Intelligent portable pulse wave measuring watch

Abstract

The application discloses a kind of intelligent portable pulse wave determination watches, it is related to pulse wave determination technical field, to solve the problem of not enough convenient when determining pulse and the problem of inaccurate measurement result.The intelligent portable pulse wave determination watch, including dial and pulse sensor, the dial upper end is with upper band, the inner wall of upper band is provided with clamping block, the dial bottom end is connected with lower band one end, a plurality of clamping holes are opened in lower band, three groups of sensors are used different measurement methods to measure pulse, three groups of collected pulse measurement data can be obtained after measurement, so that data acquisition is no longer single, through the calculation of later stage, pulse data is more accurate, different data threshold is divided into multiple groups, after division, the data is divided into different levels according to grade alarm module, alarm sound can be spread out through loudspeaker, normal pulse data can be displayed in electronic display screen.

Description

Technical Field

[0001] This invention relates to the field of pulse wave measurement technology, specifically to an intelligent portable wristwatch for pulse wave measurement. Background Technology

[0002] The pulse wave is formed by the heart's beating (vibration) propagating outwards along arteries and blood flow. Current pulse wave measurement methods still have the following problems:

[0003] 1. When measuring pulse, the measuring instruments are inconvenient and the measurement methods are too limited, resulting in inaccurate results.

[0004] 2. Once the pulse data is collected, it is transmitted directly without any evaluation or decision-making regarding the data values, thus lacking the data's ability to make its own judgments. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent portable pulse wave measuring watch. Three sets of sensors use different measurement methods to measure the pulse, resulting in three sets of collected pulse measurement data. This makes data acquisition more comprehensive, and subsequent calculations make the pulse data more accurate. Different data thresholds are distinguished into multiple groups, and then an alarm module classifies the data into different levels of intensity. A higher abnormality index threshold results in a higher alarm index, and vice versa. Finally, an alarm can be triggered, and the alarm sound can be amplified through a speaker. The higher the alarm index, the louder the sound; the lower the alarm index, the quieter the sound. Normal pulse data can be displayed on an electronic screen, thus solving the problems of existing technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A smart portable wristwatch for measuring pulse waves includes a dial and a pulse sensor. The upper end of the dial is connected to an upper strap, and a locking block is provided on the inner wall of the upper strap. The bottom end of the dial is connected to one end of a lower strap, and multiple locking holes are provided on the lower strap. A limiting strip is installed on the other end of the lower strap. An electronic display screen and a speaker are provided on the outer wall of the dial, and a pulse sensor is installed on the inner wall of the dial.

[0008] The pulse sensor includes a pressure sensing system, an AD conversion unit, a detection decision system, and an alarm system;

[0009] Pressure sensing system, used for:

[0010] Different methods of data collection are used to collect data based on the pressure of the pulse.

[0011] The AD conversion unit is used for:

[0012] The pressure sensing system converts pressure change data into electrical signals that can be observed and detected more intuitively.

[0013] Detection and decision-making system, used for:

[0014] The AD conversion unit performs data calculations on the detected data and statistical analysis on abnormal data.

[0015] Alarm systems are used for:

[0016] Based on the data calculated in the detection decision system, abnormal parts of the data are processed with alarms according to their abnormality level.

[0017] Preferably, the pressure sensing system includes:

[0018] The pulse signal acquisition module is used for:

[0019] Data on pressure changes generated during arterial pulsation were collected using multiple acquisition methods;

[0020] Categorized storage units, used for:

[0021] Data collected using different acquisition methods is categorized and saved accordingly.

[0022] Preferably, the pulse signal acquisition module includes:

[0023] Piezoelectric acquisition unit, used for:

[0024] Sensors based on the piezoelectric effect measure the non-electrical physical quantities that convert the force and energy of blood vessels into electricity during a pulse.

[0025] Piezoresistive acquisition unit, used for:

[0026] Based on piezoresistive sensors, the pressure, tension, pressure difference, and other physical quantities that can be converted into force changes in blood vessels during a pulse (such as liquid level, acceleration, weight, strain, flow rate, and vacuum) are measured.

[0027] Photoelectric acquisition unit, used for:

[0028] The photoelectric sensor detects that when blood vessels are exposed to visible light during a pulse, the photoelectric effect is generated, and the light signal is converted into an electrical signal for output.

[0029] Preferably, the piezoelectric acquisition unit is further used for:

[0030] When the crystal in a piezoelectric sensor is subjected to a fixed-direction external force during a pulse beat, polarization occurs inside, and at the same time, opposite charges are generated on the surface of the piezoresistive sensor. When the external force is removed, the crystal returns to its uncharged state. When the direction of the external force changes, the polarity of the charge also changes. The amount of charge generated by the crystal under force is proportional to the magnitude of the external force.

[0031] Preferably, the piezoresistive acquisition unit is further used for:

[0032] In a piezoresistive sensor, a set of equivalent resistors is diffused in a specific direction on a single-crystal silicon wafer using integrated circuit technology. These resistors are then connected in a bridge circuit, and the single-crystal silicon wafer is placed inside the sensor cavity. When the pressure changes, the single-crystal silicon experiences strain, causing the strain resistors diffused directly on it to change in a manner proportional to the measured pressure. The corresponding voltage output signal is then obtained by the bridge circuit.

[0033] Preferably, the photoelectric acquisition unit is further used for:

[0034] Photoelectric effect sensors utilize three types of sensors: external photoelectric effect, internal photoelectric effect, and photovoltaic effect. When a pulse beats, the optical distance between the pulse contact surface and the photoelectric effect sensor generates a photoelectric electromotive force.

[0035] Preferably, the detection decision system includes:

[0036] The data receiving module is used for:

[0037] Based on the multiple pulse acquisition methods in the pressure sensing system, various acquisition data results are received;

[0038] The data comparison module is used for:

[0039] The data transmitted in the data receiving module is compared with the data stored at the normal threshold.

[0040] The comparison data calculation module is used for:

[0041] The numerical difference is calculated between the data within the normal range in the data comparison module and the collected data.

[0042] The data reading module is used for:

[0043] The results of the difference calculation are read, and the read values ​​are effectively tested.

[0044] The anomaly classification module is used for:

[0045] The valid abnormal data is grouped and categorized according to the magnitude of the values.

[0046] A type of storage module used for:

[0047] Store multiple sets of packaged abnormal data values.

[0048] Preferably, the alarm system includes:

[0049] The abnormal data reading module is used for:

[0050] The detection and decision system receives and reads multiple sets of packaged values ​​of abnormal data.

[0051] The anomaly level classification module is used for:

[0052] Based on multiple sets of packaged values ​​read from the abnormal data reading module, threshold distinctions are made for them;

[0053] The larger the threshold value, the higher the anomaly index; the smaller the threshold value, the lower the anomaly index.

[0054] The graded alarm module is used for:

[0055] Based on the threshold values ​​distinguished in the anomaly level classification module, alarm indices are divided.

[0056] The higher the anomaly index of a threshold, the higher the alarm index; the lower the anomaly index of a threshold, the lower the alarm index.

[0057] Alarm storage module, used for:

[0058] All abnormal data in the graded alarm module is saved.

[0059] Preferably, the abnormal data reading module is further configured to:

[0060] The target values ​​of the receiving terminal importance of each group of packaged data that are greater than or equal to a preset threshold are statistically analyzed.

[0061] Acquire historical successful transmission data for each data receiving terminal, parse the historical successful transmission data to determine its integrity and security, and assess the threat risk index and vulnerability risk index of the data receiving terminal based on the integrity and security.

[0062] The security index of each data receiving terminal is calculated using a preset risk assessment system based on the threshold value of the target value for each data receiving terminal and the threat risk index and vulnerability risk index of that data receiving terminal.

[0063] Preferably, the computational data reading module further includes:

[0064] A sequence determination unit is used to acquire the numerical sequence of the data difference calculation, perform periodic detection on the numerical sequence, and determine whether the numerical sequence is a periodic sequence.

[0065] The sequence analysis unit is used to, when the numerical sequence is determined to be a periodic sequence, divide the numerical sequence into multiple identical first subsequences according to the period, determine whether all values ​​in the first subsequence are greater than a preset value, if so, extract the first abnormal value in the first subsequence that is greater than the preset value, determine the time interval between adjacent first abnormal values, and determine whether the time interval is within the preset time interval range, if so, take the first abnormal value and the time interval as the first abnormal data, otherwise, determine that the first abnormal value is invalid.

[0066] The sequence analysis unit is further configured to, after determining that the numerical sequence is an aperiodic sequence, perform clustering operations on the numerical sequence using a one-dimensional clustering method to obtain multiple split points, and divide the numerical sequence using the multiple split points to obtain multiple different second subsequences, obtain a third subsequence from the second subsequences that has a value greater than a preset value, and determine the abnormal time interval of the third subsequence based on the position of the third subsequence in the numerical sequence, and obtain a fourth subsequence from the adjacent third subsequence whose abnormal time interval is within the preset time interval range, and use the fourth subsequence and the abnormal time interval as second abnormal data;

[0067] The data integration unit is used to periodically label the first abnormal data to obtain first valid abnormal data, to non-periodically label the second abnormal data to obtain second valid abnormal data, and to send the first valid abnormal data and the second valid abnormal data as the final valid abnormal data value to the anomaly classification module.

[0068] Preferably, before converting the optical signal into an electrical signal for output, the method further includes:

[0069] The optical signal is divided into multiple sub-signal waves, and the power and wavelength of each sub-signal wave are detected;

[0070] Select an appropriate phase matching factor based on the phase change of each sub-signal wave;

[0071] The multi-wave mixing efficiency of the optical signal is calculated based on the above parameters:

[0072]

[0073] Where A represents the multi-wave mixing efficiency of the optical signal, a represents the preset signal wave mixing loss factor, Δb represents the phase matching factor, sin represents the sine function, N represents the number of sub-signal waves, i represents the i-th sub-signal wave, and S i Let represent the wavelength of the i-th sub-signal wave, and e represent the natural constant with a value of 2.72.′ It is expressed as the average wavelength of the sub-signal wave;

[0074] The peak power of the optical signal's multi-wave mixing is calculated based on the multi-wave mixing efficiency of the optical signal and the power of each sub-signal wave:

[0075]

[0076] Among them, P ′ P represents the peak power of the multi-wave mixing of the optical signal, j represents the j-th sub-signal wave, and P represents the peak power of the multi-wave mixing of the optical signal. j Let represent the power of the j-th sub-signal wave, α represent the nonlinear coefficient, ln represent the natural logarithm, and d represent the preset multi-wave mixing degeneracy factor;

[0077] The conversion parameters of the photoelectric element in the sensor based on the photoelectric effect are set according to the peak power of the multi-wave mixing of the optical signal.

[0078] The sensor based on the photoelectric effect is controlled to convert optical signals into electrical signals for output according to the set conversion parameters.

[0079] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0080] 1. The present invention provides an intelligent portable pulse wave measuring watch. The pulse sensor has three sets of sensor devices to measure the frequency of the pulse in the user's blood vessels. The three sets of sensors are a piezoelectric effect sensor, a piezoresistive sensor, and a photoelectric effect sensor. The three sets of sensors use different measurement methods to measure the pulse. After measurement, three sets of collected pulse measurement data can be obtained, making the data collection no longer singular. After subsequent calculation, the pulse data is also more accurate.

[0081] 2. This invention provides an intelligent portable pulse wave measuring watch. In a piezoelectric sensor, when the crystal is subjected to a fixed-direction external force during a pulse beat, internal polarization occurs, simultaneously generating opposite charges on the surface of the piezoresistive sensor. When the external force is removed, the crystal returns to its uncharged state. The polarity of the charge changes with the direction of the external force. The amount of charge generated by the crystal under force is proportional to the magnitude of the external force. In the piezoresistive sensor, a single-crystal silicon wafer is diffused with a set of equivalent resistors in a specific direction using integrated circuit technology, and these resistors are connected in a bridge circuit. The single-crystal silicon wafer is placed inside the sensor cavity. When the pressure changes, the single-crystal silicon experiences strain, causing the strain resistors directly diffused on it to change in a proportionate manner to the measured pressure. The corresponding voltage output signal is then obtained by the bridge circuit. The photoelectric sensor utilizes three types of effects: external photoelectric effect, internal photoelectric effect, and photovoltaic effect. During a pulse beat, the optical distance between the pulse contact surface and the photoelectric sensor generates a photoelectric electromotive force.

[0082] 3. This invention provides an intelligent portable pulse wave measuring watch. A comparison data calculation module compares abnormal data with normal threshold data to determine the value of the abnormal data. An abnormal data classification module then groups and categorizes the abnormal data according to their magnitude. An abnormal data reading module receives the grouped abnormal data and categorizes it using an abnormal level classification module. Different data thresholds are grouped into multiple sets. An alarm level module then classifies the data according to their strength, with higher threshold values ​​indicating a higher abnormality index and lower threshold values ​​indicating a lower abnormality index. A higher abnormality index corresponds to a higher alarm level, and vice versa. Finally, an alarm is triggered, with the alarm sound amplified by a speaker. The alarm sound is louder for higher alarm levels and quieter for lower alarm levels. Normal pulse data can be displayed on an electronic screen. Attached Figure Description

[0083] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0084] Figure 2 This is a schematic diagram of the rear view structure of the present invention;

[0085] Figure 3 This is a schematic diagram of the pulse sensor module of the present invention;

[0086] Figure 4 This is a schematic diagram of the pressure sensing system module of the present invention;

[0087] Figure 5 This is a schematic diagram of the pulse signal acquisition module of the present invention;

[0088] Figure 6 This is a schematic diagram of the detection decision system module of the present invention;

[0089] Figure 7 This is a schematic diagram of the alarm system module of the present invention.

[0090] In the diagram: 1. Dial; 11. Upper strap; 12. Locking block; 13. Lower strap; 14. Locking hole; 15. Limiting strip; 16. Electronic display screen; 17. Speaker; 2. Pulse sensor. Detailed Implementation

[0091] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] To address the problems of inconvenient measuring instruments, limitations in measurement methods, and inaccurate results in existing pulse measurement techniques, please refer to [link to relevant documentation]. Figures 1-5 This embodiment provides the following technical solution:

[0093] A smart portable wristwatch for measuring pulse waves includes a dial 1 and a pulse sensor 2. The upper end of the dial 1 is connected to an upper strap 11, and a latch 12 is provided on the inner wall of the upper strap 11. The bottom end of the dial 1 is connected to one end of a lower strap 13, and multiple latching holes 14 are provided on the lower strap 13. A limiting strip 15 is installed on the other end of the lower strap 13. An electronic display screen 16 and a speaker 17 are provided on the outer wall of the dial 1, and the pulse sensor 2 is installed on the inner wall of the dial 1.

[0094] The pulse sensor 2 includes a pressure sensing system, an AD conversion unit, a detection decision system, and an alarm system. The pressure sensing system is used to collect data in different ways based on the pressure of the pulse. The AD conversion unit is used to convert the pressure change data into an electrical signal that can be more intuitively observed and detected based on the pressure sensing system. The detection decision system is used to perform data calculations on the detected data based on the AD conversion unit and to statistically analyze abnormal data. The alarm system is used to trigger an alarm based on the abnormal part of the data according to the abnormality level, based on the data calculated by the detection decision system.

[0095] The pressure sensing system includes: a pulse signal acquisition module, used for:

[0096] The pulse signal acquisition module collects data on pressure changes generated during arterial pulsation using multiple acquisition methods; a classification and storage unit is used to classify and store data acquired by different acquisition methods. The module includes: a piezoelectric acquisition unit for measuring the force and non-electrical physical quantities that can be converted into electricity during pulse pulsation using a piezoelectric effect sensor; a piezoresistive acquisition unit for measuring pressure, tension, pressure difference, and other physical quantities that can be converted into force (such as liquid level, acceleration, weight, strain, flow rate, and vacuum) during pulse pulsation using a piezoresistive sensor; and a photoelectric acquisition unit for converting light signals into electrical signals by using a photoelectric effect sensor to detect the photoelectric effect generated when blood vessels are irradiated with visible light during pulse pulsation.

[0097] The piezoelectric acquisition unit is further configured to: when the crystal in the piezoelectric sensor is subjected to a fixed-direction external force during a pulse beat, an internal polarization phenomenon is generated, and at the same time, charges of opposite sign are generated on the surface of the piezoresistive sensor; when the external force is removed, the crystal returns to an uncharged state; when the direction of the external force changes, the polarity of the charge also changes accordingly; the amount of charge generated by the crystal under force is proportional to the magnitude of the external force. The piezoelectric acquisition unit is further configured to: using integrated circuit technology, a set of equivalent resistors is diffused in a specific direction on the single-crystal silicon wafer in the piezoresistive sensor, and the resistors are connected in a bridge circuit; the single-crystal silicon wafer is placed inside the sensor cavity. When the pressure changes, the single-crystal silicon generates strain, causing the strain resistor directly diffused on it to change in proportion to the measured pressure. The corresponding voltage output signal is then obtained by the bridge circuit. The photoelectric acquisition unit is also used for: the photoelectric effect sensor utilizes three types of photoelectric effects: external photoelectric effect, internal photoelectric effect, and photovoltaic effect. When the pulse beats, the optical distance between the pulse contact surface and the photoelectric effect sensor forms a photoelectric electromotive force.

[0098] Specifically, when the user wears the watch, one end of the upper strap 11 passes through the limiting strip 15, and then is adjusted according to the user's size. After adjustment, the clip 12 is inserted into the clip hole 14. After wearing, the pulse sensor 2 directly contacts the user's skin. After contact, the three sets of sensor devices in the pulse sensor 2 measure the frequency of the pulse in the user's blood vessels. The three sets of sensors are a piezoelectric effect sensor, a piezoresistive sensor, and a photoelectric effect sensor. The three sets of sensors use different measurement methods to measure the pulse. After measurement, three sets of collected pulse measurement data can be obtained, making the data collection no longer singular. After subsequent calculation, the pulse data is also more accurate.

[0099] To address the problem in existing technologies where pulse data is directly transmitted after acquisition without any evaluation or decision-making regarding the data values, thus lacking the data's self-judgment capability, please refer to [the relevant documentation / reference]. Figures 6-7 This embodiment provides the following technical solution:

[0100] The detection and decision-making system includes: a data receiving module for receiving various data acquisition results based on multiple pulse acquisition methods in the pressure sensing system; a data comparison module for comparing the data transmitted in the data receiving module with the data stored at normal thresholds; a comparison data calculation module for calculating the numerical difference between the data within the normal range in the data comparison module and the acquired data; a calculation data reading module for reading the results of the difference calculation and validally detecting the read values; an anomaly classification module for grouping and classifying valid anomaly data according to their numerical values; and a single-class storage module for storing multiple groups of packaged anomaly data values.

[0101] The alarm system includes: an abnormal data reading module, used to receive and read multiple sets of packaged values ​​of abnormal data based on the detection decision system; an abnormal level classification module, used to classify the multiple sets of packaged values ​​read from the abnormal data reading module into thresholds; wherein the larger the threshold value, the higher the abnormal index, and the smaller the threshold value, the lower the abnormal index; a level alarm module, used to classify alarm indices based on the threshold values ​​classified in the abnormal level classification module; wherein the higher the threshold value of the abnormal index, the higher the alarm index, and the lower the threshold value of the abnormal index, the lower the alarm index; and an alarm storage module, used to save all abnormal data from the level alarm module. The abnormal data reading module is further used to: statistically analyze the target values ​​of each packaged data receiving terminal whose importance is greater than or equal to a preset threshold; acquire historical successful transmission data for each data receiving terminal, analyze the historical successful transmission data to determine its integrity and security, and assess the threat risk index and vulnerability risk index of the data receiving terminal based on the integrity and security; and calculate the security index of the data receiving terminal using a preset risk assessment system based on the threshold value of the target value of each data receiving terminal and the threat risk index and vulnerability risk index of the data receiving terminal.

[0102] Specifically, the data receiving module first receives pulse data collected by piezoelectric, piezoresistive, and photoelectric sensors. After receiving the data, the data comparison module compares the three sets of collected data values ​​with threshold values ​​within the normal range. Data values ​​above or below the normal threshold are extracted; these are considered abnormal data. The abnormal data calculation module then calculates the abnormal data value by comparing it with the normal threshold. Finally, the abnormal data classification module groups and categorizes the abnormal data values ​​according to their magnitude. The abnormal data reading module then reads the grouped data. Abnormal data is received and then classified by the abnormality level classification module. Different data thresholds are divided into multiple groups. After classification, the data is further classified into different levels of intensity by the level alarm module. The larger the threshold value, the higher the abnormality index, and the smaller the threshold value, the lower the abnormality index. The higher the abnormality index of the threshold, the higher the alarm index, and the lower the abnormality index, the lower the alarm index. Finally, alarm processing can be performed. The alarm sound can be transmitted through the speaker 17. The higher the alarm index, the louder the sound, and the lower the alarm index, the quieter the sound. Normal pulse data can be displayed on the electronic display screen 16.

[0103] In one embodiment, the computational data reading module further includes:

[0104] A sequence determination unit is used to acquire the numerical sequence of the data difference calculation, perform periodic detection on the numerical sequence, and determine whether the numerical sequence is a periodic sequence.

[0105] The sequence analysis unit is used to, when the numerical sequence is determined to be a periodic sequence, divide the numerical sequence into multiple identical first subsequences according to the period, determine whether all values ​​in the first subsequence are greater than a preset value, if so, extract the first abnormal value in the first subsequence that is greater than the preset value, determine the time interval between adjacent first abnormal values, and determine whether the time interval is within the preset time interval range, if so, take the first abnormal value and the time interval as the first abnormal data, otherwise, determine that the first abnormal value is invalid.

[0106] The sequence analysis unit is further configured to, after determining that the numerical sequence is an aperiodic sequence, perform clustering operations on the numerical sequence using a one-dimensional clustering method to obtain multiple split points, and divide the numerical sequence using the multiple split points to obtain multiple different second subsequences, obtain a third subsequence from the second subsequences that has a value greater than a preset value, and determine the abnormal time interval of the third subsequence based on the position of the third subsequence in the numerical sequence, and obtain a fourth subsequence from the adjacent third subsequence whose abnormal time interval is within the preset time interval range, and use the fourth subsequence and the abnormal time interval as second abnormal data;

[0107] The data integration unit is used to periodically label the first abnormal data to obtain first valid abnormal data, to non-periodically label the second abnormal data to obtain second valid abnormal data, and to send the first valid abnormal data and the second valid abnormal data as the final valid abnormal data value to the anomaly classification module.

[0108] The working principle of the above design scheme is as follows: First, the numerical sequence is periodically determined. Different methods are used to analyze periodic and non-periodic sequences to ensure the focus and efficiency of sequence analysis. Specifically, the periodic sequence is divided into identical first subsequences. The values ​​of the first subsequences and the time intervals between the first outlier values ​​are judged. While ensuring that the values ​​meet the outlier requirements, the time intervals are also judged. A time interval exceeding a preset limit indicates that the time between two first outlier values ​​is too long and cannot be considered valid outlier data. The first outlier value and the time interval are used as the first outlier data, reducing redundancy and ensuring the accuracy of the obtained first outlier data. However, a different sequence analysis method is used for non-periodic sequences. Specifically, a one-dimensional clustering method is first used to cluster the numerical sequence to obtain multiple segmentation points, making the segmentation more accurate and objective, which is beneficial for subsequent analysis. The anomaly analysis provides a basis for segmentation. After dividing the aperiodic sequence, a third subsequence larger than a preset value is extracted. Based on the position of the third subsequence in the aperiodic sequence, the abnormal time interval of the third subsequence is determined. Similarly, the abnormal time intervals are judged, and the third subsequences outside the preset time interval range are removed to obtain a fourth subsequence. Finally, the fourth subsequence and the abnormal time intervals are used as the second anomaly data to ensure the accuracy of the obtained second anomaly data. Finally, the first anomaly data is periodically labeled to obtain the first valid anomaly data, and the second anomaly data is nonperiodically labeled to obtain the second valid anomaly data. The first and second valid anomaly data are used as the final valid anomaly data to ensure the clarity and accuracy of the final valid anomaly data, thereby providing a basis for judging and making decisions on the data values ​​and facilitating the self-judgment of the data.

[0109] The beneficial effects of the above design scheme are as follows: First, the numerical sequence is periodically judged, and different methods are used to analyze periodic and non-periodic sequences to ensure the focus and efficiency of sequence analysis. The periodic sequence is divided into the same first subsequence for numerical judgment and time interval judgment. While ensuring that the numerical value meets the abnormality requirements, the time interval is also judged. If the time interval exceeds the preset time interval, it means that the time between two first abnormal values ​​is too long and cannot be regarded as valid abnormal data. The first abnormal value and time interval are regarded as the first abnormal data, reducing the redundancy of abnormal data and ensuring the accuracy of the obtained first abnormal data. For non-periodic sequences, the numerical sequence is first clustered by a one-dimensional clustering method to obtain multiple split points, making the division of split points more accurate and objective. After the non-periodic sequence is divided, the numerical value and abnormal time interval are judged in the same way, and finally the second abnormal data is obtained, ensuring the accuracy of the obtained second abnormal data. Finally, the first and second abnormal data are labeled as the final valid abnormal data, ensuring the clarity and accuracy of the final valid abnormal data, thereby providing a basis for judging and making decisions on the numerical value of the data and facilitating the self-judgment of the data.

[0110] In one embodiment, before converting the optical signal into an electrical signal for output, the method further includes:

[0111] The optical signal is divided into multiple sub-signal waves, and the power and wavelength of each sub-signal wave are detected;

[0112] Select an appropriate phase matching factor based on the phase change of each sub-signal wave;

[0113] The multi-wave mixing efficiency of the optical signal is calculated based on the above parameters:

[0114]

[0115] Where A represents the multi-wave mixing efficiency of the optical signal, a represents the preset signal wave mixing loss factor, Δb represents the phase matching factor, sin represents the sine function, N represents the number of sub-signal waves, i represents the i-th sub-signal wave, and S i Let represent the wavelength of the i-th sub-signal wave, and e represent the natural constant with a value of 2.72. ′ It is expressed as the average wavelength of the sub-signal wave;

[0116] The peak power of the optical signal's multi-wave mixing is calculated based on the multi-wave mixing efficiency of the optical signal and the power of each sub-signal wave:

[0117]

[0118] Among them, P ′P represents the peak power of the multi-wave mixing of the optical signal, j represents the j-th sub-signal wave, and P represents the peak power of the multi-wave mixing of the optical signal. j Let represent the power of the j-th sub-signal wave, α represent the nonlinear coefficient, ln represent the natural logarithm, and d represent the preset multi-wave mixing degeneracy factor;

[0119] The conversion parameters of the photoelectric element in the sensor based on the photoelectric effect are set according to the peak power of the multi-wave mixing of the optical signal.

[0120] The sensor based on the photoelectric effect is controlled to convert optical signals into electrical signals for output according to the set conversion parameters.

[0121] The beneficial effects of the above technical solution are as follows: by calculating the multi-wave mixing efficiency of the optical signal, the mixing efficiency of the optical signal under the condition of multi-sub-signal wave division can be effectively determined, thereby evaluating the stability when it is converted into an electrical signal, improving working efficiency. Furthermore, by calculating the peak power of the multi-wave mixing of the optical signal and adjusting the conversion parameters of the photoelectric components in the sensor, the success rate and reliability of converting the optical signal into an electrical signal can be further guaranteed, further improving working efficiency and stability.

[0122] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0123] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart portable wristwatch for measuring pulse waves, comprising a dial (1) and a pulse sensor (2), characterized in that: The upper end of the dial (1) is connected to the upper strap (11), and the inner wall of the upper strap (11) is provided with a locking block (12). The bottom end of the dial (1) is connected to one end of the lower strap (13), and multiple locking holes (14) are opened on the lower strap (13). A limiting strip (15) is installed on the other end of the lower strap (13). An electronic display screen (16) and a speaker (17) are provided on the outer wall of the dial (1). A pulse sensor (2) is installed on the inner wall of the dial (1). The pulse sensor (2) includes a pressure sensing system, an AD conversion unit, a detection decision system, and an alarm system; Pressure sensing system, used for: Different methods of data collection are used to collect data based on the pressure of the pulse. The AD conversion unit is used for: The pressure sensing system converts pressure change data into electrical signals that can be observed and detected more intuitively. Detection and decision-making system, used for: The AD conversion unit performs data calculations on the detected data and statistical analysis on abnormal data. Alarm systems are used for: Based on the data calculated in the detection decision system, the abnormal parts of the data are processed with alarms according to the abnormality level; Pressure sensing system, including: The pulse signal acquisition module is used for: Data on pressure changes generated during arterial pulsation were collected using multiple acquisition methods; Categorized storage units, used for: Data collected using different acquisition methods is categorized and saved accordingly; The pulse signal acquisition module includes: Piezoelectric acquisition unit, used for: Sensors based on the piezoelectric effect measure the non-electrical physical quantities that convert the force and energy of blood vessels into electricity during a pulse. Piezoresistive acquisition unit, used for: This measurement is based on piezoresistive sensors to measure the physical quantities of pressure, tension, pressure difference, and their conversion into force in blood vessels during a pulse. Photoelectric acquisition unit, used for: The photoelectric effect-based sensor detects the photoelectric effect generated when blood vessels are exposed to visible light during a pulse beat, and converts the light signal into an electrical signal for output. Before converting the optical signal into an electrical signal for output, it also includes: The optical signal is divided into multiple sub-signal waves, and the power and wavelength of each sub-signal wave are detected; Select an appropriate phase matching factor based on the phase change of each sub-signal wave; The multi-wave mixing efficiency of the optical signal is calculated based on the above parameters: in, This is expressed as the multi-wave mixing efficiency of optical signals. This is expressed as the preset signal wave mixing loss factor. Let be the phase matching factor, sin be the sine function, N be the number of sub-signal waves, and i be the i-th sub-signal wave. Let represent the wavelength of the i-th sub-signal wave, and e represent the natural constant with a value of 2.

72. It is expressed as the average wavelength of the sub-signal wave; The peak power of the optical signal's multi-wave mixing is calculated based on the multi-wave mixing efficiency of the optical signal and the power of each sub-signal wave: in, Let represent the peak power of the multi-wave mixing of the optical signal, and j represent the j-th sub-signal wave. Let the power of the j-th sub-signal wave be denoted as . It is represented by nonlinear coefficients, ln represents the natural logarithm, and d represents the preset multi-wave mixing degeneracy factor; The conversion parameters of the photoelectric element in the sensor based on the photoelectric effect are set according to the peak power of the multi-wave mixing of the optical signal. The sensor based on the photoelectric effect is controlled to convert optical signals into electrical signals for output according to the set conversion parameters.

2. The intelligent portable pulse wave measuring watch according to claim 1, characterized in that: The piezoelectric acquisition unit is also used for: When the crystal in a piezoelectric sensor is subjected to a fixed-direction external force during a pulse beat, internal polarization occurs, and at the same time, opposite charges are generated on the surface of the piezoresistive sensor.

3. The intelligent portable pulse wave measuring watch according to claim 2, characterized in that: The piezoresistive acquisition unit is also used for: In a piezoresistive sensor, a single-crystal silicon wafer is diffused with an integrated circuit in a specific direction of the single-crystal silicon, and the resistors are connected in a bridge circuit. The single-crystal silicon wafer is placed inside the sensor cavity. When the pressure changes, the single-crystal silicon generates strain, causing the strain resistor diffused on it to change in a proportional manner to the measured pressure. The corresponding voltage output signal is then obtained by the bridge circuit. The photoelectric acquisition unit is also used for: Photoelectric effect sensors utilize the external photoelectric effect, internal photoelectric effect, and photovoltaic effect to generate a photoelectric electromotive force by the optical distance between the pulse contact surface and the photoelectric effect sensor when the pulse beats.

4. The intelligent portable pulse wave measuring watch according to claim 3, characterized in that: The detection decision system includes: The data receiving module is used for: Based on the multiple pulse acquisition methods in the pressure sensing system, various acquisition data results are received; The data comparison module is used for: The data transmitted in the data receiving module is compared with the data stored at the normal threshold. The comparison data calculation module is used for: The numerical difference is calculated between the data within the normal range in the data comparison module and the collected data. The data reading module is used for: Read the results obtained from the difference calculation and then test the read values. The anomaly classification module is used for: The valid abnormal data is grouped and categorized according to the magnitude of the values. A type of storage module used for: Store multiple sets of packaged abnormal data values.

5. The intelligent portable pulse wave measuring watch according to claim 4, characterized in that: The alarm system includes: The abnormal data reading module is used for: The detection and decision system receives and reads multiple sets of packaged values ​​of abnormal data. The anomaly level classification module is used for: Based on multiple sets of packaged values ​​read from the abnormal data reading module, threshold distinctions are made for them; The larger the threshold value, the higher the anomaly index; the smaller the threshold value, the lower the anomaly index. The graded alarm module is used for: Based on the threshold values ​​distinguished in the anomaly level classification module, alarm indices are divided. The higher the anomaly index of a threshold, the higher the alarm index; the lower the anomaly index of a threshold, the lower the alarm index. Alarm storage module, used for: All abnormal data in the graded alarm module is saved.

6. The intelligent portable pulse wave measuring watch according to claim 5, characterized in that: The abnormal data reading module is also used for: The target values ​​of the receiving terminal importance of each group of packaged data that are greater than or equal to a preset threshold are statistically analyzed. Acquire historical successful transmission data for each data receiving terminal, parse the historical successful transmission data to determine its integrity and security, and assess the threat risk index and vulnerability risk index of the data receiving terminal based on the integrity and security. The security index of each data receiving terminal is calculated using a preset risk assessment system based on the threshold value of the target value for each data receiving terminal and the threat risk index and vulnerability risk index of that data receiving terminal.

7. The intelligent portable pulse wave measuring watch according to claim 6, characterized in that: The computational data reading module further includes: A sequence determination unit is used to acquire the numerical sequence of the data difference calculation, perform periodic detection on the numerical sequence, and determine whether the numerical sequence is a periodic sequence. The sequence analysis unit is used to, when the numerical sequence is determined to be a periodic sequence, divide the numerical sequence into multiple identical first subsequences according to the period, determine whether all values ​​in the first subsequence are greater than a preset value, if so, extract the first abnormal value in the first subsequence that is greater than the preset value, determine the time interval between adjacent first abnormal values, and determine whether the time interval is within the preset time interval range, if so, take the first abnormal value and the time interval as the first abnormal data, otherwise, determine that the first abnormal value is invalid. The sequence analysis unit is further configured to, after determining that the numerical sequence is an aperiodic sequence, perform clustering operations on the numerical sequence using a one-dimensional clustering method to obtain multiple split points, and divide the numerical sequence using the multiple split points to obtain multiple different second subsequences, obtain a third subsequence from the second subsequences that has a value greater than a preset value, and determine the abnormal time interval of the third subsequence based on the position of the third subsequence in the numerical sequence, and obtain a fourth subsequence from the adjacent third subsequence whose abnormal time interval is within the preset time interval range, and use the fourth subsequence and the abnormal time interval as second abnormal data; The data integration unit is used to periodically label the first abnormal data to obtain first valid abnormal data, to non-periodically label the second abnormal data to obtain second valid abnormal data, and to send the first valid abnormal data and the second valid abnormal data as the final valid abnormal data value to the anomaly classification module.

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