A data processing and management method and system for psychological disorder assessment

Abstract

The present invention proposes a data processing and management method and system for psychological disorder assessment. The data processing and management method for psychological disorder assessment includes: using a physiological signal detection device worn by the patient to collect the patient's physiological signals in real time, and sending the physiological signals to a cloud platform; after receiving the physiological signals, the cloud platform performs data filtering and abnormal emotional tendency state judgment on the physiological signals, and stores and manages the physiological signals during the continuous abnormal emotional tendency state. The system includes modules corresponding to the steps of the method.

Description

Technical Field

[0001] The invention proposes a data processing and management method and system for psychological disorder assessment, belonging to the technical field of data processing. Background Art

[0002] With the rapid development of modern society, psychological stress and distress have increasingly become important factors affecting people's health and quality of life. As a common mental illness, early detection, accurate assessment and effective treatment of mental disorders are particularly critical. Traditional mental disorder assessment methods often rely on the subjective judgment and clinical experience of experts, lack of objective and quantitative data support, resulting in greater subjectivity and uncertainty in the assessment results.

[0003] However, the data processing and management of psychological disorder assessment and physiological signals in the existing technology often cause the problem of excessive data processing load due to the large amount of real-time detection data of patients. In addition, for the thresholds corresponding to fixed heart rate information and respiratory information, as long as the patient's heart rate and respiratory rate do not reach this threshold, the current patient condition will be considered normal, which can easily lead to misjudgment of psychological and emotional changes. Summary of the invention

[0004] The present invention provides a data processing and management method and system for psychological disorder assessment to solve the above problems in the prior art. The technical solutions adopted are as follows:

[0005] A data processing and management method for psychological disorder assessment, the data processing and management method for psychological disorder assessment comprising:

[0006] The physiological signal detection device worn by the patient is used to collect the patient's physiological signal in real time, and the physiological signal is sent to the cloud platform;

[0007] After receiving the physiological signal, the cloud platform performs data filtering and abnormal emotion tendency state judgment on the physiological signal, and stores and manages the data of the physiological signal during the continuous abnormal emotion tendency state.

[0008] Furthermore, the data processing and management method for psychological disorder assessment also includes:

[0009] After the cloud platform establishes a communication connection with the physiological signal detection device worn by the patient, the historical examination records of the patient's physiological signals in the hospital are retrieved from the medical terminal;

[0010] The cloud platform retrieves respiratory rate information and heart rate information from the historical examination records of the patient's physiological signals in the hospital;

[0011] The abnormal data boundary value is obtained according to the respiratory rate information, heart rate information, blood pressure change rate and blood oxygen saturation.

[0012] Furthermore, the abnormal data defining value is obtained according to the respiratory rate information, heart rate information, blood pressure change rate and blood oxygen saturation, including:

[0013] Retrieving critical values ​​of respiratory rate and heart rate for normal mental health and abnormal mental health from the database;

[0014] The abnormal breathing data boundary value is obtained according to the respiratory frequency information contained in the historical inspection record of the physiological signal; wherein the abnormal breathing data boundary value is obtained by the following formula:

[0015]

[0016] Among them, Q 01 Indicates the critical value of abnormal breathing data; H L Indicates the critical value of respiratory rate; H p represents the average respiratory rate in the historical examination records; a represents the base value of the first logarithm; X p Indicates the average heart rate contained in the historical examination record; H max Indicates the maximum respiratory rate contained in the historical examination record; X m Indicates the heart rate value corresponding to the maximum respiratory rate contained in the historical examination record; P x01 Indicates the blood pressure change rate contained in the historical examination records; P x02 Indicates the saturation percentage value corresponding to the blood oxygen saturation contained in the historical examination record;

[0017] The abnormal breathing data boundary value and the critical value of the respiratory frequency are used to obtain the abnormal breathing determination value range; wherein the abnormal breathing determination value range is H 01 —H L , and the lower limit of the respiratory rate H 01 Obtained through the following formula:

[0018] H 01 =H L -Q 01

[0019] Among them, H 01 Indicates the lower limit of the respiratory rate corresponding to the numerical range of abnormal breathing judgment;

[0020] The abnormal heart rate data boundary value is obtained according to the heart rate information contained in the historical inspection record of the physiological signal; wherein the abnormal heart rate data boundary value is obtained by the following formula:

[0021]

[0022] Among them, Q 02 Indicates the value of abnormal heart rate data; X L Indicates the critical value of heart rate; X p represents the average heart rate in the historical examination records; b represents the value of the second logarithmic base; X max Indicates the maximum heart rate in the historical examination record; H m Indicates the respiratory rate value corresponding to the maximum heart rate contained in the historical examination record;

[0023] The abnormal heart rate data boundary value and the critical heart rate value are used to obtain the abnormal heart rate determination value range; wherein the abnormal heart rate determination value range is X 01 —X L , and the heart rate lower limit value X 01 Obtained through the following formula:

[0024] X 01 =X L -Q 02

[0025] Among them, X 01 Indicates the lower limit of the heart rate corresponding to the abnormal heart rate judgment value range.

[0026] Furthermore, the physiological signal of the patient is collected in real time using a physiological signal detection device worn by the patient, and the physiological signal is sent to a cloud platform, including:

[0027] When the physiological signal detection device worn by the patient is started, the patient's physiological signals are collected in real time according to a preset physiological signal collection cycle, wherein the physiological signals include respiratory frequency information and heart rate information;

[0028] Send the collected physiological signals corresponding to each physiological signal collection cycle to the cloud platform;

[0029] The physiological signal collection period corresponding to the respiratory frequency information and the heart rate information is 1 minute.

[0030] Furthermore, after receiving the physiological signal, the cloud platform performs data filtering and abnormal emotion tendency state judgment on the physiological signal, and stores and manages the data of the physiological signal during the continuous abnormal emotion tendency state, including:

[0031] Performing data filtering and processing through the cloud platform to obtain target data information corresponding to the physiological signal;

[0032] Determine whether the patient is in a state of abnormal emotional tendency based on the target data information combined with the patient's psychological disorder assessment data;

[0033] When the patient develops abnormal emotional tendencies, the duration of the abnormal emotional tendencies and the physiological signal data corresponding to the duration of the abnormal emotional tendencies are stored and managed.

[0034] Furthermore, data filtering is performed through the cloud platform to obtain target data information corresponding to the physiological signal, including:

[0035] After the cloud platform obtains the patient's real-time physiological signal, it determines that the heart rate information and the respiratory rate information in the physiological signal are within the corresponding heart rate abnormality determination value range and respiratory abnormality determination value range;

[0036] When the heart rate information and the respiratory frequency information in the physiological signal are not within their corresponding heart rate abnormality determination value range and respiratory abnormality determination value range, the heart rate information and the respiratory frequency information are deleted, and the psychological dynamics of the patient's physiological signal The time period is marked as normal;

[0037] When the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory frequency information is within its corresponding abnormal determination value range, the heart rate information and the respiratory frequency information within the corresponding abnormal determination value range are retained, and the heart rate information and the respiratory frequency information within the corresponding abnormal determination value range are used as the first target data information;

[0038] or,

[0039] When the patient's heart rate information and respiratory rate information are not within their corresponding abnormal judgment value range, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the patient's heart rate information and respiratory rate information after the corresponding moment when any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value are stored in real time and used as the second target data information.

[0040] Further, judging whether the patient is in a state of abnormal emotional tendency according to the target data information combined with the patient's psychological disorder assessment data includes:

[0041] When the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within the corresponding abnormal determination value range, the first target data information of the patient is stored in real time from the moment when the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within the corresponding abnormal determination value range;

[0042] Using the first target data information in combination with the patient's psychological disorder assessment data to obtain a first abnormal tendency determination value;

[0043] When the heart rate information and respiratory rate information of the patient are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the second target data information is combined with the patient's psychological disorder assessment data to obtain a second abnormal tendency determination value;

[0044] When the first abnormal tendency determination value and the second abnormal tendency determination value exceed the preset first determination threshold and the second determination threshold, it is determined that the patient is currently in an abnormal emotional tendency state.

[0045] Further, using the first target data information in combination with the patient's psychological disorder assessment data to obtain a first abnormal tendency determination value includes:

[0046] Obtain a first physiological abnormality tendency parameter according to the first target data information; wherein the first physiological abnormality tendency parameter is obtained by the following formula:

[0047] K 01 =K x01 +K x02

[0048] Among them, K 01 Represents the first physiological abnormality tendency parameter; K x01 and K x02 They respectively represent the first parameter variable and the second parameter variable corresponding to the physiological abnormality tendency parameter; and the first parameter variable and the second parameter variable are obtained by the following formula:

[0049]

[0050] Wherein, n represents the number of respiratory frequencies in the corresponding respiratory abnormality determination value range; H1, H2, ... H n Respectively represent the values ​​corresponding to the n breathing frequencies; X1, X2, ... X n represents the heart rate value corresponding to the n-th respiratory frequency; m represents the number of heart rates in the corresponding heart rate abnormality judgment value range; X1, X2, ... X m Respectively represent the values ​​corresponding to m heart rates; H1, H2, ... H m They represent the respiratory frequency value corresponding to m heart rates; 01 and λ 02 Respectively represent the respiratory frequency distribution coefficient and the heart rate distribution coefficient; wherein the respiratory frequency distribution coefficient and the heart rate distribution coefficient are obtained by the following formula:

[0051]

[0052] Among them, H i represents the value corresponding to the i-th respiratory frequency in its corresponding respiratory abnormality determination value range; X i represents the heart rate value corresponding to the i-th respiratory frequency in its corresponding respiratory abnormality judgment value range; X j represents the jth heart rate value within the corresponding heart rate abnormality judgment value range; H j represents the respiratory frequency value corresponding to the j-th heart rate in the corresponding heart rate abnormality determination value range;

[0053] Retrieve the patient's psychological disorder assessment data;

[0054] The psychological tendency value corresponding to the patient is obtained according to the psychological disorder assessment data, wherein the psychological tendency value is obtained by the following formula:

[0055]

[0056] Among them, K y represents the psychological tendency value; k represents the number of times the psychological health characteristic words appear in the psychological disorder assessment; w i represents the tendency coefficient corresponding to the i-th mental health characteristic word; Y i represents the occurrence frequency of the i-th mental health characteristic word;

[0057] The first abnormal tendency determination value corresponding to the abnormal emotional tendency state is obtained by using the psychological tendency value and the first physiological abnormal tendency parameter; wherein the first abnormal tendency determination value is obtained by the following formula:

[0058] R 01 =lnK 01 +lnK y

[0059] Among them, R 01 Indicates the first abnormal tendency determination value.

[0060] Further, when the heart rate information and respiratory rate information of the patient are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the second target data information is combined with the patient's psychological disorder assessment data to obtain a second abnormal tendency determination value, including:

[0061] When the patient's heart rate information and respiratory rate information are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, retrieving the respiratory rate information and heart rate information exceeding the respiratory rate critical value and heart rate critical value from the second target data information;

[0062] The first physiological abnormality tendency factor is obtained by using the respiratory frequency information and the heart rate information exceeding the critical value of the respiratory frequency and the critical value of the heart rate in the second target data information; wherein the first physiological abnormality tendency factor is obtained by the following formula:

[0063]

[0064] Among them, K x03 represents the first physiological abnormality tendency factor; r 01 represents the number of respiratory frequency information exceeding the critical value of respiratory frequency in the second target data information; H t represents the tth respiratory rate value that exceeds the critical value of respiratory rate; X t represents the heart rate value corresponding to the tth respiratory frequency exceeding the critical value of respiratory frequency; δ represents the first respiratory index adjustment coefficient; X max Indicates the heart rate value corresponding to the maximum respiratory rate in the second target data information;

[0065] When the patient's heart rate information and respiratory frequency information are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory frequency critical value and heart rate critical value, retrieving the respiratory frequency information and heart rate information within the respiratory abnormal determination value range and heart rate abnormal determination value range from the second target data information;

[0066] The second physiological abnormality tendency factor is obtained by using the respiratory frequency information and the heart rate information in the second target data information that are within the respiratory abnormality determination value range and the heart rate abnormality determination value range; wherein the second physiological abnormality tendency factor is obtained by the following formula:

[0067]

[0068] Among them, K x04 Represents the second physiological abnormality tendency factor; r 02 represents the number of respiratory frequency information in the second target data information that is within the respiratory abnormality determination value range; H e represents the e-th respiratory rate value within the respiratory abnormality determination value range; X erepresents the heart rate value corresponding to the e-th respiratory frequency in the respiratory abnormality determination value range; ξ represents the second respiratory index adjustment coefficient; H max Indicates the respiratory frequency value corresponding to the maximum heart rate value within the heart rate abnormality determination value range in the second target data information;

[0069] The first physiological abnormality tendency factor and the second physiological abnormality tendency factor are used to obtain a second physiological abnormality tendency parameter, wherein the second physiological abnormality tendency parameter is obtained by the following formula:

[0070] K 02 =K x03 +K x04

[0071] Among them, K 02 Indicates the second physiological abnormality tendency parameter;

[0072] The psychological tendency value corresponding to the patient is obtained according to the psychological disorder assessment data, wherein the psychological tendency value is obtained by the following formula:

[0073]

[0074] Among them, K y represents the psychological tendency value; k represents the number of times the psychological health characteristic words appear in the psychological disorder assessment; w i represents the tendency coefficient corresponding to the i-th mental health characteristic word; Y i represents the occurrence frequency of the i-th mental health characteristic word;

[0075] The psychological tendency value and the second physiological abnormal tendency parameter are used to obtain a second abnormal tendency determination value corresponding to the abnormal emotional tendency state; wherein the second abnormal tendency determination value is obtained by the following formula:

[0076] R 02 =lnK 02 +lnK y

[0077] Among them, R 02 Indicates the second abnormal tendency determination value.

[0078] A data processing and management system for psychological disorder assessment, the data processing and management system for psychological disorder assessment comprising:

[0079] A signal acquisition module, used to collect the patient's physiological signals in real time using a physiological signal detection device worn by the patient, and send the physiological signals to a cloud platform;

[0080] The data storage and management module is used for filtering the physiological signals and judging the abnormal emotional tendency state after the cloud platform receives the physiological signals, and for storing and managing the physiological signals during the continuous abnormal emotional tendency state.

[0081] Beneficial effects of the present invention:

[0082] The data processing and management method and system for psychological disorder evaluation proposed by the present invention can realize remote monitoring and evaluation of psychological disorders by collecting the patient's physiological signals in real time and sending them to the cloud platform. This not only provides convenience for patients, but also makes the evaluation of psychological disorders no longer limited to a specific time and place, and improves the flexibility and efficiency of the evaluation. Secondly, the data filtering and abnormal emotional tendency state judgment function of the cloud platform can accurately identify the abnormal state in the physiological signal, providing strong support for the early detection of psychological disorders. At the same time, by storing and managing the physiological signals in the process of the abnormal emotional tendency state, rich data support is provided for the subsequent psychological disorder evaluation and intervention. In addition, the method also utilizes the powerful computing power and storage capacity of the cloud platform to realize the efficient processing and analysis of a large number of physiological signals. This can not only improve the accuracy and reliability of psychological disorder evaluation, but also provide a scientific basis for the intervention and treatment of psychological disorders. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 is a flow chart of the method of the present invention;

[0084] Figure 2 The system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0085] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0086] The embodiment of the present invention provides a data processing and management method for psychological disorder assessment, such as Figure 1 As shown, the data processing and management method for psychological disorder assessment includes:

[0087] S1. Using a physiological signal detection device worn by the patient to collect the patient's physiological signals in real time, and sending the physiological signals to a cloud platform;

[0088] S2. After receiving the physiological signal, the cloud platform performs data filtering and abnormal emotion tendency state judgment on the physiological signal, and stores and manages the data of the physiological signal during the continuous abnormal emotion tendency state.

[0089] The working principle of the above technical solution is as follows: First, the physiological signal detection device worn by the patient will collect the patient's physiological signals in real time. The above physiological signals include but are not limited to heart rate, respiratory rate, skin electrical activity, etc., which can reflect the patient's emotional state and psychological changes. Once the above physiological signals are collected, the device will immediately send them to the cloud platform.

[0090] After receiving the physiological signals, the cloud platform will perform a series of data processing. First, the physiological signals are filtered to remove noise and interference to ensure the accuracy and reliability of the data. Then, the cloud platform uses machine learning and pattern recognition technology to judge the abnormal emotional tendency of the filtered physiological signals. Based on a large amount of training data and algorithm models, it can identify the abnormal emotional tendency reflected in the physiological signals.

[0091] Once it is determined that there is an abnormal emotional tendency in the physiological signal, the cloud platform will immediately store and manage the data of the physiological signal in this state, including storing the data in a secure database and providing data query, analysis and visualization functions to facilitate subsequent psychological disorder assessment and intervention.

[0092] The effect of the above technical solution is that by collecting the patient's physiological signals in real time and sending them to the cloud platform, remote monitoring and evaluation of psychological disorders can be achieved. This not only provides convenience for patients, but also makes the evaluation of psychological disorders no longer limited to a specific time and place, improving the flexibility and efficiency of the evaluation.

[0093] Secondly, the data filtering and abnormal emotion tendency state judgment functions of the cloud platform can accurately identify abnormal states in physiological signals, providing strong support for the early detection of psychological disorders. At the same time, by storing and managing the data of physiological signals during the continuous abnormal emotion tendency state, it provides rich data support for the subsequent psychological disorder assessment and intervention.

[0094] In addition, the method also utilizes the powerful computing and storage capabilities of the cloud platform to achieve efficient processing and analysis of a large number of physiological signals. This can not only improve the accuracy and reliability of psychological disorder assessment, but also provide a scientific basis for the intervention and treatment of psychological disorders.

[0095] In summary, this data processing and management method for psychological disorder assessment has significant technical effects and application value, and is expected to provide new technical means for the early detection, accurate assessment and effective intervention of psychological disorders.

[0096] In one embodiment of the present invention, the data processing and management method for psychological disorder assessment further includes:

[0097] Step 1: After the cloud platform establishes a communication connection with the physiological signal detection device worn by the patient, the historical examination records of the patient's physiological signals in the hospital are retrieved from the medical terminal;

[0098] Step 2, the cloud platform retrieves respiratory rate information and heart rate information from the historical examination records of the patient's physiological signals in the hospital;

[0099] Step 3: Obtain abnormal data boundary values ​​based on the respiratory rate information, heart rate information, blood pressure change rate and blood oxygen saturation.

[0100] The working principle of the above technical solution is as follows: First, when the cloud platform successfully establishes a communication connection with the physiological signal detection device worn by the patient, the cloud platform will actively retrieve the patient's historical examination records of physiological signals in the hospital from the medical terminal. The above steps ensure that the system can obtain the patient's complete physiological signal data, including the examination records conducted in the hospital in the past.

[0101] Next, the cloud platform will further retrieve historical examination records and pay special attention to respiratory rate and heart rate information. These two types of information are important in the assessment of psychological disorders because they can reflect the patient's emotional state, stress level, and possible anxiety or depression tendencies.

[0102] Finally, the cloud platform will obtain the threshold value of abnormal data based on the retrieved respiratory rate and heart rate information, combined with algorithms and models. The above threshold values ​​are based on a large amount of clinical data and psychological research, which can help the system more accurately determine whether the patient's physiological signals are in an abnormal state.

[0103] The effects of the above technical solution are as follows: First, by retrieving the patient's historical examination records in the hospital, the system can more comprehensively understand the patient's physiological signal changes, thereby more accurately assessing his or her psychological disorder. The above technical solution helps to discover abnormal changes that may not be easily detected in a single examination.

[0104] Secondly, special attention is paid to respiratory rate and heart rate information, which enables the system to more sensitively capture physiological changes related to psychological disorders. Changes in breathing and heart rate can often directly reflect the patient's emotional state and psychological stress, so this processing method improves the sensitivity and accuracy of the system.

[0105] Finally, by obtaining the defined values ​​of abnormal data, the system can more objectively determine whether the patient's physiological signals are abnormal, avoiding errors caused by subjective judgment. The above technical solutions are helpful for the accurate assessment and effective intervention of psychological disorders.

[0106] In summary, this processing method that increases the interaction with medical terminal data and pays special attention to respiratory rate and heart rate information further improves the accuracy and reliability of data processing and management methods used for psychological disorder assessment.

[0107] In one embodiment of the present invention, obtaining an abnormal data boundary value according to the respiratory rate information, heart rate information, blood pressure change rate and blood oxygen saturation includes:

[0108] Step 301, retrieve the critical values ​​of respiratory rate and heart rate for normal mental health and abnormal mental health from the database;

[0109] Step 302: Obtain a respiratory abnormality data threshold value based on the respiratory frequency information contained in the historical inspection record of the physiological signal; wherein the respiratory abnormality data threshold value is obtained by the following formula:

[0110]

[0111] Among them, Q 01 Indicates the critical value of abnormal breathing data; H L Indicates the critical value of respiratory rate; H p represents the average respiratory rate in the historical examination records; a represents the base value of the first logarithm; X p Indicates the average heart rate contained in the historical examination record; H max Indicates the maximum respiratory rate contained in the historical examination record; X m Indicates the heart rate value corresponding to the maximum respiratory rate contained in the historical examination record; P x01 Indicates the blood pressure change rate contained in the historical examination records; P x02 Indicates the saturation percentage value corresponding to the blood oxygen saturation contained in the historical examination record;

[0112] Step 303: Obtain a breathing abnormality determination value range using the breathing abnormality data boundary value and the breathing frequency critical value; wherein the breathing abnormality determination value range is H 01 —H L , and the lower limit of the respiratory rate H 01 Obtained through the following formula:

[0113] H 01 =H L -Q 01

[0114] Among them, H 01 Indicates the lower limit of the respiratory rate corresponding to the numerical range of abnormal breathing judgment;

[0115] Step 304: Obtain a heart rate abnormality data boundary value according to the heart rate information contained in the historical inspection record of the physiological signal; wherein the heart rate abnormality data boundary value is obtained by the following formula:

[0116]

[0117] Among them, Q 02 Indicates the value of abnormal heart rate data; X L Indicates the critical value of heart rate; X p represents the average heart rate in the historical examination records; b represents the value of the second logarithmic base; X max Indicates the maximum heart rate in the historical examination record; H m Indicates the respiratory rate value corresponding to the maximum heart rate contained in the historical examination record;

[0118] Step 305: Use the heart rate abnormality data boundary value and the heart rate critical value to obtain the heart rate abnormality determination value range; wherein the heart rate abnormality determination value range is X 01 —X L , and the heart rate lower limit value X 01 Obtained through the following formula:

[0119] X 01 =X L -Q 02

[0120] Among them, X 01 Indicates the lower limit of the heart rate corresponding to the abnormal heart rate judgment value range.

[0121] The working principle of the above technical solution is: the above technical solution is based on statistical and mathematical principles, combined with the patient's historical physiological signal data, to obtain abnormal data definition values ​​and abnormal judgment value range of respiration, heart rate, blood pressure change rate and blood oxygen saturation.

[0122] First, the critical values ​​of respiratory rate (H) for normal and abnormal mental health were retrieved from the database. L ) and the critical value of heart rate (X L The above critical values ​​are usually obtained based on the statistical analysis of physiological data of a large number of normal people and patients with mental disorders, and serve as a criterion for judging abnormalities.

[0123] Then, based on the respiratory frequency information in the historical inspection record of the physiological signal, the respiratory abnormality data threshold value (Q 01). This formula comprehensively considers the critical value, historical average value, maximum value and corresponding heart rate value of respiratory rate, and combines the blood pressure change rate and blood oxygen saturation to obtain a comprehensive boundary value through logarithm, multiplication and division operations. This formula can calculate a relatively accurate boundary value of abnormal respiratory data (Q 01 ). This value helps doctors or researchers to more accurately determine whether the patient has breathing abnormalities based on the patient's physiological signal history examination records;

[0124] Then, the abnormal breathing data is used to define the value (Q 01 ) and respiratory rate critical value (H L ) Calculate the breathing abnormality judgment value range (H 01 —H L ). This range indicates under what circumstances the breathing rate may be considered abnormal.

[0125] Similarly, based on the heart rate information in the historical inspection record of the physiological signal, the heart rate abnormality data limit value (Q 02 ). This formula also takes into account the critical value, historical average, maximum value of heart rate and the corresponding respiratory rate value.

[0126] Finally, the abnormal heart rate data is used to define the value (Q 02 ) and the critical value of heart rate (X L ) Calculate the abnormal heart rate determination value range (X 01 —X L ). This range indicates under what circumstances the heart rate may be considered abnormal.

[0127] The effect of the above technical solution is: by integrating multiple physiological indicators (such as respiratory rate and heart rate) in historical examination records, as well as the correlation between them, the calculated abnormal data definition value is more accurate. This method can better reflect the patient's true physiological state than a single indicator judgment. Since the patient's historical examination records are used in the calculation process, the resulting abnormal judgment value range is more personalized and can better adapt to the physiological characteristics of different patients. By setting a clear abnormal judgment value range, the system can issue a warning in time when physiological signals are abnormal, which helps to detect psychological disorders early and intervene. The calculation method based on mathematical and statistical principles reduces the interference of human factors, making the evaluation results more objective and reliable.

[0128] On the other hand, the above technical solution uses a combination of multiple formulas and algorithms. First, by accurately defining the formula for abnormal breathing data, it can comprehensively consider multiple factors such as respiratory rate, heart rate, blood pressure change rate and blood oxygen saturation, so as to more accurately identify and determine abnormal breathing conditions, providing an important reference for clinical diagnosis and treatment. Secondly, the above method can also optimize the numerical range of abnormal breathing determination, making the determination more reasonable and accurate. At the same time, it accurately defines abnormal heart rate data, providing accurate data for more accurate determination of the patient's abnormal heart rate, which is of great significance for preventing heart disease and improving cardiovascular health.

[0129] In summary, this technical solution improves the accuracy and objectivity of psychological disorder assessment through scientific calculation methods and personalized assessment, and provides strong technical support for the early detection and intervention of psychological disorders.

[0130] In one embodiment of the present invention, a physiological signal detection device worn by a patient is used to collect the patient's physiological signal in real time, and the physiological signal is sent to a cloud platform, including:

[0131] S101, when the physiological signal detection device worn by the patient is started, the physiological signals of the patient are collected in real time according to a preset physiological signal collection cycle, wherein the physiological signals include respiratory frequency information and heart rate information;

[0132] S102, sending the physiological signals collected corresponding to each physiological signal collection cycle to the cloud platform;

[0133] The physiological signal collection period corresponding to the respiratory frequency information and the heart rate information is 1 minute.

[0134] The working principle of the above technical solution is: when the physiological signal detection device worn by the patient is activated, the device will collect the patient's physiological signals in real time according to the preset physiological signal collection cycle. The physiological signals here mainly include respiratory rate information and heart rate information, which can directly reflect the patient's physiological state and emotional changes.

[0135] Specifically, at the end of each physiological signal collection cycle (set to 1 minute in this technical solution), the device will immediately send the collected physiological signal data to the cloud platform. The cloud platform is a server that centrally processes and analyzes data and can receive and store physiological signal data from multiple devices.

[0136] During the data transmission process, the device will use wireless communication technology (such as Bluetooth, Wi-Fi, etc.) to establish a connection with the cloud platform to ensure real-time transmission and reliable reception of data. At the same time, in order to ensure the security and privacy protection of data transmission, encryption technology and identity authentication mechanisms are usually used.

[0137] The effects of the above technical solution are as follows: First, by real-time collection and transmission of physiological signal data, the system can grasp the patient's physiological state and emotional changes in real time. This is of great significance for the early detection and evaluation of psychological disorders, and helps doctors or therapists take timely intervention measures.

[0138] Secondly, the preset physiological signal collection cycle (1 minute) can ensure the continuity and stability of the data. Compared with continuous and uninterrupted data collection, this periodic collection method can not only meet the real-time requirements, but also reduce the energy consumption of the equipment and the pressure of data processing.

[0139] In addition, through centralized processing and analysis on the cloud platform, the system can efficiently process a large amount of physiological signal data and extract key information related to psychological disorders. The above technical solutions help doctors or therapists to more objectively evaluate the patient's condition and provide a scientific basis for precise treatment and personalized intervention.

[0140] Finally, this technical solution also has the advantages of easy operation and comfortable wearing, and is suitable for patients of different ages and disease severity. At the same time, with the continuous development of wireless communication technology and data processing technology, this technical solution is also expected to achieve more efficient data transmission and more accurate data analysis, providing more powerful support for the diagnosis and treatment of psychological disorders.

[0141] In one embodiment of the present invention, after receiving the physiological signal, the cloud platform performs data filtering and abnormal emotion tendency state judgment on the physiological signal, and stores and manages the data of the physiological signal during the continuous abnormal emotion tendency state, including:

[0142] S201, performing data filtering processing through the cloud platform to obtain target data information corresponding to the physiological signal;

[0143] S202, judging whether the patient is in a state of abnormal emotional tendency according to the target data information combined with the patient's psychological disorder assessment data;

[0144] S203: When the patient has abnormal emotional tendency, the duration of the abnormal emotional tendency and the physiological signal data corresponding to the duration of the abnormal emotional tendency are stored and managed.

[0145] The working principle of the above technical solution is: after receiving the physiological signal sent by the physiological signal detection device worn by the patient, the cloud platform first performs data filtering. The purpose of the above step is to remove the noise and interference in the signal and extract the key information related to the target analysis, that is, the target data information. The above data is usually the numerical value of physiological indicators such as respiratory rate and heart rate after cleaning and standardization.

[0146] Next, the cloud platform will determine whether the patient is in a state of abnormal emotion tendency based on the extracted target data information and the patient's psychological disorder assessment data (such as historical diagnosis records, psychological assessment questionnaire results, etc.). The above judgment process may be based on machine learning algorithms or expert systems, and an effective abnormal emotion recognition model is established through the analysis and learning of a large amount of similar data.

[0147] Once the patient is judged to be in a state of abnormal emotion, the cloud platform will immediately start the data storage and management mechanism. This includes recording the duration of abnormal emotion and the changes in the patient's physiological signal data during the above duration. The above data is stored in a structured or time series form for subsequent in-depth analysis and application.

[0148] The effects of the above technical solution are: first, through data filtering and processing, the quality and accuracy of physiological signal data are improved, providing a reliable data basis for subsequent judgment of abnormal emotional tendency status.

[0149] Secondly, the abnormal emotional tendency state is judged by combining the psychological disorder assessment data, making the judgment result more comprehensive and accurate. The above technical solution helps to reduce misjudgment and missed judgment and improve the early detection rate of psychological disorders.

[0150] In addition, the storage and management of physiological signal data during the continuous state of abnormal emotional tendency provides a rich data resource for subsequent in-depth analysis and personalized intervention. By mining and analyzing the above data, we can further understand the occurrence mechanism and evolution process of abnormal emotions and provide a scientific basis for the precise treatment of psychological disorders.

[0151] Finally, the above technical solution also embodies the concepts of telemedicine and smart medicine. Through centralized processing and management on the cloud platform, it realizes real-time monitoring and dynamic evaluation of patients with mental disorders, providing strong technical support for the rehabilitation and treatment of patients.

[0152] In one embodiment of the present invention, data filtering and processing are performed through the cloud platform to obtain target data information corresponding to the physiological signal, including:

[0153] S2011, after the cloud platform obtains the patient's real-time physiological signal, it determines that the heart rate information and the respiratory rate information in the physiological signal are within the corresponding heart rate abnormality determination value range and respiratory abnormality determination value range;

[0154] S2012, when the heart rate information and the respiratory rate information in the physiological signal are not within their corresponding heart rate abnormality determination value range and respiratory abnormality determination value range, the heart rate information and the respiratory rate information are deleted, and the psychological dynamics of the patient's physiological signal The time period is marked as normal;

[0155] S2013a, when the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within its corresponding abnormal determination value range, the heart rate information and the respiratory rate information within the corresponding abnormal determination value range are retained, and the heart rate information and the respiratory rate information within the corresponding abnormal determination value range are used as the first target data information;

[0156] or,

[0157] S2013b. When the patient's heart rate information and respiratory rate information are not within their corresponding abnormal judgment value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the patient's heart rate information and respiratory rate information after the corresponding moment when any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value are stored in real time and used as the second target data information.

[0158] The working principle of the above technical solution is: after receiving the patient's real-time physiological signal, the cloud platform will first determine whether the heart rate information and respiratory rate information in the above signal are within the preset abnormal judgment value range. The abnormal judgment value range is obtained through the previous data processing method and is used to identify the patient's possible emotional abnormality tendency.

[0159] Specifically, the cloud platform will first check whether the heart rate and respiratory rate in the real-time physiological signal fall within the abnormal determination value range. The above range is calculated based on the critical values ​​of normal and abnormal mental health, which can reflect whether the patient's current mental state is abnormal. If the heart rate and respiratory rate information are not within the abnormal determination value range, the cloud platform will delete the above information and mark the corresponding time period as normal psychological dynamics. The above technical solution helps to reduce data redundancy while retaining key information. If any of the heart rate and respiratory rate information is within the abnormal determination value range, the cloud platform will retain the above information and use it as the first target data information. The above abnormal data is an important basis for subsequent analysis and evaluation. In special cases, even if the heart rate and respiratory rate are not continuously within the abnormal determination value range, if any physiological signal directly exceeds its corresponding critical value (i.e., the boundary between normal and abnormal mental health), the cloud platform will store all heart rate and respiratory rate information after the above moment in real time as the second target data information. This processing method can capture sudden physiological changes and help to promptly discover and deal with potential emotional abnormalities.

[0160] The effect of the above technical solution is: by filtering the physiological signals, the data within the normal range is deleted, and only the abnormal data related to abnormal emotional tendencies is retained, thereby improving the pertinence and effectiveness of the data. By setting the abnormal judgment value range and critical value, the cloud platform can accurately identify the patient's possible abnormal emotional tendencies, providing an important basis for subsequent diagnosis and treatment.

[0161] For physiological signals that directly exceed the critical value, the cloud platform can respond in real time and store relevant information to ensure that no possible abnormal events are missed, providing a rich data source for subsequent in-depth analysis. By distinguishing the data processing methods in different situations (such as deleting normal signals, retaining abnormal signals, and storing critical situation data in real time), the above technical solution optimizes the data processing process and improves processing efficiency.

[0162] In summary, the above-mentioned technical solution realizes accurate identification and effective management of physiological signals through real-time analysis and filtering processing on the cloud platform, providing strong technical support for the assessment and intervention of psychological disorders.

[0163] In one embodiment of the present invention, judging whether the patient is in a state of abnormal emotional tendency based on the target data information combined with the patient's psychological disorder assessment data includes:

[0164] S2021, when the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within its corresponding abnormal determination value range, from the moment when the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within its corresponding abnormal determination value range, storing the first target data information of the patient in real time;

[0165] S2022, using the first target data information in combination with the patient's psychological disorder assessment data to obtain a first abnormal tendency determination value;

[0166] S2023, when the heart rate information and respiratory rate information of the patient are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, using the second target data information in combination with the patient's psychological disorder assessment data to obtain a second abnormal tendency determination value;

[0167] S2024. When the first abnormal tendency determination value and the second abnormal tendency determination value exceed the preset first determination threshold and the second determination threshold, it is determined that the patient is currently in an abnormal emotional tendency state.

[0168] The working principle of the above technical solution is as follows: the above technical solution is based on the cloud platform's analysis of target data information and the integration of psychological disorder assessment data. After the cloud platform obtains the target data information corresponding to the physiological signal through data filtering and processing, it will judge whether the patient is in an abnormal emotional state based on the above target data information and the patient's psychological disorder assessment data.

[0169] The specific steps are as follows:

[0170] When the data information of any one of the physiological signals of the heart rate information and the respiratory rate information is within the corresponding abnormal determination value range, the cloud platform will store the first target data information in real time. The above technical solution is helpful to capture the dynamic changes of abnormal states.

[0171] The cloud platform will use the stored first target data information (i.e. abnormal physiological signal data) and the patient's psychological disorder assessment data (such as historical diagnosis records, psychological assessment questionnaire results, etc.) for comprehensive analysis, and calculate a first abnormal tendency determination value through a certain algorithm or model (such as machine learning algorithm, statistical analysis method, etc.). This value can reflect the degree of the patient's current abnormal emotional tendency.

[0172] In another scenario, if the patient's heart rate information and respiratory rate information are not continuously within the abnormal judgment value range, but any physiological signal directly exceeds its corresponding critical respiratory rate value and heart rate value, the cloud platform will use the above second target data information and psychological disorder assessment data for comprehensive analysis to obtain the second abnormal tendency judgment value. The above steps pay special attention to sudden physiological changes, which helps to detect potential emotional abnormalities in a timely manner.

[0173] Finally, the cloud platform will compare the first abnormal tendency determination value and the second abnormal tendency determination value with the preset first determination threshold and the second determination threshold. When the above determination values ​​exceed the corresponding thresholds, the cloud platform will determine that the patient is currently in an abnormal emotional tendency state.

[0174] The effect of the above technical solution is: by combining real-time physiological signal data and psychological disorder assessment data, the cloud platform can more accurately determine whether the patient is in a state of abnormal emotional tendency. The above technical solution helps to reduce misjudgments and missed judgments and improve the reliability of diagnosis. The cloud platform can store and analyze physiological signal data in real time to achieve dynamic monitoring of the patient's emotional state. The above technical solution helps to timely detect and deal with emotional abnormalities and provide timely intervention and support for patients.

[0175] Due to the combination of the patient's psychological disorder assessment data, the above technical solution can achieve a more personalized assessment of abnormal emotional tendency. Based on the above assessment results, a more targeted intervention plan can be formulated for the patient. By distinguishing the data processing and analysis methods in different situations (such as real-time storage of the first target data information, processing critical situations, combining assessment data to obtain abnormal tendency judgment values, etc.), the above technical solution optimizes the data processing process and improves processing efficiency.

[0176] In summary, the above-mentioned technical solution, by combining physiological signal data and psychological disorder assessment data, can achieve accurate judgment of the patient's abnormal emotional tendency state, and provide strong technical support for the assessment and intervention of psychological disorders.

[0177] In one embodiment of the present invention, the first target data information is combined with the patient's psychological disorder assessment data to obtain a first abnormal tendency determination value, including:

[0178] Step 1: Obtain a first physiological abnormality tendency parameter according to the first target data information; wherein the first physiological abnormality tendency parameter is obtained by the following formula:

[0179] K 01 =K x01 +K x02

[0180] Among them, K 01 Represents the first physiological abnormality tendency parameter; K x01 and K x02 They respectively represent the first parameter variable and the second parameter variable corresponding to the physiological abnormality tendency parameter; and the first parameter variable and the second parameter variable are obtained by the following formula:

[0181]

[0182] Wherein, n represents the number of respiratory frequencies in the corresponding respiratory abnormality determination value range; H1, H2, ... H n Respectively represent the values ​​corresponding to the n breathing frequencies; X1, X2, ... X n represents the heart rate value corresponding to the n-th respiratory frequency; m represents the number of heart rates in the corresponding heart rate abnormality judgment value range; X1, X2, ... X m Respectively represent the values ​​corresponding to m heart rates; H1, H2, ... H m They represent the respiratory frequency value corresponding to m heart rates; 01 and λ 02 Respectively represent the respiratory frequency distribution coefficient and the heart rate distribution coefficient; wherein the respiratory frequency distribution coefficient and the heart rate distribution coefficient are obtained by the following formula:

[0183]

[0184] Among them, H i represents the value corresponding to the i-th respiratory frequency in its corresponding respiratory abnormality determination value range; X i represents the heart rate value corresponding to the i-th respiratory frequency in its corresponding respiratory abnormality judgment value range; X j represents the jth heart rate value within the corresponding heart rate abnormality judgment value range; H j represents the respiratory frequency value corresponding to the j-th heart rate in the corresponding heart rate abnormality determination value range;

[0185] Step 2: retrieve the patient's psychological disorder assessment data;

[0186] Step 3: Obtain the psychological tendency value corresponding to the patient according to the psychological disorder assessment data, wherein the psychological tendency value is obtained by the following formula:

[0187]

[0188] Among them, K y represents the psychological tendency value; k represents the number of times the psychological health characteristic words appear in the psychological disorder assessment; w i represents the tendency coefficient corresponding to the i-th mental health characteristic word; Y i represents the occurrence frequency of the i-th mental health characteristic word;

[0189] Step 4: Use the psychological tendency value and the first physiological abnormal tendency parameter to obtain a first abnormal tendency determination value corresponding to the abnormal emotional tendency state; wherein the first abnormal tendency determination value is obtained by the following formula:

[0190] R 01 =lnK 01 +lnK y

[0191] Among them, R 01 Indicates the first abnormal tendency determination value.

[0192] The working principle of the above technical solution is as follows: first, according to the first target data information (i.e., abnormal physiological signal data), a first physiological abnormality tendency parameter is calculated through a series of formulas. The above formulas take into account multiple physiological indicators such as respiratory rate and heart rate, and introduce parameters such as distribution coefficients to more comprehensively and accurately reflect the physiological abnormality state.

[0193] Next, retrieve the patient's psychological disorder assessment data. The above data usually includes the number of occurrences and frequency of psychological health characteristic words, which can reflect the patient's psychological tendency and state.

[0194] Then, based on the psychological disorder assessment data, the psychological tendency value is calculated through a specific formula. This value quantifies the patient's psychological tendency and provides an important basis for the subsequent abnormal tendency determination.

[0195] Finally, the psychological tendency value and the first physiological abnormal tendency parameter are used to calculate the first abnormal tendency determination value corresponding to the abnormal emotional tendency state through a certain algorithm or model. This value combines physiological and psychological information and can more accurately reflect the abnormal emotional tendency state of the patient.

[0196] The effect of the above technical solution is: by integrating physiological signal data and psychological disorder assessment data, the patient's emotional state can be evaluated more comprehensively. The above technical solution helps to reduce the errors and deviations that may be caused by a single data source and improve the accuracy and reliability of the assessment. Secondly, by using specific calculation formulas and parameter settings, the degree of physiological abnormalities and psychological tendencies can be quantified more accurately. The above technical solution helps to more carefully portray the patient's emotional state and provide a more accurate basis for subsequent intervention and treatment. In addition, the technical solution is also highly flexible and adaptable. The parameters and coefficients in the formula can be adjusted according to different patients and situations to meet different evaluation needs.

[0197] In summary, the above technical solution integrates physiological signal data and psychological disorder assessment data, and uses specific calculation formulas and parameter settings to accurately evaluate the patient's abnormal emotional tendency state. This provides strong technical support for the early detection and intervention of psychological disorders.

[0198] In one embodiment of the present invention, when the heart rate information and respiratory rate information of the patient are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the second target data information is combined with the patient's psychological disorder assessment data to obtain a second abnormal tendency determination value, including:

[0199] Step 1, when the patient's heart rate information and respiratory rate information are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the respiratory rate information and heart rate information exceeding the respiratory rate critical value and heart rate critical value are retrieved from the second target data information;

[0200] Step 2, using the respiratory rate information and heart rate information exceeding the critical value of the respiratory rate and the critical value of the heart rate in the second target data information to obtain the first physiological abnormality tendency factor; wherein the first physiological abnormality tendency factor is obtained by the following formula:

[0201]

[0202] Among them, K x03 represents the first physiological abnormality tendency factor; r 01 represents the number of respiratory frequency information exceeding the critical value of respiratory frequency in the second target data information; H t represents the tth respiratory rate value that exceeds the critical value of respiratory rate; X t represents the heart rate value corresponding to the tth respiratory frequency exceeding the critical value of respiratory frequency; δ represents the first respiratory index adjustment coefficient; X max Indicates the heart rate value corresponding to the maximum respiratory rate in the second target data information;

[0203] Step 3, when the patient's heart rate information and respiratory frequency information are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory frequency critical value and heart rate critical value, retrieving the respiratory frequency information and heart rate information within the respiratory abnormal determination value range and heart rate abnormal determination value range from the second target data information;

[0204] Step 4, using the respiratory frequency information and heart rate information in the second target data information that are within the respiratory abnormality determination value range and the heart rate abnormality determination value range to obtain a second physiological abnormality tendency factor; wherein the second physiological abnormality tendency factor is obtained by the following formula:

[0205]

[0206] Among them, K x04 Represents the second physiological abnormality tendency factor; r 02 represents the number of respiratory frequency information in the second target data information that is within the respiratory abnormality determination value range; H e represents the e-th respiratory rate value within the respiratory abnormality determination value range; X e represents the heart rate value corresponding to the e-th respiratory frequency in the respiratory abnormality determination value range; ξ represents the second respiratory index adjustment coefficient; H max Indicates the respiratory frequency value corresponding to the maximum heart rate value within the heart rate abnormality determination value range in the second target data information;

[0207] Step 5, using the first physiological abnormality tendency factor and the second physiological abnormality tendency factor to obtain a second physiological abnormality tendency parameter, wherein the second physiological abnormality tendency parameter is obtained by the following formula:

[0208] K 02 =K x03 +K x04

[0209] Among them, K 02 Indicates the second physiological abnormality tendency parameter;

[0210] Step 6, obtaining the psychological tendency value corresponding to the patient according to the psychological disorder assessment data, wherein the psychological tendency value is obtained by the following formula:

[0211]

[0212] Among them, K y represents the psychological tendency value; k represents the number of times the psychological health characteristic words appear in the psychological disorder assessment; w i represents the tendency coefficient corresponding to the i-th mental health characteristic word; Y i represents the occurrence frequency of the i-th mental health characteristic word;

[0213] Step 7, using the psychological tendency value and the second physiological abnormal tendency parameter to obtain a second abnormal tendency determination value corresponding to the abnormal emotional tendency state; wherein the second abnormal tendency determination value is obtained by the following formula:

[0214] R 02 =lnK 02 +lnK y

[0215] Among them, R 02 Indicates the second abnormal tendency determination value.

[0216] The working principle of the above technical solution is: first, when the patient's physiological signal (heart rate or respiratory rate) directly exceeds its corresponding critical value without being within the abnormal judgment value range, the system will retrieve the above physiological signal data exceeding the critical value from the second target data information.

[0217] Next, the physiological signal data exceeding the critical value is used to calculate the first physiological abnormality tendency factor through a specific formula, which mainly reflects the degree and frequency of the patient's physiological signal directly exceeding the critical value.

[0218] At the same time, the system will also retrieve physiological signal data within the abnormal breathing determination value range and the abnormal heart rate determination value range from the second target data information. Using the above data, the second physiological abnormality tendency factor is calculated by the above formula. This factor mainly considers the situation that the patient's physiological signal is within the abnormal determination value range.

[0219] Then, the first physiological abnormality tendency factor and the second physiological abnormality tendency factor are combined and a second physiological abnormality tendency parameter is calculated through a certain algorithm or model. This parameter integrates the physiological abnormality information under two different conditions.

[0220] After obtaining the second physiological abnormality tendency parameter, the system will also retrieve the patient's psychological disorder assessment data and calculate the psychological tendency value through a specific formula. This value reflects the patient's current psychological state.

[0221] Finally, the psychological tendency value and the second physiological abnormal tendency parameter are used to calculate the second abnormal tendency determination value corresponding to the abnormal emotional tendency state through a certain algorithm or model. This value comprehensively considers the patient's physiological and psychological state and is used to determine whether the patient is in an abnormal emotional tendency state.

[0222] The effect of the above technical solution is that by considering the situation where the physiological signal directly exceeds the critical value, the technical solution can more comprehensively capture the patient's physiological abnormal state. This helps to timely discover and deal with potential health problems and improve the accuracy of diagnosis and intervention.

[0223] Secondly, combined with the psychological disorder assessment data, this technical solution can provide a deeper understanding of the patient's psychological state and combine it with the physiological abnormality for comprehensive analysis. This helps to more accurately judge the patient's emotional abnormality tendency and provide a more reliable basis for subsequent intervention and treatment.

[0224] In addition, this technical solution can more carefully characterize the patient's physiological and psychological state by introducing multiple parameters and factors. This helps to more accurately assess the degree of abnormal emotional tendency of patients and provide support for personalized intervention and treatment plan formulation.

[0225] On the other hand, the above technical solution can accurately calculate the first physiological abnormality tendency parameter through specific mathematical formulas and algorithms, providing doctors or researchers with an accurate measure of the patient's physiological abnormality tendency, which helps to provide more comprehensive and accurate evaluation index parameters for the patient's health status. During the calculation process, the solution comprehensively considers multiple physiological parameters and ensures the accuracy of the final result through careful analysis and processing. At the same time, in order to further improve the accuracy of the calculation, the solution also introduces a distribution coefficient to optimize the calculation of the parameters. In addition, by optimizing the judgment value range, the solution can more accurately identify physiological abnormalities, thereby providing a more reliable basis for diagnosis and treatment.

[0226] At the same time, each formula in the above technical solution is accurate in evaluating and quantifying the tendency of physiological abnormality. Specifically, when any physiological signal of the patient exceeds the critical value of the respiratory frequency or heart rate, the above technical solution can quickly extract the information exceeding the critical value from the target data information, and calculate the first physiological abnormality tendency factor using a specific formula. The above method takes into account the number of respiratory frequency and heart rate and the relationship between them, so as to obtain a value that can accurately reflect the patient's physiological abnormality tendency. Further, when the patient's physiological signal is in the abnormal breathing or heart rate determination value range, the technical solution can also extract relevant information and calculate the second physiological abnormality tendency factor. The above method not only takes into account the number of physiological signals within the abnormal range, but also fully considers the specific values ​​of these signals and the relationship between them, and obtains another indicator that can quantify the patient's physiological abnormality tendency through the above method. Through the above method, not only can the information related to the abnormal tendency be extracted quickly and accurately from a large amount of physiological data, but also the value that can accurately reflect the patient's physiological state can be obtained through sophisticated mathematical calculations. These values ​​not only provide doctors with a more scientific and objective evaluation basis, but also can effectively improve the speed and accuracy of data acquisition.

[0227] In summary, this technical solution achieves an accurate assessment of the patient's abnormal emotional tendency by comprehensively considering the patient's physiological signals and psychological disorder assessment data. This provides effective technical support for the early detection and intervention of psychological disorders, and helps improve the patient's quality of life and mental health level.

[0228] The embodiment of the present invention provides a data processing and management system for psychological disorder assessment, such as Figure 2 As shown, the data processing and management system for psychological disorder assessment includes:

[0229] A signal acquisition module, used to collect the patient's physiological signals in real time using a physiological signal detection device worn by the patient, and send the physiological signals to a cloud platform;

[0230] The data storage and management module is used for filtering the physiological signals and judging the abnormal emotional tendency state after the cloud platform receives the physiological signals, and for storing and managing the physiological signals during the continuous abnormal emotional tendency state.

[0231] The working principle of the above technical solution is as follows: First, the physiological signal detection device worn by the patient will collect the patient's physiological signals in real time. The above physiological signals include but are not limited to heart rate, respiratory rate, skin electrical activity, etc., which can reflect the patient's emotional state and psychological changes. Once the above physiological signals are collected, the device will immediately send them to the cloud platform.

[0232] After receiving the physiological signals, the cloud platform will perform a series of data processing. First, the physiological signals are filtered to remove noise and interference to ensure the accuracy and reliability of the data. Then, the cloud platform uses machine learning and pattern recognition technology to judge the abnormal emotional tendency of the filtered physiological signals. Based on a large amount of training data and algorithm models, it can identify the abnormal emotional tendency reflected in the physiological signals.

[0233] Once it is determined that there is an abnormal emotional tendency in the physiological signal, the cloud platform will immediately store and manage the data of the physiological signal in this state, including storing the data in a secure database and providing data query, analysis and visualization functions to facilitate subsequent psychological disorder assessment and intervention.

[0234] The effect of the above technical solution is that by collecting the patient's physiological signals in real time and sending them to the cloud platform, remote monitoring and evaluation of psychological disorders can be achieved. This not only provides convenience for patients, but also makes the evaluation of psychological disorders no longer limited to a specific time and place, improving the flexibility and efficiency of the evaluation.

[0235] Secondly, the data filtering and abnormal emotion tendency state judgment functions of the cloud platform can accurately identify abnormal states in physiological signals, providing strong support for the early detection of psychological disorders. At the same time, by storing and managing the data of physiological signals during the continuous abnormal emotion tendency state, it provides rich data support for the subsequent psychological disorder assessment and intervention.

[0236] In addition, the method also utilizes the powerful computing and storage capabilities of the cloud platform to achieve efficient processing and analysis of a large number of physiological signals. This can not only improve the accuracy and reliability of psychological disorder assessment, but also provide a scientific basis for the intervention and treatment of psychological disorders.

[0237] In summary, this data processing and management method for psychological disorder assessment has significant technical effects and application value, and is expected to provide new technical means for the early detection, accurate assessment and effective intervention of psychological disorders.

[0238] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if the above-mentioned modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include the above-mentioned modifications and variations.

Claims

1. A data processing and management method for psychological disorder assessment, characterized in that: The data processing and management method for psychological disorder assessment includes: The physiological signal detection device worn by the patient is used to collect the patient's physiological signal in real time, and the physiological signal is sent to the cloud platform; After receiving the physiological signal, the cloud platform performs data filtering and abnormal emotion tendency state judgment on the physiological signal, and stores and manages the data of the physiological signal during the continuous abnormal emotion tendency state; After receiving the physiological signal, the cloud platform performs data filtering and abnormal emotion tendency state judgment on the physiological signal, and stores and manages the data of the physiological signal during the continuous abnormal emotion tendency state, including: Performing data filtering and processing through the cloud platform to obtain target data information corresponding to the physiological signal; Determine whether the patient is in a state of abnormal emotional tendency based on the target data information combined with the patient's psychological disorder assessment data; When the patient has abnormal emotional tendency, the duration of the abnormal emotional tendency and the physiological signal data corresponding to the duration of the abnormal emotional tendency are stored and managed; The cloud platform performs data filtering and processing to obtain target data information corresponding to the physiological signal, including: After the cloud platform obtains the patient's real-time physiological signal, it determines whether the heart rate information and respiratory rate information in the physiological signal are within the corresponding heart rate abnormality determination value range and respiratory abnormality determination value range; When the heart rate information and the respiratory frequency information in the physiological signal are not within their corresponding heart rate abnormality determination value range and respiratory abnormality determination value range, the heart rate information and the respiratory frequency information are deleted, and the psychological dynamics of the patient's physiological signal The time period is marked as normal; When the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory frequency information is within its corresponding abnormal determination value range, the heart rate information and the respiratory frequency information within the corresponding abnormal determination value range are retained, and the heart rate information and the respiratory frequency information within the corresponding abnormal determination value range are used as the first target data information; or, When the heart rate information and respiratory rate information of the patient are not within the corresponding abnormal determination value range, and any physiological signal directly exceeds the corresponding respiratory rate critical value and heart rate critical value, the heart rate information and respiratory rate information of the patient after the corresponding moment when any physiological signal directly exceeds the corresponding respiratory rate critical value and heart rate critical value are stored in real time and used as the second target data information; Judging whether the patient is in a state of abnormal emotional tendency according to the target data information combined with the patient's psychological disorder assessment data includes: When the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within the corresponding abnormal determination value range, the first target data information of the patient is stored in real time from the moment when the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within the corresponding abnormal determination value range; Using the first target data information in combination with the patient's psychological disorder assessment data to obtain a first abnormal tendency determination value; When the heart rate information and respiratory rate information of the patient are not within their corresponding abnormal determination value ranges, and any physiological signal directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the second target data information is combined with the patient's psychological disorder assessment data to obtain a second abnormal tendency determination value; When the first abnormal tendency determination value and the second abnormal tendency determination value exceed the preset first determination threshold and the second determination threshold, it is determined that the patient is currently in an abnormal emotional tendency state.

2. The data processing and management method for psychological disorder assessment according to claim 1, characterized in that: The data processing and management method for psychological disorder assessment also includes: After the cloud platform establishes a communication connection with the physiological signal detection device worn by the patient, the historical examination records of the patient's physiological signals in the hospital are retrieved from the medical terminal; The cloud platform retrieves respiratory rate information and heart rate information from the historical examination records of the patient's physiological signals in the hospital; The abnormal data boundary value is obtained according to the respiratory rate information, heart rate information, blood pressure change rate and blood oxygen saturation.

3. The data processing and management method for psychological disorder assessment according to claim 2, characterized in that: Obtaining abnormal data definition values ​​according to the respiratory rate information, heart rate information, blood pressure change rate and blood oxygen saturation, including: Retrieving critical values ​​of respiratory rate and heart rate for normal mental health and abnormal mental health from the database; The abnormal breathing data boundary value is obtained according to the respiratory frequency information contained in the historical inspection record of the physiological signal; wherein the abnormal breathing data boundary value is obtained by the following formula: Among them, Q 01 Indicates the critical value of abnormal breathing data; H L Indicates the critical value of respiratory rate; H p represents the average respiratory rate in the historical examination records; a represents the base value of the first logarithm; X p Indicates the average heart rate contained in the historical examination record; H max Indicates the maximum respiratory rate contained in the historical examination record; X m Indicates the heart rate value corresponding to the maximum respiratory rate contained in the historical examination record; P x01 Indicates the blood pressure change rate contained in the historical examination records; P x02 Indicates the saturation percentage value corresponding to the blood oxygen saturation contained in the historical examination record; The abnormal breathing data boundary value and the critical value of the respiratory frequency are used to obtain the abnormal breathing determination value range; wherein the abnormal breathing determination value range is H 01 —H L , and the lower limit of the respiratory rate H 01 Obtained through the following formula: Among them, H 01 Indicates the lower limit of the respiratory rate corresponding to the numerical range of abnormal breathing judgment; The abnormal heart rate data boundary value is obtained according to the heart rate information contained in the historical inspection record of the physiological signal; wherein the abnormal heart rate data boundary value is obtained by the following formula: Among them, Q 02 Indicates the value of abnormal heart rate data; X L Indicates the critical value of heart rate; X p represents the average heart rate in the historical examination records; b represents the value of the second logarithmic base; X max Indicates the maximum heart rate in the historical examination record; H m Indicates the respiratory rate value corresponding to the maximum heart rate contained in the historical examination record; The abnormal heart rate data boundary value and the critical heart rate value are used to obtain the abnormal heart rate determination value range; wherein the abnormal heart rate determination value range is X 01 —X L , and the heart rate lower limit value X 01 Obtained through the following formula: Among them, X 01 Indicates the lower limit of the heart rate corresponding to the abnormal heart rate judgment value range.

4. The data processing and management method for psychological disorder assessment according to claim 1, characterized in that: The physiological signal detection device worn by the patient is used to collect the patient's physiological signal in real time, and the physiological signal is sent to the cloud platform, including: When the physiological signal detection device worn by the patient is started, the patient's physiological signals are collected in real time according to a preset physiological signal collection cycle, wherein the physiological signals include respiratory frequency information and heart rate information; Send the collected physiological signals corresponding to each physiological signal collection cycle to the cloud platform; The physiological signal collection period corresponding to the respiratory frequency information and the heart rate information is 1 minute.

5. The data processing and management method for psychological disorder assessment according to claim 1, characterized in that: The first target data information is combined with the patient's psychological disorder assessment data to obtain a first abnormal tendency determination value, including: Obtain a first physiological abnormality tendency parameter according to the first target data information; wherein the first physiological abnormality tendency parameter is obtained by the following formula: Among them, K 01 Represents the first physiological abnormality tendency parameter; K x01 and K x02 They respectively represent the first parameter variable and the second parameter variable corresponding to the physiological abnormality tendency parameter; and the first parameter variable and the second parameter variable are obtained by the following formula: Wherein, n represents the number of respiratory frequencies in the corresponding respiratory abnormality determination value range; H1, H2, ... H n Respectively represent the values ​​corresponding to the n breathing frequencies; X1, X2, ... X n represents the heart rate value corresponding to the n-th respiratory frequency; m represents the number of heart rates in the corresponding heart rate abnormality judgment value range; X1, X2, ... X m Respectively represent the values ​​corresponding to m heart rates; H1, H2, ... H m They represent the respiratory frequency value corresponding to m heart rates; 01 and λ 02 Represent the respiratory frequency distribution coefficient and the heart rate distribution coefficient respectively; wherein the respiratory frequency distribution coefficient and the heart rate distribution coefficient are obtained by the following formula Among them, H i represents the value corresponding to the i-th respiratory frequency in its corresponding respiratory abnormality determination value range; X i represents the heart rate value corresponding to the i-th respiratory frequency in its corresponding respiratory abnormality judgment value range; X j represents the jth heart rate value within the corresponding heart rate abnormality judgment value range; H j represents the respiratory frequency value corresponding to the j-th heart rate in the corresponding heart rate abnormality determination value range; Retrieve the patient's psychological disorder assessment data; The psychological tendency value corresponding to the patient is obtained according to the psychological disorder assessment data, wherein the psychological tendency value is obtained by the following formula: Among them, K y represents the psychological tendency value; k represents the number of times the psychological health characteristic words appear in the psychological disorder assessment; w i represents the tendency coefficient corresponding to the i-th mental health characteristic word; Y i represents the occurrence frequency of the i-th mental health characteristic word; The first abnormal tendency determination value corresponding to the abnormal emotional tendency state is obtained by using the psychological tendency value and the first physiological abnormal tendency parameter; wherein the first abnormal tendency determination value is obtained by the following formula: Among them, R 01 Indicates the first abnormal tendency determination value.

6. The data processing and management method for psychological disorder assessment according to claim 1, characterized in that: When the heart rate information and respiratory rate information of the patient are not within their corresponding abnormal determination value ranges, and any physiological signal directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the second target data information is combined with the patient's psychological disorder assessment data to obtain a second abnormal tendency determination value, including: When the heart rate information and respiratory rate information of the patient are not within the corresponding abnormal determination value range, and any physiological signal directly exceeds the corresponding respiratory rate critical value and heart rate critical value, the respiratory rate information and heart rate information exceeding the respiratory rate critical value and heart rate critical value are retrieved from the second target data information; The first physiological abnormality tendency factor is obtained by using the respiratory frequency information and the heart rate information exceeding the critical value of the respiratory frequency and the critical value of the heart rate in the second target data information; wherein the first physiological abnormality tendency factor is obtained by the following formula: Among them, K x03 represents the first physiological abnormality tendency factor; r 01 represents the number of respiratory frequency information exceeding the critical value of respiratory frequency in the second target data information; H t represents the tth respiratory rate value that exceeds the critical value of respiratory rate; X t represents the heart rate value corresponding to the tth respiratory frequency exceeding the critical value of respiratory frequency; δ represents the first respiratory index adjustment coefficient; X max Indicates the heart rate value corresponding to the maximum respiratory rate in the second target data information; When the patient's heart rate information and respiratory frequency information are not within their corresponding abnormal determination value ranges, and any one of the physiological signals directly exceeds its corresponding respiratory frequency critical value and heart rate critical value, retrieving the respiratory frequency information and heart rate information within the respiratory abnormal determination value range and heart rate abnormal determination value range from the second target data information; The second physiological abnormality tendency factor is obtained by using the respiratory frequency information and the heart rate information in the second target data information that are within the respiratory abnormality determination value range and the heart rate abnormality determination value range; wherein the second physiological abnormality tendency factor is obtained by the following formula: Among them, K x04 Represents the second physiological abnormality tendency factor; r 02 represents the number of respiratory frequency information in the second target data information that is within the respiratory abnormality determination value range; H e represents the e-th respiratory rate value within the respiratory abnormality determination value range; X e represents the heart rate value corresponding to the e-th respiratory frequency in the respiratory abnormality determination value range; ξ represents the second respiratory index adjustment coefficient; H max Indicates the respiratory frequency value corresponding to the maximum heart rate value within the heart rate abnormality determination value range in the second target data information; The first physiological abnormality tendency factor and the second physiological abnormality tendency factor are used to obtain a second physiological abnormality tendency parameter, wherein the second physiological abnormality tendency parameter is obtained by the following formula: Among them, K 02 Indicates the second physiological abnormality tendency parameter; The psychological tendency value corresponding to the patient is obtained according to the psychological disorder assessment data, wherein the psychological tendency value is obtained by the following formula: Among them, K y represents the psychological tendency value; k represents the number of times the psychological health characteristic words appear in the psychological disorder assessment; w i represents the tendency coefficient corresponding to the i-th mental health characteristic word; Y i represents the occurrence frequency of the i-th mental health characteristic word; The psychological tendency value and the second physiological abnormal tendency parameter are used to obtain a second abnormal tendency determination value corresponding to the abnormal emotional tendency state; wherein the second abnormal tendency determination value is obtained by the following formula: Among them, R 02 Indicates the second abnormal tendency determination value.

7. A data processing and management system for psychological disorder assessment, characterized in that: The data processing and management system for psychological disorder assessment includes: A signal acquisition module, used to collect the patient's physiological signals in real time using a physiological signal detection device worn by the patient, and send the physiological signals to a cloud platform; A data storage and management module, which is used for the cloud platform to filter the physiological signals and determine the abnormal emotional tendency state after receiving the physiological signals, and to store and manage the physiological signals during the continuous abnormal emotional tendency state; After receiving the physiological signal, the cloud platform performs data filtering and abnormal emotion tendency state judgment on the physiological signal, and stores and manages the data of the physiological signal during the continuous abnormal emotion tendency state, including: Performing data filtering and processing through the cloud platform to obtain target data information corresponding to the physiological signal; Determine whether the patient is in a state of abnormal emotional tendency based on the target data information combined with the patient's psychological disorder assessment data; When the patient has abnormal emotional tendency, the duration of the abnormal emotional tendency and the physiological signal data corresponding to the duration of the abnormal emotional tendency are stored and managed; The cloud platform performs data filtering and processing to obtain target data information corresponding to the physiological signal, including: After the cloud platform obtains the patient's real-time physiological signal, it determines whether the heart rate information and respiratory rate information in the physiological signal are within the corresponding heart rate abnormality determination value range and respiratory abnormality determination value range; When the heart rate information and the respiratory frequency information in the physiological signal are not within their corresponding heart rate abnormality determination value range and respiratory abnormality determination value range, the heart rate information and the respiratory frequency information are deleted, and the psychological dynamics of the patient's physiological signal The time period is marked as normal; When the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory frequency information is within its corresponding abnormal determination value range, the heart rate information and the respiratory frequency information within the corresponding abnormal determination value range are retained, and the heart rate information and the respiratory frequency information within the corresponding abnormal determination value range are used as the first target data information; or, When the heart rate information and respiratory rate information of the patient are not within the corresponding abnormal determination value range, and any physiological signal directly exceeds the corresponding respiratory rate critical value and heart rate critical value, the heart rate information and respiratory rate information of the patient after the corresponding moment when any physiological signal directly exceeds the corresponding respiratory rate critical value and heart rate critical value are stored in real time and used as the second target data information; Judging whether the patient is in a state of abnormal emotional tendency according to the target data information combined with the patient's psychological disorder assessment data includes: When the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within the corresponding abnormal determination value range, the first target data information of the patient is stored in real time from the moment when the data information corresponding to any one of the physiological signals of the heart rate information and the respiratory rate information is within the corresponding abnormal determination value range; Using the first target data information in combination with the patient's psychological disorder assessment data to obtain a first abnormal tendency determination value; When the heart rate information and respiratory rate information of the patient are not within their corresponding abnormal determination value ranges, and any physiological signal directly exceeds its corresponding respiratory rate critical value and heart rate critical value, the second target data information is combined with the patient's psychological disorder assessment data to obtain a second abnormal tendency determination value; When the first abnormal tendency determination value and the second abnormal tendency determination value exceed the preset first determination threshold and the second determination threshold, it is determined that the patient is currently in an abnormal emotional tendency state.

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