Remote monitoring method and system for pregnant women in late pregnancy based on cloud service

Abstract

The invention discloses a remote monitoring method and system for pregnant women in late pregnancy based on cloud service, and relates to the technical field of remote monitoring and computer-assisted health. The method comprises the following steps: S100, collecting physical sign data of a pregnant woman by using a pregnant woman wearable device every preset period; s200, uploading the pregnant woman sign data to a cloud platform database, and determining a first sub-database and a second sub-database to which the pregnant woman sign data belong currently based on a preset period; s300, performing late pregnancy data monitoring analysis on different parts of data of the pregnant woman sign data in the first sub-database and the second sub-database; and S400, adjusting a preset period based on a monitoring analysis result, and returning to the step S100. The system is used for implementing the method. According to the technical scheme, multi-dimensional remote monitoring of the pregnant women in the late pregnancy period can be achieved, whole-process multi-node data updating monitoring analysis is carried out based on the group data clustering analysis method, and the accuracy and real-time performance of remote computer-assisted health monitoring are improved.

Description

Technical Field

[0001] The present invention belongs to the field of remote monitoring and computer-assisted health technology, and in particular, relates to a method and system for remote monitoring of pregnant women in late pregnancy based on cloud services. Background Art

[0002] The third trimester generally refers to the period from the 28th week of pregnancy to the end of delivery, which includes the three months of pregnancy, August, September, and October. The changes in the third trimester are the biggest during the entire pregnancy. As the body becomes more cumbersome and the date of the baby's birth is approaching, mothers may experience varying degrees of anxiety and tension.

[0003] Many pregnant women will gain weight rapidly when they enter the late stage of pregnancy, and their body flexibility will continue to decrease. This series of changes are under the influence of progesterone, which will prompt pregnant women to pay more and more attention to themselves and their babies, and anxiety and comparative psychology may also increase.

[0004] The Chinese invention patent application document with publication number CN108852284A proposes a late pregnancy analysis system based on a cloud platform, which sends the results of the late pregnancy analysis to a medical application management platform. The medical application management platform manages mobile medical applications and connects with the hospital information system and the medical big data platform, thereby realizing remote monitoring of pregnant women in the late pregnancy, improving the accuracy and real-time nature of late pregnancy monitoring; the Chinese invention patent application document with publication number CN117524451A proposes a pregnancy hypertension care system based on remote monitoring technology, which can generate personalized medical advice based on the prediction results of the AI ​​model, accurately reflect the health status of pregnant women, and effectively help pregnant women manage stress, thereby providing more effective medical services.

[0005] The related late pregnancy monitoring schemes in the existing technology only conduct relevant monitoring analysis and suggestions based on the personal data of the target pregnant woman, and do not take into account the comparison of other groups of users under the same data labels. The results obtained are not very comparable, which is not conducive to alleviating the anxiety of users in the late pregnancy. In addition, the large models and telemedicine technologies in the existing technology all take the data of pregnant women as a whole, and do not distinguish between the two dimensions of pregnant woman and fetal physical sign data. The dimensions of the results obtained are also relatively single, which does not conform to the actual situation that pregnant women in the late pregnancy pay attention to both their own data and the fetal data at the same time. Summary of the invention

[0006] In response to the above technical problems, the present invention proposes a method and system for remote monitoring of pregnant women in the late pregnancy based on cloud services, a computer-readable storage medium, a computer program product and an electronic device for implementing the method.

[0007] In a first aspect of the present invention, a remote monitoring method for pregnant women in late pregnancy based on cloud services is proposed. The method is implemented based on a wearable device for pregnant women, and the method comprises the following steps: S100: collecting the pregnant woman's physical sign data by using the pregnant woman's wearable device at every preset period, wherein the pregnant woman's physical sign data includes the pregnant woman's personal physical sign sensor data and the fetus' physical sign sensor data; S200: uploading the pregnant woman's vital sign data to a cloud platform database, wherein the cloud platform database determines at least a first sub-database and at least a second sub-database to which the pregnant woman's vital sign data currently belongs based on the preset period; S300: performing late pregnancy data monitoring and analysis on different parts of the pregnant woman's physical sign data in the first sub-database and the second sub-database respectively; S400: Based on the monitoring and analysis results, adjust the preset period and return to step S100.

[0008] The first sub-database corresponds to the pregnant woman's personal vital sign sensor data, and the second sub-database corresponds to the fetus's vital sign sensor data; Different first sub-databases and the second sub-databases are generated at different preset periodic nodes.

[0009] In step S200, the cloud platform database determines at least a first sub-database and at least a second sub-database to which the pregnant woman's physical sign data currently belongs based on the preset period, specifically including: S210: Determine a current preset period node based on the preset period; S220: Acquire multiple candidate first sub-databases and second candidate sub-databases corresponding to the current preset periodic node; S230: Determine at least a first sub-database to which the pregnant woman's vital sign data currently belongs from the multiple candidate first sub-databases based on the pregnant woman's personal vital sign sensor data, and determine at least a second sub-database to which the pregnant woman's vital sign data currently belongs from the multiple candidate second sub-databases based on the fetal vital sign sensor data.

[0010] The step S300 specifically includes: Performing late pregnancy data monitoring and analysis on the pregnant woman's personal vital sign sensor data in the first sub-database to obtain a first monitoring and analysis result; The fetal vital sign sensor data is subjected to late pregnancy data monitoring and analysis in the second sub-database to obtain a second monitoring and analysis result.

[0011] The step S400 specifically includes: When either the first monitoring and analysis result or the second monitoring and analysis result does not meet the preset condition, the preset period is adjusted and the process returns to step S100.

[0012] In a second aspect of the present invention, in order to implement the method described in the first aspect, a remote monitoring system for pregnant women in the third trimester based on cloud services is proposed, wherein the system uses a wearable device for pregnant women to collect vital sign data of pregnant women according to a preset cycle, wherein the vital sign data of pregnant women includes personal vital sign sensor data of pregnant women and fetal vital sign sensor data; The system further comprises: A sub-database matching unit, after the pregnant woman's wearable device uploads the pregnant woman's vital sign data to the cloud platform database, the sub-database matching unit determines, within the preset period, at least a first sub-database and at least a second sub-database to which the pregnant woman's vital sign data currently belongs; A data monitoring and analysis unit, which performs late pregnancy data monitoring and analysis on different parts of the pregnant woman's physical sign data in the first sub-database and the second sub-database respectively; A cycle adjustment unit, configured to adjust the preset cycle based on the monitoring and analysis result of the data monitoring and analysis unit; The monitoring and analysis results of the data monitoring and analysis unit include a first analysis result for the pregnant woman's personal vital sign sensor data and a second analysis result for the fetal vital sign sensor data, and different first sub-databases and second sub-databases are generated at different preset periodic nodes.

[0013] The sub-database matching unit determines, within the preset period, at least a first sub-database and at least a second sub-database to which the pregnant woman's physical sign data currently belongs, specifically including: Determine a current preset period node based on the preset period; Acquire multiple candidate first sub-databases and second candidate sub-databases corresponding to the current preset periodic node; Based on the pregnant woman's personal vital sign sensor data, at least a first sub-database to which the pregnant woman's vital sign data currently belongs is determined from the multiple candidate first sub-databases, and based on the fetal vital sign sensor data, at least a second sub-database to which the pregnant woman's vital sign data currently belongs is determined from the multiple candidate second sub-databases.

[0014] The generating different first sub-databases and second sub-databases at different preset periodic nodes specifically includes: At each preset cycle node, obtain all uploaded pregnant women's vital signs data at the current preset cycle node; All uploaded pregnant women's vital signs data are grouped according to the pregnant women's vital signs data labels to obtain a first sub-database and a second sub-database corresponding to the current preset cycle node.

[0015] The pregnant woman's vital sign data label includes the pregnant woman's personal vital sign sensor data label and the fetal vital sign sensor data label; The first sub-database corresponding to the current preset period node includes multiple groups of pregnant women's personal vital sign sensor data, and the multiple groups of pregnant women's personal vital sign sensor data have the same labels; The second sub-database corresponding to the current preset periodic node includes multiple groups of fetal vital sign sensor data, and the fetal vital sign sensor data labels of the multiple groups of fetal vital sign sensor data are the same.

[0016] The aforementioned cloud-based remote monitoring method for pregnant women in the late stage of pregnancy can be automatically implemented through various forms of electronic devices through computer program instructions; the computer program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0017] Therefore, in the third aspect of the present invention, a computer-readable storage medium is also provided for storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes a cloud service-based remote monitoring method for pregnant women in the late pregnancy according to the first aspect.

[0018] In the fourth aspect of the present invention, a computer device is also proposed, which includes a processor and a memory, the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the computer device executes a cloud service-based remote monitoring method for pregnant women in the late pregnancy of the first aspect mentioned above.

[0019] In a fifth aspect of the present invention, a computer program product is also proposed, which includes a computer program. When the computer program is executed, the cloud service-based method for remote monitoring of pregnant women in the late pregnancy of the first aspect mentioned above is implemented.

[0020] The technical solution of the present invention distinguishes between the two dimensions of maternal and fetal vital signs data during data processing, and therefore focuses on the comparison results of the two dimensions, which is in line with the actual situation that pregnant women in the late pregnancy pay attention to both their own and fetal data at the same time; in addition, the remote monitoring processing of the present invention is based on the comparison of other group users under the same data label, and the comparison of the results obtained is very strong, which is conducive to alleviating the anxiety of users in the late pregnancy. Therefore, the technical solution of the present invention can realize multi-dimensional remote monitoring of pregnant women in the late pregnancy, and perform data update monitoring and analysis of multiple nodes throughout the process based on the group data clustering analysis method, which improves the accuracy and real-time performance of remote computer-assisted health monitoring.

[0021] Further advantages of the present invention will be further reflected in detail in the specific embodiments section in conjunction with the drawings of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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 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 work.

[0023] Figure 1 It is a main flow diagram of a method for remote monitoring of pregnant women in late pregnancy based on cloud services according to an embodiment of the present invention; Figure 2 is a schematic diagram of a first sub-database and a second sub-database generated at different predetermined periodic nodes; Figure 3 yes Figure 1 Schematic diagram of the principle of the method for determining the first sub-database and the second sub-database to which the current belongs; Figure 4 This is an overall architecture diagram of a remote monitoring system for pregnant women in late pregnancy based on cloud services according to an embodiment of the present invention; Figure 5 It is a schematic diagram of the composition of functional module units of a remote monitoring system for pregnant women in late pregnancy based on cloud services according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] First of all, it should be pointed out that the embodiment of the remote monitoring method for pregnant women in late pregnancy based on cloud services mentioned in this section can be implemented through a computer program on an electronic device or system configured with a memory and a processor. The electronic device or system can be in the form of a physical machine, a virtual machine, a server, a cluster, or any combination thereof.

[0025] Preferably, the specific form of the electronic device may also be a human-computer interaction terminal, and the human-computer interaction terminal may be a desktop terminal with a human-computer interaction interface, a smart handheld terminal, a mobile terminal, a wearable device for pregnant women, etc.

[0026] See first Figure 1 , Figure 1 A schematic diagram of the main process of a method for remote monitoring of pregnant women in late pregnancy based on cloud services according to an embodiment of the present invention.

[0027] Figure 1 The method is implemented based on a wearable device for pregnant women, and the method comprises the following steps: S100: collecting pregnant women's vital sign data using the wearable device for pregnant women at every preset period, wherein the pregnant women's vital sign data comprises pregnant women's personal vital sign sensor data and fetal vital sign sensor data; As an example, the pregnant woman's personal vital sign sensing data includes at least weight and blood pressure; the fetal vital sign sensing data includes at least fetal movement data and fetal heart rate data.

[0028] S200: uploading the pregnant woman's vital sign data to a cloud platform database, wherein the cloud platform database determines at least a first sub-database and at least a second sub-database to which the pregnant woman's vital sign data currently belongs based on the preset period; S300: performing late pregnancy data monitoring and analysis on different parts of the pregnant woman's physical sign data in the first sub-database and the second sub-database respectively; S400: Based on the monitoring and analysis results, adjust the preset period and return to step S100.

[0029] The first sub-database corresponds to the pregnant woman's personal vital sign sensor data, and the second sub-database corresponds to the fetus's vital sign sensor data; Different first sub-databases and the second sub-databases are generated at different preset periodic nodes.

[0030] Next, combine Figure 2 A schematic diagram is provided to introduce in detail the generation principle of the first sub-database and the second sub-database used in various embodiments of the present invention.

[0031] The preset cycle nodes are marked with gestational age. Figure 2 In the figure, three preset cycle nodes are schematically given, namely, the 29th week of pregnancy stage node, the 30th week of pregnancy stage node and the 37th week of pregnancy stage node. The data collection time point of each preset cycle in step S100 may correspond to three different preset cycle nodes respectively.

[0032] For example, assuming that the preset cycle of data collection is 1 day. The current data is at 29 weeks + 5 days (less than 30 weeks), then the corresponding preset cycle node is the 29-week node; the next data collection (one day later) is at 29 weeks + 6 days (less than 30 weeks), then the corresponding preset cycle node is the 29-week node; the next data collection (2 days later) is at 29 weeks + 7 days (full 30 weeks), which is actually the full 30 weeks of pregnancy, then the corresponding preset cycle node is the 30-week node.

[0033] When the preset cycle node is the 29th week of pregnancy node, all currently uploaded maternal vital sign data (maternal personal vital sign sensor data and fetal vital sign sensor data) are obtained, and maternal vital sign labels and fetal vital sign labels are generated for different maternal vital sign data for each pregnant woman as a unit.

[0034] Assume that N pregnant women have uploaded their vital signs data, as described below: The pregnant woman Oi's vital sign data uploaded is ODi=(ODMi, ODBi), where ODMi is the personal vital sign sensor data uploaded by the pregnant woman Oi, and ODBi is the fetal vital sign sensor data uploaded by the pregnant woman, i=1, 2,…,N.

[0035] Generate maternal vital signs labels for ODMi and fetal vital signs labels for ODBi; Taking the pregnant woman's physical sign data as age and weight as an example, the pregnant woman's physical sign labels generated for ODMi can be age labels and weight labels; Age labels and weight labels can be either stage descriptions or numerical descriptions; For example: Age label: middle-aged; or, Age label: 30-35 (years old); For example: weight label: normal, obese, thin; or, weight label: 60-65 (kg); The fetal vital sign label generated for ODBi may be a fetal heart rate label and / or a fetal movement rate label.

[0036] Age labels and weight labels can be either stage descriptions or numerical descriptions; For example: fetal heart rate labels: below threshold, normal range, above threshold; Or, fetal heart rate label: 120-160 (beats / min).

[0037] In order to facilitate computer program automation processing, in actual application, the label can be digitized, for example, to form Figure 2 The age label 1 (for example, representing the age label: middle-aged) or the fetal heart rate label 2 (for example, representing the fetal heart rate label: 120-160 (beats / minute)).

[0038] Taking the preset cycle node as the 29th week of pregnancy stage node as an example, it is assumed that at this time, the personal vital sign sensor data of 100 pregnant women in the late pregnancy of 29 weeks of pregnancy and the fetal vital sign sensor data of a certain period of time have been obtained; Among them, the data labels of the personal vital sign sensor data of 50 pregnant women are all (age label 1, weight label 2); the data labels of the personal vital sign sensor data of 45 pregnant women are all (age label 2, weight label 2), then the personal vital sign sensor data of 50 pregnant women are used as a first sub-database corresponding to the 29th week of pregnancy node of the preset period node, and the personal vital sign sensor data of 45 pregnant women are used as another first sub-database corresponding to the 29th week of pregnancy node of the preset period node; That is to say, corresponding to the preset cycle node (29-week gestational stage node), at least two first sub-databases are generated at this time; Further, assuming that among the fetal vital sign sensor data uploaded by 100 pregnant women in the late pregnancy of 29 weeks, the data labels of the fetal vital sign sensor data of 48 pregnant women are all (fetal heart rate label 1, fetal movement rate label 1), and the data labels of the fetal vital sign sensor data of 49 pregnant women are all (fetal heart rate label 2, fetal movement rate label 2), then the fetal vital sign sensor data of the 48 pregnant women are used as a second sub-database corresponding to the stage node of 29 weeks of pregnancy with a preset period node, and the fetal vital sign sensor data of the 49 pregnant women are used as another second sub-database corresponding to the stage node of 29 weeks of pregnancy with a preset period node; That is to say, corresponding to the preset cycle node (the 29th week of pregnancy stage node), at least two second sub-databases are generated at this time.

[0039] In specific implementation, at each preset cycle node, all uploaded pregnant women's vital signs data of the current preset cycle node are obtained; all uploaded pregnant women's vital signs data are grouped according to the pregnant women's vital signs data labels to obtain the first sub-database and the second sub-database corresponding to the current preset cycle node.

[0040] The pregnant woman's vital sign data label includes the pregnant woman's personal vital sign sensor data label and the fetal vital sign sensor data label; The first sub-database corresponding to the current preset cycle node includes multiple groups of pregnant women's personal vital sign sensor data, and the multiple groups of pregnant women's personal vital sign sensor data have the same personal vital sign sensor data labels; the second sub-database corresponding to the current preset cycle node includes multiple groups of fetal vital sign sensor data, and the multiple groups of fetal vital sign sensor data have the same fetal vital sign sensor data labels.

[0041] Figure 2 Such a principle diagram is shown, that is, the two first sub-databases correspond to a certain part of the pregnant woman's personal vital sign sensor data set of (age label 1, weight label 1) and (age label 2, weight label 2); the two second sub-databases correspond to a certain part of the fetal vital sign sensor data set of (fetal heart rate label 1, fetal movement rate label 1) and (fetal heart rate label 2, fetal movement rate label 2).

[0042] Similarly, at another preset cycle node (30 weeks), at least two second sub-databases will be generated, namely, the two first sub-databases correspond to a certain partial pregnant woman's personal vital sign sensor data set of (age label 1, weight label 11) and (age label 2, weight label 21); the two second sub-databases correspond to a certain partial fetal vital sign sensor data set of (fetal heart rate label 11, fetal movement rate label 11) and (fetal heart rate label 22, fetal movement rate label 22).

[0043] The above examples only describe the situation where a pregnant woman's vital signs data (pregnant woman's personal vital signs sensor data and fetal vital signs sensor data) belongs to the same sub-database, and there are only two first sub-databases and two second sub-databases. However, in actual applications, as the number of pregnant women uploading data increases and the level of data labels becomes richer, a pregnant woman's vital signs data (pregnant woman's personal vital signs sensor data and fetal vital signs sensor data) may belong to multiple sub-databases, and the number of first sub-databases and second sub-databases may be greater than 2.

[0044] For the convenience of description, the following Figure 2 The illustrated case where there are two first sub-databases and two second sub-databases is taken as an example, and embodiments of other cases can be derived similarly.

[0045] Figure 3 Show Figure 1 The step S200 of the method is a schematic diagram showing the principle of determining the first sub-database and the second sub-database currently belonging to the method.

[0046] In step S200, the cloud platform database determines at least a first sub-database and at least a second sub-database to which the pregnant woman's physical sign data currently belongs based on the preset period, specifically including: S210: Determine a current preset period node based on the preset period; Specifically, determine which preset cycle node the current preset cycle corresponds to. For example, assuming that the preset cycle for data collection is 1 day. If the current pregnancy is 29 weeks + 5 days (less than 30 weeks), and the pregnant woman's wearable device is used to collect the pregnant woman's vital signs data, the corresponding preset cycle node is the 29th week of pregnancy node; The next time the collection is done (one day later), it is at 29 weeks + 6 days of pregnancy (less than 30 weeks), so the corresponding preset cycle node is the 29th week of pregnancy node; the next time the collection is done (2 days later), it is at 29 weeks + 7 days of pregnancy (30 weeks), which is actually the 30th week of pregnancy, so the corresponding preset cycle node is the 30th week of pregnancy node.

[0047] S220: Acquire multiple candidate first sub-databases and second candidate sub-databases corresponding to the current preset periodic node; Continuing with the previous example, we can see that the two candidate first sub-databases correspond to a certain partial pregnant woman's personal vital sign sensor data set of (age label 1, weight label 1) and (age label 2, weight label 2) respectively; the two candidate second sub-databases correspond to a certain partial fetal vital sign sensor data set of (fetal heart rate label 1, fetal movement rate label 1) and (fetal heart rate label 2, fetal movement rate label 2) respectively.

[0048] S230: Determine at least a first sub-database to which the pregnant woman's vital sign data currently belongs from the multiple candidate first sub-databases based on the pregnant woman's personal vital sign sensor data, and determine at least a second sub-database to which the pregnant woman's vital sign data currently belongs from the multiple candidate second sub-databases based on the fetal vital sign sensor data.

[0049] Assuming that for the current target pregnant woman, the pregnant woman's wearable device is used to collect the pregnant woman's vital signs data for labeling, the obtained pregnant woman's personal vital signs sensor data label is (age label 1, weight label 1), and the obtained fetal vital signs sensor data is (fetal heart rate label 2, fetal movement rate label 2), then the first sub-database to which the corresponding pregnant woman's vital signs data currently belongs can be matched: Figure 2 The first sub-database corresponding to (age label 1, weight label 11), and the second sub-database corresponding to (fetal heart rate label 2, fetal movement rate label 2).

[0050] It should be noted that in the above embodiment, the second sub-database and the first sub-database correspond to two tags respectively. However, in actual applications, the second sub-database and the first sub-database can be set to correspond to more or fewer data tags (of course, at least one tag), and the present invention does not make specific restrictions on this.

[0051] On this basis, step S300 is executed: performing late pregnancy data monitoring and analysis on different parts of the pregnant woman's physical sign data in the first sub-database and the second sub-database respectively.

[0052] Specifically, in the first sub-database, the pregnant woman's personal vital sign sensor data is monitored and analyzed in the third trimester to obtain a first monitoring and analysis result; in the second sub-database, the fetal vital sign sensor data is monitored and analyzed in the third trimester to obtain a second monitoring and analysis result.

[0053] It can be seen that compared with the prior art that directly performs overall data analysis in all databases or open large models, this embodiment distinguishes between the two dimensions of maternal and fetal vital signs data, and performs monitoring and analysis in pre-matched database subsets. This not only reduces invalid data and the amount of data processing, but also can produce at least two aspects of analysis results, which is in line with the actual situation that pregnant women in the late pregnancy are concerned about both their own and fetal data.

[0054] In addition, since the monitoring and analysis are performed on the same comparison range (people of the same age and weight range) of the same object (pregnant woman or fetus), pregnant women can obtain the comparison results of the monitoring and analysis more intuitively.

[0055] It can be understood that the specific process of the first monitoring and analysis or the second monitoring and analysis can refer to the relevant technical solutions of the prior art, such as the AI ​​model prediction mentioned in the background technology, the application results of the medical remote platform, etc. The difference between this embodiment and the prior art is that the monitoring data of the pregnant woman or the fetus are monitored and analyzed separately, and the benchmark data used for the analysis is at least the first sub-database and at least the second sub-database to which the current preset cycle node of the pregnant woman's vital sign data belongs, taking into account the comparison of other groups of users under the same data label, and the results obtained are more comparative.

[0056] Preferably, each monitoring and analysis is not only for the current data, but also can be combined with the historical data of the current data to perform trend analysis.

[0057] Preferably, the first monitoring and analysis result and the second monitoring and analysis result both include their own data analysis results and a comparative analysis of deviation results from the average baseline values ​​of the corresponding first sub-database and the second sub-database, etc. For example, the first monitoring and analysis result and the second monitoring and analysis result may include an individual data change trend chart, an overall data change trend chart included in the first sub-database / the second sub-database, and a comparative analysis trend chart of the two.

[0058] Preferably, the method further includes step S400: when any one of the first monitoring and analysis result or the second monitoring and analysis result does not meet a preset condition, adjusting the preset period and returning to step S100.

[0059] An example of not meeting the preset condition may be: the deviation between the individual data change trend and the overall data change trend is greater than a preset change threshold, etc.

[0060] Specifically, adjusting the preset period may be: shortening the preset period. For example, the original preset collection period is 1 day, which is adjusted to 4 hours.

[0061] exist Figure 1-Figure 3 Based on the method embodiment of Figure 4 , Figure 4 The overall architecture diagram of a remote monitoring system for pregnant women in late pregnancy based on cloud services according to an embodiment of the present invention is shown.

[0062] Among them, the system uses the pregnant woman's wearable device to collect the pregnant woman's vital sign data according to a preset period and uploads it to the cloud service platform for processing. The cloud service platform specifically includes a cloud platform database.

[0063] exist Figure 4 Based on this, see Figure 5 , a schematic diagram of the functional module units of a remote monitoring system for pregnant women in late pregnancy based on cloud services according to an embodiment of the present invention.

[0064] exist Figure 5 In the system, the wearable device for pregnant women is used to collect the vital sign data of pregnant women according to a preset cycle, and the vital sign data of pregnant women includes the personal vital sign sensor data of pregnant women and the vital sign sensor data of fetuses;

[0065] The system further comprises: A sub-database matching unit, after the pregnant woman's wearable device uploads the pregnant woman's vital sign data to the cloud platform database, the sub-database matching unit determines, within the preset period, at least a first sub-database and at least a second sub-database to which the pregnant woman's vital sign data currently belongs; A data monitoring and analysis unit, which performs late pregnancy data monitoring and analysis on different parts of the pregnant woman's physical sign data in the first sub-database and the second sub-database respectively; A cycle adjustment unit, configured to adjust the preset cycle based on the monitoring and analysis result of the data monitoring and analysis unit; The monitoring and analysis results of the data monitoring and analysis unit include a first analysis result for the pregnant woman's personal vital sign sensor data and a second analysis result for the fetal vital sign sensor data, and different first sub-databases and second sub-databases are generated at different preset periodic nodes.

[0066] The sub-database matching unit determines, within the preset period, at least a first sub-database and at least a second sub-database to which the pregnant woman's physical sign data currently belongs, specifically including: Determine a current preset period node based on the preset period; Acquire multiple candidate first sub-databases and second candidate sub-databases corresponding to the current preset periodic node; Based on the pregnant woman's personal vital sign sensor data, at least a first sub-database to which the pregnant woman's vital sign data currently belongs is determined from the multiple candidate first sub-databases, and based on the fetal vital sign sensor data, at least a second sub-database to which the pregnant woman's vital sign data currently belongs is determined from the multiple candidate second sub-databases.

[0067] The generating different first sub-databases and second sub-databases at different preset periodic nodes specifically includes: At each preset cycle node, obtain all uploaded pregnant women's vital signs data at the current preset cycle node; All uploaded pregnant women's vital signs data are grouped according to the pregnant women's vital signs data labels to obtain a first sub-database and a second sub-database corresponding to the current preset cycle node.

[0068] The pregnant woman's vital sign data label includes the pregnant woman's personal vital sign sensor data label and the fetal vital sign sensor data label; The first sub-database corresponding to the current preset period node includes multiple groups of pregnant women's personal vital sign sensor data, and the multiple groups of pregnant women's personal vital sign sensor data have the same labels; The second sub-database corresponding to the current preset periodic node includes multiple groups of fetal vital sign sensor data, and the fetal vital sign sensor data labels of the multiple groups of fetal vital sign sensor data are the same.

[0069] The aforementioned cloud-based remote monitoring method for pregnant women in the late stage of pregnancy can be automatically implemented through various forms of electronic devices through computer-readable program instructions; the computer-readable program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0070] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0071] The technical solution of the present invention distinguishes between the two dimensions of maternal and fetal vital signs data during data processing, and therefore focuses on the comparison results of the two dimensions, which is in line with the actual situation that pregnant women in the late pregnancy pay attention to both their own and fetal data at the same time; in addition, the remote monitoring processing of the present invention is based on the comparison of other group users under the same data label, and the comparison of the results obtained is very strong, which is conducive to alleviating the anxiety of users in the late pregnancy. Therefore, the technical solution of the present invention can realize multi-dimensional remote monitoring of pregnant women in the late pregnancy, and perform data update monitoring and analysis of multiple nodes throughout the process based on the group data clustering analysis method, which improves the accuracy and real-time performance of remote computer-assisted health monitoring.

[0072] For other technologies, principles, algorithms or models not elaborated in detail in this application, please refer to the prior art.

[0073] In the aforementioned embodiment section, the present invention provides multiple embodiments, each of which can constitute an independent technical solution and may contribute to the prior art and solve corresponding technical problems.

[0074] However, it should be pointed out that different embodiments can be combined with each other without violating logic; at the same time, each embodiment can solve at least one technical problem, but it is not required that each individual embodiment must solve multiple or all technical problems.

[0075] At the same time, in the specific implementation of this application, if user-related data is involved, when the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions. The user-related data involved in the embodiments of the present invention does not include information that can be used for personal identification and does not involve personal identification.

[0076] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

1. A remote monitoring method for pregnant women in late pregnancy based on cloud services, the method is implemented based on a wearable device for pregnant women, characterized in that: The method comprises the following steps: S100: collecting the pregnant woman's physical sign data by using the pregnant woman's wearable device at every preset period, wherein the pregnant woman's physical sign data includes the pregnant woman's personal physical sign sensor data and the fetus' physical sign sensor data; S200: uploading the pregnant woman's vital sign data to a cloud platform database, wherein the cloud platform database determines at least a first sub-database and at least a second sub-database to which the pregnant woman's vital sign data currently belongs based on the preset period; S300: performing late pregnancy data monitoring and analysis on different parts of the pregnant woman's physical sign data in the first sub-database and the second sub-database respectively; S400: Based on the monitoring and analysis results, adjust the preset period and return to step S100.

2. A remote monitoring method for pregnant women in the third trimester based on cloud services as claimed in claim 1, characterized in that: The first sub-database corresponds to the pregnant woman's personal vital sign sensor data, and the second sub-database corresponds to the fetus's vital sign sensor data; Different first sub-databases and the second sub-databases are generated at different preset periodic nodes.

3. A remote monitoring method for pregnant women in late pregnancy based on cloud services as claimed in claim 2, characterized in that: In step S200, the cloud platform database determines at least a first sub-database and at least a second sub-database to which the pregnant woman's physical sign data currently belongs based on the preset period, specifically including: S210: Determine a current preset period node based on the preset period; S220: Acquire multiple candidate first sub-databases and second candidate sub-databases corresponding to the current preset periodic node; S230: Determine at least a first sub-database to which the pregnant woman's vital sign data currently belongs from the multiple candidate first sub-databases based on the pregnant woman's personal vital sign sensor data, and determine at least a second sub-database to which the pregnant woman's vital sign data currently belongs from the multiple candidate second sub-databases based on the fetal vital sign sensor data.

4. A method for remote monitoring of pregnant women in late pregnancy based on cloud services as claimed in claim 1, characterized in that , the step S300 specifically includes: Performing late pregnancy data monitoring and analysis on the pregnant woman's personal vital sign sensor data in the first sub-database to obtain a first monitoring and analysis result; The fetal vital sign sensor data is subjected to late pregnancy data monitoring and analysis in the second sub-database to obtain a second monitoring and analysis result.

5. A method for remote monitoring of pregnant women in late pregnancy based on cloud services as claimed in claim 4, characterized in that , the step S400 specifically includes: When either the first monitoring and analysis result or the second monitoring and analysis result does not meet the preset condition, the preset period is adjusted and the process returns to step S100.

6. A remote monitoring system for pregnant women in the third trimester based on cloud services, wherein the system uses a wearable device for pregnant women to collect the vital signs data of pregnant women according to a preset cycle, wherein the vital signs data of pregnant women include the personal vital signs sensor data of pregnant women and the vital signs sensor data of fetuses; It is characterized in that The system further comprises: A sub-database matching unit, after the pregnant woman's wearable device uploads the pregnant woman's vital sign data to the cloud platform database, the sub-database matching unit determines, within the preset period, at least a first sub-database and at least a second sub-database to which the pregnant woman's vital sign data currently belongs; A data monitoring and analysis unit, which performs late pregnancy data monitoring and analysis on different parts of the pregnant woman's physical sign data in the first sub-database and the second sub-database respectively; A cycle adjustment unit, configured to adjust the preset cycle based on the monitoring and analysis result of the data monitoring and analysis unit; The monitoring and analysis results of the data monitoring and analysis unit include a first analysis result for the pregnant woman's personal vital sign sensor data and a second analysis result for the fetal vital sign sensor data, and different first sub-databases and second sub-databases are generated at different preset periodic nodes.

7. A remote monitoring system for pregnant women in late pregnancy based on cloud services as claimed in claim 6, characterized in that: The sub-database matching unit determines, within the preset period, at least a first sub-database and at least a second sub-database to which the pregnant woman's physical sign data currently belongs, specifically including: Determine a current preset period node based on the preset period; Acquire multiple candidate first sub-databases and second candidate sub-databases corresponding to the current preset periodic node; Based on the pregnant woman's personal vital sign sensor data, at least a first sub-database to which the pregnant woman's vital sign data currently belongs is determined from the multiple candidate first sub-databases, and based on the fetal vital sign sensor data, at least a second sub-database to which the pregnant woman's vital sign data currently belongs is determined from the multiple candidate second sub-databases.

8. A remote monitoring system for pregnant women in late pregnancy based on cloud services as claimed in claim 6, characterized in that: The generating different first sub-databases and second sub-databases at different preset periodic nodes specifically includes: At each preset cycle node, obtain all uploaded pregnant women's vital signs data at the current preset cycle node; All uploaded pregnant women's vital signs data are grouped according to the pregnant women's vital signs data labels to obtain a first sub-database and a second sub-database corresponding to the current preset cycle node.

9. A remote monitoring system for pregnant women in late pregnancy based on cloud services as claimed in claim 8, characterized in that: The pregnant woman's vital sign data label includes the pregnant woman's personal vital sign sensor data label and the fetal vital sign sensor data label; The first sub-database corresponding to the current preset period node includes multiple groups of pregnant women's personal vital sign sensor data, and the multiple groups of pregnant women's personal vital sign sensor data have the same labels; The second sub-database corresponding to the current preset periodic node includes multiple groups of fetal vital sign sensor data, and the fetal vital sign sensor data labels of the multiple groups of fetal vital sign sensor data are the same.

10. A computer program product, comprising a computer program or computer executable instructions, which, when executed by a processor, implements the cloud service-based remote monitoring method for pregnant women in the late pregnancy as described in any one of claims 1 to 5.

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