Pregnancy health care control system based on data feedback analysis

Abstract

The invention relates to the technical field of data feedback control, and discloses a pregnancy health care control system based on data feedback analysis, which comprises a data acquisition module, a dynamic physiological feature mapping module, an implicit feedback channel module, a self-stabilization control module and a care execution module. By constructing the dynamic physiological feature mapping matrix, the multi-dimensional physiological data and the historical trend of the pregnant woman are deeply fused, the continuous health state vector is generated, and the limitation that in the prior art, a single threshold value is relied on, and dynamic adaptive capacity is lacked is broken through. Meanwhile, an implicit feedback and self-stabilization control mechanism is introduced into the system, the nursing strategy can be automatically adjusted when data abnormity or environment change is detected, pregnancy health management with individualized, continuous and robust coexistence is achieved, and the sensitivity, the stability and the individualized nursing capacity of the monitoring system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data feedback control, and in particular to a pregnancy health care control system based on data feedback analysis. Background Art

[0002] At present, pregnancy health monitoring technology has been widely used in clinical and home care fields. It usually assists in assessing the health status of pregnant women and fetuses by periodically testing physiological indicators such as the heart rate, fetal movement, and blood pressure of pregnant women. However, existing pregnancy health monitoring systems generally have the following technical limitations:

[0003] First, existing systems mostly rely on regular testing or single data collection, lacking real-time and continuous monitoring of pregnant women's physiological data, which can easily lead to key health risk signals being ignored in a short period of time, and the timeliness of monitoring is insufficient. Secondly, most existing technologies use fixed threshold models or empirical rules to judge health risks, failing to fully consider the differences in historical health data and physiological characteristics of individual pregnant women, with limited personalized adaptability and the risk of misjudgment or missed judgment. In addition, traditional systems usually only focus on a single or a few physiological parameters, lack effective integration and correlation analysis of pregnant women's daily activity pattern data (such as gait, sleep quality) and environmental data (such as temperature, humidity, and air quality), making it difficult to comprehensively evaluate the dynamic changes in the health status of pregnant women.

[0004] Existing technologies are also deficient in their ability to handle abnormal data. During the monitoring process, sensor signals may be affected by factors such as environmental noise, equipment interference, or user behavior, resulting in data anomalies. Existing systems often lack effective abnormality identification and correction mechanisms, which can easily lead to false alarms or excessive intervention, affecting the monitoring experience. In response to the above problems, some technical solutions attempt to improve monitoring capabilities by increasing sensor types and introducing simple data analysis algorithms, but they usually only stay at the data aggregation level and lack dynamic fusion and feedback control mechanisms for multi-dimensional data, making it impossible to achieve continuous tracking and real-time response to the individualized health status of pregnant women. Summary of the invention

[0005] The technical problem solved by the present invention is to provide a pregnancy health care control system based on data feedback analysis in response to the defects existing in the above-mentioned prior art, so as to solve the problems proposed in the above-mentioned background technology, such as insufficient continuity of data collection, limited individualized risk assessment capability, lack of multi-dimensional data integration and analysis, insufficient data exception processing capability and poor dynamic adaptability of nursing strategies.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows: a pregnancy health care control system based on data feedback analysis, comprising: The data collection module is used to collect real-time physiological data of pregnant women, including heart rate, fetal movement, blood pressure, and daily activity pattern data of pregnant women, including gait and sleep quality, and collect environmental data, including temperature, humidity and air quality; The dynamic physiological characteristic mapping module is connected to the data acquisition module and is used to construct a dynamic physiological characteristic mapping matrix based on the collected real-time physiological data and historical health trend data, and convert the discrete physiological data stream into a continuous health status vector. : , in, is the number of physiological data types, The first The weight of physiological data, For the Real-time value of physiological data, To determine the range of the historical data of the pregnant woman The normalized value is used to match the initial care strategy in real time by calculating the similarity between the current health status vector and the historical health status vector; An implicit feedback channel module, which is in communication with the data acquisition module and the dynamic physiological characteristic mapping module, is used to analyze the implicit association between the daily activity pattern data of the pregnant woman and the environmental data, and automatically correct the priority of the preliminary nursing strategy according to the analysis result to generate a final nursing strategy; A self-stabilizing control module is communicatively connected with the dynamic physiological characteristic mapping module and the implicit feedback channel module, and is used to monitor the health state vector, and when data abnormality is detected, automatically switches to a robust mode based on the historical fluctuation range of the health state vector, and in the robust mode, fuzzy logic inference is used instead of precise calculation to generate a robust nursing strategy; A nursing execution module is communicatively connected with the implicit feedback channel module and the self-stabilizing control module, and is used for executing the final nursing strategy or the robust nursing strategy.

[0007] As a further solution of the present invention, the implicit feedback channel module further includes: an activity pattern-physiological response relationship library, which is used to store personalized activity-response models established based on the historical activity data of pregnant women and changes in corresponding physiological indicators; a real-time strategy fine-tuning unit, which is used to automatically reduce the weight of the associated nursing strategy when it is detected that the current activity pattern deviates from the historical model in the activity pattern-physiological response relationship library, and give priority to the conservative intervention plan as the final nursing strategy.

[0008] As a further solution of the present invention, the dynamic physiological feature mapping module further includes: a cross-modal consistency verification unit, which is used to determine whether the anomaly is a real risk or noise interference by analyzing the consistency in trend between other modal data and the abnormal data when an indication of an abnormality in single sensor data is received; an asymmetric correction unit, which is used to perform local downgrading on only the data source that generates the anomaly without adjusting the global care strategy when the cross-modal consistency verification unit determines that it is noise interference.

[0009] As a further solution of the present invention, the implicit feedback channel module further includes: an environmental stress index calculation unit for generating an environmental stress index based on real-time environmental data and sensitivity parameters in the health records of pregnant women. : , in, is the number of environmental data types, The pregnant woman's health record is determined based on the pregnant woman's Sensitivity parameters of environmental data, For the A real-time value of environmental data; a nursing strategy elasticity correction unit, which is used to automatically increase the monitoring frequency of physiological indicators related to the environment when the environmental stress index exceeds a preset threshold, and trigger active intervention measures, including playing soothing music.

[0010] As a further solution of the present invention, the self-stabilizing control module further includes: a fuzzy health status partitioning unit, used to map the health status vector to a pre-set safety zone, warning zone and dangerous fuzzy interval, wherein the boundary of the fuzzy interval is dynamically adjusted according to the individual differences of pregnant women; a dual threshold triggering unit, used to initiate progressive intervention measures, including pushing reminder information, when the health status vector continuously deviates from the safety zone but does not reach the dangerous threshold.

[0011] As a further solution of the present invention, the dynamic physiological feature mapping module adopts an incremental update algorithm to update the dynamic physiological feature mapping matrix.

[0012] As a further solution of the present invention, the data acquisition module is compatible with mainstream wearable devices, including smart bracelets and fetal heart monitors.

[0013] As a further solution of the present invention, the fuzzy logic inference in the self-stabilizing control module sets fuzzy rules based on the historical fluctuation range of the health status vector of the pregnant woman.

[0014] As a further solution of the present invention, the nursing execution module generates nursing suggestions according to the final nursing strategy or the robust nursing strategy and sends the suggestions to the user terminal.

[0015] As a further solution of the present invention, the system is deployed on an edge computing device.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. In view of the fact that physiological data is not updated in time and it is difficult to fully reflect individual differences in traditional pregnancy health monitoring, the present invention introduces a dynamic physiological feature mapping mechanism, constructs a continuous health status vector, and combines historical data for similarity matching, thereby achieving real-time and personalized assessment of the health status of pregnant women. This method no longer relies on discrete threshold judgments, but can capture the dynamic changes of health status more delicately, thereby providing a more accurate basis for the formulation of subsequent nursing strategies.

[0017] 2. By analyzing daily activity patterns such as pregnant women's gait and sleep quality, as well as data such as environmental temperature, humidity, and air quality, the system can automatically correct the priority of nursing strategies. This multi-dimensional data fusion method can more comprehensively assess the health risks of pregnant women and improve the accuracy of early warnings without the need for additional sensors or manual intervention, thereby avoiding the one-sidedness that may be caused by relying solely on single-dimensional physiological data, and avoiding focusing only on physiological indicators while ignoring the potential impact of the environment and daily activities on the health of pregnant women. When data anomalies are detected, drastic intervention measures will not be taken immediately, but will be switched to robust mode based on the fluctuation range of historical data, and fuzzy logic will be used for inference. This mechanism effectively reduces the misjudgment rate, ensures the stability and reliability of the system in complex dynamic environments, and is more in line with the dual needs of pregnancy care for safety and psychological comfort.

[0018] 3. It can combine the behavior habits of pregnant women to issue more inappropriate intervention instructions. Specifically, it further integrates the activity pattern-physiological response relationship library through implicit feedback channels. By learning the personalized model between the historical activity data of pregnant women and the changes in their physiological indicators, the system can identify whether the current activity pattern deviates from the normal range, and adjust the weight of the nursing strategy accordingly, giving priority to conservative intervention plans. This dynamic adaptation method effectively reduces the risk of excessive intervention and improves the accuracy and humanity of nursing. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1It is a schematic diagram of the functional module structure of the pregnancy health care control system based on data feedback analysis of the present invention.

[0021] Figure 2 Schematic diagram of data processing flow of dynamic physiological characteristic mapping module of the present invention.

[0022] Figure 3 This is a schematic diagram of the health status zoning and intervention mechanism of the present invention.

[0023] Figure 4 It is a functional structure diagram of the implicit feedback channel module of the present invention.

[0024] Figure 5 The present invention provides a flow chart for data feedback analysis and dynamic generation of health status vectors.

[0025] Figure 6 This is a flowchart of robust control mode switching and anomaly detection of the present invention. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 — Figure 6 , a pregnancy health care control system based on data feedback analysis, comprising: The data collection module is used to collect real-time physiological data of pregnant women, including heart rate, fetal movement, blood pressure, and daily activity pattern data of pregnant women, including gait and sleep quality, and collect environmental data, including temperature, humidity and air quality; The dynamic physiological characteristic mapping module is connected to the data acquisition module and is used to construct a dynamic physiological characteristic mapping matrix based on the collected real-time physiological data and historical health trend data, and convert the discrete physiological data stream into a continuous health status vector. : , in, is the number of physiological data types, The first The weight of physiological data, For the Real-time value of physiological data, To determine the range of the historical data of the pregnant woman The normalized value is used to match the initial care strategy in real time by calculating the similarity between the current health status vector and the historical health status vector; An implicit feedback channel module, which is in communication with the data acquisition module and the dynamic physiological characteristic mapping module, is used to analyze the implicit association between the daily activity pattern data of the pregnant woman and the environmental data, and automatically correct the priority of the preliminary nursing strategy according to the analysis result to generate a final nursing strategy; A self-stabilizing control module is communicatively connected with the dynamic physiological characteristic mapping module and the implicit feedback channel module, and is used to monitor the health state vector, and when data abnormality is detected, automatically switches to a robust mode based on the historical fluctuation range of the health state vector, and in the robust mode, fuzzy logic inference is used instead of precise calculation to generate a robust nursing strategy; A nursing execution module is communicatively connected with the implicit feedback channel module and the self-stabilizing control module, and is used for executing the final nursing strategy or the robust nursing strategy.

[0028] As a further solution of the present invention, the implicit feedback channel module further includes: an activity pattern-physiological response relationship library, which is used to store personalized activity-response models established based on the historical activity data of pregnant women and changes in corresponding physiological indicators; a real-time strategy fine-tuning unit, which is used to automatically reduce the weight of the associated nursing strategy when it is detected that the current activity pattern deviates from the historical model in the activity pattern-physiological response relationship library, and give priority to the conservative intervention plan as the final nursing strategy.

[0029] As a further solution of the present invention, the dynamic physiological feature mapping module further includes: a cross-modal consistency verification unit, which is used to determine whether the anomaly is a real risk or noise interference by analyzing the consistency in trend between other modal data and the abnormal data when an indication of an abnormality in single sensor data is received; an asymmetric correction unit, which is used to perform local downgrading on only the data source that generates the anomaly without adjusting the global care strategy when the cross-modal consistency verification unit determines that it is noise interference.

[0030] As a further solution of the present invention, the implicit feedback channel module further includes: an environmental stress index calculation unit for generating an environmental stress index based on real-time environmental data and sensitivity parameters in the health records of pregnant women. : , in, is the number of environmental data types, The pregnant woman's health record is determined based on the pregnant woman's Sensitivity parameters of environmental data, For the A real-time value of environmental data; a nursing strategy elasticity correction unit, which is used to automatically increase the monitoring frequency of physiological indicators related to the environment when the environmental stress index exceeds a preset threshold, and trigger active intervention measures, including playing soothing music.

[0031] As a further solution of the present invention, the self-stabilizing control module further includes: a fuzzy health status partitioning unit, used to map the health status vector to a pre-set safety zone, warning zone and dangerous fuzzy interval, wherein the boundary of the fuzzy interval is dynamically adjusted according to the individual differences of pregnant women; a dual threshold triggering unit, used to initiate progressive intervention measures, including pushing reminder information, when the health status vector continuously deviates from the safety zone but does not reach the dangerous threshold.

[0032] As a further solution of the present invention, the dynamic physiological feature mapping module adopts an incremental update algorithm to update the dynamic physiological feature mapping matrix.

[0033] As a further solution of the present invention, the data acquisition module is compatible with mainstream wearable devices, including smart bracelets and fetal heart monitors.

[0034] As a further solution of the present invention, the fuzzy logic inference in the self-stabilizing control module sets fuzzy rules based on the historical fluctuation range of the health status vector of the pregnant woman.

[0035] As a further solution of the present invention, the nursing execution module generates nursing suggestions according to the final nursing strategy or the robust nursing strategy and sends the suggestions to the user terminal.

[0036] As a further solution of the present invention, the system is deployed on an edge computing device.

[0037] Example 1: In this example, the pregnancy health care control system is deployed in a smart health terminal device with edge computing capabilities. The device can receive and process multi-dimensional data collected by smart bracelets and fetal heart rate monitors worn by pregnant women in real time, including heart rate, fetal movement, blood pressure, gait, sleep quality, and environmental data (temperature, humidity, air quality). All data are transmitted wirelessly to the data acquisition module of the system.

[0038] Specifically, the data acquisition module pre-processes the above data to generate real-time physiological data sequences and activity pattern data sequences with time stamp alignment, and simultaneously obtains external environmental data and archives them synchronously. On this basis, the system uses the dynamic physiological feature mapping module to weight and normalize various physiological data to form a continuous health status vector In this embodiment, the calculation formula of the health state vector is: , Among them, the variable Indicates the number of physiological data types monitored by the system, specifically heart rate, fetal movement, and blood pressure. .variable The system determines the first The weight of each physiological data is determined based on the pregnant woman’s personal health record and risk sensitivity assessment during pregnancy. For example, if historical data shows that the blood pressure fluctuations of the pregnant woman in the second trimester have a greater impact on her overall health status, the system will appropriately increase the corresponding weight of blood pressure. .variable Indicates The real-time collection value of physiological data comes from the sensor data of the smart bracelet and fetal heart rate monitor. Express The normalized results were obtained by using the minimum-maximum normalization method of the same type of historical data of the pregnant woman to ensure that each indicator was within In addition, to prevent abnormal fluctuations in the health state vector from misleading the nursing strategy, the system introduces a cross-modal consistency verification unit in the dynamic physiological feature mapping module. When a sensor data has a mutation or abnormal signal, the system automatically compares the trend of other modal data in the same time window. If the trend is inconsistent, the system determines that the anomaly is noise interference and locally downgrades the data source through the asymmetric correction unit without affecting the overall health state vector. weight distribution, thereby reducing the risk of misjudgment.

[0039] The system further mines the potential correlation between pregnant women's activity patterns and environmental data through the implicit feedback channel module. In this embodiment, the activity pattern-physiological response relationship library establishes a personalized activity response model based on the activity data of pregnant women in the past 30 days and the changes in corresponding physiological indicators. When the system detects that the current activity pattern (such as gait rhythm or sleep time) deviates significantly from the historical pattern, and this deviation trend is consistent with the health status vector When there is a correlation between abnormal fluctuations in the activity mode and the activity pattern, the system automatically reduces the intervention priority related to the activity mode in the current nursing strategy and adopts a more conservative nursing plan. In terms of environmental data, the system uses the environmental stress index calculation unit to dynamically weight environmental parameters such as temperature, humidity and air quality to generate an environmental stress index. In this embodiment, the weight parameter The sensitivity parameters are set according to the health records of pregnant women. If pregnant women have basic respiratory diseases, the weight of air quality indicators is The calculation formula of environmental stress index is: , in, is the number of environmental data types, For the Real-time values ​​of environmental data, is the corresponding sensitivity weight. When the threshold value set by the system is exceeded, the system automatically increases the monitoring frequency of environmental related physiological indicators (such as heart rate fluctuations) and pushes soothing music to the user terminal, forming a flexible intervention mechanism. For the self-stabilizing control module of the system, this embodiment sets the triggering conditions and implementation mechanism of the robust mode in detail. When the data deviates from the historical fluctuation range for 5 consecutive minutes and the cross-modal consistency check determines that the data anomaly is non-noise, the system starts the robust mode. In this mode, the system no longer relies on precise calculations, but uses preset fuzzy logic rules to The values ​​are mapped to three fuzzy intervals of safety, warning, and danger, and different intervention strategies are adopted according to the mapping results. The boundaries of the fuzzy intervals are dynamically adjusted according to the individual historical data of pregnant women to ensure that the intervention measures are individualized and adaptable. In order to ensure the timeliness and dynamic adaptability of the health status mapping matrix, the dynamic physiological characteristic mapping module adopts an incremental update algorithm. Whenever new data is collected, the system will partially update the historical statistical data in the mapping matrix without the need for full reconstruction, thereby achieving low-latency, high-real-time dynamic adjustment of nursing strategies, which are all extended implementation methods known to ordinary technicians in this field.

[0040] Embodiment 2: In the dynamic physiological feature mapping module, the health state vector The calculation formula is: , where the variable Indicates the number of physiological data types monitored by the system, including heart rate, fetal movement, and blood pressure. .variable Indicates The weights of various physiological data are determined based on the historical health records and individual physiological characteristics of the pregnant woman. In this embodiment, the system uses a statistical analysis method based on historical data, combined with the sensitivity of the pregnancy stage to various physiological data, to determine the weights. For example, if a pregnant woman's blood pressure fluctuations in the second trimester have a greater impact on her overall health, the system will determine the corresponding weight of blood pressure by analyzing the standard deviation of the physiological data of the past 30 days. Higher, the specific value range is , and updated in real time in the data acquisition module.

[0041] variable Indicates The real-time collection value of physiological data is from the smart bracelet and fetal heart rate monitor worn by pregnant women, ensuring that the data source is clear and verifiable. Express The result after normalization processing, this embodiment adopts the minimum-maximum normalization method, and the specific calculation method is: , in, and They represent the first The minimum and maximum values ​​of various physiological data are derived from historical monitoring data in the past 90 days. This normalization process ensures that various physiological data are fused and calculated at a unified scale to avoid affecting the accuracy of the health state vector due to differences in data dimensions. In the implicit feedback channel module, the environmental stress index The calculation formula is: , In this embodiment, the parameters Indicates the number of environmental data types, specifically temperature, humidity, and air quality. .parameter Pregnant women The sensitivity weight of the environmental data is derived from the individual sensitivity records in the health records of pregnant women, and the value range is For example, when a pregnant woman has respiratory disease, the air quality corresponds to the sensitivity weight Set to 0.8. Parameters Indicates The real-time value of environmental data is from the environmental monitoring equipment deployed in the living environment of pregnant women. When the threshold value set by the system (set to 0.7 in this embodiment) is exceeded, the system automatically increases the monitoring frequency of relevant physiological indicators (such as heart rate), and triggers flexible intervention measures in the nursing execution module, such as pushing soothing music or reminding pregnant women to adjust their rest environment. In addition, in the self-stabilizing control module, the system sets the switching condition of the robust mode as follows: the health state vector The historical fluctuation range is determined by the health status vector in the past 14 days. The mean and standard deviation of are calculated dynamically, with the specific range being: , in, and Represent the health status vector The mean and standard deviation in the past 14 days are derived from the history of the dynamic physiological feature mapping module. In robust mode, the system uses a fuzzy logic inference mechanism to vector the current health status. The fuzzy intervals are mapped to three types: safe zone, warning zone, and danger zone. The specific interval boundaries are dynamically adjusted according to the individual differences of pregnant women. In this embodiment, the fuzzy interval boundaries are set based on the health risk sensitivity parameters and health status vectors in the historical health records of pregnant women. The historical fluctuation characteristics of the disease and ensuring that the intervention measures have individual adaptability are all extended implementation methods known to ordinary technicians in this field.

[0042] Embodiment 3: In this embodiment, the structure of each functional module and the data processing flow in the embodiment of the present invention are further described in detail in combination with the accompanying drawings.

[0043] like Figure 1 As shown, the pregnancy health care control system based on data feedback analysis in this embodiment includes a data acquisition module, a dynamic physiological feature mapping module, an implicit feedback channel module, a self-stabilizing control module and a nursing execution module. Among them, the data acquisition module is used to collect real-time physiological data, activity mode data and environmental data, and form a data stream to be transmitted to the dynamic physiological feature mapping module. The dynamic physiological feature mapping module constructs the health state vector of the pregnant woman based on the received data stream and outputs the health state vector. The health state vector is transmitted to the implicit feedback channel module for realizing strategy fine-tuning and calculation of the environmental stress index, and generating the final nursing strategy. At the same time, the health state vector is also input into the self-stabilizing control module for abnormal detection and robust mode determination to form a robust nursing strategy. When the final nursing strategy output by the implicit feedback channel module or the robust nursing strategy output by the self-stabilizing control module is generated, it is handed over to the nursing execution module for strategy push and intervention, so as to realize personalized, dynamic and continuous pregnancy health care control for pregnant women. Through this system structure, the information interaction of data flow, health state vector, final nursing strategy and robust nursing strategy among the data acquisition module, dynamic physiological feature mapping module, implicit feedback channel module, self-stabilizing control module and nursing execution module is realized, which improves the real-time performance, individual adaptability and robustness of the system.

[0044] like Figure 2As shown, this embodiment further specifically demonstrates the data processing flow in the dynamic physiological feature mapping module. First, the system receives multi-source physiological data, including multi-dimensional real-time physiological data such as heart rate, fetal movement, and blood pressure. The received multi-source physiological data is normalized by historical data, and the minimum-maximum normalization method is adopted to ensure the comparability of various physiological indicators according to the data range in the historical health file of the pregnant woman. The normalized data enters the dynamic weight allocation step. The system dynamically determines the weight parameters of each physiological data type through historical data statistical analysis based on the historical health trend of the pregnant woman and the current stage of pregnancy, reflecting the degree of influence of different physiological indicators on the health status. Subsequently, the system calculates the health state vector based on the normalized data and dynamic weight parameters to form a health state vector for subsequent nursing decisions. After the health state vector calculation is completed, the system starts the cross-modal verification link, and identifies and verifies the abnormal data in the current data through the trend consistency analysis method. When the cross-modal verification determines that it is normal data, the data will be directly used for strategy matching; if the verification determines that it is abnormal data, it triggers local weight reduction processing, and locally adjusts the weight of the data source that generates the abnormal data to avoid the abnormal data from misleading the overall health status assessment. After cross-modal verification and local weight reduction, the data enters the strategy matching step. The system compares the health status vector with the historical nursing strategy library of pregnant women to determine the applicable preliminary nursing strategy, which serves as the decision-making basis for the subsequent nursing process. Through the above process, the system realizes the standardized processing of multi-source physiological data, individualized weight allocation, abnormal data identification and correction, and dynamic matching of preliminary nursing strategies, which improves the accuracy of pregnancy health status assessment and the scientific nature of nursing strategy generation.

[0045] like Figure 3 As shown, this embodiment further clarifies the specific structure of the health status partition and intervention mechanism. The mechanism includes a fuzzy health status partition unit, a dual threshold trigger unit and a progressive intervention strategy. Among them, the fuzzy health status partition unit is used to map the health status vector of the pregnant woman to the preset partition information, specifically three intervals: the safe zone, the early warning zone and the dangerous zone. The partition information is used as a basis for judgment and is transmitted to the dual threshold trigger unit for intervention triggering according to the distribution of the health status vector between the safe zone, the early warning zone and the dangerous zone. When the health status vector continuously deviates from the safe zone but does not reach the dangerous zone threshold, the system starts the progressive intervention strategy through the dual threshold trigger unit. The intervention methods include reminders, music and intervention measures. Through flexible partitioning and hierarchical intervention, the mechanism effectively reduces the occurrence of excessive intervention or omission risks in pregnancy care, realizes the dynamic division of the safe zone, the early warning zone and the dangerous zone, and the organic coordination of the dual threshold trigger based on the partition information and the progressive intervention strategy, and improves the personalized adaptability and real-time intervention capability of the nursing system.

[0046] like Figure 4As shown, this embodiment further clarifies the functional structure of the implicit feedback channel module. The module includes an activity pattern physiological response relationship library, an environmental stress index calculation unit, a real-time strategy fine-tuning unit, and a nursing strategy elastic correction unit. Among them, the activity pattern physiological response relationship library is used to store the historical activity model of individual pregnant women and establish the corresponding relationship between activity patterns and physiological responses. The environmental stress index calculation unit is used to calculate the environmental stress index of the environment in which the pregnant woman is located in real time, specifically based on the sensitivity parameters and current environmental data in the health records of the pregnant woman. The real-time strategy fine-tuning unit fine-tunes the priority of the current nursing strategy according to the degree of deviation between the historical activity model and the current activity pattern. After receiving the fine-tuning strategy and the environmental stress index, the nursing strategy elastic correction unit dynamically corrects the current nursing strategy to ensure that the nursing strategy can be adjusted in real time according to the individual historical behavior pattern of the pregnant woman and the current environmental state. Through the above structure, the implicit feedback channel module realizes the collaborative work between the activity pattern physiological response relationship library, the environmental stress index calculation unit, the real-time strategy fine-tuning unit, and the nursing strategy elastic correction unit, effectively enhancing the individualized adaptability and environmental sensitivity in the nursing decision-making process, and improving the dynamic optimization ability and robustness of the nursing strategy.

[0047] Figure 5 shows the complete process of dynamic generation of health status vector and abnormality detection in the pregnancy health care control system based on data feedback analysis of the present invention. The input layer first obtains real-time sensor data through the real-time sensor data acquisition module, and temporarily stores the collected data using the sliding window cache mechanism to ensure the continuity and real-time nature of the data flow. At the same time, a historical feature library is also provided in the input layer to store the historical health trend data of individual pregnant women, and based on the historical feature library, a covariance matrix is ​​dynamically generated through the covariance matrix generation module to provide historical reference data for subsequent health status assessment. The processing layer includes a data normalization module and a dynamic weight calculator. The data normalization module performs standardized processing on the real-time sensor data in the sliding window cache to generate a feature vector to ensure the comparability of physiological data of different dimensions; the dynamic weight calculator dynamically updates the weight vector according to the individual difference information in the historical feature library to form a weight vector, thereby improving the personalized adaptability of the system. Subsequently, the system completes the vector dot product operation of the feature vector and the weight vector at the processing layer to obtain the health status vector. The output layer is based on the health state vector Carry out abnormal detection. If an abnormal signal is detected, the system starts the feedback update mechanism, transmits the current abnormal situation back to the input layer, updates the data in the historical feature library and the covariance matrix generation module, so as to enhance the adaptability and robustness of the model to the dynamic changes of the health status of pregnant women. The present invention realizes the dynamic fusion of multi-dimensional health data during pregnancy and the health status vector through the above process. Real-time generation and anomaly detection, and through sliding window cache, historical feature library, covariance matrix generation, data normalization module, feature vector, dynamic weight calculator, weight vector, vector dot product operation, health state vector The collaborative work between modules such as anomaly detection and feedback update forms a complete data feedback analysis and dynamic nursing strategy support system.

[0048] FIG6 shows the processing flow of the self-stabilizing control module in the pregnancy health care control system based on data feedback analysis of the present invention when the health state vector H fluctuates abnormally. The system continuously monitors the health state vector H in normal mode. , when detected When it exceeds the historical fluctuation, the cross-modal verification operation is immediately triggered. The cross-modal verification is used to compare the trend consistency of the current abnormal data with other modal data. When the cross-modal verification results show that the data is consistent, the system determines that the current abnormality is a real abnormality, and then immediately initiates the emergency protocol, takes active nursing intervention measures, and switches to the robust control mode to ensure the safety and continuity of care for pregnant women and fetuses; when the cross-modal verification results show that the data is inconsistent, the system determines that the abnormality is noise interference, and then performs a sensor de-weighting operation to reduce the weight of the sensor from which the abnormal data comes, to avoid noise data from misleading the overall health status assessment. At the same time, the system switches to the robust control mode to continue to maintain health monitoring. When the health status vector is subsequently monitored Return to normal, the system automatically exits the robust control mode and returns to normal mode operation. Exceeding historical fluctuations, cross-modal verification, data consistency, real anomalies, starting emergency protocols, noise interference, sensor demotion, robust control mode, Specific process nodes such as return to normal and normal mode realize accurate identification and dynamic response to abnormal fluctuations in health status, and improve the robustness, stability and personalized adaptability of the pregnancy health care control system, which are all extended implementation methods known to ordinary technicians in this field.

[0049] Embodiment 4: In this embodiment, during the modeling of the health status during pregnancy, the system uses the dynamic physiological feature mapping module to standardize and weight the collected multi-dimensional physiological parameters to generate a continuous health status vector In this embodiment, : The number of physiological data types is fixed to the three core physiological data types currently accessed by the system: heart rate (HR), fetal movement (FM) and blood pressure (BP). . : No. Real-time values ​​of physiological data are directly collected by wearable smart bracelets and fetal heart rate monitoring devices to ensure that the data source is verifiable; :right The result after minimum-maximum normalization processing, the normalization interval is set according to the extreme value of the pregnant woman's historical data in the past 90 days. Specifically, the normalization processing adopts: , in, and Represent the minimum and maximum values ​​in historical observations, respectively, to ensure that the normalized data is within the range Internal fluctuations, enhancing the comparability of different data dimensions; : No. The dynamic weight of physiological data is set to The weight calculation is based on the contribution of each physiological parameter to the system status fluctuation in the historical health trend, and is adjusted by comparing the standard deviation results in the last 30 days. For example, if a pregnant woman's blood pressure fluctuations in the second trimester significantly affect the health status assessment results, the system will increase the weight of blood pressure accordingly. ; The system is building a health status vector After that, the result is not used directly to generate nursing strategies, but it is used as an intermediate state and combined with the vector in the historical health status record database. Perform similarity matching. The matching method uses cosine similarity measurement: , The point multiplication and norm operations are based on the basic linear algebra definition of three-dimensional vectors, and the matching results will be directly used to determine whether the current state is in a historically known health mode or a deviation interval; in terms of adjusting the priority of nursing strategies, the implicit feedback channel module introduces an activity mode-physiological response relationship library mechanism. The construction of this mechanism is based on the following path, that is, the system uses the daily gait data (cadence, stride change), sleep structure (deep sleep duration, number of sleep interruptions) and other daily activity mode data of pregnant women and physiological data of the corresponding time period for multivariate linear regression modeling; each type of activity mode establishes a corresponding physiological response function to obtain a set of personalized mapping models to determine whether the current activity state is within the normal variation range; when the current activity state deviates significantly from the model prediction result, and the deviation is accompanied by a sharp fluctuation of the health state vector, the system will automatically trigger the strategy fine-tuning process, reduce the execution priority of the strategy associated with the abnormal activity, and instead guide it to a conservative nursing path; in order to further enhance the robustness of the system in a highly uncertain environment, the fuzzy logic inference mechanism in the self-stabilizing control module is standardized in this embodiment. The system will continuously sample within 5 minutes The vector standard deviation and mean construct a dynamic fuzzy interval, which is divided into three levels: safety, warning, and danger. The mapping rules are as follows:

[0050] Safe Zone: ; Warning area: ; Danger zone: values ​​beyond the warning zone; in and The system uses a hierarchical strategy to trigger logic in different intervals to avoid misjudging short-term fluctuations as major abnormalities and improve the accuracy of nursing recommendations, which are all extended implementation methods known to ordinary technicians in this field.

[0051] Embodiment 5: In the dynamic physiological characteristic mapping module, the calculation of the health state vector 𝐻 adopts the weighted normalized summation formula, which is expressed as follows: , Among them, the variable Indicates the number of physiological data types collected by the system. The current setting is 3, corresponding to the indicators of heart rate (HR), fetal movement (FM) and blood pressure (BP). Indicates Real-time collection of physiological data, the data source is the smart bracelet and fetal heart monitoring equipment worn by pregnant women, to ensure the authenticity and verifiability of the data. Indicates real-time collection value The result after normalization is the minimum-maximum normalization method, and the specific calculation method is: , in, and They represent the number of The minimum and maximum values ​​of physiological data in the past 90 days have clear sources and are traceable.

[0052] Weight Parameters The sensitivity of fluctuations of various physiological indicators in the individual historical health records of pregnant women is dynamically set. Specifically, the standard deviation of various physiological data of pregnant women in the past 30 days is statistically analyzed, combined with the risk assessment results of the pregnancy stage, to determine the weight of each indicator on the overall health status. The value range of is set to [0.1, 0.6]. The basis for determining this range is to avoid an imbalance in the health state vector caused by a certain indicator being too high or too low, and to ensure the reasonable distribution of the contribution of each indicator. The design logic of the above health state vector calculation formula is that, different from the traditional method that relies on fixed thresholds or single indicator monitoring, this system introduces historical individual data normalization processing and dynamic weight allocation to establish a continuous and interpretable health status assessment model, which enhances the system's ability to adapt to individual differences in pregnant women and avoids the risk of misjudgment caused by abnormal single indicators.

[0053] In the implicit feedback channel module, the system further enhances the personalized adaptability of the nursing strategy by establishing an activity pattern-physiological response relationship library. This relationship library is based on the daily activity pattern data of pregnant women in the past 30 days (including gait frequency, sleep structure, etc.) and the physiological data of the corresponding time period. Through multivariate linear regression analysis, an individualized mapping model between each activity pattern and the fluctuation of physiological indicators is established. The current activity pattern data comes from the real-time monitoring results of the data acquisition module. The system compares the deviation degree of the current activity pattern with the historical model in real time. When it is detected that the current activity pattern deviates significantly from the historical model, and the deviation trend is consistent with the health status vector When the abnormal fluctuation is associated with the abnormal activity pattern, the system automatically reduces the weight of the nursing strategy related to the abnormal activity pattern and gives priority to conservative intervention plans. The design logic of this fine-tuning process is to avoid excessive intervention caused by short-term behavioral abnormalities and ensure the rationality and stability of the nursing strategy. In addition, the environmental stress index in the system The calculation formula is: , Among them, the variable Indicates the number of environmental data types, currently set to 3, corresponding to the indicators of temperature, humidity and air quality. For the The real-time collection value of environmental data is derived from the environmental monitoring equipment in the living environment of pregnant women. It is derived from the records of environmental sensitivity in the health records of pregnant women. The value range is set to [0.1, 0.9] and is dynamically adjusted according to the individual health status and medical history of pregnant women. When the system preset threshold is exceeded (the threshold is specifically set to 0.7 in the embodiment), the system automatically increases the monitoring frequency of physiological indicators related to the environment (such as heart rate), and pushes flexible intervention measures to the user terminal through the nursing execution module, such as playing soothing music or prompting to adjust the rest environment. The original intention of the design of this mechanism is to dynamically quantify the potential impact of environmental factors on the health status of pregnant women and provide real-time feedback to avoid delayed nursing strategy response due to ignoring environmental changes.

[0054] In the self-stabilizing control module, in order to effectively distinguish between real anomalies and noise interference, the system introduces a cross-modal consistency check unit and an asymmetric correction unit. When a single sensor data anomaly is detected, the system compares the trend consistency of other modal data in the same period. If there is a trend consistency, it is determined to be a real anomaly and the robust mode is activated; if the trend is inconsistent, it is determined to be noise interference, and only the weight of the abnormal data source is partially reduced to avoid misjudgment from spreading to the overall care strategy. The switching condition of the robust mode is set as follows: the health state vector is within 5 consecutive minutes. Deviates from the historical fluctuation range, and the cross-modal verification result is a true anomaly. The historical fluctuation range is based on the past 14 days. The mean and standard deviation of are calculated dynamically, and the specific range is ,in and represent the historical mean and standard deviation respectively.

[0055] In the robust mode, the system uses preset fuzzy logic rules to transform the health state vector Mapping to three intervals: safety, warning, and danger, with the interval boundaries dynamically set based on the risk sensitivity in the pregnant woman's historical health records, ensures that the intervention measures are individualized and adaptable, and effectively avoids excessive intervention due to short-term abnormalities. These are all extended implementation methods that are known to ordinary technicians in this field.

[0056] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. A pregnancy health care control system based on data feedback analysis, characterized in that: include: The data collection module is used to collect real-time physiological data of pregnant women, including heart rate, fetal movement, blood pressure, and daily activity pattern data of pregnant women, including gait and sleep quality, and collect environmental data, including temperature, humidity and air quality; The dynamic physiological characteristic mapping module is connected to the data acquisition module and is used to construct a dynamic physiological characteristic mapping matrix based on the collected real-time physiological data and historical health trend data, and convert the discrete physiological data stream into a continuous health status vector. : ， in, is the number of physiological data types, The first The weight of physiological data, For the Real-time value of physiological data, To determine the range of the historical data of the pregnant woman The normalized value is used to match the initial care strategy in real time by calculating the similarity between the current health status vector and the historical health status vector; An implicit feedback channel module, which is in communication with the data acquisition module and the dynamic physiological characteristic mapping module, is used to analyze the implicit association between the daily activity pattern data of the pregnant woman and the environmental data, and automatically correct the priority of the preliminary nursing strategy according to the analysis result to generate a final nursing strategy; A self-stabilizing control module is communicatively connected with the dynamic physiological characteristic mapping module and the implicit feedback channel module, and is used to monitor the health state vector, and when data abnormality is detected, automatically switches to a robust mode based on the historical fluctuation range of the health state vector, and in the robust mode, fuzzy logic inference is used instead of precise calculation to generate a robust nursing strategy; A nursing execution module is communicatively connected with the implicit feedback channel module and the self-stabilizing control module, and is used for executing the final nursing strategy or the robust nursing strategy.

2. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The implicit feedback channel module further includes: an activity pattern-physiological response relationship library, which is used to store a personalized activity-response model established based on the historical activity data of pregnant women and the changes in corresponding physiological indicators; a real-time strategy fine-tuning unit, which is used to automatically reduce the weight of the associated nursing strategy when it is detected that the current activity pattern deviates from the historical model in the activity pattern-physiological response relationship library, and give priority to the conservative intervention plan as the final nursing strategy.

3. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The dynamic physiological feature mapping module further includes: a cross-modal consistency verification unit, which is used to determine whether the anomaly is a real risk or noise interference by analyzing the consistency in trend between other modal data and the abnormal data when an indication of an abnormality in single sensor data is received; and an asymmetric correction unit, which is used to perform local downgrading on the data source that generates the anomaly without adjusting the global care strategy when the cross-modal consistency verification unit determines that it is noise interference.

4. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The implicit feedback channel module further includes: an environmental stress index calculation unit for generating an environmental stress index based on real-time environmental data and sensitivity parameters in the health records of pregnant women. : ， in, is the number of environmental data types, The pregnant woman's health record is determined based on the pregnant woman's Sensitivity parameters of environmental data, For the A real-time value of environmental data; a nursing strategy elasticity correction unit, which is used to automatically increase the monitoring frequency of physiological indicators related to the environment when the environmental stress index exceeds a preset threshold, and trigger active intervention measures, including playing soothing music.

5. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The self-stabilizing control module further includes: a fuzzy health status partitioning unit, used to map the health status vector to a preset safety zone, a warning zone and a dangerous fuzzy interval, wherein the boundary of the dangerous fuzzy interval is dynamically adjusted according to the individual differences of pregnant women; a dual threshold triggering unit, used to initiate progressive intervention measures, including pushing reminder information, when the health status vector continuously deviates from the safety zone but does not reach the dangerous fuzzy interval.

6. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The dynamic physiological feature mapping module uses an incremental update algorithm to update the dynamic physiological feature mapping matrix.

7. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The data acquisition module is compatible with mainstream wearable devices, including smart bracelets and fetal heart monitors.

8. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The fuzzy logic inference in the self-stabilizing control module sets fuzzy rules based on the historical fluctuation range of the health state vector of the pregnant woman.

9. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The nursing execution module generates nursing suggestions according to the final nursing strategy or the robust nursing strategy and sends the suggestions to the user terminal.

10. The pregnancy health care control system based on data feedback analysis according to claim 1, characterized in that: The system is deployed on an edge computing device.

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