Data analysis platform for obstetrical mobile ultrasonic equipment

Abstract

The invention relates to the technical field of ultrasonic data analysis, and discloses a data analysis platform for obstetrical mobile ultrasonic equipment, which comprises a multi-dimensional acquisition module and an intelligent analysis module. According to the data analysis platform, physiological data of all obstetrical patients and heart rate monitoring data and ultrasonic detection data of all fetuses are acquired through the multi-dimensional acquisition module and are classified to form a data set, and the intelligent analysis module analyzes the health coefficient of each obstetrical patient, so that misdiagnosis or omission caused by a single index is avoided; then analyzing the difference index between the obstetrical patient and the fetus and the development coefficient of each fetus, carrying out personalized analysis for multi-fetal pregnancy, recognizing possible maternal and infant health problems in advance, accurately evaluating the development state of each fetus, helping doctors to make reasonable judgments under complex conditions, and being high in multi-dimensional analysis precision and high in accuracy. The intelligent analysis module judges the health state of the obstetrical patient and the health state and development state of the fetus, corresponding early warning signals are generated, and the personalized diagnosis and treatment efficiency is high.

Description

Technical Field

[0001] The present invention relates to the technical field of ultrasonic data analysis, and specifically to a data analysis platform for a mobile ultrasonic device in obstetrics. Background Art

[0002] The mobile ultrasonic device in obstetrics is a high-tech medical instrument that facilitates rapid and convenient examinations in the obstetric environment. It is mainly used for prenatal examinations of pregnant women, fetal development monitoring, fetal heart rate monitoring, placental position assessment, etc. In emergency situations, the mobility and efficiency of the ultrasonic device are particularly important. The mobile ultrasonic device can be used in different places such as the obstetrics department, emergency room, operating room, and intensive care unit of the hospital, and has high image quality and accuracy. The mobile ultrasonic device in obstetrics can provide efficient and accurate examination services, which not only improves the efficiency of medical work but also provides better safety guarantees for pregnant women and fetuses. This convenient and flexible device is especially suitable for areas with limited resources or situations where bedside examinations are required, greatly improving the accessibility and quality of medical services. The data analysis system for the mobile ultrasonic device in obstetrics usually combines advanced technologies such as image processing, intelligent AI, data mining, and cloud computing, aiming to improve the accuracy of diagnosis, reduce the workload of doctors, and enhance the quality and efficiency of obstetric medical services.

[0003] Currently, the traditional data analysis platform for the mobile ultrasonic device in obstetrics lacks the ability of personalized analysis, is difficult to comprehensively and accurately identify abnormal physiological indicators, easily leads to misdiagnosis or missed diagnosis, and cannot customize appropriate diagnosis and treatment suggestions for each obstetric patient, thus missing the opportunity to deeply evaluate the health status of pregnant women and fetuses. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides a data analysis platform for a mobile ultrasonic device in obstetrics, which has the advantages of high multidimensional analysis accuracy and high personalized diagnosis and treatment efficiency, and solves the problems that the traditional data analysis platform for the mobile ultrasonic device in obstetrics lacks the ability of personalized analysis and is difficult to provide personalized diagnosis and treatment suggestions.

[0006] (II) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solution: A data analysis platform for a mobile ultrasonic device in obstetrics, including a multidimensional acquisition module and an intelligent analysis module;

[0008] The multi-dimensional acquisition module consists of a physiological data unit, a fetal heart rate data unit, and an ultrasonic data unit. The physiological data unit collects patient data sets through a network connection to the electronic medical record system. The patient data sets include the physiological data of all obstetric patients. The fetal heart rate data unit collects fetal heart rate data sets through a network connection to a fetal heart monitor. The fetal heart rate data sets include the heart rate monitoring data of all fetuses. The ultrasonic data unit collects ultrasonic data sets through a network connection to an ultrasonic machine. The ultrasonic data sets include the ultrasonic examination data of all fetuses;

[0009] The intelligent analysis module consists of a patient analysis unit, a correlation analysis unit, and a warning management unit. The patient analysis unit analyzes the health coefficient of each obstetric patient based on the patient data set , the correlation analysis unit analyzes the difference index between the obstetric patient and the fetus based on the patient data set and the fetal heart rate data set , the warning management unit analyzes the development coefficient of each fetus based on the fetal heart rate data set and the ultrasonic data set , the warning management unit is set with a fixed range of health thresholds and development thresholds , and then combined with the health coefficient , the difference index and the development coefficient , to judge the health status of the obstetric patient, the health status and development status of the fetus, and generate corresponding warning signals.

[0010] Preferably, the expression of the patient data set is , to are respectively the physiological data of the first to the th obstetric patients. The physiological data includes heart rate, blood pressure, blood sugar, blood oxygen saturation, fetal movement frequency, and weight, represents the age of each patient.

[0011] Preferably, the expression of the fetal heart rate data set is , to are respectively the heart rate monitoring data of the first to the th fetuses. The heart rate monitoring data is the heart rate, represents the gestational period of each fetus's mother.

[0012] Preferably, the expression of the ultrasonic data set is , to are respectively the ultrasonic examination data of the first to the th fetuses. The ultrasonic examination data includes head circumference, abdominal circumference, femur length, weight, and blood flow velocity, Indicates the specific time points for obtaining each fetal ultrasound examination data.

[0013] Preferably, the health coefficient The calculation process is as follows:

[0014] Extract the physiological data of the th obstetric patient in the patient dataset, and mark the heart rate of the th obstetric patient as , mark the blood pressure of the th obstetric patient as , mark the blood glucose of the th obstetric patient as , mark the blood oxygen saturation of the th obstetric patient as , mark the fetal movement frequency of the th obstetric patient as , mark the weight of the th obstetric patient as , mark the age of the th obstetric patient as ;

[0015]

[0016] In the formula, represents the standard heart rate, represents the evaluation weight for the ratio of heart rate to the standard heart rate, represents the standard blood pressure, represents the evaluation weight for the ratio of blood pressure to the standard blood pressure, represents the standard blood glucose, represents the evaluation weight for the ratio of blood glucose to the standard blood glucose, represents the standard blood oxygen saturation, represents the evaluation weight for the ratio of blood oxygen saturation to the standard blood oxygen saturation, represents the standard fetal movement frequency, represents the evaluation weight for the ratio of fetal movement frequency to the standard fetal movement frequency, represents the safe weight, represents the evaluation weight for the ratio of weight to the safe weight, represents the safe age, represents the evaluation weight for the ratio of age to the safe age, , represents according to , , , , , and Weight to obtain the health coefficient of the th obstetric patient.

[0017] Preferably, the calculation process of the difference index is as follows:

[0018] According to the fetal heart rate dataset, mark the heart rate of the th fetus as , and mark the heart rate of the th fetus as ;

[0019] If the th obstetric patient has a pregnancy relationship only with the th fetus;

[0020]

[0021] In the formula, represents the ratio of the fetal heart rate to the heart rate of the obstetric patient, that is, the difference index of the th fetus and the th obstetric patient;

[0022] If the th fetus and the th fetus are both in a pregnancy relationship with the th obstetric patient;

[0023]

[0024] In the formula, represents the ratio of the heart rate of the th fetus to the heart rate of the th fetus, represents the ratio of the heart rate of the th fetus to the heart rate of the th obstetric patient, represents the ratio of the heart rate of the th fetus to the heart rate of the th obstetric patient, represents the difference index of the th fetus and the th fetus.

[0025] Preferably, the calculation process of the development coefficient is as follows:

[0026] According to the fetal heart rate dataset, mark the pregnancy cycle of the mother of the th fetus as ;

[0027] Based on the ultrasound dataset, mark the head circumference of the th fetus as , mark the abdominal circumference of the th fetus as , mark the femur length of the th fetus as , mark the weight of the th fetus as , mark the blood flow velocity of the th fetus as ;

[0028]

[0029] In the formula, represents the evaluation weight for the gestational age, represents the evaluation weight for the fetal head circumference, represents the evaluation weight for the fetal abdominal circumference, represents the evaluation weight for the fetal femur length, represents the evaluation weight for the fetal weight, represents the evaluation weight for the fetal blood flow velocity, , represents that according to the , , , , and weights, the development coefficient of the th fetus is obtained.

[0030] Preferably, when the health coefficient is lower than the health threshold , it indicates that the health status of the obstetric patient is poor, and a corresponding maternal emergency signal is generated.

[0031] Preferably, when any one of the values in the difference index increases continuously three times, it indicates that the health status of the obstetric patient is poor, and a corresponding maternal emergency signal is generated. When any one of the values in the difference index decreases continuously three times, it indicates that the health status of the fetus is poor, and a corresponding fetal emergency signal is generated.

[0032] Preferably, when the development coefficient is lower than the development threshold , it indicates that the development status of the fetus is poor, and a corresponding fetal danger signal is generated.

[0033] Compared with the prior art, the present invention provides a data analysis platform for an obstetric mobile ultrasound device, which has the following beneficial effects:

[0034] 1. The present invention connects an electronic medical record system, a fetal heart monitor, and an ultrasound machine through a multi-dimensional acquisition module network, obtains the physiological data of all obstetric patients, the heart rate monitoring data of all fetuses, and the ultrasound examination data, and classifies and forms a patient data set, a fetal heart data set, and an ultrasound data set. The intelligent analysis module analyzes the health coefficient of each obstetric patient , avoiding misdiagnosis or omission caused by a single indicator, and then analyzes the difference index between the obstetric patient and the fetus , and the development coefficient of each fetus , conducts personalized analysis for multiple pregnancies, identifies possible maternal and fetal health problems in advance, accurately evaluates the development status of each fetus, helps doctors make reasonable judgments in complex situations, and has high multi-dimensional analysis accuracy.

[0035] 2. The present invention sets health thresholds and development thresholds within a fixed range through the intelligent analysis module , and then combines the health coefficient , the difference index , and the development coefficient to judge the health status of the obstetric patient, the health status of the fetus, and the development status. When the health coefficient is lower than the health threshold , it indicates that the health status of the obstetric patient is poor, and a corresponding maternal emergency signal is generated. When any one of the values in the difference index rises continuously three times, it indicates that the health status of the obstetric patient is poor, and a corresponding maternal emergency signal is generated. When any one of the values in the difference index drops continuously three times, it indicates that the health status of the fetus is poor, and a corresponding fetal emergency signal is generated. When the development coefficient is lower than the development threshold , it indicates that the development status of the fetus is poor, and a corresponding fetal danger signal is generated, reducing the time of manual intervention, helping doctors intervene in time, and avoiding serious problems such as postpartum complications or fetal growth retardation, with high personalized diagnosis and treatment efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic flow diagram of the data analysis platform of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Due to the lack of personalized analysis capabilities in the traditional data analysis platform for obstetric mobile ultrasound devices, it is difficult to comprehensively and accurately identify abnormal physiological indicators, which easily leads to misdiagnosis or missed diagnosis, and it is impossible to customize appropriate diagnosis and treatment suggestions for each obstetric patient, thus missing the opportunity to deeply evaluate the health status of pregnant women and fetuses. Therefore, a data analysis platform for obstetric mobile ultrasound devices is provided. Please refer to Figure 1 A data analysis platform for obstetric mobile ultrasound devices includes a multi-dimensional acquisition module and an intelligent analysis module;

[0039] The multi-dimensional acquisition module consists of a physiological data unit, a fetal heart rate data unit, and an ultrasound data unit. The physiological data unit collects patient data sets through a network connection to the electronic medical record system. The patient data set includes the physiological data of all obstetric patients. The expression of the patient data set is , to are respectively the physiological data of the first to the th obstetric patients. The physiological data includes heart rate, blood pressure, blood sugar, blood oxygen saturation, fetal movement frequency, and weight. represents the age of each patient. Especially during high-risk pregnancies, complications, or the labor process, comprehensively collecting physiological data can help doctors evaluate the physiological responses of the mother in real time and make timely adjustments;

[0040] The fetal heart rate data unit collects fetal heart rate data sets through a network connection to the fetal heart monitor. The fetal heart rate data set includes the heart rate monitoring data of all fetuses. The expression of the fetal heart rate data set is , to are respectively the heart rate monitoring data of the first to the th fetuses, which helps to quickly judge the health status of the fetus in the follow-up. The heart rate monitoring data is the heart rate. represents the gestational period of each fetus's mother;

[0041] The ultrasound data unit collects ultrasound data sets through a network connection to the ultrasound machine. The ultrasound data set includes the ultrasound examination data of all fetuses. The expression of the ultrasound data set is , to are respectively the ultrasound examination data of the first to the th fetuses. The ultrasound examination data includes head circumference, abdominal circumference, femur length, weight, and blood flow velocity, which is convenient for tracking the health status of the fetus. represents the specific time point for obtaining the ultrasound examination data of each fetus;

[0042] The intelligent analysis module consists of a patient analysis unit, a correlation analysis unit, and a warning management unit. The patient analysis unit analyzes the health coefficient of each obstetric patient based on the patient dataset. Its calculation process is as follows:

[0043] Extract the physiological data of the th obstetric patient in the patient dataset, and mark the heart rate of the th obstetric patient as , mark the blood pressure of the th obstetric patient as , mark the blood glucose of the th obstetric patient as , mark the blood oxygen saturation of the th obstetric patient as , mark the fetal movement frequency of the th obstetric patient as , mark the weight of the th obstetric patient as , mark the age of the th obstetric patient as ;

[0044]

[0045] In the formula, represents the standard heart rate, represents the evaluation weight for the ratio of heart rate to the standard heart rate, represents the standard blood pressure, represents the evaluation weight for the ratio of blood pressure to the standard blood pressure, represents the standard blood glucose, represents the evaluation weight for the ratio of blood glucose to the standard blood glucose, represents the standard blood oxygen saturation, represents the evaluation weight for the ratio of blood oxygen saturation to the standard blood oxygen saturation, represents the standard fetal movement frequency, represents the evaluation weight for the ratio of fetal movement frequency to the standard fetal movement frequency, represents the safe weight, represents the evaluation weight for the ratio of weight to the safe weight, represents the safe age, represents the evaluation weight for the ratio of age to the safe age, , represents according to , , , , , and The weights are used to obtain the health coefficient of the th obstetric patient . By combining multiple physiological indicators such as heart rate, blood pressure, and blood sugar, a comprehensive health coefficient is calculated. This can avoid misdiagnosis or omission caused by a single indicator and can customize health advice and monitoring plans for each pregnant woman;

[0046] The association analysis unit analyzes the difference index between the obstetric patient and the fetus based on the patient dataset and the fetal heart rate dataset . The calculation process is as follows:

[0047] Based on the fetal heart rate dataset, the heart rate of the th fetus is marked as , and the heart rate of the th fetus is marked as ;

[0048] If the th obstetric patient has a pregnancy relationship only with the th fetus;

[0049]

[0050] In the formula, represents the ratio of the fetal heart rate to the heart rate of the obstetric patient, which is the difference index between the th fetus and the th obstetric patient . An abnormally fast or slow heart rate of the mother may indicate insufficient oxygen supply to the mother's body, which may lead to abnormal fetal heart rate;

[0051] If the th fetus and the th fetus are both related to the th obstetric patient;

[0052]

[0053] In the formula, represents the ratio of the heart rate of the th fetus to the heart rate of the th fetus, represents the ratio of the heart rate of the th fetus to the heart rate of the th obstetric patient, represents the ratio of the heart rate of the th fetus to the heart rate of the th obstetric patient, represents the difference index between the th fetus and the th fetus , conduct personalized analysis for multiple pregnancies to identify potential maternal and fetal health problems in advance;

[0054] The early warning management unit analyzes the development coefficient of each fetus based on the fetal heart rate data set and the ultrasound data set , and its calculation process is as follows:

[0055] Based on the fetal heart rate data set, mark the pregnancy cycle of the mother of the th fetus as ;

[0056] Based on the ultrasound data set, mark the head circumference of the th fetus as , mark the abdominal circumference of the th fetus as , mark the femur length of the th fetus as , mark the weight of the th fetus as , mark the blood flow velocity of the th fetus as ;

[0057]

[0058] In the formula, represents the evaluation weight for the pregnancy cycle, represents the evaluation weight for the fetal head circumference, represents the evaluation weight for the fetal abdominal circumference, represents the evaluation weight for the fetal femur length, represents the evaluation weight for the fetal weight, represents the evaluation weight for the fetal blood flow velocity, , represents that according to the , , , , and weights, obtain the development coefficient of the th fetus, accurately evaluate the development status of each fetus, help doctors make reasonable judgments in complex situations, and have high precision in multi-dimensional analysis;

[0059] The early warning management unit is set with a fixed range of health thresholds and development thresholds , and then combined with the health coefficient , the difference index and the development coefficient , determine the health status of the obstetric patient, the health status and development status of the fetus, and the health coefficient below the health threshold When it is, it indicates that the health status of the obstetric patient is poor, and a corresponding maternal emergency signal is generated. When any one of the difference indices rises continuously three times, it indicates that the health status of the obstetric patient is poor, and a corresponding maternal emergency signal is generated. When any one of the difference indices drops continuously three times, it indicates that the health status of the fetus is poor, and a corresponding fetal emergency signal is generated. When the development coefficient is below the development threshold When it is, it indicates that the development status of the fetus is poor, and a corresponding fetal danger signal is generated, reducing the time of artificial intervention, helping the doctor to intervene in time, and avoiding serious problems such as postpartum complications or fetal growth retardation, with high personalized diagnosis and treatment efficiency.

[0060] Example 1: In this experiment, a 30-year-old pregnant woman was selected as the experimental subject. After testing, the heart rate of this pregnant woman was 72 bpm, blood pressure was 120 / 80 mmHg, blood sugar was 5.0 mmol / L, blood oxygen saturation was 98%, fetal movement frequency was 10 times / hour, weight was 70 kg, standard heart rate was 70 bpm, standard blood pressure was 120 / 80 mmHg, standard blood sugar was 5.5 mmol / L, standard blood oxygen saturation was 95%, standard fetal movement frequency was 15 times / hour, safe weight was 65 kg, and safe age was 25 years. The health coefficient of this pregnant woman The calculation formula is as follows:

[0061]

[0062] In the formula, represents the evaluation weight for the ratio of heart rate to standard heart rate, represents the evaluation weight for the ratio of blood pressure to standard blood pressure, represents the evaluation weight for the ratio of blood sugar to standard blood sugar, represents the evaluation weight for the ratio of blood oxygen saturation to standard blood oxygen saturation, represents the evaluation weight for the ratio of fetal movement frequency to standard fetal movement frequency, represents the evaluation weight for the ratio of weight to safe weight, represents the evaluation weight for the ratio of age to safe age, , according to , , , , , and weights, the health coefficient of this pregnant woman is obtained is approximately 。

[0063] Example 2: In this experiment, twin parturients were selected as the experimental subjects. After detection, the heart rate of the parturient was 80 beats per minute, the heart rate of the first fetus was 120 beats per minute, and the heart rate of the second fetus was 100 beats per minute. The difference index between the parturient and the fetuses The calculation formula is as follows:

[0064]

[0065] In the formula, represents the ratio of the heart rates of the two fetuses, represents the ratio of the heart rate of the first fetus to the heart rate of the parturient, represents the ratio of the heart rate of the second fetus to the heart rate of the parturient.

[0066] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data analysis platform for a mobile ultrasonic device in obstetrics, characterized in that, It includes a multi-dimensional acquisition module and an intelligent analysis module; The multi-dimensional acquisition module consists of a physiological data unit, a fetal heart rate data unit, and an ultrasound data unit. The physiological data unit collects a patient data set by connecting to an electronic medical record system through a network. The patient data set includes the physiological data of all obstetric patients. The fetal heart rate data unit collects a fetal heart rate data set by connecting to a fetal heart rate monitor through a network. The fetal heart rate data set includes the heart rate monitoring data of all fetuses. The ultrasound data unit collects an ultrasound data set by connecting to an ultrasound machine through a network. The ultrasound data set includes the ultrasound examination data of all fetuses; The intelligent analysis module consists of a patient analysis unit, a correlation analysis unit, and a warning management unit. The patient analysis unit analyzes the health coefficient of each obstetric patient according to the patient data set. , the correlation analysis unit analyzes the difference index between the obstetric patient and the fetus according to the patient data set and the fetal heart rate data set. , the warning management unit analyzes the development coefficient of each fetus according to the fetal heart rate data set and the ultrasound data set. , the warning management unit is set with a health threshold within a fixed range. and a development threshold. , and then combined with the health coefficient , the difference index and the development coefficient , to judge the health status of the obstetric patient, the health status and development status of the fetus, and generate corresponding warning signals.

2. The data analysis platform for an obstetric mobile ultrasound device according to claim 1, characterized in that: The expression of the patient dataset is , to are the physiological data of the first to the th obstetric patients respectively. The physiological data include heart rate, blood pressure, blood glucose, blood oxygen saturation, fetal movement frequency and weight, represents the age of each patient.

3. The data analysis platform for a mobile obstetric ultrasound device according to claim 2, wherein: The expression of the fetal heart rate dataset is , to are the heart rate monitoring data of the first to the th fetuses respectively. The heart rate monitoring data is the heart rate, represents the gestational period of each fetus's mother.

4. The data analysis platform for an obstetric mobile ultrasound device according to claim 3, wherein: The expression of the ultrasound data set is , to are the ultrasound detection data of the first to the th fetuses respectively. The ultrasound detection data includes head circumference, abdominal circumference, femur length, weight and blood flow velocity, represents the specific time points for obtaining the ultrasound detection data of each fetus.

5. The data analysis platform for an obstetric mobile ultrasound device according to claim 4, characterized in that: The health coefficient The calculation process is as follows: Extract the physiological data of the th obstetric patient in the patient dataset, and mark the heart rate of the th obstetric patient as , mark the blood pressure of the th obstetric patient as , mark the blood glucose of the th obstetric patient as , mark the blood oxygen saturation of the th obstetric patient as , mark the fetal movement frequency of the th obstetric patient as , mark the weight of the th obstetric patient as , mark the age of the th obstetric patient as ; ； In the formula, represents the standard heart rate, represents the evaluation weight for the ratio of heart rate to the standard heart rate, represents the standard blood pressure, represents the evaluation weight for the ratio of blood pressure to the standard blood pressure, represents the standard blood glucose, represents the evaluation weight for the ratio of blood glucose to the standard blood glucose, represents the standard blood oxygen saturation, represents the evaluation weight for the ratio of blood oxygen saturation to the standard blood oxygen saturation, represents the standard fetal movement frequency, represents the evaluation weight for the ratio of fetal movement frequency to the standard fetal movement frequency, represents the safe weight, represents the evaluation weight for the ratio of weight to the safe weight, represents the safe age, represents the evaluation weight for the ratio of age to the safe age, , represents according to , , , , , and weights, the health coefficient of the th obstetric patient is obtained .

6. The data analysis platform for a mobile ultrasonic device for obstetrics according to claim 5, characterized in that: The difference index The calculation process is as follows: According to the fetal heart rate dataset, mark the heart rate of the th fetus as , and mark the heart rate of the th fetus as ; If the th obstetric patient has a pregnancy relationship only with the th fetus; ； In the formula, represents the ratio of the fetal heart rate to the heart rate of the obstetric patient, which is the th fetus and the th difference index of the obstetric patient ; If the th fetus and the th fetus are simultaneously in a pregnancy relationship with the th obstetric patient; ； In the formula, represents the ratio of the th fetal heart rate to the th fetal heart rate, represents the ratio of the th fetal heart rate to the th heart rate of an obstetric patient, represents the ratio of the th fetal heart rate to the th heart rate of an obstetric patient, represents the difference index between the th fetus and the th fetus .

7. The data analysis platform for a mobile obstetric ultrasound device according to claim 6, characterized in that: The development coefficient The calculation process is as follows: According to the fetal heart rate dataset, mark the gestational age of the th fetal mother as ; Based on the ultrasound dataset, label the head circumference of the th fetus as , label the abdominal circumference of the th fetus as , label the femur length of the th fetus as , label the weight of the th fetus as , label the blood flow velocity of the th fetus as ; ； In the formula, represents the evaluation weight for the gestational age, represents the evaluation weight for the fetal head circumference, represents the evaluation weight for the fetal abdominal circumference, represents the evaluation weight for the fetal femur length, represents the evaluation weight for the fetal weight, represents the evaluation weight for the fetal blood flow velocity, , represents that according to , , , , and weights, the development coefficient of the th fetus is obtained .

8. The data analysis platform for an obstetric mobile ultrasound device according to claim 7, characterized in that: The health coefficient is lower than the health threshold , indicating that the health status of the obstetric patient is poor, and generating a corresponding maternal emergency signal.

9. The data analysis platform for a mobile ultrasonic device for obstetrics according to claim 8, wherein: The difference index When any one of the numerical values continuously rises three times, it indicates a poor health status of the obstetric patient, and a corresponding maternal emergency signal is generated. The difference index When any one of the numerical values continuously drops three times, it indicates a poor health status of the fetus, and a corresponding fetal emergency signal is generated.

10. The data analysis platform for a mobile ultrasonic device in obstetrics according to claim 9, characterized in that: The developmental coefficient is lower than the developmental threshold , indicating that the fetal development status is poor and generating a corresponding fetal danger signal.

Images