Ovulation and menstrual period estimation method, device and storage medium based on body temperature

Abstract

The present invention discloses a method for estimating ovulation and menstrual period based on body temperature, comprising: obtaining the user's basal body temperature just before waking up every day through a wearable device; when the number of monitoring days reaches a preset number of days, obtaining the basal body temperature of each day within the monitoring days, and traversing the user's daily basal body temperature in chronological order of the dates, recording the dates that meet the preset requirements and recording them as suspected ovulation days, and then judging whether the corresponding suspected ovulation day is an ovulation day based on the basal body temperature of each suspected ovulation day and the basal body temperature of the previous day and the basal body temperature of the following day; and determining the first day of the user's current menstrual period based on the determined ovulation day. The present invention can automatically obtain the user's body temperature and predict the date of ovulation and menstrual period, solving the problems of the prior art that require manual measurement and recording of body temperature and manual calculation of ovulation date and menstrual period, resulting in operational difficulties. The present invention also discloses an ovulation and menstrual period estimation device and storage medium based on body temperature.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring, and in particular to a method, device and storage medium for estimating ovulation and menstrual periods based on body temperature. Background Art

[0002] Basal body temperature, also known as resting body temperature, is the temperature measured after waking from a long sleep period and before engaging in any activity. Basal body temperature fluctuates with the normal female cycle. Therefore, long-term basal body temperature monitoring, through data curves and patterns, can effectively understand the physiological cycle of women of childbearing age, such as ovulation date, ovulation period, luteal phase, and menstrual period. This allows women to better understand the timing of each phase of their menstrual cycle, thereby identifying the most effective time periods for conception or contraception, and also allows them to prepare for their menstrual period.

[0003] Currently, the most common method for monitoring basal body temperature is to use a thermometer to measure the temperature under the armpit for three minutes immediately after waking up each morning. The user then records their temperature on a temperature form, creating a temperature summary chart for analysis. However, this method has certain drawbacks. It is very cumbersome to operate, requiring the individual to measure their temperature daily and maintain this routine over a long period of time. This consumes a lot of time and energy, making it difficult for individuals to adhere to this practice. Furthermore, the data in the form needs to be calculated and analyzed later, requiring the user to perform these calculations themselves, which is inconvenient and difficult to operate. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, one of the purposes of the present invention is to provide an ovulation and menstrual period estimation method based on body temperature, which can solve the problems of existing menstrual period prediction requiring users to manually measure body temperature and calculate, resulting in operational difficulties.

[0005] The second purpose of the present invention is to provide an ovulation and menstrual period prediction device based on body temperature, which can solve the problems of existing menstrual period prediction that requires users to manually measure body temperature and calculate, resulting in operational difficulties.

[0006] The third object of the present invention is to provide a storage medium that can solve the problems of existing menstrual period prediction that requires users to manually measure body temperature and calculate, resulting in operational difficulties.

[0007] One of the purposes of the present invention is achieved by the following technical solution:

[0008] The method for estimating ovulation and menstrual period based on body temperature includes the following steps:

[0009] Data acquisition steps: Obtain the user's basal body temperature just before waking up every day through a wearable device;

[0010] Judgment step: when the number of monitoring days reaches the preset number of days, the daily basal body temperature during the monitoring days is obtained from the system, and the user's daily basal body temperature is judged in order of date to see whether it meets the preset requirements. If so, the corresponding date is recorded;

[0011] Ovulation day determination steps: Obtain all recorded dates and record them as suspected ovulation days, then determine whether the suspected ovulation day is the ovulation day based on the basal body temperature of each suspected ovulation day and the basal body temperature of the previous day and the basal body temperature of the next day;

[0012] Recommendation steps: Determine the first day of the user's menstrual period based on the determined ovulation day, and recommend the determined ovulation day and the first day of the user's menstrual period to the user; wherein, the first day of the user's menstrual period refers to the first day of the user's first menstrual period after the ovulation day.

[0013] Furthermore, the recommendation step also includes: based on the determined ovulation day, calculating the date M days backward as the first day of the user's menstrual period, and determining the date range of the first day of the user's menstrual period based on the calculated first day of the user's menstrual period and then recommending it to the user; wherein M is a natural number greater than 1.

[0014] Furthermore, the judgment step of judging whether the user's daily basal body temperature meets the preset requirements is specifically as follows: judging whether the difference between the user's basal body temperature on one day and the basal body temperature on the day before the date meets the preset threshold, and then judging whether the date is a suspected ovulation day; wherein, the suspected ovulation day refers to the difference between the basal body temperature on the date and the basal body temperature on the day before the date is within the preset temperature range; while recording the corresponding date in the judgment step, the body temperature variable of the corresponding date is set to 1; the suspected ovulation day in the ovulation day determination step refers to the date when the body temperature variable is 1.

[0015] Furthermore, the preset temperature range is obtained based on the average value during the post-menstrual period; wherein the average value during the post-menstrual period refers to the average value of the basal body temperature of the user N days after the end of the last menstrual period; wherein N=5.

[0016] Furthermore, the ovulation day determination step further includes:

[0017] For any suspected ovulation day: obtain the basal body temperature on the suspected ovulation day, the basal body temperature one day before the suspected ovulation day, and the basal body temperature one day after the suspected ovulation day, and construct a combined curve based on the basal body temperatures of the three days, and determine whether the suspected ovulation day is the ovulation day based on the trend of the combined curve.

[0018] Furthermore, according to: when the trend of the corresponding combined curve is in a descending state, the suspected ovulation day is not the ovulation day; when the trend of the corresponding combined curve is in an ascending state or a flat state, the suspected ovulation day is the ovulation day.

[0019] Furthermore, the data acquisition step specifically includes: monitoring the user's sleep state through a wrist activity monitor to determine the time when the user wakes up, and then obtaining the time before the user woke up; at the same time, obtaining the user's body temperature at each moment in real time through the wrist activity monitor, and then matching the user's body temperature at the moment before the user woke up from the system according to the moment before the user woke up, and recording it as the basal body temperature.

[0020] Furthermore, the method further includes a correction step: obtaining the actual first day of the user's menstrual period, and determining whether the actual first day of the user's menstrual period calculated in the recommendation step is the same as the first day of the user's menstrual period;

[0021] and when the actual first day of the user's menstrual period is different from the first day of the user's menstrual period, correcting the ovulation date calculated in the recommendation step according to the actual first day of the user's menstrual period, and predicting the user's next ovulation date and first day of the menstrual period based on the actual first day of the user's menstrual period and the corrected ovulation date;

[0022] When the actual first day of the user's current menstrual period is the same as the first day of the user's current menstrual period, the first day of the user's next menstrual period is predicted based on the first day of the user's current menstrual period and the ovulation day calculated in the recommendation step.

[0023] The second object of the present invention is achieved by adopting the following technical solution:

[0024] An ovulation and menstrual period prediction device based on body temperature includes a memory and a processor, wherein the memory stores an ovulation and menstrual period prediction program running on the processor, and the ovulation and menstrual period prediction program is a computer program. When the processor executes the ovulation and menstrual period prediction program, the steps of the ovulation and menstrual period prediction method based on body temperature are implemented as one of the purposes of the present invention.

[0025] The third object of the present invention is achieved by adopting the following technical solution:

[0026] A storage medium, which is a computer-readable storage medium and stores a computer program, wherein the computer program is an ovulation period and menstrual period prediction program. When the ovulation period and menstrual period prediction program is executed by a processor, the steps of the ovulation period and menstrual period prediction method based on body temperature as one of the purposes of the present invention are implemented.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention automatically obtains the user's body temperature just before waking up every day by combining with a wearable device, and then determines the user's ovulation day based on the changes in the body temperature data just before waking up every day within a month, and further determines the first day of the user's menstrual period, thereby automatically calculating the user's menstrual date and ovulation day without the need for human intervention or manual data recording, thereby solving the problems of manual data recording and manual calculation in the prior art that cause inconvenience in operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of the method for estimating ovulation and menstrual period based on body temperature provided by the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0031] Example 1

[0032] The present invention automatically captures the user's daily basal body temperature with the help of a lightweight wearable device worn by the user, thereby automatically calculating the user's ovulation period and other physiological cycle times, accurately predicting the user's menstrual period, saving the user's time and energy, and providing convenience for the user.

[0033] Preferably, if Figure 1 As shown, the present invention provides a preferred embodiment of a method for estimating ovulation and menstrual period based on body temperature, comprising the following steps:

[0034] Step S1: Obtain the user's body temperature just before waking up every day through a wearable device, and record it as the basal body temperature.

[0035] The user's basal body temperature refers to the user's body temperature at the moment before waking up every day, wherein the moment in this article is in minutes. Specifically, by setting relevant sensors inside the wearable device to detect the user's sleep state, to determine the moment when the user wakes up, and then to obtain the moment before the user wakes up, and setting a body temperature measuring device inside the wearable device to obtain the user's body temperature in real time, once the user is detected to wake up, the moment before the user wakes up can be determined according to the moment when the user wakes up, and then the body temperature data detected by the body temperature measuring device at the moment before the user wakes up can be obtained and recorded as the basal body temperature. When obtaining the user's basal body temperature, the present invention does not require manual operation by the user and recording of data, and automatically obtains it through the wearable device; at the same time, a communication module can be set inside the wearable device to realize automatic uploading of body temperature data, which is convenient and fast.

[0036] In the prior art, the user's body temperature data is recorded by manually operating a thermometer or temperature device. Therefore, the recorded body temperature is the body temperature of the user after waking up or after the user has been awake for a period of time. This body temperature deviates greatly from the basal body temperature mentioned in this application. This application sets a wearable device to detect the user's sleeping state, so as to automatically obtain the user's body temperature just before waking up, and measure it automatically and accurately.

[0037] Preferably, the wearable device is a wrist activity sleep monitor that monitors the user's wrist activity in real time 24 hours a day to determine whether the user is asleep or awake, thereby obtaining the user's body temperature just before waking up. Specifically, a time detection mode can be set in the wearable device to detect the user's body temperature just before waking up each day, and this temperature is used as the basal body temperature and uploaded to the system database or cloud disk. In addition, when recording the user's basal body temperature, it is also necessary to record data such as the date and time the basal body temperature was obtained.

[0038] Preferably, the wrist activity sleep monitor used in the present invention detects the activity state of the user's wrist through an acceleration sensor. Specifically, the acceleration sensor is built into the wrist activity sleep monitor, and the three-axis acceleration is measured by the acceleration sensor to reduce the influence of different postures on the sampling results, and the three-axis combined acceleration is used to describe the user's body movement status. That is, the intensity of human body movement is indicated by the three-axis combined acceleration, which combines the acceleration changes of the human body's X-axis, Y-axis, and Z-axis into a vector. The larger the value, the more intense the movement. That is, the three-axis combined acceleration within one minute is recorded by a wearable device, and the number of times the three-axis combined acceleration is greater than the reference acceleration is determined to determine whether the user is in a body movement state.

[0039] The calculation formula for the three-axis combined acceleration is:

[0040] Among them, α xt is the measurement data of the X-axis of the acceleration sensor, α yt is the measurement data of the X-axis of the acceleration sensor, α zt The X-axis measurement data of the acceleration sensor.

[0041] Preferably, the wearable device of the present invention can be implemented as a wrist activity sleep monitor. For example, it can be implemented by using a body movement sleep monitoring model. The model formula is:

[0042] Wherein, R is the result of the user's sleep status determination in the current minute, and it is set that when R<1, it is determined to be sleeping, and when R>1, it is determined to be awake (body movement); A iis the number of wrist movements in the i-th minute, for example: A0 is the number of wrist movements in the current minute, and A1 is the number of wrist movements in the first minute after the current minute. i is the coefficient, which can be obtained by multivariate linear regression. α is the triaxial acceleration.

[0043] The above sleep model is fitted by the multivariate linear regression method to obtain the various coefficients in the above sleep model, and then the sleep algorithm formula can be achieved as follows:

[0044] R=0.0046(0.06A i-2 +0.24A i-1 +A i +0.22A i+1 +0.04A i+2 )(3).

[0045] Among them, R is the final decision result of the sleep state in the i-th minute. When R<1, it is judged as a sleep state, and when R≥1, it is judged as a wakeful state.

[0046] A i+k is the number of wrist movements in the i±kth minute. This number is calculated based on the number of times the wearable device's accelerometer sensor's detection value exceeds a preset threshold within one minute. For example, the wearable device's accelerometer's detection value is acquired in real time within one minute and determined to be greater than the preset threshold. If so, the number of wrist movements is recorded plus one. The measurement can be performed in seconds, depending on the actual situation.

[0047] That is, the user's body temperature is continuously monitored through the wearable device, and at the same time, the detection data of the acceleration sensor of the wearable device is used to determine whether the user is in a sleeping state or a moving state, so as to obtain the time when the user wakes up every day. In this way, the time before the user wakes up every day can be obtained, and the user's basal body temperature can be obtained based on the detected body temperature.

[0048] The present invention eliminates the need for users to manually measure their body temperature using a thermometer. Instead, the device automatically acquires and stores the data in the system, enabling automated data acquisition and storage. Furthermore, users only need to carry a wearable device to measure their temperature daily, eliminating the need for daily monitoring, thus enabling continuous temperature monitoring.

[0049] Step S2: When the number of monitoring days reaches a preset number of days, the user's daily basal body temperature collected during the monitoring days is obtained.

[0050] Preferably, based on the physiological characteristics of the human body, the number of monitoring days is generally set to one month (generally calculated as a 30-day cycle). The specific number of monitoring days can be set according to the different physiological periods of each person. The present invention continuously monitors the user's basal body temperature for one month as an example to calculate the user's ovulation day.

[0051] Step S3: Determine whether the user's daily basal body temperature meets the preset requirements in order of date. If so, record the corresponding date.

[0052] Preferably, when recording the corresponding date, the body temperature variable of the corresponding date is also set to 1, so as to distinguish the dates later.

[0053] Furthermore, the determination of whether the user's daily basal body temperature meets the requirements is specifically as follows: determining whether the difference between the user's daily basal body temperature and the basal body temperature of the previous day is within the preset temperature range; if so, recording the corresponding date and setting the temperature variable of the corresponding date to 1.

[0054] The preset temperature range is determined by the average value of the post-menstrual period. The average value of the post-menstrual period refers to the average value of the user's basal body temperature N days after the end of the last menstrual period; where N = 5. The initial value of the average value of the post-menstrual period can be given by experience. During the continuous measurement process of the system, the average value of the post-menstrual period can be corrected according to the actual measurement value. Selecting the average value of the post-menstrual period to compare the difference to monitor the ovulation period is more stable and reliable than relying solely on the difference between two days of basal body temperature. It effectively eliminates the influence of some abnormal values, making ovulation day detection and menstrual period prediction more accurate.

[0055] Step S4: Obtain all recorded dates and record them as suspected ovulation days.

[0056] That is, all days with a temperature variable of 1 are recorded as suspected ovulation days. As can be seen from the above, the basal body temperature on a day with a temperature variable of 1 must meet a predetermined temperature difference from the basal body temperature of the previous day. For a normal woman, her basal body temperature on an ovulation day will be somewhat different from that on a non-ovulation day. Therefore, the present invention first uses basal body temperature to screen suspected ovulation days, and then further confirms the suspected ovulation day to obtain the final ovulation day.

[0057] Step S5: judging whether the suspected ovulation day is the ovulation day according to the basal body temperature of each suspected ovulation day, the basal body temperature of the previous day, and the basal body temperature of the next day.

[0058] Specifically, for each suspected ovulation day, the basal body temperature of the day before and the day after is obtained, and then the basal body temperature of the three days is used to determine whether the suspected ovulation day is an ovulation day. Specifically, a combined curve is constructed based on the basal body temperatures of the three days, and then the trend of the combined curve is used to determine whether the corresponding suspected ovulation day is an ovulation day. More preferably, for a suspected ovulation day, when the trend of the corresponding combined curve is in a downward state, the suspected ovulation day is a non-ovulation day; conversely, when the trend of the corresponding combined curve is in an upward state or remains flat, the suspected ovulation day is an ovulation day.

[0059] Step S6: derive the first day of the user's menstrual period based on the determined ovulation day, and recommend the determined ovulation day and the first day of the user's menstrual period to the user.

[0060] The system calculates ovulation based on the user's body temperature, and then predicts the first day of the menstrual period. This prediction is then shared with the user, and the system can remind them of their period's first day, allowing them to understand their period and prioritize lifestyle habits and preparations. For example, before their period, they should avoid contact with cold water, cold food, and sanitary products. Furthermore, the system can also provide reminders for those experiencing menstrual cramps, allowing them to pre-emptively take appropriate medication or rest, thus reducing dysmenorrhea and the unnecessary complications it brings.

[0061] The first day of the user's menstrual period refers to the first day of the user's first menstrual period after the ovulation day.

[0062] In addition, when recommending the first day of menstruation to the user, the interval range of the first day of menstruation can also be determined based on the calculated first day of menstruation. The predicted interval is a few days before and after the first day of menstruation.

[0063] More preferably, the monitoring days in the present invention are one month for cyclic monitoring. Therefore, the next ovulation day and the first day of the menstrual period can also be calculated based on the calculated ovulation day and the first day of the menstrual period, as well as the changes in the user's basal body temperature, to achieve predictions of the ovulation day and the first day of the menstrual period, providing data support for the user so that the user can prepare for pregnancy, contraception, etc. based on the predicted ovulation day, and prepare materials based on the predicted first day of the menstrual period. For example, after the user is recommended the first day of the next menstrual period, the user can make accurate predictions in advance based on the predicted results, avoiding unnecessary trouble for the user.

[0064] In addition, in actual practice, different individuals have different physiological characteristics, which may lead to inconsistencies between the predicted and actual dates. In this case, the present invention also adjusts the predicted ovulation date and the first day of the user's menstrual period. Specifically, the actual first day of the user's menstrual period can be collected and compared with the predicted first day of the user's menstrual period to determine whether the predicted first day of the user's menstrual period needs to be adjusted.

[0065] Specifically, when the actual first day of the user's menstrual period is different from the first day of the user's menstrual period, the ovulation date calculated in the recommendation step is corrected according to the actual first day of the user's menstrual period, and the user's next ovulation date and first day of menstrual period are predicted based on the actual first day of the user's menstrual period and the corrected ovulation date.

[0066] When the actual first day of the user's current menstrual period is the same as the first day of the user's current menstrual period, the first day of the user's next menstrual period and the ovulation day are predicted based on the first day of the user's current menstrual period and the ovulation day calculated in the recommendation step.

[0067] The present invention is particularly suitable for women with unstable menstrual cycles. It predicts the ovulation date and the first day of menstruation by the user's body temperature, which is convenient for users. Generally speaking, women's menstrual cycles are regular and cyclical, but in many cases, due to external factors such as physical reasons and psychological reasons, women's menstrual periods are irregular. In this way, for women, it is easy to be inaccurate to speculate on the menstrual period according to normal regularity. The present invention uses body temperature to predict the ovulation date, and then predicts the menstrual period, which can greatly improve the accuracy of the prediction. At the same time, for users, there is no need to perform manual operations. They only need to wear the corresponding wearable device to automatically obtain and record body temperature and calculate the menstrual period, which is convenient for users to use.

[0068] The present invention provides convenience for user use, basal body temperature capture, basal body temperature data analysis and processing, menstrual cycle time prompts, etc., which can let female users know the time range of their ovulation day and the first day of their menstrual period, improve the accuracy of the prediction, facilitate the majority of women of childbearing age to implement contraception and conception measures, and remind users to prepare for care when the menstrual period comes.

[0069] Example 2

[0070] An ovulation and menstrual period estimation device based on body temperature includes a memory and a processor. The memory stores an ovulation and menstrual period estimation program that runs on the processor. The ovulation and menstrual period estimation program is a computer program. When the processor executes the ovulation and menstrual period estimation program, the following steps are implemented:

[0071] Data acquisition steps: Obtain the user's basal body temperature just before waking up every day through a wearable device;

[0072] Judgment step: when the number of monitoring days reaches the preset number of days, the daily basal body temperature during the monitoring days is obtained from the system, and the user's daily basal body temperature is judged in order of date to see whether it meets the preset requirements. If so, the corresponding date is recorded;

[0073] Ovulation day determination steps: Obtain all recorded dates and record them as suspected ovulation days, then determine whether the suspected ovulation day is the ovulation day based on the basal body temperature of each suspected ovulation day and the basal body temperature of the previous day and the basal body temperature of the next day;

[0074] Recommendation steps: Determine the first day of the user's menstrual period based on the determined ovulation day, and recommend the determined ovulation day and the first day of the user's menstrual period to the user; wherein, the first day of the user's menstrual period refers to the first day of the user's first menstrual period after the ovulation day.

[0075] Furthermore, the recommendation step also includes: based on the determined ovulation day, calculating the date M days backward as the first day of the user's menstrual period, and determining the date range of the first day of the user's menstrual period based on the calculated first day of the user's menstrual period and then recommending it to the user; wherein M is a natural number greater than 1.

[0076] Furthermore, the judgment step of judging whether the user's daily basal body temperature meets the preset requirements is specifically as follows: judging whether the difference between the user's basal body temperature on one day and the basal body temperature on the day before the date meets the preset threshold, and then judging whether the date is a suspected ovulation day; wherein, the suspected ovulation day refers to the difference between the basal body temperature on the date and the basal body temperature on the day before the date is within the preset temperature range; while recording the corresponding date in the judgment step, the body temperature variable of the corresponding date is set to 1; the suspected ovulation day in the ovulation day determination step refers to the date when the body temperature variable is 1.

[0077] Furthermore, the preset temperature range is obtained based on the average value during the post-menstrual period; wherein the average value during the post-menstrual period refers to the average value of the basal body temperature of the user N days after the end of the last menstrual period; wherein N=5.

[0078] Furthermore, the ovulation day determination step further includes:

[0079] For any suspected ovulation day: obtain the basal body temperature on the suspected ovulation day, the basal body temperature one day before the suspected ovulation day, and the basal body temperature one day after the suspected ovulation day, and construct a combined curve based on the basal body temperatures of the three days, and determine whether the suspected ovulation day is the ovulation day based on the trend of the combined curve.

[0080] Furthermore, according to: when the trend of the corresponding combined curve is in a descending state, the suspected ovulation day is not the ovulation day; when the trend of the corresponding combined curve is in an ascending state or a flat state, the suspected ovulation day is the ovulation day.

[0081] Furthermore, the data acquisition step specifically includes: monitoring the user's sleep state through a wrist activity monitor to determine the time when the user wakes up, and then obtaining the time before the user woke up; at the same time, obtaining the user's body temperature at each moment in real time through the wrist activity monitor, and then matching the user's body temperature at the moment before the user woke up from the system according to the moment before the user woke up, and recording it as the basal body temperature.

[0082] Furthermore, the method further includes a correction step: obtaining the actual first day of the user's menstrual period, and determining whether the actual first day of the user's menstrual period calculated in the recommendation step is the same as the first day of the user's menstrual period;

[0083] and when the actual first day of the user's menstrual period is different from the first day of the user's menstrual period, correcting the ovulation date calculated in the recommendation step according to the actual first day of the user's menstrual period, and predicting the user's next ovulation date and first day of the menstrual period based on the actual first day of the user's menstrual period and the corrected ovulation date;

[0084] When the actual first day of the user's current menstrual period is the same as the first day of the user's current menstrual period, the first day of the user's next menstrual period is predicted based on the first day of the user's current menstrual period and the ovulation day calculated in the recommendation step.

[0085] Example 3

[0086] A storage medium is a computer-readable storage medium having a computer program stored thereon, wherein the computer program is an ovulation period and menstrual period estimation program. When the ovulation period and menstrual period estimation program is executed by a processor, the following steps are implemented:

[0087] Data acquisition steps: Obtain the user's basal body temperature just before waking up every day through a wearable device;

[0088] Judgment step: when the number of monitoring days reaches the preset number of days, the daily basal body temperature during the monitoring days is obtained from the system, and the user's daily basal body temperature is judged in order of date to see whether it meets the preset requirements. If so, the corresponding date is recorded;

[0089] Ovulation day determination steps: Obtain all recorded dates and record them as suspected ovulation days, then determine whether the suspected ovulation day is the ovulation day based on the basal body temperature of each suspected ovulation day and the basal body temperature of the previous day and the basal body temperature of the next day;

[0090] Recommendation steps: Determine the first day of the user's menstrual period based on the determined ovulation day, and recommend the determined ovulation day and the first day of the user's menstrual period to the user; wherein, the first day of the user's menstrual period refers to the first day of the user's first menstrual period after the ovulation day.

[0091] Furthermore, the recommendation step also includes: based on the determined ovulation day, calculating the date M days backward as the first day of the user's menstrual period, and determining the date range of the first day of the user's menstrual period based on the calculated first day of the user's menstrual period and then recommending it to the user; wherein M is a natural number greater than 1.

[0092] Furthermore, the judgment step of judging whether the user's daily basal body temperature meets the preset requirements is specifically as follows: judging whether the difference between the user's basal body temperature on one day and the basal body temperature on the day before the date meets the preset threshold, and then judging whether the date is a suspected ovulation day; wherein, the suspected ovulation day refers to the difference between the basal body temperature on the date and the basal body temperature on the day before the date is within the preset temperature range; while recording the corresponding date in the judgment step, the body temperature variable of the corresponding date is set to 1; the suspected ovulation day in the ovulation day determination step refers to the date when the body temperature variable is 1.

[0093] Furthermore, the preset temperature range is obtained based on the average value during the post-menstrual period; wherein the average value during the post-menstrual period refers to the average value of the basal body temperature of the user N days after the end of the last menstrual period; wherein N=5.

[0094] Furthermore, the ovulation day determination step further includes:

[0095] For any suspected ovulation day: obtain the basal body temperature on the suspected ovulation day, the basal body temperature one day before the suspected ovulation day, and the basal body temperature one day after the suspected ovulation day, and construct a combined curve based on the basal body temperatures of the three days, and determine whether the suspected ovulation day is the ovulation day based on the trend of the combined curve.

[0096] Furthermore, according to: when the trend of the corresponding combined curve is in a descending state, the suspected ovulation day is not the ovulation day; when the trend of the corresponding combined curve is in an ascending state or a flat state, the suspected ovulation day is the ovulation day.

[0097] Furthermore, the data acquisition step specifically includes: monitoring the user's sleep state through a wrist activity monitor to determine the time when the user wakes up, and then obtaining the time before the user woke up; at the same time, obtaining the user's body temperature at each moment in real time through the wrist activity monitor, and then matching the user's body temperature at the moment before the user woke up from the system according to the moment before the user woke up, and recording it as the basal body temperature.

[0098] Furthermore, the method further includes a correction step: obtaining the actual first day of the user's menstrual period, and determining whether the actual first day of the user's menstrual period calculated in the recommendation step is the same as the first day of the user's menstrual period;

[0099] and when the actual first day of the user's menstrual period is different from the first day of the user's menstrual period, correcting the ovulation date calculated in the recommendation step according to the actual first day of the user's menstrual period, and predicting the user's next ovulation date and first day of the menstrual period based on the actual first day of the user's menstrual period and the corrected ovulation date;

[0100] When the actual first day of the user's current menstrual period is the same as the first day of the user's current menstrual period, the first day of the user's next menstrual period is predicted based on the first day of the user's current menstrual period and the ovulation day calculated in the recommendation step.

[0101] The above embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and replacements made by technicians in this field on the basis of the present invention fall within the scope of protection required by the present invention.

Claims

1. A method for estimating ovulation and menstrual period based on body temperature, characterized in that: The method for estimating menstrual period and ovulation date includes the following steps: Data acquisition steps: The user's basal body temperature before waking up every day is obtained through a wearable device; the wearable device is a wrist activity monitor, which uses an accelerometer to detect the activity status of the user's wrist, and measures the three-axis combined acceleration through the accelerometer to describe the user's body movement status. The calculation formula is: （1） in, is the measurement data of the X-axis of the acceleration sensor, is the measurement data of the X-axis of the acceleration sensor, The measurement data of the X axis of the acceleration sensor; The sleep status is determined using the body movement sleep monitoring model. The formula of the body movement sleep monitoring model is: （2） in, The result of the user's sleep status in the current minute is set. <1 is considered as sleep. >1 is considered awake, is the number of wrist movements in the i-th minute, is the coefficient, obtained by fitting the multiple linear regression method; The user's sleep state is monitored through a wrist activity monitor to determine the time when the user wakes up, and then the time before the user woke up is obtained; at the same time, the wrist activity monitor is used to obtain the user's body temperature at each moment in real time, and then the user's body temperature before waking up is matched with the system based on the time before the user woke up and recorded as the basal body temperature; Judgment step: when the number of monitoring days reaches the preset number of days, the daily basal body temperature during the monitoring days is obtained from the system, and the user's daily basal body temperature is judged in order of date to see whether it meets the preset requirements. If so, the corresponding date is recorded; Steps for determining the ovulation day: obtain all recorded dates and record them as suspected ovulation days, and then determine whether the corresponding suspected ovulation day is the ovulation day based on the basal body temperature of each suspected ovulation day, the basal body temperature of the previous day, and the basal body temperature of the next day; for any suspected ovulation day: obtain the basal body temperature of the suspected ovulation day, the basal body temperature of the day before the suspected ovulation day, and the basal body temperature of the day after the suspected ovulation day, and construct a combined curve based on the basal body temperatures of the three days, and determine whether the suspected ovulation day is the ovulation day based on the trend of the combined curve; when the trend of the corresponding combined curve is in a downward state, the suspected ovulation day is not the ovulation day; when the trend of the corresponding combined curve is in an upward state or a flat state, the suspected ovulation day is the ovulation day; Recommendation step: determining the first day of the user's menstrual period based on the determined ovulation day, and recommending the determined ovulation day and the first day of the user's menstrual period to the user; wherein the first day of the user's menstrual period refers to the first day of the user's first menstrual period after the ovulation day; Based on the determined ovulation day, the date calculated M days backward is recorded as the first day of the user's menstrual period, and the date range of the first day of the user's menstrual period is calculated based on the calculated first day of the user's menstrual period and then recommended to the user; where M is a natural number greater than 1.

2. The method for estimating ovulation and menstrual period based on body temperature according to claim 1, characterized in that: The determination step of determining whether the user's daily basal body temperature meets the preset requirements is specifically as follows: determining whether the difference between the user's basal body temperature on one day and the basal body temperature on the day before the date meets a preset threshold, and then determining whether the date is a suspected ovulation day; wherein, a suspected ovulation day refers to a date on which the difference between the basal body temperature on the date and the basal body temperature on the day before the date is within a preset temperature range; while recording the corresponding date in the determination step, the body temperature variable of the corresponding date is set to 1; the suspected ovulation day in the ovulation day determination step refers to a date on which the body temperature variable is 1.

3. The method for estimating ovulation and menstrual period based on body temperature according to claim 2, characterized in that: The preset temperature range is obtained based on the average value in the post-menstrual period; wherein the average value in the post-menstrual period refers to the average value of the basal body temperature of the user N days after the end of the last menstrual period; wherein N=5.

4. The method for estimating ovulation and menstrual period based on body temperature according to claim 1, wherein: The method also includes a correction step: obtaining the actual first day of the user's menstrual period and determining whether the actual first day of the user's menstrual period calculated in the recommendation step is the same as the first day of the user's menstrual period; and when the actual first day of the user's menstrual period is different from the first day of the user's menstrual period, correcting the ovulation date calculated in the recommendation step according to the actual first day of the user's menstrual period, and predicting the user's next ovulation date and first day of the menstrual period based on the actual first day of the user's menstrual period and the corrected ovulation date; When the actual first day of the user's current menstrual period is the same as the first day of the user's current menstrual period, the first day of the user's next menstrual period is predicted based on the first day of the user's current menstrual period and the ovulation day calculated in the recommendation step.

5. An ovulation and menstrual period estimation device based on body temperature, comprising a memory and a processor, wherein the memory stores an ovulation and menstrual period estimation program running on the processor, the ovulation and menstrual period estimation program being a computer program, characterized in that: When the processor executes the ovulation period and menstrual period prediction program, the steps of the ovulation period and menstrual period prediction method based on body temperature as described in any one of claims 1 to 4 are implemented.

6. A storage medium, wherein the storage medium is a computer-readable storage medium and stores a computer program, wherein the computer program is an ovulation and menstrual period estimation program, characterized in that: When the ovulation period and menstrual period estimation program is executed by a processor, the steps of the ovulation period and menstrual period estimation method based on body temperature as described in any one of claims 1 to 4 are implemented.

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