Internet-based gynecological and obstetrical information processing method and system

Abstract

The application discloses an obstetrics and gynecology information processing method and system based on the Internet, relates to the technical field of Internet, and integrates feature data set F by extracting and processing data set D, so that the nursing scheme is more personalized and dynamic. By establishing a time series analysis algorithm, the system can calculate the adjustment index C reflecting the fluctuation of the health state of the lying-in woman, match it with the health fluctuation evaluation threshold TZ, generate a nursing control suggestion and a nursing scheme corresponding to the health state of the lying-in woman, and thus avoid the disadvantage that the nursing scheme cannot follow the health change of the lying-in woman in real time. In addition, an effect monitoring mechanism is introduced, the change trend of the adjustment index C is calculated, the effect execution index R is generated, the execution effect of the nursing scheme is continuously evaluated, and the effect of the nursing scheme is ensured to always meet the health needs of the lying-in woman. The nursing plan is matched with the actual recovery state of the lying-in woman, and thus the recovery effect of the lying-in woman is improved.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, specifically to an Internet-based method and system for processing obstetric and gynecological information. Background Technology

[0002] Medical data and patient health information are processed and managed using information technology to improve the efficiency and accuracy of healthcare services. With the development of internet and mobile communication technologies, telemedicine and online health management have become important subfields of medical informatics. These technologies are particularly suitable for obstetrics and gynecology, as pregnant women and postpartum women typically require continuous and dynamic health monitoring and care guidance.

[0003] Although the application of internet technology in obstetrics and gynecology has made some progress in medical informatization, many shortcomings still exist in obstetrics and gynecology nursing. Currently, most traditional nursing plans are still based on fixed care schedules, usually formulated after the mother is discharged from the hospital, and rarely adjusted in real time according to the patient's actual situation. The drawback of this static nursing plan is that it cannot adequately address the dynamic health changes of mothers during the recovery process. For example, a mother's mood fluctuations, sleep quality, and physical recovery speed vary from person to person, and a fixed nursing plan can hardly meet the individualized needs of each mother. Furthermore, although modern medical equipment can collect physiological data of mothers, such as heart rate and blood pressure, this data is mostly used only for doctors' diagnoses and is not fully utilized to dynamically adjust the nursing plan. This results in insufficient responsiveness and personalization of the nursing plan, making it unable to accurately follow the mother's health changes and make timely adjustments. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention provides an Internet-based method and system for obstetrics and gynecology information processing, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an Internet-based method for processing obstetric and gynecological information, comprising the following steps:

[0006] S1. Collect the mother's physiological parameters through smartwatches, mobile phones and other wearable devices to form a collection dataset D, and upload it to the cloud server via the Internet;

[0007] S2. Extract and process the acquired dataset D, and integrate it into a feature dataset F;

[0008] S3. By establishing a time series analysis algorithm, the obtained feature dataset F is substituted into the calculation to obtain the adjustment index C that reflects the fluctuation of maternal health status;

[0009] S4. Match the obtained adjustment index C with the preset health fluctuation assessment threshold TZ to obtain the maternal health regulation and generation suggestion results, and generate a suggested nursing plan based on the maternal health regulation and generation suggestion results.

[0010] S5. Based on the results of the maternal health regulation, a mechanism for monitoring the effectiveness of the recommended nursing plan is triggered, and the effect execution index R is obtained by calculating the trend of the adjustment index C.

[0011] S6. Match the effect execution index R with the preset suggested nursing execution effect threshold ZZ to obtain a nursing effectiveness assessment strategy plan, and notify and remind relevant personnel according to the nursing effectiveness assessment strategy plan.

[0012] Preferably, S1 includes S11 and S12;

[0013] S11. Real-time collection of postpartum physiological parameters, including heart rate time series data HRp, sleep time series data SP, mood time series data PS, and exercise time series data St, through smartwatches, mobile phones, and other wearable devices;

[0014] Among them, heart rate time series data HRp includes average heart rate HRm, peak heart rate HRmax, and heart rate fluctuation amplitude HRv; sleep time series data SP includes sleep duration SPc, deep sleep duration SPd, light sleep duration SPl, number of deep sleeps Dc, and number of light sleeps Qc; mood time series data PS includes average stress PSm, peak stress PSmax, and stress fluctuation amplitude PSv; exercise time series data St includes total walking time Stc, number of steps Stn, and walking distance Std;

[0015] The heart rate time series data HRp is specifically defined as HRp = {HRm, HRmax, HRv};

[0016] The sleep time series data SP is specifically defined as SP = {SPc, SPd, SPl, Dc, Qc};

[0017] The emotional time series data PS is specifically defined as PS = {PSm, PSmax, PSv};

[0018] The motion time series data St is specifically defined as St = {Stc, Stn, Std}.

[0019] Preferably, in step S12, the acquired heart rate time series data HRp, sleep time series data SP, emotion time series data PS, and exercise time series data St are integrated to form a collection dataset D, and after a fixed period N, the internet is connected to upload the collection dataset D to the cloud server.

[0020] The collected dataset D is specifically defined as D = {HRp, SP, PS, St}.

[0021] Preferably, S2 includes S21 and S22;

[0022] S21. Normalize the heart rate time series data HRp, sleep time series data SP, emotion time series data PS, and exercise time series data St in the acquired dataset D to obtain the heart rate load index HRint, sleep quality index SPint, emotion stress index PSint, and exercise recovery index Stint. The normalization process includes using min-max normalization.

[0023] The Heart Rate Load Index (HRint) is obtained through... Obtain the calculation formula;

[0024] The sleep quality index SPint is obtained through Obtain the calculation formula;

[0025] The emotional stress index PSint is obtained through Obtain the calculation formula;

[0026] The Stint sports recovery index is obtained through Obtain the calculation formula;

[0027] S22. Integrate the obtained heart rate load index HRint, sleep quality index SPint, emotional stress index PSint, and exercise recovery index Stint to form a feature dataset F = {HRint, SPint, PSint, Stint}.

[0028] Preferably, S3 includes S31 and S32;

[0029] S31. By establishing a time series analysis algorithm, the obtained feature dataset F is substituted into the calculation to obtain the heart rate load index difference △HRint, sleep quality index difference △SPint, emotional stress index difference △PSint, and exercise recovery index difference △Stint.

[0030] The heart rate load index difference △HRint is obtained by the following formula:

[0031]

[0032] In the formula, HRint(t) and HRint(t-1) represent the heart rate load index HRint at time t and time t-1, respectively, and log represents the natural logarithm function;

[0033] The difference in sleep quality index △SPint is obtained using the following formula:

[0034]

[0035] In the formula, SPint(t) and SPint(t-1) represent the sleep quality index SPint at time t and time t-1, respectively;

[0036] The difference in the emotional stress index △PSint is obtained using the following formula:

[0037]

[0038] In the formula, PSint(t) and PSint(t-1) represent the emotional stress index PSint at time t and time t-1, respectively;

[0039] The difference in the sports recovery index ΔStint is obtained using the following formula:

[0040]

[0041] In the formula, Stint(t) and Stint(t-1) represent the motion recovery index Stint at time t and time t-1, respectively.

[0042] Preferably, in step S32, the heart rate load index difference △HRint, sleep quality index difference △SPint, emotional stress index difference △PSint, and exercise recovery index difference △Stint are fitted to obtain the adjustment index C that reflects the fluctuation of the postpartum woman's health status.

[0043] The adjustment index C is obtained through the following calculation formula:

[0044]

[0045] In the formula, C(N) represents the adjustment index C within a fixed period N, N represents the fixed period, specifically the total number of data samples acquired within the fixed period, and t represents the time, specifically the data samples acquired within time t within the fixed period.

[0046] Preferably, the results of the maternal health regulation and recommendation generation are obtained through the following matching method:

[0047] When the adjustment index C is less than the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is considered a qualified result.

[0048] When the adjustment index C is greater than or equal to the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is unqualified.

[0049] When the adjustment index C is greater than or equal to twice the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is considered an abnormal result.

[0050] The recommended care plan was obtained through the following generation method:

[0051] When the maternal health regulation recommendation is deemed unsatisfactory, a recommended nursing care plan is generated that includes increasing psychological counseling, dietary adjustments, and improving sleep.

[0052] When the maternal health monitoring results are abnormal, a doctor's intervention examination and nursing plan will be generated.

[0053] Preferably, S5 includes S51 and S52;

[0054] S51. Based on the results of the suggestions generated by maternal health regulation, a mechanism for monitoring the effectiveness of the suggested nursing plan is triggered.

[0055] When the maternal health regulation recommendation result is unqualified, a mechanism is triggered to double the frequency of monitoring the effect of the recommended nursing plan. Specifically, the sample data volume of time t within a fixed period N is multiplied by 2 to adjust the total number of data samples collected within the fixed period N, and a new fixed period XN is obtained.

[0056] When the maternal health regulation generates a recommendation result that is unqualified, a mechanism is triggered to monitor the effectiveness of the recommended nursing plan at a frequency of four times. Specifically, the sample data volume of time t within a fixed period N is multiplied by 4 to adjust the total number of data samples collected within the fixed period N, and a new fixed period XN is obtained.

[0057] S52. Calculate and adjust the trend of the adjustment index C based on the effect monitoring mechanism to obtain the effect execution index R;

[0058] The performance index R for the trend of change is obtained through the following formula:

[0059]

[0060] In the formula, XN represents the new fixed period, specifically the total number of new data samples after adjusting the amount of sample data within the fixed period N.

[0061] Preferably, the strategy for establishing nursing effectiveness assessment is obtained through the following matching method:

[0062] When the effectiveness execution index R ≥ the recommended nursing execution effectiveness threshold ZZ, obtain the evaluation result of the ineffective implementation of the nursing recommendation plan, adjust the recommended nursing plan, generate an ineffective recommended nursing plan record, and notify relevant staff and the preset emergency contact person;

[0063] When the effectiveness execution index R is less than the recommended nursing execution effectiveness threshold ZZ, the effective evaluation result of the nursing recommendation plan is obtained, and the recommended nursing plan continues to be implemented, generating an effective recommended nursing plan record.

[0064] The Internet-based obstetrics and gynecology information processing system includes a physiological data acquisition module, a data processing module, a fluctuation analysis module, a suggestion generation module, a trend analysis module, and an iterative optimization module.

[0065] The physiological data acquisition module collects the physiological parameters of the mother through smartwatches, mobile phones and other wearable devices, forming a collection dataset D, and uploads it to the cloud server via the Internet;

[0066] The data processing module extracts and processes the acquired dataset D, integrating it into a feature dataset F;

[0067] The fluctuation analysis module establishes a time series analysis algorithm, substitutes the acquired feature dataset F into the calculation, and obtains the adjustment index C that reflects the fluctuation of the maternal health status.

[0068] The suggestion generation module matches the obtained adjustment index C with the preset health fluctuation assessment threshold TZ to obtain the maternal health regulation suggestion result, and generates a suggested nursing plan based on the maternal health regulation suggestion result.

[0069] The trend analysis module generates suggestions based on maternal health regulation, triggering an effect monitoring mechanism for the suggested nursing plan, and obtains the effect execution index R by calculating the changing trend of the adjustment index C.

[0070] The iterative optimization module matches the effect execution index R with the preset suggested nursing execution effect threshold ZZ to obtain a nursing effectiveness assessment strategy plan, and notifies and prompts relevant personnel according to the established nursing effectiveness assessment strategy plan.

[0071] This invention provides an internet-based method and system for processing obstetric and gynecological information, which has the following beneficial effects:

[0072] (1) By extracting and processing dataset D, a feature dataset F is formed, making the nursing plan more personalized and dynamic. By establishing a time series analysis algorithm, the system can calculate the adjustment index C reflecting the fluctuation of the mother's health status and match it with the health fluctuation assessment threshold TZ to generate nursing control suggestions and nursing plans that are consistent with the mother's health status, thereby avoiding the drawback of the nursing plan not being able to keep up with the changes in the mother's health in real time. In addition, by introducing an effect monitoring mechanism, the effect execution index R is generated by calculating the changing trend of the adjustment index C, continuously evaluating the execution effect of the nursing plan, and generating an effectiveness evaluation strategy plan by matching it with the recommended nursing execution effect threshold ZZ, ensuring that the effect of the nursing plan always meets the health needs of the mother. Compared with the traditional static nursing model, this dynamic adjustment mechanism can adjust the nursing plan according to real-time health data, respond to factors such as heart rate fluctuations and stress fluctuations in a timely manner, and ensure that the nursing plan matches the actual recovery status of the mother, thereby improving the recovery effect of the mother and reducing potential health risks.

[0073] (2) Data sets (D) of postpartum women are collected and uploaded to a cloud server periodically. This yields more standardized heart rate load index (HRint), sleep quality index (SPint), emotional stress index (PSint), and exercise recovery index (Stint). Normalization allows for precise comparison and analysis of these physiological parameters from different sources, making adjustments to the nursing care plan more objective and accurate. Furthermore, by integrating these data to generate a feature dataset (F), the nursing care plan can not only be based on standardized data for decision-making but also better capture the dynamic health changes of postpartum women.

[0074] (3) By establishing a time series analysis algorithm, subtle changes in the health status of postpartum women can be captured. This not only reflects the trend of health fluctuations through data differences at multiple time points, but also generates an adjustment index C reflecting overall health status fluctuations through fitting these differences. Covering the total number of data samples N within a fixed period, the system can comprehensively assess changes in health status over a longer time frame. This improves the accuracy of capturing health fluctuations and allows the adjustment index C to comprehensively reflect the postpartum woman's recovery progress and the adaptability of the current care plan, avoiding the situation where the care plan blindly follows short-term data changes. This provides a more stable and scientific basis for adjusting postpartum care over a long period.

[0075] (4) By precisely matching the adjustment index C with the health fluctuation assessment threshold TZ, the system can promptly assess the mother's health status and generate corresponding nursing care recommendations. Especially when the adjustment index C exceeds twice the health fluctuation assessment threshold TZ, the system quickly generates a nursing care recommendation plan for doctor intervention, thus avoiding risks caused by ineffective or delayed adjustments to the nursing care plan. Furthermore, dynamic adjustments are made through an effectiveness monitoring mechanism. When the generated recommendations for mother's health regulation are unqualified or abnormal, the monitoring frequency is automatically doubled or quadrupled, ensuring real-time capture of changes in health status under higher-density data collection, and generating a new fixed cycle XN to accurately assess the nursing care effect. This significantly improves the responsiveness and execution effectiveness of the nursing care plan and maintains continuous monitoring of health fluctuations. Attached Figure Description

[0076] Figure 1 This is a schematic diagram illustrating the steps of the Internet-based obstetrics and gynecology information processing method of the present invention;

[0077] Figure 2 This is a schematic diagram of the Internet-based obstetrics and gynecology information processing system of the present invention. Detailed Implementation

[0078] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0079] Example 1

[0080] This invention provides an internet-based method for processing obstetric and gynecological information. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0081] S1. Collect the mother's physiological parameters through smartwatches, mobile phones and other wearable devices to form a collection dataset D, and upload it to the cloud server via the Internet;

[0082] S2. Extract and process the acquired dataset D, and integrate it into a feature dataset F;

[0083] S3. By establishing a time series analysis algorithm, the obtained feature dataset F is substituted into the calculation to obtain the adjustment index C that reflects the fluctuation of maternal health status;

[0084] S4. Match the obtained adjustment index C with the preset health fluctuation assessment threshold TZ to obtain the maternal health regulation and generation suggestion results, and generate a suggested nursing plan based on the maternal health regulation and generation suggestion results.

[0085] S5. Based on the results of the maternal health regulation, a mechanism for monitoring the effectiveness of the recommended nursing plan is triggered, and the effect execution index R is obtained by calculating the trend of the adjustment index C.

[0086] S6. Match the effect execution index R with the preset suggested nursing execution effect threshold ZZ to obtain a nursing effectiveness assessment strategy plan, and notify and remind relevant personnel according to the nursing effectiveness assessment strategy plan.

[0087] In this embodiment, by extracting and processing dataset D, a feature dataset F is formed, making the nursing plan more personalized and dynamic. By establishing a time-series analysis algorithm, the system can calculate an adjustment index C reflecting fluctuations in the mother's health status and match it with a health fluctuation assessment threshold TZ. This generates nursing control suggestions and plans that align with the mother's health status, thus avoiding the drawback of nursing plans failing to keep pace with changes in the mother's health in real time. Furthermore, by introducing an effect monitoring mechanism, the system calculates the trend of the adjustment index C to generate an effect execution index R, continuously evaluating the effectiveness of the nursing plan. By matching this R with the suggested nursing execution effect threshold ZZ, an effectiveness assessment strategy is generated, ensuring that the nursing plan always meets the mother's health needs. Compared to the traditional static nursing model, this dynamic adjustment mechanism can adjust the nursing plan based on real-time health data, promptly addressing factors such as heart rate fluctuations and stress fluctuations, ensuring that the nursing plan matches the mother's actual recovery status, thereby improving the mother's recovery and reducing potential health risks.

[0088] Example 2

[0089] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S1 includes S11 and S12;

[0090] S11. Real-time collection of postpartum physiological parameters, including heart rate time series data HRp, sleep time series data SP, mood time series data PS, and exercise time series data St, through smartwatches, mobile phones, and other wearable devices;

[0091] Among them, heart rate time series data HRp includes average heart rate HRm, peak heart rate HRmax, and heart rate fluctuation amplitude HRv; sleep time series data SP includes sleep duration SPc, deep sleep duration SPd, light sleep duration SPl, number of deep sleeps Dc, and number of light sleeps Qc; mood time series data PS includes average stress PSm, peak stress PSmax, and stress fluctuation amplitude PSv; exercise time series data St includes total walking time Stc, number of steps Stn, and walking distance Std;

[0092] The heart rate time series data HRp is specifically defined as HRp = {HRm, HRmax, HRv};

[0093] The sleep time series data SP is specifically defined as SP = {SPc, SPd, SPl, Dc, Qc};

[0094] The emotional time series data PS is specifically defined as PS = {PSm, PSmax, PSv};

[0095] The motion time series data St is specifically defined as St = {Stc, Stn, Std}.

[0096] S12. Integrate the acquired heart rate time series data HRp, sleep time series data SP, emotion time series data PS, and exercise time series data St to form a collection dataset D, and connect to the Internet after a fixed period N to upload the collection dataset D to the cloud server.

[0097] The collected dataset D is specifically defined as D = {HRp, SP, PS, St}.

[0098] S2 includes S21 and S22;

[0099] S21. Normalize the heart rate time series data HRp, sleep time series data SP, emotion time series data PS, and exercise time series data St in the acquired dataset D to obtain the heart rate load index HRint, sleep quality index SPint, emotion stress index PSint, and exercise recovery index Stint. The normalization process includes using min-max normalization.

[0100] The Heart Rate Load Index (HRint) is obtained through... Obtain the calculation formula;

[0101] The sleep quality index SPint is obtained through Obtain the calculation formula;

[0102] The emotional stress index PSint is obtained through Obtain the calculation formula;

[0103] The Stint sports recovery index is obtained through Obtain the calculation formula;

[0104] S22. Integrate the obtained heart rate load index HRint, sleep quality index SPint, emotional stress index PSint, and exercise recovery index Stint to form a feature dataset F = {HRint, SPint, PSint, Stint}.

[0105] In this embodiment, heart rate time-series data (HRp), sleep time-series data (SP), mood time-series data (PS), and exercise time-series data (St) of postpartum women are collected and integrated into a dataset D, which is then periodically uploaded to a cloud server. By performing min-max normalization on the dataset D, the magnitude differences between different physiological parameters can be effectively eliminated, resulting in more standardized heart rate load index (HRint), sleep quality index (SPint), mood stress index (PSint), and exercise recovery index (Stint). Normalization ensures that these physiological parameters from different sources can be accurately compared and analyzed, making adjustments to the care plan more objective and accurate. Simultaneously, by integrating these data to generate a feature dataset F, the care plan can not only be based on standardized data for decision-making but also better capture the dynamic health changes of postpartum women.

[0106] Example 3

[0107] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: S3 includes S31 and S32;

[0108] S31. By establishing a time series analysis algorithm, the obtained feature dataset F is substituted into the calculation to obtain the heart rate load index difference △HRint, sleep quality index difference △SPint, emotional stress index difference △PSint, and exercise recovery index difference △Stint.

[0109] The heart rate load index difference △HRint is obtained by the following formula:

[0110]

[0111] In the formula, HRint(t) and HRint(t-1) represent the heart rate load index HRint at time t and time t-1, respectively, and log represents the natural logarithm function;

[0112] The difference in sleep quality index △SPint is obtained using the following formula:

[0113]

[0114] In the formula, SPint(t) and SPint(t-1) represent the sleep quality index SPint at time t and time t-1, respectively;

[0115] The difference in the emotional stress index △PSint is obtained using the following formula:

[0116]

[0117] In the formula, PSint(t) and PSint(t-1) represent the emotional stress index PSint at time t and time t-1, respectively;

[0118] The difference in the sports recovery index ΔStint is obtained using the following formula:

[0119]

[0120] In the formula, Stint(t) and Stint(t-1) represent the motion recovery index Stint at time t and time t-1, respectively.

[0121] S32. Based on the obtained differences in heart rate load index △HRint, sleep quality index △SPint, emotional stress index △PSint, and exercise recovery index △Stint, a fitting process is performed to obtain the adjustment index C that reflects the fluctuation of the postpartum woman's health status.

[0122] The adjustment index C is obtained through the following calculation formula:

[0123]

[0124] In the formula, C(N) represents the adjustment index C within a fixed period N, N represents the fixed period, specifically the total number of data samples acquired within the fixed period, and t represents the time, specifically the data samples acquired within time t within the fixed period.

[0125] In this embodiment, a time-series analysis algorithm is established to obtain differences in heart rate load index ΔHRint, sleep quality index ΔSPint, emotional stress index ΔPSint, and exercise recovery index ΔStint, thereby capturing subtle changes in the postpartum woman's health status. Unlike traditional static care plans, this system not only reflects the postpartum woman's health fluctuation trends through data differences at multiple time points, but also generates an adjustment index C reflecting overall health status fluctuations through fitting these difference values. The calculation of adjustment index C covers the total number of data samples N within a fixed period, enabling a comprehensive assessment of health status changes over a longer time frame. This improves the accuracy of capturing health fluctuations and allows the adjustment index C to comprehensively reflect the postpartum woman's recovery progress and the adaptability of the current care plan, avoiding the situation where the care plan blindly follows short-term data changes, thus providing a more stable and scientific basis for care adjustments for postpartum women over a long period.

[0126] Example 4

[0127] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically, the results of the maternal health regulation recommendations are obtained through the following matching methods:

[0128] When the adjustment index C is less than the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is considered a qualified result.

[0129] When the adjustment index C is greater than or equal to the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is unqualified.

[0130] When the adjustment index C is greater than or equal to twice the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is considered an abnormal result.

[0131] The recommended care plan was obtained through the following generation method:

[0132] When the maternal health regulation recommendation is deemed unsatisfactory, a recommended nursing care plan is generated that includes increasing psychological counseling, dietary adjustments, and improving sleep.

[0133] When the maternal health monitoring results are abnormal, a doctor's intervention examination and nursing plan will be generated.

[0134] S5 includes S51 and S52;

[0135] S51. Based on the results of the suggestions generated by maternal health regulation, a mechanism for monitoring the effectiveness of the suggested nursing plan is triggered.

[0136] When the maternal health regulation recommendation result is unqualified, a mechanism is triggered to double the frequency of monitoring the effect of the recommended nursing plan. Specifically, the sample data volume of time t within a fixed period N is multiplied by 2 to adjust the total number of data samples collected within the fixed period N, and a new fixed period XN is obtained.

[0137] When the maternal health regulation generates a recommendation result that is unqualified, a mechanism is triggered to monitor the effectiveness of the recommended nursing plan at a frequency of four times. Specifically, the sample data volume of time t within a fixed period N is multiplied by 4 to adjust the total number of data samples collected within the fixed period N, and a new fixed period XN is obtained.

[0138] S52. Calculate and adjust the trend of the adjustment index C based on the effect monitoring mechanism to obtain the effect execution index R;

[0139] The performance index R for the trend of change is obtained through the following formula:

[0140]

[0141] In the formula, XN represents the new fixed period, specifically the total number of new data samples after adjusting the amount of sample data within the fixed period N.

[0142] The established nursing effectiveness assessment strategy was obtained through the following matching method:

[0143] When the effectiveness execution index R ≥ the recommended nursing execution effectiveness threshold ZZ, obtain the evaluation result of the ineffective implementation of the nursing recommendation plan, adjust the recommended nursing plan, generate an ineffective recommended nursing plan record, and notify relevant staff and the preset emergency contact person;

[0144] When the effectiveness execution index R is less than the recommended nursing execution effectiveness threshold ZZ, the effective evaluation result of the nursing recommendation plan is obtained, and the recommended nursing plan continues to be implemented, generating an effective recommended nursing plan record.

[0145] In this embodiment, by precisely matching the adjustment index C with the health fluctuation assessment threshold TZ, the health status of the postpartum woman can be assessed in a timely manner, and corresponding nursing care recommendations can be generated. Specifically, when the adjustment index C exceeds twice the health fluctuation assessment threshold TZ, the system quickly generates a nursing care recommendation plan for doctor intervention, thereby avoiding risks caused by ineffective or delayed adjustments to the nursing care plan. Furthermore, dynamic adjustments are made through an effectiveness monitoring mechanism. When the generated recommendations for postpartum health regulation are unqualified or abnormal, the monitoring frequency is automatically doubled or quadrupled to ensure real-time capture of changes in health status under higher-density data collection, and to generate a new fixed cycle XN for accurate assessment of nursing care effectiveness. By calculating the effectiveness execution index R, the effectiveness of the nursing care plan can be judged based on its execution effect. If the effectiveness execution index R does not reach the recommended nursing care execution effect threshold ZZ, an invalid recommended nursing care plan record will be automatically generated, and relevant staff and emergency contacts will be notified to ensure that the postpartum woman receives more rapid nursing care adjustments and necessary medical intervention. This continuous assessment and monitoring mechanism significantly improves the responsiveness and execution effectiveness of the nursing care plan and maintains continuous monitoring of health fluctuations.

[0146] Example 5

[0147] For an internet-based obstetrics and gynecology information processing system, please refer to [reference needed]. Figure 2 Specifically, it includes a physiological data acquisition module, a data processing module, a fluctuation analysis module, a suggestion generation module, a trend analysis module, and an iterative optimization module;

[0148] The physiological data acquisition module collects the physiological parameters of the mother through smartwatches, mobile phones and other wearable devices, forming a collection dataset D, and uploads it to the cloud server via the Internet;

[0149] The data processing module extracts and processes the acquired dataset D, integrating it into a feature dataset F;

[0150] The fluctuation analysis module establishes a time series analysis algorithm, substitutes the acquired feature dataset F into the calculation, and obtains the adjustment index C that reflects the fluctuation of the maternal health status.

[0151] The suggestion generation module matches the obtained adjustment index C with the preset health fluctuation assessment threshold TZ to obtain the maternal health regulation suggestion result, and generates a suggested nursing plan based on the maternal health regulation suggestion result.

[0152] The trend analysis module generates suggestions based on maternal health regulation, triggering an effect monitoring mechanism for the suggested nursing plan, and obtains the effect execution index R by calculating the changing trend of the adjustment index C.

[0153] The iterative optimization module matches the effect execution index R with the preset suggested nursing execution effect threshold ZZ to obtain a nursing effectiveness assessment strategy plan, and notifies and prompts relevant personnel according to the established nursing effectiveness assessment strategy plan.

[0154] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An internet-based method for processing obstetric and gynecological information, characterized in that: Includes the following steps: S1. Collect the mother's physiological parameters through smartwatches, mobile phones and other wearable devices to form a collection dataset D, and upload it to the cloud server via the Internet; S1 includes S11; S11. Real-time collection of postpartum physiological parameters, including heart rate time series data HRp, sleep time series data SP, mood time series data PS, and exercise time series data St, through smartwatches, mobile phones, and other wearable devices; Among them, heart rate time series data HRp includes average heart rate HRm, peak heart rate HRmax, and heart rate fluctuation amplitude HRv; sleep time series data SP includes sleep duration SPc, deep sleep duration SPd, light sleep duration SPl, number of deep sleeps Dc, and number of light sleeps Qc; mood time series data PS includes average stress PSm, peak stress PSmax, and stress fluctuation amplitude PSv; exercise time series data St includes total walking time Stc, number of steps Stn, and walking distance Std; The heart rate time series data HRp is specifically defined as HRp = {HRm, HRmax, HRv}. The sleep time series data SP is specifically defined as SP = {SPc, SPd, SPl, Dc, Qc}; The emotional time series data PS is specifically defined as PS = {PSm, PSmax, PSv}; The motion time series data St is specifically St={Stc, Stn, Std}; S2. Extract and process the acquired dataset D, and integrate it into a feature dataset F; S2 includes S21 and S22; S21. Normalize the acquired heart rate time series data HRp, sleep time series data SP, emotion time series data PS, and exercise time series data St to obtain the heart rate load index HRint, sleep quality index SPint, emotion stress index PSint, and exercise recovery index Stint. The normalization process includes using min-max normalization. The Heart Rate Load Index (HRint) is obtained through... Obtain the calculation formula; The sleep quality index SPint is obtained through Obtain the calculation formula; The emotional stress index PSint is obtained through Obtain the calculation formula; The Stint sports recovery index is obtained through Obtain the calculation formula; S22. Integrate the obtained heart rate load index HRint, sleep quality index SPint, emotional stress index PSint, and exercise recovery index Stint to form a feature dataset F={HRint, SPint, PSint, Stint}. S3. By establishing a time series analysis algorithm, the obtained feature dataset F is substituted into the calculation to obtain the adjustment index C that reflects the fluctuation of maternal health status; S3 includes S31 and S32; S31. By establishing a time series analysis algorithm, the obtained feature dataset F is substituted into the calculation to obtain the heart rate load index difference △HRint, sleep quality index difference △SPint, emotional stress index difference △PSint, and exercise recovery index difference △Stint. The heart rate load index difference △HRint is obtained by the following formula: ； In the formula, HRint(t) and HRint(t-1) represent the heart rate load index HRint at time t and time t-1, respectively, and log represents the natural logarithm function; The difference in sleep quality index △SPint is obtained using the following formula: ； In the formula, SPint(t) and SPint(t-1) represent the sleep quality index SPint at time t and time t-1, respectively; The difference in the emotional stress index △PSint is obtained using the following formula: ； In the formula, PSint(t) and PSint(t-1) represent the emotional stress index PSint at time t and time t-1, respectively; The difference in the sports recovery index ΔStint is obtained using the following formula: ； In the formula, Stint(t) and Stint(t-1) represent the exercise recovery index Stint at time t and time t-1, respectively; S32, based on the obtained differences in heart rate load index △HRint, sleep quality index △SPint, emotional stress index △PSint and exercise recovery index △Stint, the adjustment index C reflecting the fluctuation of the maternal health status is obtained by fitting the data. The adjustment index C is obtained through the following calculation formula: ； In the formula, C(N) represents the adjustment index C within a fixed period N, N represents the fixed period, and t represents time; S4. Match the obtained adjustment index C with the preset health fluctuation assessment threshold TZ to obtain the maternal health regulation and generation suggestion results, and generate a suggested nursing plan based on the maternal health regulation and generation suggestion results. The results of the maternal health regulation recommendations were obtained through the following matching method: When the adjustment index C is less than the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is considered a qualified result. When the adjustment index C is greater than or equal to the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is unqualified. When the adjustment index C is greater than or equal to twice the health fluctuation assessment threshold TZ, the result of obtaining the maternal health regulation and generation suggestion is considered an abnormal result. The recommended care plan was obtained through the following generation method: When the maternal health regulation recommendation is deemed unsatisfactory, a recommended nursing care plan is generated that includes increasing psychological counseling, dietary adjustments, and improving sleep. When the maternal health monitoring results are abnormal, a doctor's intervention examination and nursing plan will be generated. S5. Based on the results of the maternal health regulation, a mechanism for monitoring the effectiveness of the recommended nursing plan is triggered, and the effect execution index R is obtained by calculating the trend of the adjustment index C. S5 includes S51 and S52; S51. Based on the results of the suggestions generated by maternal health regulation, a mechanism for monitoring the effectiveness of the suggested nursing plan is triggered. When the maternal health regulation recommendation result is unqualified, a mechanism is triggered to double the frequency of monitoring the effect of the recommended nursing plan. Specifically, the sample data volume of time t within a fixed period N is multiplied by 2 to adjust the total number of data samples collected within the fixed period N, and a new fixed period XN is obtained. When the result of the maternal health regulation suggestion is abnormal, the mechanism of monitoring the effect of the suggested nursing plan is triggered four times. Specifically, the sample data volume of time t within the fixed period N is multiplied by 4 to adjust the total number of data samples collected within the fixed period N, and a new fixed period XN is obtained. S52. Calculate and adjust the trend of the adjustment index C based on the effect monitoring mechanism to obtain the effect execution index R; The performance index R for the trend of change is obtained through the following formula: ； In the formula, XN represents the new fixed period; S6. Match the effect execution index R with the preset suggested nursing execution effect threshold ZZ to obtain a nursing effectiveness assessment strategy plan, and notify and remind relevant personnel according to the nursing effectiveness assessment strategy plan.

2. The Internet-based obstetrics and gynecology information processing method according to claim 1, characterized in that: S1 further includes S12; S12. Integrate the acquired heart rate time series data HRp, sleep time series data SP, emotion time series data PS, and exercise time series data St to form a collection dataset D, and connect to the Internet after a fixed period N to upload the collection dataset D to the cloud server. The collected dataset D is specifically defined as D = {HRp, SP, PS, St}.

3. The Internet-based obstetrics and gynecology information processing method according to claim 1, characterized in that: The established nursing effectiveness assessment strategy was obtained through the following matching method: When the effectiveness execution index R ≥ the recommended nursing execution effectiveness threshold ZZ, obtain the evaluation result of the ineffective implementation of the nursing recommendation plan, adjust the recommended nursing plan, generate an ineffective recommended nursing plan record, and notify relevant staff and the preset emergency contact person; When the effectiveness execution index R is less than the recommended nursing execution effectiveness threshold ZZ, the effective evaluation result of the nursing recommendation plan is obtained, and the recommended nursing plan continues to be implemented, generating an effective recommended nursing plan record.

4. An Internet-based obstetrics and gynecology information processing system, employing the Internet-based obstetrics and gynecology information processing method according to any one of claims 1 to 3, characterized in that: It includes a physiological data acquisition module, a data processing module, a fluctuation analysis module, a suggestion generation module, a trend analysis module, and an iterative optimization module; The physiological data acquisition module collects the physiological parameters of the mother through smartwatches, mobile phones and other wearable devices, forming a collection dataset D, and uploads it to the cloud server via the Internet; The data processing module extracts and processes the acquired dataset D, integrating it into a feature dataset F; The fluctuation analysis module establishes a time series analysis algorithm, substitutes the acquired feature dataset F into the calculation, and obtains the adjustment index C that reflects the fluctuation of the maternal health status. The suggestion generation module matches the obtained adjustment index C with the preset health fluctuation assessment threshold TZ to obtain the maternal health regulation suggestion result, and generates a suggested nursing plan based on the maternal health regulation suggestion result. The trend analysis module generates suggestions based on maternal health regulation, triggering an effect monitoring mechanism for the suggested nursing plan, and obtains the effect execution index R by calculating the changing trend of the adjustment index C. The iterative optimization module matches the effect execution index R with the preset suggested nursing execution effect threshold ZZ to obtain a nursing effectiveness assessment strategy plan, and notifies and prompts relevant personnel according to the established nursing effectiveness assessment strategy plan.

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