Data processing method and system for monitoring depression in perinatal period

Abstract

The invention provides a data processing method and system for monitoring depression in a perinatal period. The data processing method for monitoring the depression in the perinatal period comprises the following steps: extracting a current pregnancy stage of a patient in the perinatal period, and setting a data acquisition frequency according to the current pregnancy stage of the patient in the perinatal period, the data acquisition frequency of the current pregnancy stage of the patient in the perinatal period is dynamically adjusted in real time in the data acquisition process; performing data acquisition on the patient in the perinatal period according to the data acquisition frequency to obtain behavior data and physiological data of the patient in the perinatal period, and performing data quality evaluation and data quality abnormity early warning on the behavior data and the physiological data; the behavior data and the physiological data, meeting the data quality requirements, of the perinatal patients are stored in a grouped mode. The system comprises modules corresponding to the steps of the method.

Description

Technical Field

[0001] The invention provides a data processing method and system for monitoring perinatal depression, belonging to the technical field of data processing. Background Art

[0002] The perinatal period, that is, the period around childbirth, is a special stage of physiological and psychological changes in women's lives. During this period, pregnant women not only have to face many physical changes, but also have to endure psychological pressure, all of which may lead to the occurrence of perinatal depression. Perinatal depression not only affects the physical and mental health of pregnant women, but may also have adverse effects on the health and development of the fetus. Therefore, effective monitoring and intervention of perinatal depression is particularly important. Traditional perinatal depression monitoring methods mainly rely on clinician observations and patient self-reports. These methods have problems such as strong subjectivity, unfixed monitoring frequency, and incomplete data collection, making it difficult to achieve accurate and real-time monitoring of perinatal depression. With the development of information technology and medical technology, the use of advanced data processing methods to collect, analyze and monitor the behavioral and physiological data of perinatal patients has become a new and potential means.

[0003] However, existing data processing methods still face some challenges when applied to perinatal depression monitoring. On the one hand, the physiological and psychological states of perinatal patients change with the different stages of pregnancy, so the frequency and content of data collection also need to be adjusted accordingly to meet the needs of different stages. On the other hand, the quality of the collected data varies, and the presence of data anomalies and noise may affect the accuracy of the monitoring results. Summary of the invention

[0004] The present invention provides a data processing method and system for perinatal depression monitoring, which is used to solve the problem of low data management and processing efficiency in the above-mentioned prior art. The technical solution adopted is as follows: A data processing method for monitoring perinatal depression, the data processing method for monitoring perinatal depression comprising: Extracting the current pregnancy stage of the perinatal patient, setting the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjusting the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process; Collecting data from perinatal patients according to the data collection frequency, obtaining behavioral data and physiological data of the perinatal patients, and performing data quality assessment and data quality abnormality warning on the behavioral data and physiological data; The behavioral data and physiological data of the perinatal patients that meet the data quality requirements are grouped and stored.

[0005] Further, extracting the current pregnancy stage of the perinatal patient, setting the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjusting the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process, including: Determining the current pregnancy stage of the perinatal patient according to a preset pregnancy stage classification strategy; Setting an initial frequency of data collection according to the current pregnancy stage of the perinatal patient; Behavioral data and physiological data of perinatal patients are collected according to the initial data collection frequency, and the data collection frequency is dynamically adjusted according to the behavioral data and physiological data.

[0006] Furthermore, the stage cycle of the pregnancy stage division strategy is as follows: The early pregnancy stage cycle includes a first early pregnancy stage, a second early pregnancy stage and a third early pregnancy stage, wherein the early pregnancy stage is from 0 to 12 weeks of pregnancy, and the first early pregnancy stage is from 0 to 3 weeks of the early pregnancy stage; the second early pregnancy stage is from 4 to 9 weeks of the early pregnancy stage; the third early pregnancy stage is from 10 to 12 weeks of the early pregnancy stage; The mid-pregnancy stage cycle includes the first mid-pregnancy stage, the second mid-pregnancy stage, the third mid-pregnancy stage and the fourth mid-pregnancy stage, wherein the mid-pregnancy stage is from 13 weeks to 28 weeks of pregnancy, and the first mid-pregnancy stage is from 13 weeks to 16 weeks of the mid-pregnancy stage; the second mid-pregnancy stage is from 17 weeks to 20 weeks of the mid-pregnancy stage; the third mid-pregnancy stage is from 21 weeks to 25 weeks of the mid-pregnancy stage; the fourth mid-pregnancy stage is from 26 weeks to 28 weeks of the mid-pregnancy stage; The late pregnancy stage cycle includes a first late pregnancy stage, a second late pregnancy stage and a third late pregnancy stage, wherein the late pregnancy stage is from 29 weeks to 40 weeks of pregnancy, and the first late pregnancy stage is from 29 weeks to 31 weeks of the late pregnancy stage; the second late pregnancy stage is from 32 weeks to 36 weeks of the late pregnancy stage; the third late pregnancy stage is from 37 weeks to 40 weeks of the late pregnancy stage; The postpartum period cycle includes a first postpartum period and a second postpartum period, wherein the postpartum period includes 0 to 6 weeks after delivery; and the first postpartum period is 0 to 4 weeks after delivery; and the second postpartum period is 5 to 6 weeks after delivery.

[0007] Further, the initial frequency of data collection is set according to the current pregnancy stage of the perinatal patient, including: Extracting the period of the current pregnancy of the perinatal patient and the starting week of the stage of the current pregnancy as the first time period; Extracting the period of the end week of the current pregnancy stage of the perinatal patient as the second time period; Extracting a data collection time interval corresponding to a benchmark data collection frequency, wherein the data collection time interval corresponding to the benchmark data collection frequency ranges from 1 day to 3 days; The data collection time interval corresponding to the initial data collection frequency is obtained by combining the first time period and the second time period with the data collection time interval corresponding to the reference data collection frequency; wherein the data collection time interval corresponding to the initial data collection frequency is obtained by the following formula: Where D represents the data collection time interval corresponding to the initial frequency of data collection; B represents the data collection time interval corresponding to the baseline data collection frequency; W represents the importance weight value corresponding to the pregnancy stage, and the importance weight value range of the early pregnancy is 0.2-0.5, the importance weight value range of the mid-pregnancy is 0.4-0.6, and the importance weight value range of the late pregnancy is 0.8-1.0; T 01 Indicates the length of time corresponding to the first time period; T 02 Indicates the time length corresponding to the second time period; e represents a constant, and its value is 2.71.

[0008] Furthermore, behavioral data and physiological data are collected from perinatal patients according to the initial frequency of data collection, and the data collection frequency is dynamically adjusted according to the behavioral data and physiological data, including: Collecting behavioral data and physiological data of perinatal patients according to the initial frequency of data collection; wherein the behavioral data includes sleep duration, number of sleep interruptions and deep sleep duration; and the physiological data includes heart rate data, blood pressure data, respiratory rate and blood oxygen saturation; The first adjustment coefficient is set using the sleep duration, the number of sleep interruptions, and the deep sleep duration; wherein the first adjustment coefficient is obtained by the following formula: Among them, K 01 represents the first adjustment coefficient; S t Indicates the total sleep length; S d Indicates the duration of deep sleep; S max Indicates the preset deep sleep duration reference value; I s Indicates the number of sleep interruptions; I max Indicates the preset reference value of sleep interruption times; α 01 and β 01 Indicates the weight values ​​corresponding to the sleep duration data and the sleep interruption data; The second adjustment coefficient is set using the heart rate data, blood pressure data, respiratory rate and blood oxygen saturation; wherein the second adjustment coefficient is obtained by the following formula: Among them, K 02 Represents the second adjustment coefficient; H c , R c and S c Indicates the corresponding values ​​of heart rate data, respiratory rate and blood oxygen saturation; B c Indicates the difference between diastolic and systolic blood pressure; B x Indicates the preset reference value of the difference between diastolic and systolic pressure; H x , R x and S x Indicates the preset reference values ​​corresponding to heart rate data, respiratory rate and blood oxygen saturation; The data collection time interval corresponding to the initial frequency of data collection is adjusted by using the first adjustment coefficient and the second adjustment coefficient to obtain the data collection time interval corresponding to the adjusted data collection frequency; wherein the data collection time interval corresponding to the adjusted data collection frequency is obtained by the following formula: Among them, D t represents the data collection time interval corresponding to the adjusted data collection frequency; D represents the data collection time interval corresponding to the initial data collection frequency; e represents a constant, with a value of 2.71; K 01 Represents the first adjustment coefficient; K 02 Represents the second adjustment coefficient.

[0009] Furthermore, data is collected from perinatal patients according to the data collection frequency to obtain behavioral data and physiological data of the perinatal patients, and data quality assessment and data quality abnormality warning are performed on the behavioral data and physiological data, including: Collecting data from perinatal patients according to the data collection frequency to obtain behavioral data and physiological data of the perinatal patients; Performing data preprocessing on the behavioral data and physiological data of the perinatal patients, wherein the data preprocessing includes information extraction processing of garbled data and its corresponding data type and information extraction processing of the data type corresponding to the missing data; Data quality anomaly determination and warning are performed based on the data type weight values ​​of the garbled data and its corresponding data type and the data type corresponding to the missing data.

[0010] Furthermore, data quality abnormality determination and early warning are performed according to the data type weight value of the garbled data and its corresponding data type and the data type corresponding to the missing data, including: Extract the data type weight value corresponding to the data type of each garbled data and the data type weight value corresponding to the data type of each missing data; The importance weight value corresponding to the current pregnancy stage of the perinatal patient is extracted to obtain the data quality abnormality severity coefficient; wherein the data quality abnormality severity coefficient is obtained by the following formula: Among them, S q Indicates the severity coefficient of data quality abnormality; W s Indicates the importance weight value corresponding to the current stage of pregnancy; N ci Indicates the number of garbled data in the i-th data type; N mi represents the number of missing data in the i-th data type; W ti Indicates the data type weight value of the i-th data type; N t Indicates the total number of data; n indicates the total number of data types; When the data quality abnormality severity coefficient exceeds a preset severity coefficient threshold, it is determined that data quality abnormalities exist in the data collection of the current behavioral data and physiological data of the perinatal patient, and a data quality abnormality warning is issued.

[0011] Furthermore, the behavioral data and physiological data of the perinatal patients that meet the data quality requirements are grouped and stored, including: Setting a time window according to the current pregnancy stage of the perinatal patient; Grouping the behavioral data and physiological data collected during the current pregnancy stage according to the time window to form multiple groups of behavioral data and physiological data; Each set of behavioral data and physiological data is stored in groups.

[0012] Furthermore, the behavioral data and physiological data of the perinatal patients that meet the data quality requirements are grouped and stored, further comprising: Data aggregation processing is performed on each set of behavioral data and physiological data to generate a state comprehensive index parameter corresponding to each set of behavioral data and physiological data; wherein the state comprehensive index parameter is obtained by the following formula: Among them, E represents the comprehensive indicator parameter of the state; S t Indicates the total sleep length; S tc Indicates the total sleep length reference value; S max Indicates the preset deep sleep duration reference value; S d Indicates the duration of deep sleep; I s Indicates the number of sleep interruptions; I symaxIndicates the maximum number of sleep interruptions allowed; C max Indicates the maximum difference between the value of the physiological number type in the physiological data and its standard value; C ymax Indicates the maximum allowable difference; Comparing the comprehensive index parameters of the status corresponding to the perinatal patient with the preset index parameters; When the state comprehensive index parameter corresponding to the perinatal patient exceeds the preset index parameter, a group of behavioral data and physiological data whose state comprehensive index parameter exceeds the preset index parameter is marked; When the number of groups of marked behavioral data and physiological data exceeds the preset group number threshold, an early warning prompt will be issued.

[0013] A data processing system for monitoring perinatal depression, the data processing system for monitoring perinatal depression comprising: A data collection frequency setting and adjustment module, used to extract the current pregnancy stage of a perinatal patient, set the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjust the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process; A data quality abnormality warning module is used to collect data from perinatal patients according to the data collection frequency, obtain behavioral data and physiological data of the perinatal patients, and perform data quality assessment and data quality abnormality warning on the behavioral data and physiological data; The grouping storage module is used to group and store the behavioral data and physiological data of the perinatal patients that meet the data quality requirements.

[0014] Beneficial effects of the present invention: The present invention proposes a data processing method and system for monitoring perinatal depression. By dynamically adjusting the data collection frequency according to the pregnancy stage, the technical solution can more accurately monitor the physiological and psychological state of perinatal patients. This helps medical staff to detect abnormal conditions of patients in a timely manner and take effective intervention measures. Real-time dynamic adjustment of the data collection frequency can ensure that more valuable data is collected during critical periods, while reducing data redundancy during stable periods. This helps to improve the quality and analysis efficiency of data. This technical solution can perform personalized data monitoring according to needs to avoid excessive collection or omission of important data. This helps to reduce the burden on patients and improve their overall experience. By dynamically adjusting the data collection frequency in real time, the technical solution can ensure the rational allocation and utilization of medical resources. During critical periods, the system can automatically increase the data collection frequency to monitor the patient's condition in more detail; while in stable periods, the data collection frequency can be reduced to save medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the method of the present invention; Figure 2 The system block diagram of the method described in the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] The embodiment of the present invention provides a data processing method for monitoring perinatal depression. Figure 1 As shown, the data processing method for perinatal depression monitoring includes: S1. extracting the current pregnancy stage of the perinatal patient, setting the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjusting the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process; S2. Collecting data from perinatal patients according to the data collection frequency, obtaining behavioral data and physiological data of the perinatal patients, and performing data quality assessment and data quality abnormality warning on the behavioral data and physiological data; S3. The behavioral data and physiological data of the perinatal patients that meet the data quality requirements are grouped and stored.

[0018] The working principle of the above technical solution is: with the help of the preset pregnancy stage division strategy, the system can accurately determine the current pregnancy stage of the perinatal patient. This strategy is usually based on medical knowledge and experience, and divides the pregnancy into several key stages, such as early pregnancy, mid-pregnancy, late pregnancy, etc.

[0019] Once the patient's pregnancy stage is determined, the system will set a reasonable initial frequency for data collection based on the characteristics and needs of that stage. For example, in the early and late stages of pregnancy, a higher frequency of data collection may be required due to the more significant physiological and psychological changes of the patient.

[0020] During the data collection process at the initial frequency, the system analyzes the collected behavioral and physiological data in real time. Based on this data, the system dynamically adjusts the frequency of data collection to ensure that important changes in the patient's physiological and psychological state are captured. For example, if the system detects an abnormal fluctuation in a patient's physiological indicator, it may automatically increase the frequency of data collection to monitor the patient's condition in more detail.

[0021] The effect of the above technical solution is: by dynamically adjusting the frequency of data collection according to the stage of pregnancy, this technical solution can more accurately monitor the physiological and psychological state of perinatal patients. This helps medical staff to detect abnormal conditions of patients in a timely manner and take effective intervention measures. Real-time dynamic adjustment of data collection frequency can ensure that more valuable data is collected during critical periods, while reducing data redundancy during stable periods. This helps to improve the quality and analysis efficiency of data. This technical solution can perform personalized data monitoring according to needs to avoid excessive collection or omission of important data. This helps to reduce the burden on patients and improve their overall experience. By dynamically adjusting the frequency of data collection in real time, this technical solution can ensure the rational allocation and utilization of medical resources. During critical periods, the system can automatically increase the frequency of data collection to monitor the patient's condition in more detail; while in stable periods, the frequency of data collection can be reduced to save medical resources.

[0022] In one embodiment of the present invention, the current pregnancy stage of a perinatal patient is extracted, a data collection frequency is set according to the current pregnancy stage of the perinatal patient, and the data collection frequency of the current pregnancy stage of the perinatal patient is dynamically adjusted in real time during the data collection process, including: S101, determining the current pregnancy stage of the perinatal patient according to a preset pregnancy stage classification strategy; S102, setting an initial frequency of data collection according to the current pregnancy stage of the perinatal patient; S103, collecting behavioral data and physiological data of perinatal patients according to the initial frequency of data collection, and dynamically adjusting the data collection frequency according to the behavioral data and physiological data.

[0023] The working principle of the above technical solution is: with the help of the preset pregnancy stage division strategy, the system can accurately determine the current pregnancy stage of the perinatal patient. This strategy is usually based on medical knowledge and experience, and divides pregnancy into several key stages, such as early pregnancy, mid-pregnancy, and late pregnancy. Once the patient's pregnancy stage is determined, the system will set a reasonable initial frequency of data collection according to the characteristics and needs of that stage. For example, in the early and late pregnancy, due to the significant physiological and psychological changes of the patient, a higher data collection frequency may be required. In the process of data collection at the initial frequency, the system will analyze the collected behavioral data and physiological data in real time. Based on this data, the system will dynamically adjust the frequency of data collection to ensure that important changes in the patient's physiological and psychological state can be captured. For example, if the system detects an abnormal fluctuation in a physiological indicator of the patient, it may automatically increase the frequency of data collection to monitor the patient's condition in more detail.

[0024] The effect of the above technical solution is: by dynamically adjusting the frequency of data collection according to the stage of pregnancy, this technical solution can more accurately monitor the physiological and psychological state of perinatal patients. This helps medical staff to detect abnormal conditions of patients in a timely manner and take effective intervention measures. Real-time dynamic adjustment of the frequency of data collection can ensure that more valuable data is collected during critical periods, while reducing data redundancy during stable periods. This helps to improve the quality and analysis efficiency of data. This technical solution can perform personalized data monitoring according to the actual needs of patients, avoiding excessive collection or omission of important data. This helps to reduce the burden on patients and improve their overall experience. By dynamically adjusting the frequency of data collection in real time, this technical solution can ensure the rational allocation and utilization of medical resources. During critical periods, the system can automatically increase the frequency of data collection to monitor the patient's condition in more detail; while in stable periods, the frequency of data collection can be reduced to save medical resources.

[0025] In one embodiment of the present invention, the stage cycle of the pregnancy stage division strategy is as follows: The early pregnancy stage cycle includes a first early pregnancy stage, a second early pregnancy stage and a third early pregnancy stage, wherein the early pregnancy stage is from 0 to 12 weeks of pregnancy, and the first early pregnancy stage is from 0 to 3 weeks of the early pregnancy stage; the second early pregnancy stage is from 4 to 9 weeks of the early pregnancy stage; the third early pregnancy stage is from 10 to 12 weeks of the early pregnancy stage; The mid-pregnancy stage cycle includes the first mid-pregnancy stage, the second mid-pregnancy stage, the third mid-pregnancy stage and the fourth mid-pregnancy stage, wherein the mid-pregnancy stage is from 13 weeks to 28 weeks of pregnancy, and the first mid-pregnancy stage is from 13 weeks to 16 weeks of the mid-pregnancy stage; the second mid-pregnancy stage is from 17 weeks to 20 weeks of the mid-pregnancy stage; the third mid-pregnancy stage is from 21 weeks to 25 weeks of the mid-pregnancy stage; the fourth mid-pregnancy stage is from 26 weeks to 28 weeks of the mid-pregnancy stage; The late pregnancy stage cycle includes a first late pregnancy stage, a second late pregnancy stage and a third late pregnancy stage, wherein the late pregnancy stage is from 29 weeks to 40 weeks of pregnancy, and the first late pregnancy stage is from 29 weeks to 31 weeks of the late pregnancy stage; the second late pregnancy stage is from 32 weeks to 36 weeks of the late pregnancy stage; the third late pregnancy stage is from 37 weeks to 40 weeks of the late pregnancy stage; The postpartum period cycle includes a first postpartum period and a second postpartum period, wherein the postpartum period includes 0 to 6 weeks after delivery; and the first postpartum period is 0 to 4 weeks after delivery; and the second postpartum period is 5 to 6 weeks after delivery.

[0026] The working principle of the above technical solution is: through the preset pregnancy stage division strategy, the system can automatically or assist medical staff to accurately identify the specific stage and cycle of the perinatal patient. This helps to formulate corresponding data collection and processing plans based on the characteristics of different stages and cycles. Based on the patient's current stage and cycle, the system can automatically adjust the frequency of data collection. For example, in the early and late pregnancy, due to the significant physiological and psychological changes of pregnant women, the system may increase the frequency of data collection; while in the relatively stable stage of the second trimester, the collection frequency can be appropriately reduced. Through detailed stage division, the system can formulate a personalized monitoring plan for each perinatal patient. This includes the content, time, and method of data collection to ensure the pertinence and effectiveness of monitoring. During the data collection process, the system can also dynamically adjust the collection frequency according to the patient's real-time physiological and behavioral data. If an abnormal fluctuation is detected in a patient's indicator, the system will immediately increase the collection frequency to obtain more detailed information, so as to timely discover and warn potential problems.

[0027] The effect of the above technical solution is: through detailed stage division and personalized monitoring plan, this technical solution can more accurately monitor the changes in the physiological and psychological status of perinatal patients and improve the accuracy and reliability of monitoring. The frequency of data collection is dynamically adjusted according to the actual needs of the patient to avoid waste of resources and excessive collection. This helps to optimize the allocation and utilization efficiency of medical resources. Personalized monitoring plans can reduce the burden and pressure on patients and improve their overall experience. At the same time, timely warnings and intervention measures can also help alleviate patients' anxiety and uneasiness. Real-time and accurate monitoring data provides an important reference for medical staff, helping them to more accurately assess the health status of patients and formulate effective treatment plans.

[0028] In summary, this technical solution achieves accurate monitoring and dynamic adjustment of the physiological and psychological state of perinatal patients through detailed stage division and personalized monitoring plan, providing strong support for clinical decision-making and improving patient experience.

[0029] In one embodiment of the present invention, the initial frequency of data collection is set according to the current pregnancy stage of the perinatal patient, including: S1021, extracting the period of the current pregnancy of the perinatal patient and the starting week of the stage of the current pregnancy as the first time period; S1022, extracting the period of the end week of the current pregnancy stage of the perinatal patient as the second time period; S1023, extracting a data collection time interval corresponding to a benchmark data collection frequency, wherein the data collection time interval corresponding to the benchmark data collection frequency ranges from 1 day to 3 days; S1024. Obtain a data collection time interval corresponding to an initial data collection frequency by using the first time period and the second time period in combination with a data collection time interval corresponding to a reference data collection frequency; wherein the data collection time interval corresponding to the initial data collection frequency is obtained by the following formula: Where D represents the data collection time interval corresponding to the initial frequency of data collection; B represents the data collection time interval corresponding to the baseline data collection frequency; W represents the importance weight value corresponding to the pregnancy stage, and the importance weight value range of the early pregnancy is 0.2-0.5, the importance weight value range of the mid-pregnancy is 0.4-0.6, and the importance weight value range of the late pregnancy is 0.8-1.0; T 01 Indicates the length of time corresponding to the first time period; T 02 Indicates the time length corresponding to the second time period; e represents a constant, and its value is 2.71.

[0030] The working principle of the above technical solution is as follows: the system first extracts the current pregnancy of the perinatal patient and determines the starting week and ending week of the stage to which it belongs, thereby calculating the first time period (the time from the current pregnancy period to the starting week of the stage) and the second time period (the remaining time of the stage, that is, the time from the starting week of the stage to the ending week of the stage). The system presets a benchmark data collection frequency, which corresponds to a data collection time interval, ranging from 1 day to 3 days. This benchmark frequency serves as a reference point for subsequent calculations. According to the importance of the pregnancy stage (early pregnancy, mid-pregnancy, and late pregnancy), the system assigns an importance weight value (W) to each stage. The importance of the early pregnancy is relatively low, with a weight value between 0.2 and 0.5; the mid-pregnancy is moderate, with a weight value between 0.4 and 0.6; the late pregnancy is the most critical, with a weight value between 0.8 and 1.0. The data collection time interval (D) corresponding to the initial frequency of data collection is calculated using a formula. The formula takes into account the benchmark data collection time interval (B), the length of the first time period (T01), the length of the second time period (T02), and the constant e (the base of the natural logarithm, approximately equal to 2.71). By adjusting these parameters, the system can dynamically calculate the data collection frequency appropriate for the current stage of pregnancy.

[0031] The effect of the above technical solution is that the technical solution can dynamically adjust the data collection frequency according to the specific pregnancy period and stage of the perinatal patient. This personalized data collection method is more in line with the actual needs of patients and improves the pertinence and effectiveness of monitoring. By reasonably setting the data collection frequency, waste of resources and excessive collection are avoided. In the relatively stable stage of pregnancy, the collection frequency can be appropriately reduced; while in the critical or highly variable stage, the collection frequency is increased to obtain more detailed information. The introduction of the concept of importance weight value enables the system to fully consider the importance of the pregnancy stage when calculating the data collection frequency. This is of great significance for timely detection of potential problems, early warning of complications, etc., and improves the accuracy and reliability of monitoring. Real-time and accurate data collection provides an important reference for medical staff. By continuously monitoring the changes in the patient's physiological and psychological state, medical staff can more accurately assess the patient's health status and formulate effective treatment plans.

[0032] On the other hand, by introducing the importance weight value (W) corresponding to the pregnancy stage, the formula can automatically adjust the initial frequency of data collection according to the importance of different pregnancy stages. The importance weight values ​​of early, mid and late pregnancy gradually increase, reflecting that the urgency and importance of monitoring maternal and child health status are also increasing as the pregnancy progresses. At the same time, the length of the first time period (T01, the period from the current pregnancy to the start week of the stage) and the second time period (T02, the period from the current pregnancy to the end week of the stage) are used in the formula. This helps to fine-tune the data collection frequency according to the length of the remaining time in the pregnancy, ensuring the balance and effectiveness of data collection throughout the pregnancy stage. The data collection time interval corresponding to the benchmark data collection frequency (B) ranges from 1 day to 3 days, which provides a basic and adjustable framework for data collection. By adjusting the benchmark frequency, the initial frequency of data collection can be flexibly set according to actual conditions (such as medical resources, patient needs, etc.). Reasonableness of the mathematical model: The natural logarithm form (exponential form of e) is used in the formula. This mathematical expression can smoothly adjust the data collection time interval and avoid sudden changes in the data collection frequency when the pregnancy stage changes. At the same time, the introduction of the constant e (approximately equal to 2.71) increases the scientificity and accuracy of the formula. By dynamically adjusting the frequency of data collection, medical resources can be allocated more reasonably. In the relatively stable stage of pregnancy, the frequency of data collection can be appropriately reduced to save medical resources and costs; while in the critical stage of pregnancy, the frequency of data collection can be increased to ensure timely monitoring and intervention of maternal and child health.

[0033] In summary, this technical solution calculates the initial frequency of data collection by comprehensively considering multiple factors, achieving personalized, efficient and accurate monitoring of perinatal patients, and providing strong support for clinical decision-making. At the same time, the above formula realizes dynamic adjustment and optimal configuration of the initial frequency of data collection by comprehensively considering factors such as the importance of the pregnancy stage, the length of time, and the frequency of baseline data collection, providing more scientific, reasonable and effective technical support for the health monitoring of perinatal patients.

[0034] In one embodiment of the present invention, behavioral data and physiological data of perinatal patients are collected according to the initial frequency of data collection, and the data collection frequency is dynamically adjusted according to the behavioral data and physiological data, including: S1031, collecting behavioral data and physiological data of perinatal patients according to the data collection initial frequency; wherein the behavioral data includes sleep duration, number of sleep interruptions and deep sleep duration; and the physiological data includes heart rate data, blood pressure data, respiratory rate and blood oxygen saturation; S1032, using the sleep duration, the number of sleep interruptions, and the deep sleep duration to set a first adjustment coefficient; wherein the first adjustment coefficient is obtained by the following formula: Among them, K 01 Represents the first adjustment coefficient; S t Indicates the total sleep length; S d Indicates the duration of deep sleep; S max Indicates the preset deep sleep duration reference value; I s Indicates the number of sleep interruptions; I max Indicates the preset reference value of sleep interruption times; α 01 and β 01 Indicates the weight values ​​corresponding to the sleep duration data and the sleep interruption data; S1033. Set a second adjustment coefficient using the heart rate data, blood pressure data, respiratory rate, and blood oxygen saturation; wherein the second adjustment coefficient is obtained by the following formula: Among them, K 02 Represents the second adjustment coefficient; H c , R c and S c Indicates the corresponding values ​​of heart rate data, respiratory rate and blood oxygen saturation; B c Indicates the difference between diastolic and systolic blood pressure; B x Indicates the preset reference value of the difference between diastolic and systolic pressure; H x , R x and S xIndicates the preset reference values ​​corresponding to heart rate data, respiratory rate and blood oxygen saturation; S1034. Use the first adjustment coefficient and the second adjustment coefficient to adjust the data collection time interval corresponding to the initial frequency of data collection to obtain the data collection time interval corresponding to the adjusted data collection frequency; wherein the data collection time interval corresponding to the adjusted data collection frequency is obtained by the following formula: Among them, D t represents the data collection time interval corresponding to the adjusted data collection frequency; D represents the data collection time interval corresponding to the initial data collection frequency; e represents a constant, with a value of 2.71; K 01 Represents the first adjustment coefficient; K 02 Represents the second adjustment coefficient.

[0035] The working principle of the above technical solution is as follows: according to the preset initial frequency of data collection, the system starts to collect behavioral data and physiological data of perinatal patients. Behavioral data include sleep duration, number of sleep interruptions and deep sleep duration, while physiological data covers key indicators such as heart rate, blood pressure, respiratory rate and blood oxygen saturation. The system uses the collected behavioral data (sleep duration, number of sleep interruptions, deep sleep duration) to calculate the first adjustment coefficient (K01) through a specific formula. This coefficient reflects the quality of the patient's sleep, and balances the impact of sleep duration and number of sleep interruptions on sleep quality through weight values ​​(α01 and β01). Similarly, the system uses physiological data (heart rate, blood pressure, respiratory rate, blood oxygen saturation) to calculate the second adjustment coefficient (K02) through another formula. This coefficient comprehensively evaluates the patient's physiological condition and reflects whether the patient's physiological indicators are within the normal range by comparing the actual measured value with the preset reference value. Based on the first adjustment coefficient and the second adjustment coefficient, the system dynamically adjusts the initial frequency of data collection. Through a specific formula, the system combines these two adjustment coefficients with the initial data collection time interval (D) to calculate the adjusted data collection time interval (Dt). This adjustment process ensures that the frequency of data collection can be changed in real time based on the patient's actual condition, thereby increasing monitoring density when necessary to capture important changes.

[0036] The effect of the above technical solution is that in the data collection of perinatal patients, the initial collection frequency may not accurately reflect the actual changes in the patient's condition. If the data collection frequency is too low, important emotional or health change signals may be missed; if it is too high, it may lead to data redundancy and patient burden. Therefore, a flexible and sensitive mechanism is needed to dynamically adjust the collection frequency according to the patient's real-time data so that it can capture subtle changes without over-collection. Dynamic adjustment of the data collection frequency enables the system to more accurately capture the physiological and behavioral changes of perinatal patients. Reduce the collection frequency to save resources when the patient's condition is stable; increase the collection frequency to obtain more detailed information when abnormal conditions occur, thereby improving the accuracy and effectiveness of monitoring. Through an intelligent dynamic adjustment mechanism, the technical solution avoids waste of resources and over-collection. The system can reasonably allocate collection resources according to actual needs to ensure that sufficient data is obtained to support clinical decision-making during critical periods. Real-time and dynamic monitoring methods help medical staff to promptly detect abnormal conditions of patients and take intervention measures, thereby alleviating patients' pain and anxiety. At the same time, a reasonable data collection frequency also reduces the monitoring burden of patients and improves their overall experience. High-quality, real-time monitoring data provides an important reference for medical staff. By continuously monitoring changes in patients' physiological and behavioral conditions, medical staff can more accurately assess patients' health conditions and develop personalized treatment plans, thereby improving the scientificity and effectiveness of clinical decision-making.

[0037] On the other hand, according to the initially set data collection frequency, the behavioral data (such as sleep duration, number of sleep interruptions, and deep sleep duration) and physiological data (such as heart rate, blood pressure, respiratory rate, and blood oxygen saturation) of perinatal patients are systematically collected. These data provide a basis for subsequent health assessments and frequency adjustments. The first adjustment coefficient (K01) is calculated by a formula, which is based on the patient's sleep quality and sleep interruptions. When the patient's sleep quality decreases (such as a decrease in deep sleep duration and an increase in sleep interruptions), the value of K01 will increase accordingly, indicating that more frequent data collection is needed to monitor the patient's health. This adjustment mechanism helps to promptly detect and resolve potential sleep problems. At the same time, the second adjustment coefficient (K02) is calculated by a formula, which is based on the difference between the patient's physiological indicators (heart rate, respiratory rate, blood oxygen saturation) and blood pressure (the difference between diastolic and systolic blood pressure). When these physiological indicators deviate from the normal range, the value of K02 will increase accordingly, indicating that there may be problems with the patient's physiological condition and that closer monitoring is required. This adjustment mechanism helps to promptly detect and respond to possible health risks.

[0038] The initial data collection frequency is adjusted using the first adjustment coefficient and the second adjustment coefficient to obtain the adjusted data collection frequency. This adjustment is based on the patient's current behavior and physiological data, which can ensure that the frequency of data collection can be increased or decreased accordingly when the patient's health status changes. Specifically, when the values ​​of K01 and K02 increase, the adjusted data collection time interval (Dt) will be shortened, that is, the data collection frequency will increase; conversely, when the values ​​of K01 and K02 decrease, Dt will be extended, that is, the data collection frequency will decrease. This dynamic adjustment mechanism helps to optimize the allocation of medical resources and improve the efficiency and accuracy of health monitoring. Through the above steps and formulas, the technical solution realizes the dynamic adjustment of the data collection frequency of perinatal patients. This adjustment mechanism not only takes into account the importance of the pregnancy stage, but also combines the patient's actual health status (reflected by behavioral data and physiological data). This personalized data collection strategy helps to provide more accurate and timely health monitoring services and provide strong support for the health management of perinatal patients.

[0039] In one embodiment of the present invention, data is collected from perinatal patients according to the data collection frequency, behavioral data and physiological data of the perinatal patients are obtained, and data quality assessment and data quality abnormality warning are performed on the behavioral data and physiological data, including: S201, collecting data from perinatal patients according to the data collection frequency to obtain behavioral data and physiological data of the perinatal patients; S202, performing data preprocessing on the behavioral data and physiological data of the perinatal patient, wherein the data preprocessing includes information extraction processing of garbled data and its corresponding data type and information extraction processing of data type corresponding to missing data; S203: Perform data quality anomaly determination and early warning based on the data type weight values ​​of the garbled data and its corresponding data type and the data type corresponding to the missing data.

[0040] The working principle of the above technical solution is as follows: according to the preset data collection frequency, the system continuously collects data from perinatal patients to obtain their behavioral data and physiological data. These data are the basis for subsequent analysis and are crucial for assessing the health status of patients. Before the data enters the analysis stage, preprocessing is first performed to ensure the quality of the data. The preprocessing step includes the extraction and processing of garbled data and its corresponding data type and the extraction and processing of the data type corresponding to missing data. Garbled data may be caused by encoding errors, transmission errors, etc., while missing data may be caused by equipment failure, patient non-cooperation, etc. By extracting this information, the system can identify potential data quality problems. Based on data preprocessing, the system performs quality assessment on garbled data and missing data according to the preset data type weight value. The data type weight value reflects the importance of different types of data in the overall assessment. If the garbled or missing situation of a certain type of data exceeds the preset threshold, the system will trigger an abnormal warning, prompting relevant personnel to pay attention to data quality issues, and may take further remedial measures.

[0041] The effect of the above technical solution is: through data preprocessing and quality assessment steps, the system can timely discover and deal with garbled and missing problems in the data, thereby improving the overall data quality. High-quality data is the basis for subsequent analysis and decision-making, which helps to improve the accuracy and reliability of monitoring. The data quality abnormality warning mechanism enables the system to respond quickly to potential data quality problems, enhancing the robustness and stability of the system. Even if some unforeseen problems occur during the data collection process, the system can timely discover and solve these problems through the early warning mechanism to ensure the smooth progress of monitoring. The data quality assessment results help optimize resource allocation. For monitoring points or equipment with poor data quality, the data collection strategy can be adjusted in time or equipment maintenance can be performed to reduce resource waste and improve monitoring efficiency. High-quality data provides an important reference for medical staff. By continuously monitoring and evaluating the behavioral and physiological data of perinatal patients, medical staff can more accurately assess the health status of patients and formulate personalized treatment plans, thereby improving the scientificity and effectiveness of clinical decision-making.

[0042] In summary, this technical solution achieves comprehensive management and quality control of perinatal patient data through steps such as data collection, preprocessing, quality assessment and abnormal warning, providing strong support for clinical decision-making.

[0043] In one embodiment of the present invention, data quality anomaly determination and early warning are performed based on the data type weight value of the garbled data and its corresponding data type and the data type corresponding to the missing data, including: S2031, extracting a data type weight value corresponding to the data type of each garbled data and a data type weight value corresponding to the data type of each missing data; S2032. Extract the importance weight value corresponding to the current pregnancy stage of the perinatal patient to obtain the data quality abnormality severity coefficient; wherein the data quality abnormality severity coefficient is obtained by the following formula: Among them, S q Indicates the severity coefficient of data quality abnormality; W s Indicates the importance weight value corresponding to the current stage of pregnancy; N ci Indicates the number of garbled data in the i-th data type; N mi represents the number of missing data in the i-th data type; W ti Indicates the data type weight value of the i-th data type; N t Indicates the total number of data; n indicates the total number of data types; S2033. When the data quality abnormality severity coefficient exceeds a preset severity coefficient threshold, it is determined that data quality abnormalities exist in the data collection of the current behavioral data and physiological data of the perinatal patient, and a data quality abnormality warning is issued.

[0044] The working principle of the above technical solution is: the system first extracts each garbled data and its corresponding data type weight value, as well as each missing data and its corresponding data type weight value. The data type weight value reflects the importance of different types of data in the overall assessment and is the basis for the subsequent calculation of the data quality anomaly severity coefficient.

[0045] Next, the system extracts the importance weight value corresponding to the current pregnancy stage of the perinatal patient. This weight value reflects the importance of the current pregnancy stage in the overall monitoring process.

[0046] Then, the system uses a preset formula to calculate the data quality abnormality severity coefficient (Sq). This coefficient takes into account the importance of the current pregnancy stage, the number of garbled and missing data types, and the weight of each data type. Through weighted summation, the system can quantitatively evaluate the data quality abnormalities in the current data collection process.

[0047] Finally, the system compares the calculated data quality abnormality severity coefficient with the preset severity coefficient threshold. If Sq exceeds the threshold, it is determined that the data collection of the current behavioral data and physiological data of the perinatal patient has data quality abnormalities, and the early warning mechanism is triggered. The early warning mechanism may include sending alarm information to medical staff, displaying abnormal prompts on the monitoring interface, etc., so that relevant personnel can take timely measures to solve the problem.

[0048] The effect of the above technical solution is that in the perinatal data collection, different data types (such as heart rate data, sleep duration, blood oxygen saturation, etc.) have different importance for data analysis. If some key data (such as heart rate and blood pressure) are garbled or missing, it will have a more serious impact on the judgment of the patient's health status. Even if some minor data (such as the frequency of social media use) are abnormal, the impact is small. Therefore, data quality assessment needs to consider the weight differences of each data type to accurately reflect the severity of data anomalies. By comprehensively considering the weight value of the data type, the importance of the current pregnancy stage, and the number of garbled and missing data, the system can accurately determine the data quality anomalies during the data collection process. This judgment method is more scientific and objective, and helps to improve the accuracy of early warning. Once the data quality is determined to be abnormal, the system can quickly trigger the early warning mechanism and provide timely and effective alarm information to relevant personnel. This helps medical staff to promptly discover problems, assess the impact and formulate corresponding solutions, thereby ensuring the monitoring effect and safety of perinatal patients. The data quality abnormality judgment and early warning mechanism also help to optimize resource allocation. Through continuous monitoring and analysis of data quality anomalies, the system can identify weak links and potential problems in the data collection process, providing strong support for resource allocation and process optimization. This helps improve monitoring efficiency, reduce resource waste and improve overall service quality.

[0049] At the same time, the above formula comprehensively evaluates the impact of garbled and missing data on the overall data quality during data collection by calculating the data quality abnormality severity coefficient (Sq). This comprehensive evaluation method is more comprehensive and accurate than considering garbled or missing data alone. The formula introduces the importance weight value (Ws) corresponding to the current stage of pregnancy, which reflects the differences in data quality requirements at different stages of pregnancy. In the more critical stages of pregnancy, the importance of data quality is higher, so even a small amount of garbled or missing data may lead to a higher data quality abnormality severity coefficient. The formula considers the weight values ​​(Wti) of different types of data (such as different indicators in behavioral data and physiological data). This reflects the difference in importance of different types of data in evaluating data quality. For key indicators (such as heart rate, blood pressure, etc.), their weight values ​​may be higher, so when garbled or missing data occur, the impact on the overall data quality is also greater. The formula quantifies the specific impact of garbled and missing data on the overall data quality by calculating the number of garbled data (Nc) and the number of missing data (Nmi) in each data type, and combining the data type weight value (Wti). This quantitative method makes the assessment of data quality abnormalities more objective and accurate. When the data quality abnormality severity coefficient (Sq) exceeds the preset severity coefficient threshold, the system will determine that the data collection of the current behavioral data and physiological data of perinatal patients has data quality abnormalities and trigger an early warning mechanism. This helps to promptly discover and address data quality issues and ensure the accuracy and reliability of subsequent analysis and decision-making. By regularly evaluating the data quality abnormality severity coefficient and triggering an early warning mechanism, medical institutions can continuously optimize the data collection process and improve the accuracy and completeness of data collection. For example, data verification and completion measures can be strengthened for data types that frequently appear garbled or missing, or the frequency and method of data collection can be adjusted to meet the needs of different stages of pregnancy.

[0050] In summary, this technical solution provides a strong guarantee for the monitoring of perinatal patients through accurate data quality abnormality judgment and timely early warning mechanism. It can not only improve the accuracy and reliability of monitoring, but also optimize resource allocation, improve service efficiency, and provide more scientific and effective support for clinical decision-making.

[0051] In one embodiment of the present invention, the behavioral data and physiological data of the perinatal patient that meet the data quality requirements are grouped and stored, including: S301, setting a time window according to the current pregnancy stage of the perinatal patient; S302, grouping the behavior data and physiological data collected at the current stage of pregnancy according to the time window to form multiple groups of behavior data and physiological data; S303: Group and store each set of behavior data and physiological data.

[0052] The working principle of the above technical solution is: according to the current stage of pregnancy of the perinatal patient, the system sets a reasonable time window. This time window is determined based on the natural laws of pregnancy development and monitoring needs, and is intended to ensure that the data in each time window can reflect the specific physiological and behavioral characteristics of the patient at that stage. According to the set time window, the system groups the behavioral data and physiological data collected at the current stage of pregnancy. This step cuts the continuously collected data stream into multiple time segments, each of which corresponds to a data set within a time window. Such grouping helps with subsequent data analysis and processing, so that the data at each stage can be evaluated and applied independently. The behavioral data and physiological data in each time window are stored in groups. This means that the system will classify and store the relevant data in different storage units according to the chronological order and the stage of pregnancy. This storage method facilitates the rapid retrieval, access and analysis of data, and also provides convenience for future data mining and scientific research.

[0053] The effect of the above technical solution is: by setting time windows according to the stages of pregnancy and storing the data in groups, the system can achieve orderly management of perinatal patient data. This helps to reduce data confusion and redundancy and improve the efficiency and accuracy of data processing. The grouped stored data enables the data of each stage of pregnancy to be evaluated and analyzed independently. This helps medical staff to understand the physiological and behavioral trends of patients more accurately and provide a scientific basis for clinical decision-making. At the same time, it also provides rich data resources for future scientific research and helps to promote research progress in the field of perinatal medicine. Grouped storage also helps to improve the security and privacy protection of data. By storing data at different stages in different storage units, the system can more effectively control data access rights and the security of the data transmission process, reducing the risk of data leakage and abuse.

[0054] In summary, this technical solution achieves orderly management and efficient use of data by reasonably setting time windows and grouping and storing the behavioral and physiological data of perinatal patients, providing strong support for clinical decision-making and scientific research.

[0055] In one embodiment of the present invention, the behavioral data and physiological data of the perinatal patients that meet the data quality requirements are grouped and stored, further comprising: Step 1: Perform data aggregation processing on each set of behavioral data and physiological data to generate a state comprehensive index parameter corresponding to each set of behavioral data and physiological data; wherein the state comprehensive index parameter is obtained by the following formula: Among them, E represents the comprehensive indicator parameter of the state; S t Indicates the total sleep length; Stc Indicates the total sleep length reference value; S max Indicates the preset deep sleep duration reference value; S d Indicates the duration of deep sleep; I s Indicates the number of sleep interruptions; I symax Indicates the maximum number of sleep interruptions allowed; C max Indicates the maximum difference between the value of the physiological number type in the physiological data and its standard value; C ymax Indicates the maximum allowable difference; Step 2, comparing the comprehensive index parameters corresponding to the perinatal patients with the preset index parameters; Step 3: When the comprehensive index parameter of the state corresponding to the perinatal patient exceeds the preset index parameter, a group of behavioral data and physiological data whose comprehensive index parameter exceeds the preset index parameter is marked; Step 4: When the number of groups of marked behavioral data and physiological data exceeds a preset group number threshold, an early warning prompt is issued.

[0056] The working principle of the above technical solution is as follows: First, the system collects behavioral data and physiological data of perinatal patients, which need to meet data quality requirements to ensure accuracy and completeness. For each group of behavioral data and physiological data (grouped by time window or pregnancy stage), the system performs data aggregation processing. This step calculates the state comprehensive index parameter (E) through a preset formula, which comprehensively considers multiple factors related to health status, such as sleep length, deep sleep duration, number of sleep interruptions, and the difference between physiological values ​​and their standard values. The weights and thresholds of these factors can be set based on clinical experience and research results. The calculated state comprehensive index parameter (E) is then compared with the preset index parameters. The preset index parameters are set based on authoritative sources such as health standards, clinical guidelines or expert consensus, and are intended to reflect the normal or acceptable health status range of perinatal patients. By comparison, the system can determine whether the current health status of perinatal patients is within the normal range. If the state comprehensive index parameter exceeds the preset index parameter, it may mean that the patient's health status is abnormal or has potential risks.

[0057] When the comprehensive indicator parameters of the status exceed the preset indicator parameters, the system will mark the corresponding behavioral data and physiological data. This marking mechanism helps medical staff quickly identify abnormal data groups. The system further determines whether the number of marked data groups exceeds the preset group number threshold. This threshold is set based on actual clinical needs and risk assessment results, and aims to balance the timeliness and accuracy of early warning. If the number of marked data groups exceeds the preset threshold, the system will issue an early warning prompt. The early warning prompt may be presented in the form of sound, light signals, pop-up windows, etc., to attract the attention of medical staff and take timely measures to intervene in the patient's health risks.

[0058] This technical solution generates comprehensive status indicator parameters through data aggregation processing, and compares and analyzes them with preset indicator parameters, and finally issues early warning prompts based on the comparison results and early warning threshold judgments. This workflow realizes real-time monitoring and risk assessment of the health status of perinatal patients, providing timely and accurate decision-making support for medical staff.

[0059] The effect of the above technical solution is: by calculating the comprehensive indicator parameter (E), the solution comprehensively considers the sleep quality and multiple dimensions of physiological data of perinatal patients, such as total sleep length, deep sleep duration, number of sleep interruptions, and the difference between physiological values ​​and their standard values, thereby providing a more comprehensive and accurate health status assessment. Comparing the actual data with the reference values ​​(such as the total sleep length reference value, deep sleep duration reference value, maximum allowable number of sleep interruptions, maximum allowable difference, etc.) helps to eliminate individual differences and make the assessment results more objective and comparable.

[0060] The scheme can calculate the comprehensive indicator parameters of the state in real time or periodically, and compare them with the preset indicator parameters, so as to realize the dynamic monitoring of the health status of perinatal patients. When the comprehensive indicator parameters of the state exceed the preset indicator parameters, the corresponding behavioral data and physiological data are marked; when the number of data groups with marks exceeds the preset threshold, the early warning prompt is triggered. This mechanism helps to timely discover potential health risks and provide a time window for medical staff to take intervention measures. Through the marking and early warning mechanism, medical staff can quickly locate perinatal patients with health risks and intervene accurately to avoid waste of resources and ineffective intervention. The scheme has a high degree of automation, which can reduce the workload of manual evaluation and improve the utilization efficiency of medical resources. The timeliness of the early warning prompt helps medical staff to pay attention to the health changes of patients in time, give patients more care and support, and thus improve the medical experience of patients. Based on the evaluation results of the comprehensive indicator parameters of the state, medical staff can provide patients with more personalized care plans to meet the different needs of patients.

[0061] On the other hand, the formula generates a comprehensive state index parameter (E) by integrating data from multiple dimensions (such as total sleep length, deep sleep duration, number of sleep interruptions, and the difference between physiological data types and standard values). This comprehensive evaluation method can more comprehensively reflect the patient's health status, not just the change of a single indicator. Reference values ​​(such as total sleep length reference value Sct, deep sleep duration reference value Smax, maximum allowable number of sleep interruptions Isymax, and maximum allowable difference Cymax) are introduced into the formula to standardize the original data. This processing method makes the data between different patients comparable and can also more accurately assess the patient's health status. Although the formula itself does not directly show the weight distribution, the position and calculation method of different indicators in the formula imply their different effects on the patient's overall health status. For example, deep sleep duration and number of sleep interruptions may have an important impact on the patient's sleep quality, so they occupy an important position in the formula. By comparing the calculated state comprehensive index parameters with the preset index parameters, it is possible to detect whether the patient's health status is abnormal. When the state comprehensive index parameters exceed the preset values, it indicates that there may be problems with the patient's health status and further attention is needed.

[0062] For behavioral data and physiological data groups whose status comprehensive indicator parameters exceed the preset values, they are marked. When the number of marked data groups exceeds the preset group threshold, the system will issue an early warning prompt. This mechanism helps to promptly discover and deal with patients' health problems to avoid worsening of their condition. By regularly calculating and comparing the status comprehensive indicator parameters, medical institutions can more accurately understand the changing trends of patients' health conditions and thus develop more personalized health management plans. At the same time, the triggering of the early warning mechanism can also prompt medical institutions to take timely intervention measures to ensure the health and safety of patients.

[0063] In summary, this technical solution achieves timely discovery and early warning of health risks by comprehensively evaluating the behavioral data and physiological data of perinatal patients, which helps to optimize the allocation of medical resources, improve patient experience, and improve the overall quality of medical care. At the same time, the above formula of this embodiment generates comprehensive status indicator parameters by comprehensively evaluating the behavioral data and physiological data of patients, providing medical institutions with an effective means of health monitoring and early warning. This technical means helps to improve the quality and efficiency of medical services and provide patients with more comprehensive and accurate health management services.

[0064] The embodiment of the present invention provides a data processing system for monitoring perinatal depression. Figure 2 As shown, the data processing system for perinatal depression monitoring includes: A data collection frequency setting and adjustment module, used to extract the current pregnancy stage of a perinatal patient, set the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjust the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process; A data quality abnormality warning module is used to collect data from perinatal patients according to the data collection frequency, obtain behavioral data and physiological data of the perinatal patients, and perform data quality assessment and data quality abnormality warning on the behavioral data and physiological data; The grouping storage module is used to group and store the behavioral data and physiological data of the perinatal patients that meet the data quality requirements.

[0065] The working principle of the above technical solution is: with the help of the preset pregnancy stage division strategy, the system can accurately determine the current pregnancy stage of the perinatal patient. This strategy is usually based on medical knowledge and experience, and divides the pregnancy into several key stages, such as early pregnancy, mid-pregnancy, late pregnancy, etc.

[0066] Once the patient's pregnancy stage is determined, the system will set a reasonable initial frequency for data collection based on the characteristics and needs of that stage. For example, in the early and late stages of pregnancy, a higher frequency of data collection may be required due to the more significant physiological and psychological changes of the patient.

[0067] During the data collection process at the initial frequency, the system analyzes the collected behavioral and physiological data in real time. Based on this data, the system dynamically adjusts the frequency of data collection to ensure that important changes in the patient's physiological and psychological state are captured. For example, if the system detects an abnormal fluctuation in a patient's physiological indicator, it may automatically increase the frequency of data collection to monitor the patient's condition in more detail.

[0068] The effect of the above technical solution is: by dynamically adjusting the frequency of data collection according to the stage of pregnancy, this technical solution can more accurately monitor the physiological and psychological state of perinatal patients. This helps medical staff to detect abnormal conditions of patients in a timely manner and take effective intervention measures. Real-time dynamic adjustment of data collection frequency can ensure that more valuable data is collected during critical periods, while reducing data redundancy during stable periods. This helps to improve the quality and analysis efficiency of data. This technical solution can perform personalized data monitoring according to needs to avoid excessive collection or omission of important data. This helps to reduce the burden on patients and improve their overall experience. By dynamically adjusting the frequency of data collection in real time, this technical solution can ensure the rational allocation and utilization of medical resources. During critical periods, the system can automatically increase the frequency of data collection to monitor the patient's condition in more detail; while in stable periods, the frequency of data collection can be reduced to save medical resources.

[0069] 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 these 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 these modifications and variations.

Claims

1. A data processing method for perinatal depression monitoring, characterized in that: The data processing method for perinatal depression monitoring comprises: Extracting the current pregnancy stage of the perinatal patient, setting the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjusting the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process; Collecting data from perinatal patients according to the data collection frequency, obtaining behavioral data and physiological data of the perinatal patients, and performing data quality assessment and data quality abnormality warning on the behavioral data and physiological data; The behavioral data and physiological data of the perinatal patients that meet the data quality requirements are grouped and stored.

2. The data processing method for perinatal depression monitoring according to claim 1, characterized in that: Extracting the current pregnancy stage of the perinatal patient, setting the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjusting the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process, including: Determining the current pregnancy stage of the perinatal patient according to a preset pregnancy stage classification strategy; Setting an initial frequency of data collection according to the current pregnancy stage of the perinatal patient; Behavioral data and physiological data of perinatal patients are collected according to the initial data collection frequency, and the data collection frequency is dynamically adjusted according to the behavioral data and physiological data.

3. The data processing method for perinatal depression monitoring according to claim 2, characterized in that: The stage cycle of the pregnancy stage division strategy is as follows: The early pregnancy stage cycle includes a first early pregnancy stage, a second early pregnancy stage and a third early pregnancy stage, wherein the early pregnancy stage is from 0 to 12 weeks of pregnancy, and the first early pregnancy stage is from 0 to 3 weeks of the early pregnancy stage; the second early pregnancy stage is from 4 to 9 weeks of the early pregnancy stage; the third early pregnancy stage is from 10 to 12 weeks of the early pregnancy stage; The mid-pregnancy stage cycle includes the first mid-pregnancy stage, the second mid-pregnancy stage, the third mid-pregnancy stage and the fourth mid-pregnancy stage, wherein the mid-pregnancy stage is from 13 weeks to 28 weeks of pregnancy, and the first mid-pregnancy stage is from 13 weeks to 16 weeks of the mid-pregnancy stage; the second mid-pregnancy stage is from 17 weeks to 20 weeks of the mid-pregnancy stage; the third mid-pregnancy stage is from 21 weeks to 25 weeks of the mid-pregnancy stage; the fourth mid-pregnancy stage is from 26 weeks to 28 weeks of the mid-pregnancy stage; The late pregnancy stage cycle includes a first late pregnancy stage, a second late pregnancy stage and a third late pregnancy stage, wherein the late pregnancy stage is from 29 weeks to 40 weeks of pregnancy, and the first late pregnancy stage is from 29 weeks to 31 weeks of the late pregnancy stage; the second late pregnancy stage is from 32 weeks to 36 weeks of the late pregnancy stage; the third late pregnancy stage is from 37 weeks to 40 weeks of the late pregnancy stage; The postpartum period cycle includes a first postpartum period and a second postpartum period, wherein the postpartum period includes 0 to 6 weeks after delivery; and the first postpartum period is 0 to 4 weeks after delivery; and the second postpartum period is 5 to 6 weeks after delivery.

4. The data processing method for perinatal depression monitoring according to claim 2, characterized in that: The initial frequency of data collection is set according to the current pregnancy stage of the perinatal patient, including: Extracting the period of the current pregnancy of the perinatal patient and the starting week of the stage of the current pregnancy as the first time period; Extracting the period of the end week of the current pregnancy stage of the perinatal patient as the second time period; Extracting a data collection time interval corresponding to a benchmark data collection frequency, wherein the data collection time interval corresponding to the benchmark data collection frequency ranges from 1 day to 3 days; The data collection time interval corresponding to the initial data collection frequency is obtained by combining the first time period and the second time period with the data collection time interval corresponding to the reference data collection frequency; wherein the data collection time interval corresponding to the initial data collection frequency is obtained by the following formula: Where D represents the data collection time interval corresponding to the initial frequency of data collection; B represents the data collection time interval corresponding to the baseline data collection frequency; W represents the importance weight value corresponding to the pregnancy stage, and the importance weight value range of the early pregnancy is 0.2-0.5, the importance weight value range of the mid-pregnancy is 0.4-0.6, and the importance weight value range of the late pregnancy is 0.8-1.0; T 01 Indicates the length of time corresponding to the first time period; T 02 Indicates the time length corresponding to the second time period; e represents a constant, and its value is 2.

71.

5. The data processing method for perinatal depression monitoring according to claim 2, characterized in that: Collecting behavioral data and physiological data of perinatal patients according to the initial frequency of data collection, and dynamically adjusting the data collection frequency according to the behavioral data and physiological data, including: Collecting behavioral data and physiological data of perinatal patients according to the initial frequency of data collection; wherein the behavioral data includes sleep duration, number of sleep interruptions and deep sleep duration; and the physiological data includes heart rate data, blood pressure data, respiratory rate and blood oxygen saturation; The first adjustment coefficient is set using the sleep duration, the number of sleep interruptions, and the deep sleep duration; wherein the first adjustment coefficient is obtained by the following formula: Among them, K 01 represents the first adjustment coefficient; S t Indicates the total sleep length; S d Indicates the duration of deep sleep; S max Indicates the preset deep sleep duration reference value; I s Indicates the number of sleep interruptions; I max Indicates the preset reference value of sleep interruption times; α 01 and β 01 Indicates the weight values ​​corresponding to the sleep duration data and the sleep interruption data; The second adjustment coefficient is set using the heart rate data, blood pressure data, respiratory rate and blood oxygen saturation; wherein the second adjustment coefficient is obtained by the following formula: Among them, K 02 Represents the second adjustment coefficient; H c , R c and S c Indicates the corresponding values ​​of heart rate data, respiratory rate and blood oxygen saturation; B c Indicates the difference between diastolic and systolic blood pressure; B x Indicates the preset reference value of the difference between diastolic and systolic pressure; H x , R x and S x Indicates the preset reference values ​​corresponding to heart rate data, respiratory rate and blood oxygen saturation; The data collection time interval corresponding to the initial frequency of data collection is adjusted by using the first adjustment coefficient and the second adjustment coefficient to obtain the data collection time interval corresponding to the adjusted data collection frequency; wherein the data collection time interval corresponding to the adjusted data collection frequency is obtained by the following formula: Among them, D t represents the data collection time interval corresponding to the adjusted data collection frequency; D represents the data collection time interval corresponding to the initial data collection frequency; e represents a constant, with a value of 2.71; K 01 Represents the first adjustment coefficient; K 02 Represents the second adjustment coefficient.

6. The data processing method for perinatal depression monitoring according to claim 1, characterized in that: Data is collected from perinatal patients according to the data collection frequency to obtain behavioral data and physiological data of the perinatal patients, and data quality assessment and data quality abnormality warning are performed on the behavioral data and physiological data, including: Collecting data from perinatal patients according to the data collection frequency to obtain behavioral data and physiological data of the perinatal patients; Performing data preprocessing on the behavioral data and physiological data of the perinatal patients, wherein the data preprocessing includes information extraction processing of garbled data and its corresponding data type and information extraction processing of the data type corresponding to the missing data; Data quality anomaly determination and warning are performed based on the data type weight values ​​of the garbled data and its corresponding data type and the data type corresponding to the missing data.

7. The data processing method for perinatal depression monitoring according to claim 6, characterized in that: According to the data type weight value of the garbled data and its corresponding data type and the data type corresponding to the missing data, data quality abnormality judgment and warning are performed, including: Extract the data type weight value corresponding to the data type of each garbled data and the data type weight value corresponding to the data type of each missing data; The importance weight value corresponding to the current pregnancy stage of the perinatal patient is extracted to obtain the data quality abnormality severity coefficient; wherein the data quality abnormality severity coefficient is obtained by the following formula: Among them, S q Indicates the severity coefficient of data quality abnormality; W s Indicates the importance weight value corresponding to the current stage of pregnancy; N ci Indicates the number of garbled data in the i-th data type; N mi represents the number of missing data in the i-th data type; W ti Indicates the data type weight value of the i-th data type; N t Indicates the total number of data; n indicates the total number of data types; When the data quality abnormality severity coefficient exceeds a preset severity coefficient threshold, it is determined that data quality abnormalities exist in the data collection of the current behavioral data and physiological data of the perinatal patient, and a data quality abnormality warning is issued.

8. The data processing method for perinatal depression monitoring according to claim 1, characterized in that: The behavioral data and physiological data of the perinatal patient that meet the data quality requirements are grouped and stored, including: Setting a time window according to the current pregnancy stage of the perinatal patient; Grouping the behavioral data and physiological data collected during the current pregnancy stage according to the time window to form multiple groups of behavioral data and physiological data; Each set of behavioral data and physiological data is stored in groups.

9. The data processing method for perinatal depression monitoring according to claim 8, characterized in that: The behavioral data and physiological data of the perinatal patients that meet the data quality requirements are grouped and stored, further comprising: Data aggregation processing is performed on each set of behavioral data and physiological data to generate a state comprehensive index parameter corresponding to each set of behavioral data and physiological data; wherein the state comprehensive index parameter is obtained by the following formula: Among them, E represents the comprehensive indicator parameter of the state; S t Indicates the total sleep length; S tc Indicates the total sleep length reference value; S max Indicates the preset deep sleep duration reference value; S d Indicates the duration of deep sleep; I s Indicates the number of sleep interruptions; I symax Indicates the maximum number of sleep interruptions allowed; C max Indicates the maximum difference between the value of the physiological number type in the physiological data and its standard value; C ymax Indicates the maximum allowable difference; Comparing the comprehensive index parameters of the status corresponding to the perinatal patient with the preset index parameters; When the state comprehensive index parameter corresponding to the perinatal patient exceeds the preset index parameter, a group of behavioral data and physiological data whose state comprehensive index parameter exceeds the preset index parameter is marked; When the number of groups of marked behavioral data and physiological data exceeds the preset group number threshold, an early warning prompt will be issued.

10. A data processing system for monitoring perinatal depression, characterized in that: The data processing system for perinatal depression monitoring comprises: A data collection frequency setting and adjustment module, used to extract the current pregnancy stage of a perinatal patient, set the data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjust the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process; A data quality abnormality warning module is used to collect data from perinatal patients according to the data collection frequency, obtain behavioral data and physiological data of the perinatal patients, and perform data quality assessment and data quality abnormality warning on the behavioral data and physiological data; The grouping storage module is used to group and store the behavioral data and physiological data of the perinatal patients that meet the data quality requirements.

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