An ovulation detector based on cloud analysis technology and its detection method

Abstract

An ovulation detector based on cloud analysis technology and its detection method of the present invention relate to an ovulation detection and analysis method. It includes a device terminal and a cloud computing server. The device terminal includes a body, a body control unit, a sensing probe, and a lithium battery power supply. The body control unit includes a temperature detection module connected to a temperature calculation module, a conductivity detection module connected to a conductivity calculation module, a data compression module and a data transmission module connected in sequence, and a data receiving module. The temperature calculation module and the conductivity calculation module are respectively connected to the data compression module. The body control unit is connected to the cloud computing server through the data transmission module and the data receiving module. The sensing probe is connected to the temperature detection module and the conductivity detection module. The cloud computing server has an information feature extraction module, an information feature matching module, a trend judgment module, and a sending module connected in sequence. The prediction result of the present invention is more stable.

Description

Technical Field

[0001] This invention relates to an ovulation detection and analysis method based on cloud analysis technology. Background Technology

[0002] Studies abroad have investigated the use of electrical resistance measurements in saliva and cervical mucus to reflect ovulation timing. These studies involved inserting electrode probes into the vagina to measure resistance, using serum LH analysis as a control. After measuring the menstrual cycles of 13 subjects over 18 months (10 patients who underwent artificial insemination, the others were volunteers), it was found that LH levels peaked on the day of ovulation, while the resistance of cervical mucus reached its minimum. Conversely, the resistance of oral saliva reached its maximum 5 to 6 days before ovulation. In my country, Qiu Xiuguang et al. from Shandong Medical University conducted research on the resistance values ​​of saliva and cervical mucus, confirming the effectiveness of this method. However, this method is complex and cannot be performed by an individual.

[0003] Basal body temperature (BBT) is the temperature measured immediately upon waking, assuming no activity. It reflects the body temperature at complete rest. In women with normal ovarian function, one follicle matures and releases an egg during each menstrual cycle, subsequently forming the corpus luteum. The follicle before ovulation produces estrogen, while the follicle after ovulation primarily produces progesterone. Progesterone has a thermogenic effect, raising body temperature by 0.3-0.5°C through the thermoregulatory center, creating a biphasic temperature pattern. When serum lactone concentration reaches 12.72 nmol / L or higher, body temperature rises significantly, and this temperature rise coincides with the luteinization time. Therefore, BBT can objectively reflect the ovulation and luteinization process in women of reproductive age. However, current measurement instruments cannot comprehensively analyze a user's historical measurements, resulting in unstable predictions and limitations in guiding women towards natural methods of contraception and selecting the optimal conception period. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing detection methods and provide an ovulation predictor and its method based on cloud analysis technology. It is a next-generation ovulation predictor that uses a cloud computing server as a data processing center and has powerful computing power and storage space, thereby improving the accuracy and stability of ovulation prediction.

[0005] This invention is achieved by the following technical solution:

[0006] The ovulation analyzer based on cloud analysis technology includes a device terminal and a cloud computing server. The device terminal includes a main body, a main body control unit, a sensor probe, and a lithium battery power supply. A display screen and buttons are located on the outside of the main body, and the main body control unit and lithium battery power supply are located inside the main body. The display screen and buttons are respectively connected to the main body control unit.

[0007] The lithium battery power supply provides power to the display screen, sensor probes, and body control unit;

[0008] The display screen is used to display operation prompts, detection results, and historical detection results, etc.

[0009] The buttons are used for powering on / off the instrument, controlling the testing process, and viewing information.

[0010] The body control unit includes a temperature detection module connected to the temperature calculation module, a conductivity detection module connected to the conductivity calculation module, a data compression module and a data transmission module connected in sequence, and a data receiving module; the temperature calculation module and the conductivity calculation module are respectively connected to the data compression module;

[0011] The machine control unit is connected to the cloud computing server through a data transmission module and a data receiving module;

[0012] The sensing probe is connected to the temperature detection module and the conductivity detection module;

[0013] The sensing probe includes four detection electrodes: an NTC sensing electrode, an AC signal output electrode, and two AC signal input electrodes. The resistance of the NTC sensing electrode changes with temperature.

[0014] The temperature detection module and the temperature calculation module are controlled by the first microcontroller. The temperature detection module samples the voltage signal of the NTC sensing electrode of the sensor probe and outputs the sampled value to the temperature calculation module. The temperature calculation module converts and calculates the basal body temperature and outputs the basal body temperature value to the data compression module. At the same time, the basal body temperature value is stored in the Flash of the first microcontroller according to the date. The first microcontroller is a commercially available BH66F5233 microcontroller, which contains a 24-bit high-precision digital-to-analog converter.

[0015] The conductivity detection module and conductivity calculation module are controlled by a second microcontroller. The conductivity detection module samples saliva and outputs the sampled value to the conductivity calculation module. The conductivity calculation module converts the sampled value into conductivity and outputs it to the data compression module. At the same time, the conductivity is stored in the Flash memory of the second microcontroller according to the date. The second microcontroller is a commercially available HT66F0185 microcontroller.

[0016] The control unit of the machine body uses a microcontroller as the control chip for the display screen, buttons and data compression module; the microcontroller is a commercially available NRF52832 microcontroller.

[0017] The data compression module encapsulates and compresses the transmitted conductivity and base temperature data according to the communication protocol, and then outputs the compressed data to the data transmission module.

[0018] The data transmission module and the data receiving module both use GPRS modules. The data transmission module uploads the compressed data packets from the data compression module to the cloud computing server. The data receiving module receives the prediction data sent by the cloud computing server and outputs the prediction data to the microcontroller, which then controls the display screen to show the detection results. The GPRS module uses the commercially available Neoway M680 communication module.

[0019] The cloud computing server has an information feature extraction module, an information feature matching module, a trend judgment module, and a sending module that are connected in sequence.

[0020] The information feature extraction module decompresses and analyzes the compressed data transmitted from the data transmission module, and outputs the basal body temperature and salivary conductivity obtained from the decompression analysis to the information feature matching module.

[0021] The information feature matching module analyzes basal body temperature and salivary conductivity, performs big data model matching, and transmits the matching results to the trend judgment module for prediction and determination.

[0022] The trend judgment module sends the judgment result to the data receiving module through the sending module.

[0023] The ovulation tracking method based on cloud-based analytics includes the following steps:

[0024] 1) The temperature detection module converts the resistance value of the NTC sensing electrode into a voltage signal, performs AD sampling on the voltage signal, and outputs the sampled value to the temperature calculation module. The temperature calculation module, based on the principle of voltage division, converts the voltage signal into a resistance value and calculates the basal body temperature using a formula. The temperature calculation module outputs the basal body temperature value to the data compression module, and simultaneously stores the basal body temperature value in the Flash memory of the first microcontroller according to the date.

[0025] 2) The conductivity detection module applies a bipolar square wave pulse to the AC signal output electrode of the sensing probe, and simultaneously performs AD sampling on the input signals of the two AC signal input electrodes; the obtained sampled values ​​are output to the conductivity calculation module, using the basal body temperature obtained in step 1). Temperature compensation was applied to the salivary conductivity to obtain the final salivary conductivity. The conductivity calculation module outputs the conductivity to the data compression module, and simultaneously stores the conductivity in the Flash memory of the second microcontroller according to the date.

[0026] 3) The data compression module compresses the basal body temperature data obtained in steps 1) and 2). and salivary conductivity The packets are compressed and then transmitted to the cloud computing server via the data transmission module.

[0027] 4) The information feature extraction module in the cloud computing server parses the compressed data to obtain the basal body temperature. and salivary conductivity The information feature matching module matches the basal body temperature. and salivary conductivity The big data model is matched, and the trend judgment module makes the final prediction based on the output of the information feature matching module. The cloud computing server sends the prediction result to the data receiving module of the terminal device through the sending module.

[0028] Temperature sampling and basal body temperature detection in step 1) The method includes the following steps:

[0029] 1-1) The temperature detection module performs n consecutive AD samplings on the NTC sensing electrode of the sensing probe;

[0030] 1-2) Discard the maximum and minimum sampled values ​​from the n samples;

[0031] 1-3) The temperature value is calculated by averaging the remaining n-2 samples.

[0032]

[0033] 1-4) Convert the temperature value Ad obtained in step 1-3) into a resistance value Rt according to the principle of voltage division;

[0034] 1-5) Substitute the resistance value Rt obtained in step 1-4) into the polynomial fitting cubic curve formula:

[0035]

[0036] Obtain basal body temperature The accuracy can reach ±0.01℃;

[0037] 1-6) Store according to the testing date To the Flash memory of the BH66F5233 microcontroller;

[0038] The n≥5.

[0039] In step 2), AD sampling and salivary conductivity are performed. The method for obtaining it includes the following steps:

[0040] 2-1) The conductivity detection module applies a bipolar square wave pulse to the AC signal output electrode of the sensing probe, causing the AC signal output electrode to output a bipolar square wave signal.

[0041] 2-2) The conductivity calculation module performs AD sampling on the input signals, i.e., the voltage signals, of the two AC signal input electrodes;

[0042] 2-3) The conductivity calculation module performs a weighted average of the voltage signals from the two input electrodes and stores the sampled values;

[0043] 2-4) Repeat steps (a) to (c) n times, where n ≥ 5;

[0044] 2-5) Discard the maximum and minimum values ​​from the n samples, and take the average of the remaining n-2 samples. ,Will And substitute into the formula:

[0045]

[0046] Saliva conductivity before compensation In the formula

[0047] , , The value of n is 2473.32 (obtained from saliva tests on 33 women of childbearing age), Rc is the constant value of the feedback resistor (10 KΩ, determined by the circuit design), Vinput is the average voltage of the input AC square wave signal, and n≥5.

[0048] 2-6) Saliva conductivity before compensation obtained in step 2-5) Substitute into the temperature compensation formula and replace :

[0049]

[0050] The final temperature-compensated salivary conductivity In the formula

[0051] The temperature compensation coefficient is set to 0.142 (obtained from saliva tests on 33 women of childbearing age). Basal body temperature is The salivary conductivity before time compensation, k0 is the salivary conductivity value detected when the method is first used (no temperature compensation will be performed when it is first used).

[0052] 2-7) Saliva conductivity The data is stored in the Flash memory of the second microcontroller based on the detection date.

[0053] The present invention has the following advantages over the prior art:

[0054] 1. It enables simultaneous collection of oral saliva conductivity and basal body temperature in a single operation;

[0055] 2. Uploading data to cloud computing servers provides opportunities for big data analysis;

[0056] 3. Statistical analysis is performed using the powerful data processing and analysis capabilities of cloud computing servers, resulting in more accurate and stable results;

[0057] 4. During use, simply place the sensor probe under the tongue in the mouth to begin measurement, greatly simplifying the user experience. Attached Figure Description

[0058] The present invention will be further described below with reference to the accompanying drawings:

[0059] Figure 1 This is a structural principle block diagram of the present invention;

[0060] Figure 2 This is a schematic diagram of the saliva conductivity detection module of the present invention;

[0061] Figure 3 It is the fitted curve of resistance and temperature values, and the discrete value distribution of each resistance value on the curve.

[0062] In the diagram: 1. Body control unit; 2. Sensor probe; 3. Display screen; 4. Buttons; 5. Temperature detection module; 6. Temperature calculation module; 7. Conductivity detection module; 8. Conductivity calculation module; 9. Data compression module; 10. Lithium battery power supply; 11. Data transmission module; 12. Data receiving module; 13. Information feature extraction module; 14. Trend judgment module; 15. Transmission module; 16. Information feature matching module. Detailed Implementation

[0063] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0064] See attached document Figure 1 The ovulation analyzer based on cloud analysis technology includes a device terminal and a cloud computing server. The device terminal includes a body, a body control unit 1, a sensor probe 2, and a lithium battery power supply 10. A display screen 3 and buttons 4 are provided on the outside of the body, and the body control unit 1 and the lithium battery power supply 10 are provided inside the body. The display screen 3 and buttons 4 are respectively connected to the body control unit 1.

[0065] The body control unit 1 includes a temperature detection module 5 connected to the temperature calculation module 6, a conductivity detection module 7 connected to the conductivity calculation module 8, a data compression module 9 and a data transmission module 11 connected in sequence, and a data receiving module 12; the temperature calculation module 6 and the conductivity calculation module 8 are respectively connected to the data compression module 9;

[0066] The body control unit 1 is connected to the cloud computing server through the data transmission module 11 and the data receiving module 12;

[0067] The sensing probe 2 is connected to the temperature detection module 5 and the conductivity detection module 7;

[0068] The sensing probe 2 includes four detection electrodes, namely an NTC sensing electrode, an AC signal output electrode, and two AC signal input electrodes;

[0069] The cloud computing server has an information feature extraction module 13, an information feature matching module 16, a trend judgment module 14, and a sending module 15 connected in sequence.

[0070] Upon waking, without engaging in any activity, the user connects sensor probe 2 to the main control unit 1, places sensor probe 2 under the tongue in the mouth, and presses button 4 to begin detecting basal body temperature and salivary conductivity. The specific steps are as follows:

[0071] (1) Temperature detection module 5 samples the temperature of the NTC sensing electrode to detect the basal body temperature. And store according to date;

[0072] (2) The conductivity detection module 6 applies a bipolar square wave pulse to the output electrode and simultaneously performs AD sampling on the input signals of the two electrodes. Finally, it uses the basal body temperature. Temperature compensation was applied to the conductivity to obtain the final salivary conductivity. And store them according to date;

[0073] (3) The data compression module 9 will detect the basal body temperature. and salivary conductivity Packet compression is performed and transmitted to the cloud computing server through the data transmission module 11. The information extraction module 13 extracts the detection data and performs big data model matching through the information matching module 16. The trend judgment module 14 makes a prediction and judgment based on the final matching result. The cloud computing server sends the prediction and judgment result to the data receiving module 12 of the terminal device through the sending module 13.

[0074] Step (1) includes the following steps:

[0075] (a) Perform 5 consecutive AD samplings;

[0076] (b) Discard the largest and smallest sampled values ​​from the 5 samples;

[0077] (c) The temperature value is calculated by averaging the remaining 3 samples.

[0078] ;

[0079] (d) Convert the Ad obtained in step (c) into a resistance value Rt according to the principle of voltage divider;

[0080] (e) Substitute the resistance value Rt obtained in step (d) into the polynomial fitting cubic curve formula,

[0081]

[0082] Obtain basal body temperature The accuracy can reach ±0.01℃;

[0083] (f) Store according to measurement date It is stored in the Flash memory of the first microcontroller.

[0084] The resolution of the temperature value obtained by the lookup table method is ±1℃. Generally, the linear interpolation method is used to calculate the temperature value corresponding to Rt by substituting Rt as the independent variable into the interpolation formula formed by the correspondence between the two resistance values ​​and the temperature. However, the change of NTC with temperature is not linear, which leads to a certain error when using the linear interpolation method.

[0085] This implementation employs a polynomial curve fitting method, which better solves this problem. The normal body temperature range is set as 32℃-42℃. Using the resistance value within this temperature range as the independent variable and the temperature value as the dependent variable, a cubic curve fitting is performed. Figure 3 The figure shows the fitted curve and the discrete difference of each resistance value on the curve. It can be seen from the figure that the discrete difference is much smaller than ±0.01.

[0086] The flowchart for the execution of the conductivity detection module in step (2) is as follows: Figure 2 As shown:

[0087] (a) The AC signal output electrode outputs a 100Hz bipolar square wave signal;

[0088] (b) The input signals of the two AC signal input electrodes are rectified, filtered and amplified, and finally acquired as digital voltage signals by analog-to-digital conversion;

[0089] (c) Perform a weighted average of the voltage signals from the two AC signal input electrodes and store the sampled values;

[0090] (d) Repeat steps (a) to (c) 5 times;

[0091] (e) Discard the maximum and minimum values ​​from the 5 samples, and take the average of the remaining 3 samples. ,Will And substitute it into the formula,

[0092]

[0093] Saliva conductivity before compensation In the formula

[0094] , , Rc is the salivary conductivity coefficient (obtained from saliva tests on 33 women of childbearing age, with a value of 2473.32), Rc is the constant value of the feedback resistance (10 KΩ, determined by the circuit design), and Vinput is the average voltage of the input AC square wave signal.

[0095] (f) will Substitute into the temperature compensation formula and replace :

[0096]

[0097] The final temperature-compensated salivary conductivity In the formula

[0098] The temperature compensation coefficient (obtained from saliva tests on 33 women of childbearing age, with a value of 0.142) is used. Basal body temperature is The salivary conductivity before time compensation, k0 is the salivary conductivity value detected when the method is first used (no temperature compensation will be performed when it is first used).

[0099] (g) Saliva conductivity The date is stored in the Flash memory of the second microcontroller.

[0100] This invention fully leverages the advantages of cloud computing servers in terms of computational intensity and data mining capabilities, and has significant advantages in accuracy, stability, and additional diagnostic information.

[0101] Traditional ovulation detection methods typically use large medical imaging equipment or test strips to determine ovulation. This invention breaks down this process into two distributed stages, completed separately on the device terminal and a cloud computing server. This approach benefits from the powerful computing capabilities of the remote cloud server, allowing for statistical analysis of more data, thus providing more accurate and stable results and richer measurement feedback.

[0102] In this invention, the control unit built into the device terminal only needs to complete the preliminary processing of the collection and transmission of basic body temperature and salivary conductivity data, and send the data to a remote cloud computing server using a GSM / GPRS network. The cloud computing server combines and analyzes the current detection data with historical measurement data, and performs model matching using a more complex artificial intelligence algorithm. After obtaining the prediction result, the cloud computing server sends the prediction result to the device terminal, which then presents it to the user on a display screen.

Claims

1. An ovulation detection instrument based on cloud analysis technology, characterized in that: It includes a device terminal and a cloud computing server. The device terminal includes a body, a body control unit, a sensor probe, and a lithium battery power supply. A display screen and buttons are provided on the outside of the body, and a body control unit and a lithium battery power supply are provided inside the body. The display screen and buttons are respectively connected to the body control unit. The lithium battery power supply provides power to the display screen, sensor probes, and body control unit; The display screen is used to display operation prompts, detection results, and historical detection result information; The buttons are used for powering on / off the instrument, controlling the testing process, and viewing information. The body control unit includes a temperature detection module connected to the temperature calculation module, a conductivity detection module connected to the conductivity calculation module, a data compression module and a data transmission module connected in sequence, and a data receiving module; the temperature calculation module and the conductivity calculation module are respectively connected to the data compression module; The machine control unit is connected to the cloud computing server through a data transmission module and a data receiving module; The sensing probe is connected to the temperature detection module and the conductivity detection module; The sensing probe includes four detection electrodes: an NTC sensing electrode, an AC signal output electrode, and two AC signal input electrodes. The temperature detection module and the temperature calculation module are controlled by the first microcontroller; The conductivity detection module and the conductivity calculation module are controlled by a second microcontroller; The control unit of the machine body uses a microcontroller as the control chip for the display screen, buttons and data compression module; The data compression module encapsulates and compresses the transmitted conductivity and base temperature data according to the communication protocol, and then outputs the compressed data to the data transmission module. The data transmission module and the data receiving module both use a GPRS module. The data transmission module uploads the compressed data from the data compression module to the cloud computing server. The data receiving module is used to receive the prediction data sent by the cloud computing server and output the prediction data to the microcontroller, which then controls the display screen to show the detection results. The cloud computing server has an information feature extraction module, an information feature matching module, a trend judgment module, and a sending module connected in sequence; The information feature extraction module decompresses and analyzes the compressed data transmitted from the data transmission module, and outputs the basal body temperature and salivary conductivity obtained from the decompression analysis to the information feature matching module. The information feature matching module analyzes basal body temperature and salivary conductivity, performs big data model matching, and transmits the matching results to the trend judgment module for prediction and determination. The trend judgment module sends the judgment result to the data receiving module through the sending module.

2. The ovulation analyzer based on cloud analysis technology according to claim 1, characterized in that: The temperature detection module samples the voltage signal of the NTC sensing electrode of the sensor probe and outputs the sampled value to the temperature calculation module. The temperature calculation module converts and calculates the basal body temperature, and outputs the basal body temperature value to the data compression module. At the same time, the basal body temperature value is stored in the Flash memory of the first microcontroller according to the date.

3. The ovulation analyzer based on cloud analysis technology according to claim 1, characterized in that, The conductivity detection module samples saliva and outputs the sampled value to the conductivity calculation module. The conductivity calculation module converts the sampled value into conductivity and outputs it to the data compression module. At the same time, the conductivity is stored in the Flash memory of the second microcontroller according to the date.

4. A method for measuring ovulation using a cloud-based analysis technology as described in claim 1, characterized in that, Includes the following steps: 1) The temperature detection module converts the resistance of the NTC sensing electrode into a voltage signal, performs AD sampling on the voltage signal, and outputs the sampled value to the temperature calculation module. The temperature calculation module converts the voltage signal into a resistance value based on the principle of voltage division, and then calculates the basal body temperature according to the formula. The temperature calculation module outputs the basal body temperature value to the data compression module, and simultaneously stores the basal body temperature value in the Flash memory of the first microcontroller according to the date. 2) The conductivity detection module applies a bipolar square wave pulse to the AC signal output electrode of the sensing probe, and simultaneously performs AD sampling on the input signals of the two AC signal input electrodes; the obtained sampled values ​​are output to the conductivity calculation module, using the basal body temperature obtained in step 1). Temperature compensation was applied to the salivary conductivity to obtain the final salivary conductivity. The conductivity calculation module outputs the conductivity to the data compression module, and simultaneously stores the conductivity in the Flash memory of the second microcontroller according to the date. 3) The data compression module compresses the basal body temperature data obtained in steps 1) and 2). and salivary conductivity The packets are compressed and then transmitted to the cloud computing server via the data transmission module. 4) The information feature extraction module in the cloud computing server parses the compressed data to obtain the basal body temperature. and salivary conductivity The information feature matching module matches the basal body temperature. and salivary conductivity The big data model is matched, and the trend judgment module makes the final prediction based on the output of the information feature matching module. The cloud computing server sends the prediction result to the data receiving module of the terminal device through the sending module.

5. The ovulation detection method of the cloud-based analysis technology according to claim 4, characterized in that, Temperature sampling and basal body temperature detection in step 1) The method includes the following steps: 1-1) The temperature detection module performs n consecutive AD samplings on the NTC sensing electrode of the sensing probe; 1-2) Discard the maximum and minimum sampled values ​​from the n samples; 1-3) The temperature value is calculated by averaging the remaining n-2 samples. 1-4) Convert the temperature value Ad obtained in step 1-3) into a resistance value Rt according to the principle of voltage division; 1-5) Substitute the resistance value Rt obtained in step 1-4) into the polynomial fitting cubic curve formula: Obtain basal body temperature The accuracy can reach ±0.01℃; 1-6) Store according to the testing date To the Flash memory of the BH66F5233 microcontroller; The n≥5.

6. The ovulation detection method of the cloud-based analysis technology according to claim 4, characterized in that, In step 2), AD sampling and salivary conductivity are performed. The method for obtaining it includes the following steps: 2-1) The conductivity detection module applies a bipolar square wave pulse to the AC signal output electrode of the sensing probe, causing the AC signal output electrode to output a bipolar square wave signal. 2-2) The conductivity calculation module performs AD sampling on the input signals, i.e., the voltage signals, of the two AC signal input electrodes; 2-3) The conductivity calculation module performs a weighted average of the voltage signals from the two input electrodes and stores the sampled values; 2-4) Repeat steps (a) to (c) n times, where n ≥ 5; 2-5) Discard the maximum and minimum values ​​from the n samples, and take the average of the remaining n-2 samples. ,Will And substitute into the formula: Saliva conductivity before compensation In the formula , , is the salivary conductivity coefficient, with a value of 2473.32, Rc is the constant value of the feedback resistor, Vinput is the average voltage of the input AC square wave signal, and n≥5; 2-6) Saliva conductivity before compensation obtained in step 2-5) Substitute into the temperature compensation formula and replace : The final temperature-compensated salivary conductivity In the formula The temperature compensation coefficient is set to 0.142 (obtained from saliva tests on 33 women of childbearing age). Basal body temperature is The salivary conductivity before temperature compensation, k0 is the salivary conductivity value detected when the method is first used. No temperature compensation will be performed when the method is first used. 2-7) Saliva conductivity The data is stored in the Flash memory of the second microcontroller based on the detection date.

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