. In this embodiment, the process of establishing the relationship model between known brain wave data and gender, age, climate, and temperature, constructing a query library, inputting the gender, age, and permanent residence of the patient to obtain the brain wave threshold of the patient, and further obtaining the sensitivity of the patient to acupuncture and acupuncture advice data based on the obtained brain wave threshold is as follows: The relationship model between brain wave data and gender, age, climate, and temperature is: ; Among them, Y is the brain wave threshold, a is the global scaling factor of the brain wave threshold, b is the age steepness, specifically controlling the slope of the age curve, is the age power exponent, used to adjust the age responsiveness, o is the actual age, is the gender coefficient, g is the gender code, taking the value of 0 for males and 1 for females, c is the temperature influence intensity coefficient, is the temperature damping, specifically the extreme temperature correction factor, t is the centralized temperature, T(C) is the climate function, and ; where K is the total number of climate types, taking the value of 5, is the Köppen climate serial number of the k-th category, is the weight coefficient of climate type k, is the indicator function, is the seasonal fluctuation sensitivity coefficient, reflecting the sensitivity of brain waves to temperature fluctuations, is the seasonal temperature difference, used to quantify climate instability; It should be noted that brain waves include α, β, and θ waves. When using this formula to obtain the thresholds of α, β, and θ waves, completely independent parameter groups will be used. Gender and age are adjusted through specific variables. When calculating the thresholds of α, β, and θ waves, the parameter values are different for different ages, different climates, and different genders, and these parameters are all obtained by fitting known brain wave data; Integrate the trained brain wave parameters, construct a query library, input the gender, age, and permanent residence of the patient to obtain the brain wave threshold of the patient, and finally obtain the sensitivity of the patient to acupuncture and acupuncture advice data based on the obtained brain wave threshold; In this embodiment, the process of integrating the trained brain wave parameters, constructing a query library, inputting the gender, age, and permanent residence of the patient to obtain the brain wave threshold of the patient is as follows: Convert the patient's usual residence to a standard geographical location and obtain climate information from WorldClim and Köppen climate classification data, including the temperature index of the location and obtain the Köppen climate classification data of the location; match the age group according to the age and gender input by the patient; Combine the age group data and temperature climate data, load the brain wave parameters, calculate the brain wave threshold, and finally output the calculated brain wave threshold; In this embodiment, according to the obtained brain wave threshold, further obtain the patient's sensitivity to acupuncture and the data processing process of acupuncture advice is as follows: Based on the obtained brain wave threshold data, first analyze the brain wave threshold offset, compare the brain wave threshold data with the standard neural activity range, the standard range of α wave is [8.0, 10.5] μV², the standard range of β wave is [12.0, 15.0] μV², and the standard range of θ wave is [4.0, 6.5] μV²; Calculate the climate sensitivity, and the specific formula is: ; Among them, 0.35 is the global adjustment coefficient, is the seasonal temperature difference, 15 is the temperature span, is the climate function; Combine the threshold offset and climate sensitivity to comprehensively evaluate the acupuncture sensitivity level. When any brain wave exceeds the low-sensitivity threshold boundary: α < 7.5 or α > 11.0, β < 11.0 or β > 16.0, θ < 3.5 or θ > 7.0, it is classified as the high-sensitivity benchmark; when the brain wave is in the high-sensitivity transition interval: α wave is in [7.5, 8.0) ∪ (10.5, 11.0] or β wave is in [11.0, 12.0) ∪ (15.0, 16.0] or θ wave is in [3.5, 4.0) ∪ (6.5, 7.0], it is classified as the medium-sensitivity benchmark; if all brain waves are within the normal range, it is the low-sensitivity benchmark, and then dynamically correct it by combining the climate sensitivity: if > 0.4, the sensitivity level is upgraded by one level; It should be noted that the low-sensitivity threshold of the brain wave is taken as 25% outside the standard range. When the seasonal temperature difference > 25 °C and the tropical climate patient reaches 0.5, the sensitivity is forced to be upgraded; at the same time, when both α wave and β wave are abnormal, it is directly determined as high-sensitivity, while when θ wave is single abnormal and maintain the medium-sensitivity benchmark; Match the acupuncture advice in the acupuncture rule library according to the acupuncture sensitivity data; In this embodiment, the process of matching the acupuncture advice in the acupuncture rule library according to the acupuncture sensitivity data is as follows: Pre-set an acupuncture rule library. The content of the acupuncture rule library is as follows: When the acupuncture sensitivity is highly sensitive, it is recommended to use shallow acupuncture, rapid acupuncture, reducing method, and short needle retention time; when the acupuncture sensitivity is low-sensitive, it is recommended to use deep acupuncture, warm needling, reinforcing method, and long needle retention time; when the acupuncture sensitivity is moderately sensitive, it is recommended to use medium acupuncture and balanced reinforcing-reducing method; Match acupuncture suggestions according to the sensitivity level and output the acupuncture suggestions; It should be noted that due to the influence of factors such as climate environment and living habits on patients in different regions, there are differences in acupuncture sensitivity and brain wave parameters. Therefore, setting personalized brain wave thresholds and recommending acupuncture suggestions based on the patient's acupuncture sensitivity and brain wave parameters can not only fit the individual differences of patients, but also enhance the pertinence and adaptability of acupuncture treatment; A brain wave judgment module judges the patient's emotional state according to the obtained brain wave threshold. When the emotion is abnormal, conduct artificial counseling and continuous monitoring until the brain wave returns to normal; In this embodiment, the process of the brain wave judgment module judging the patient's emotional state according to the obtained brain wave threshold and conducting artificial counseling and continuous monitoring until the brain wave returns to normal is as follows: Compare the patient's real-time brain wave data with the brain wave threshold. If the β wave power > the threshold, and the θ / β ratio < 1.5, the α wave power < the threshold, and LI < -0.2, the θ wave power < the threshold, and the θ / β ratio < 1.8, all are judged as abnormal; where LI = the α power of the left prefrontal lobe / the α power of the right prefrontal lobe, that is, the α wave laterality index; When the brain wave is normal, perform acupuncture in combination with the acupuncture suggestion. When the brain wave is abnormal, conduct psychological counseling manually and continuously monitor the patient's brain wave data. When the brain wave data remains abnormal, transmit the abnormality to the acupuncture plan dynamic adjustment module; The acupuncture plan dynamic adjustment module receives the data of the brain wave judgment module, sets the basic stimulation threshold according to the patient's brain wave threshold, and continuously monitors the patient's brain wave data to dynamically adjust the acupuncture parameters; In this embodiment, the process of the acupuncture plan dynamic adjustment module receiving the data of the brain wave judgment module, setting the basic stimulation threshold according to the patient's brain wave threshold, and continuously monitoring the patient's brain wave data to dynamically adjust the acupuncture parameters is as follows: Receive the data of the brain wave judgment module. When the patient's emotional state remains abnormal, dynamically adjust the acupuncture plan. Divide the treatment time of the acupuncture plan into the initial stage, the middle stage, and the later stage. In the initial stage, relieve the patient's emotion. In the middle stage, perform transitional acupuncture. In the later stage, acupuncture the patient's original symptoms; during acupuncture, continuously monitor the patient's brain wave data and dynamically adjust the acupuncture parameters; When the patient's emotion is normal, continuously monitor the patient's brain wave data and dynamically adjust the acupuncture parameters according to the brain wave data.

[0022] It should be noted that in the initial stage, acupuncture is implemented to relieve the patient's emotions. According to the continuously received brain wave data, it is judged whether the patient's emotions are relieved. When the patient's emotions return to normal, in the middle stage, progressive acupuncture is performed according to the patient's original symptoms, and in the later stage, continuous acupuncture is performed according to the patient's original symptoms. In this embodiment, when the patient's emotions are normal, the brain wave data of the patient is continuously monitored, and the process of dynamically adjusting the acupuncture parameters according to the brain wave data is as follows: According to the continuously obtained brain wave data, it is set to calculate the current brain wave index every 5 seconds. The brain wave index is compared with the brain wave threshold. When the deviation degree > 15%, it is a mild deviation. When the deviation degree > 30%, it is a significant deviation. The acupuncture state of the patient is judged according to the degree of brain wave deviation, and the acupuncture parameters are adjusted. The feedback module outputs the state during the patient's acupuncture process for state feedback.

[0023] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An acupuncture data monitoring system based on a brain-computer interface, characterized in that: It includes data collection module, acupuncture difference analysis module, brain wave judgment module, acupuncture program dynamic adjustment and feedback module; The data acquisition module is used to collect WorldClim temperature data, Köppen climate classification data, brain wave basic value data of different regions and age groups, patients' real-time brain wave signals and acupuncture parameters; The acupuncture difference analysis module studies the relationship between known brain wave data and WorldClim temperature data and Köppen climate classification data, and builds a query library. It inputs the patient's age, gender, and permanent residence, and provides the patient's brain wave threshold, acupuncture sensitivity, and acupuncture recommendations; The brain wave judgment module judges the patient's emotional state according to the obtained brain wave threshold. If the patient's emotions are abnormal, manual guidance and continuous monitoring are performed until the brain waves return to normal. The acupuncture scheme dynamic adjustment module receives data from the brain wave judgment module, sets the basic stimulation threshold according to the patient's brain wave threshold, and continuously monitors the patient's brain wave data to dynamically adjust the acupuncture parameters; The feedback module outputs the status of the patient during acupuncture and performs status feedback.

2. The acupuncture data monitoring system based on a brain-computer interface according to claim 1, wherein The data acquisition module is used to collect WorldClim temperature data, Köppen climate classification data, brain wave basic value data of different regions and age groups, patients' real-time brain wave signals and acupuncture parameter processing process is as follows: Collect 1km resolution annual average temperature and seasonal fluctuation raster data from the WorldClim platform; collect vector data of Köppen climate classification; Collect basic EEG data of different regions and age groups. The data sources include EEG data published by hospitals or research institutions and EEG data from the EEGbase platform. The brain wave data and acupuncture parameters of patients during acupuncture treatment are collected, and the brain wave data are aligned with the acupuncture parameters according to the timestamp. The acupuncture parameters include acupoint name, needle insertion depth, manipulation, and electroacupuncture parameters.

3. The acupuncture data monitoring system based on a brain-computer interface according to claim 1, wherein: The acupuncture difference analysis module studies the relationship between known brain wave data and WorldClim temperature data and Köppen climate classification data, and builds a query library. By inputting the patient's age, gender, and permanent residence, the module provides the patient's brain wave threshold, acupuncture sensitivity, and acupuncture recommendations: The EEG data was cleaned to remove abnormal values beyond the normal physiological range. At the same time, the corresponding individual climate data and temperature data were matched according to the geographical coordinates of the sampling points. Finally, the EEG data was divided into groups by age, specifically into developmental period, adulthood, and old age. The developmental period was 0-18 years old, and was further divided into developmental period at intervals of 2 years. The adult period was 19-60 years old, and was further divided into intervals of 5 years. The old age was over 60 years old, and was further divided into intervals of 10 years. Standardize WorldClim temperature data and Köppen climate classification data; A relationship model between known brain wave data and gender, age, climate, and temperature is established, and a query library is constructed. The patient's gender, age, and permanent residence are input to obtain the patient's brain wave threshold. At the same time, based on the obtained brain wave threshold, the patient's sensitivity to acupuncture and acupuncture recommendation data are further obtained.

4. The acupuncture data monitoring system based on a brain-computer interface according to claim 3, wherein The process of establishing a relationship model between known brain wave data and gender, age, climate, and temperature, constructing a query library, inputting the patient's gender, age, and usual residence to obtain the patient's brain wave threshold, and further obtaining the patient's sensitivity to acupuncture and acupuncture advice data based on the obtained brain wave threshold is as follows: The relationship model between brain wave data and gender, age, climate, and temperature is: ； Among them, Y is the brain wave threshold, a is the global scaling factor of the brain wave threshold, b is the age steepness, specifically controlling the slope of the age curve, is the age power exponent for adjusting the age responsiveness, o is the actual age, is the gender coefficient, g is the gender code, taking the value of 0 for males and 1 for females, c is the temperature influence intensity coefficient, is the temperature damping, specifically the extreme temperature correction factor, t is the centralized temperature, T(C) is the climate function, and ; where K is the total number of climate types, taking the value of 5, is the Köppen climate serial number of the k-th category, is the weight coefficient of climate type k, is the indicator function, is the seasonal fluctuation sensitivity coefficient, reflecting the sensitivity of brain waves to temperature fluctuations, is the seasonal temperature difference, used to quantify climate instability; Integrate the brain wave parameters obtained from training, construct a query library, input the patient's gender, age, and usual residence to obtain the patient's brain wave threshold, and finally, based on the obtained brain wave threshold, further obtain the patient's sensitivity to acupuncture and acupuncture advice data.

5. The acupuncture data monitoring system based on a brain-computer interface according to claim 4, characterized in that, The process of integrating the brain wave parameters obtained from training, constructing a query library, inputting the patient's gender, age, and usual residence to obtain the patient's brain wave threshold is as follows: Convert the patient's usual residence to a standard geographical location, and obtain climate information from WorldClim and Köppen climate classification data, including the temperature index of this location and obtain the Köppen climate classification data of this location; perform age group matching according to the age and gender input by the patient; Combine the age group data and temperature climate data, load the brain wave parameters, calculate the brain wave threshold, and finally output the calculated brain wave threshold.

6. The acupuncture data monitoring system based on a brain-computer interface according to claim 5, wherein: The process of further obtaining the patient's sensitivity to acupuncture and acupuncture advice data based on the obtained brain wave threshold is as follows: Based on the obtained brain wave threshold data, first analyze the brain wave threshold offset, compare the brain wave threshold data with the standard neural activity range, the standard range of α wave is [8.0, 10.5] μV², the standard range of β wave is [12.0, 15.0] μV², and the standard range of θ wave is [4.0, 6.5] μV²; Calculate the climate sensitivity, and the specific formula is: ； Among them, 0.35 is the global adjustment coefficient, is the seasonal temperature difference, and 15 is the temperature span, is the climate function; Combined with the climate sensitivity, the threshold offset is used to comprehensively evaluate the acupuncture sensitivity level. When any brain wave exceeds the low-sensitivity threshold boundary: α < 7.5 or α > 11.0, β < 11.0 or β > 16.0, θ < 3.5 or θ > 7.0, it is classified as a high-sensitivity benchmark; when the brain wave is in the high-sensitivity transition interval: the α wave is in [7.5, 8.0) ∪ (10.5, 11.0], or the β wave is in [11.0, 12.0) ∪ (15.0, 16.0], or the θ wave is in [3.5, 4.0) ∪ (6.5, 7.0], it is classified as a medium-sensitivity benchmark; if all brain waves are within the normal range, it is a low-sensitivity benchmark, and then the climate sensitivity is combined for dynamic correction: if > 0.4, the sensitivity level is increased by one level; Match the acupuncture advice in the acupuncture rule library according to the acupuncture sensitivity data.

7. The acupuncture data monitoring system based on a brain-computer interface according to claim 6, characterized in that: The process of matching the acupuncture advice in the acupuncture rule library according to the acupuncture sensitivity data is as follows: Pre-set an acupuncture rule library, and the content of the acupuncture rule library is: when the acupuncture sensitivity is highly sensitive, it is recommended to use shallow needling, fast needling, reducing method, and short needle retention time; when the acupuncture sensitivity is low sensitive, it is recommended to use deep needling, warm needling, reinforcing method, and long needle retention time; when the acupuncture sensitivity is moderately sensitive, it is recommended to use medium needling, balanced reinforcing-reducing method; Match the acupuncture advice according to the sensitivity level and output the acupuncture advice.

8. An acupuncture data monitoring system based on a brain-computer interface according to claim 1, characterized in that: The brain wave judgment module judges the patient's emotional state according to the obtained brain wave threshold. When the emotion is abnormal, conduct artificial counseling and continuous monitoring until the brain wave returns to normal. The process is as follows: Compare the patient's real-time brain wave data with the brain wave threshold. If the β wave power > threshold, and the θ / β ratio < 1.5, the α wave power < threshold, and LI < -0.2, the θ wave power < threshold, and the θ / β ratio < 1.8, all are judged as abnormal; where, LI = left prefrontal α power / right prefrontal α power, that is, the α wave lateralization index; When the brain wave is normal, perform acupuncture in combination with the acupuncture advice. When the brain wave is abnormal, conduct artificial psychological counseling and continuously monitor the patient's brain wave data. When the brain wave data is continuously abnormal, transmit the abnormality to the acupuncture plan dynamic adjustment module.

9. The acupuncture data monitoring system based on a brain-computer interface according to claim 1, wherein: The acupuncture plan dynamic adjustment module receives the data from the brain wave judgment module, sets the basic stimulation threshold according to the patient's brain wave threshold, and continuously monitors the patient's brain wave data. The process of dynamically adjusting the acupuncture parameters is as follows: Receive the data from the brain wave judgment module. When the patient's emotional state remains abnormal, dynamically adjust the acupuncture plan. Divide the treatment time of the acupuncture plan into the initial stage, the middle stage, and the later stage. In the initial stage, relieve the patient's emotions. In the middle stage, perform transitional acupuncture. In the later stage, perform acupuncture on the patient's original symptoms. During acupuncture, continuously monitor the patient's brain wave data and dynamically adjust the acupuncture parameters. When the patient's emotion is normal, continuously monitor the patient's brain wave data and dynamically adjust the acupuncture parameters according to the brain wave data.

10. The acupuncture data monitoring system based on a brain-computer interface according to claim 9, characterized in that: When the patient's emotion is normal, the process of continuously monitoring the patient's brain wave data and dynamically adjusting the acupuncture parameters according to the brain wave data is as follows: According to the continuously obtained brain wave data, set the current brain wave index to be calculated every 5 seconds. Compare the brain wave index with the brain wave threshold. If the deviation degree > 15%, it is a mild deviation. If the deviation degree > 30%, it is a significant deviation. Judge the patient's acupuncture state according to the degree of brain wave deviation and adjust the acupuncture parameters.

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