CN114694347A - Transaminase data monitoring system - Google Patents
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- 102000003929 Transaminases Human genes 0.000 title claims abstract description 404
- 108090000340 Transaminases Proteins 0.000 title claims abstract description 404
- 238000012544 monitoring process Methods 0.000 title claims abstract description 235
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- 238000004364 calculation method Methods 0.000 claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 17
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- 230000001174 ascending effect Effects 0.000 claims description 11
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- 210000004304 subcutaneous tissue Anatomy 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
- G08B21/0438—Sensor means for detecting
- G08B21/0453—Sensor means for detecting worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
- G08B29/18—Prevention or correction of operating errors
- G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
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- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H—ELECTRICITY
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
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Abstract
The invention provides a transaminase data monitoring system, which is characterized in that a monitoring receiving terminal is connected with a transmitter through Bluetooth, so that data can be transmitted in a poor network environment, the monitoring receiving terminal calculates through a data processing module after receiving the data, the accuracy of transaminase data is ensured by adopting calculation methods such as an abnormal data elimination algorithm, an initial wearing algorithm, a reference correction algorithm and the like, and the transaminase data is displayed through a monitoring data display module, so that delay is reduced as much as possible; the cloud server synchronizes the data to the monitoring receiving terminal after receiving the data of the monitoring receiving terminal, performs predictive calculation by adopting calculation methods such as a transaminase value segmentation algorithm and a transaminase prediction alarm algorithm, and feeds the prediction result back to the monitoring receiving terminal and the monitoring receiving terminal to remind a user of transaminase control.
Description
Technical Field
The invention relates to the field of transaminase data communication, monitoring and monitoring, in particular to a transaminase data monitoring system, a transaminase data communication monitoring method and an application method.
Background
The dynamic transaminase monitoring system (RGMS) is a new continuous dynamic transaminase monitoring system that has been put into clinical use in recent years and is connected to a probe, similar to a needle, for placement in subcutaneous tissue. The diameter of the probe is very small, and the patient does not feel pain or discomfort obviously when the probe is placed in the body. The instrument receives an electric signal reflecting the change of transaminase from a probe at certain time intervals, and converts the average value of the electric signals acquired for many times into a transaminase value to be stored. Several hundred transaminase values can be recorded per day. The dynamic transaminase monitor can also store the time for eating, exercising, taking medicine and the like at the same time. This allows patients to avoid suffering from daily needle sticks and it provides a daily transaminase profile, a trend analysis of the fluctuations of the multiple-day transaminase profile and a summary of the daily transaminase data, which is a new breakthrough in transaminase detection.
The transaminase value generates current through a sensor, and current data are transmitted to terminal equipment in a wireless transmission mode and calculated to obtain the transaminase value. The current of the sensor is influenced by human rejection reaction and other reasons, so that the current value abnormally jumps, and the subsequently calculated transaminase value is inaccurate; in addition, data transmission modes between devices such as transaminase monitoring and monitoring, monitoring modes and application modes utilizing data transmission all affect the use experience of sensor wearers and monitoring personnel, and the data communication mode and the algorithm for ensuring data accuracy are also technical problems. The invention patent with publication number CN107788994B discloses an intelligent real-time dynamic transaminase monitoring system and method based on cloud big data, and the method adopted by the patent is to transmit current data to a cloud big data server for calculation and correction of a conversion coefficient, and then transmit the transaminase data obtained by calculation back to transaminase monitoring software for display. The technique of this patent presents some problems: firstly, the data is transmitted to the cloud for calculation and then transmitted back to the monitoring software for display, so that delay is generated, and particularly, under the environment with poor network conditions, the situation that the data cannot be received may exist; secondly, the algorithm calibrates the transaminase value according to the measured value of the transaminase and the electrochemical impedance data, but factors influencing the current value are many, the current value is not only the electrochemical impedance data, but also the historical data stored in the cloud is inaccurate after the current data is unstable due to various complex factors, and the subsequent calibration algorithm has little meaning; thirdly, the change of the transaminase value of the human body is mainly influenced by the change of food intake and insulin secretion, the existing regularity is not strong, and the calculation modes such as the least square method, the historical data regression algorithm and the like adopted by the patent need strong regularity of the historical data, so the accuracy of the algorithm is uncertain; fourthly, the transaminase value displayed to the user after calibration deviates from the actually measured transaminase value, which may cause misleading to the user and may endanger life in severe cases.
Therefore, the processing and calculation of transaminase data not only affect the user experience, but also affect the accuracy of monitoring transaminase values, so that it is very important to provide a reliable transaminase data monitoring system, and a monitoring method and an application method using data communication.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a transaminase data monitoring system, which is characterized in that a monitoring receiving terminal and a transmitter are connected through Bluetooth, so that data can still be transmitted in a network poor environment, the monitoring receiving terminal firstly calculates through a data processing module after receiving the data, the accuracy of the transaminase data is ensured by adopting calculation methods such as an abnormal data elimination algorithm, an initial wearing algorithm and a reference correction algorithm, the transaminase data is displayed through a monitoring data display module, and the delay is reduced as much as possible; the cloud server synchronizes the data to the monitoring receiving terminal after receiving the data of the monitoring receiving terminal, performs predictive calculation by adopting calculation methods such as a transaminase value segmentation algorithm, a transaminase prediction alarm algorithm and the like, and feeds prediction results back to the monitoring receiving terminal and the monitoring receiving terminal to remind a user of attention to transaminase control; in addition, the invention also provides a monitoring method and an application method for transaminase data communication, the monitoring receiving terminal can be used by multiple people, different pushing conditions are set according to the priority, so that transaminase early warning information is preferentially pushed to relatives of wearers, and then pushed to medical staff in emergency, the working pressure of the medical staff can be reduced, a better monitoring effect is achieved, and the use experience of users is enhanced.
The specific technical scheme is that the transaminase data monitoring system comprises a sensor, a transmitter, a monitoring receiving terminal and a cloud server; the sensor is provided with an electrode and a first conductive contact which is conducted with the electrode; the transmitter comprises a battery module, a memory module, a first Bluetooth module and a second conductive contact, data transmission is realized between the sensor and the transmitter through the electric connection of the first conductive contact and the second conductive contact, the battery module supplies power for the transmitter and the electrode, and the memory module receives micro-current data of the electrode and transmits the micro-current data to the monitoring receiving terminal through the first Bluetooth module; the monitoring receiving terminal comprises a second Bluetooth module, a first wireless network module and monitoring software, the second Bluetooth module and the first Bluetooth module are paired to realize data transmission between the monitoring receiving terminal and the transmitter, the first wireless network module can realize data transmission between the monitoring receiving terminal and the cloud server, and the monitoring software comprises a data processing module, a monitoring data display module, a reference input module and a monitoring prompt alarm module; the monitoring receiving terminal comprises a second wireless network module and monitoring software, the second wireless network module can realize data transmission with the cloud server, and the monitoring software comprises a monitoring data display module and a monitoring prompt alarm module; the cloud server can receive the data of the monitoring receiving terminal and transmit the data to the monitoring receiving terminal.
Therefore, the monitoring receiving terminal is connected with the transmitter through the Bluetooth, and data can still be transmitted in a network poor environment; after receiving the data, the monitoring receiving terminal firstly calculates through the data processing module and displays transaminase data through the monitoring data display module, so that delay is reduced as much as possible; and the cloud server synchronizes data to the monitoring receiving terminal after receiving the data of the monitoring receiving terminal, performs predictive calculation, and feeds a prediction result back to the monitoring receiving terminal and the monitoring receiving terminal to remind a user of attention to transaminase control.
Preferably, the data processing module comprises an abnormal data exclusion algorithm, an initial wearing algorithm and a reference correction algorithm.
Therefore, when the current value of the sensor jumps due to human rejection reaction and other reasons, abnormal current data can be eliminated through the abnormal data elimination algorithm, so that the transaminase value is more practical; when the sensor is worn at the initial time, the electrode has a polarization process, and the polarization end time can be calculated through the initial wearing algorithm, so that the monitoring receiving terminal starts to calculate the transaminase value; in order to ensure the accuracy of transaminase data in the wearing process of the sensor, the correction of the transaminase value of blood is required for a plurality of times, and the transaminase value and the transaminase reference can be corrected in time by the reference correction algorithm, so that the accuracy of the transaminase data is ensured.
Preferably, the cloud server comprises a transaminase value segmentation algorithm and a transaminase prediction alarm algorithm.
Therefore, the transaminase value of a human body obviously rises after ingestion and then slowly falls, and because ingestion has certain randomness, the prediction of the transaminase value is not very accurate according to the whole historical transaminase data; the transaminase value segmentation algorithm distinguishes the transaminase value in an empty stomach state from the transaminase value increased by the transaminase after eating, so that the transaminase prediction alarm algorithm carries out prediction calculation according to the distinguished transaminase values, and prediction is more accurate.
Preferably, the initial wearing algorithm comprises the following calculation steps: step A, the transmitter receives current data of the sensor once every interval time t and stores the current data into the memory module, and after a times of reception, a current data are packaged into a data packet to be sent to the monitoring receiving terminal; step B, after the monitoring receiving terminal receives the data packet of the transmitter, the data processing module adds time data, and compares a current data in the data packet with the previous current data respectively, wherein the first current data in the data packet is compared with the last current data in the previous data packet; setting a polarization threshold value p, comparing the difference value of the latter current data minus the former current data with the polarization threshold value p, and considering that the polarization time is ended and calculating the transaminase value when 5 continuous difference values are less than p.
Therefore, the general time interval t is set to be 1min, the receiving times a are set to be 3 times, namely, the transmitter sends a data packet at an interval of 3 minutes, and therefore the Bluetooth power consumption can be reduced; in the polarization process, the current data is in a slowly increasing process, the difference value of the current data after the current data is judged to be subtracted from the current data before the current data is judged to be distinguished, and when 5 continuous difference values are smaller than p, the current value is shown to be restored to a more stable state, namely, the polarization is completed.
Preferably, the abnormal data exclusion algorithm includes the following calculation steps: step A, the transmitter receives current data of the sensor once every interval time t and stores the current data into the memory module, and after a times of reception, a current data are packaged into a data packet to be sent to the monitoring receiving terminal; step B, after the monitoring receiving terminal receives the data packet of the transmitter, the data processing module adds time data, and compares a current data in the data packet with the previous current data respectively, wherein the first current data in the data packet is compared with the last current data in the previous data packet; setting a current data rising threshold m and a current data falling threshold n, if the current data after the current data is subtracted from the current data before the current data is positive, comparing a difference value with the rising threshold m, and if the difference value is larger than m, determining that the current data is abnormal and removing the current data; if the current data is negative after the current data is subtracted from the current data before the current data is negative, comparing the difference value with the drop threshold value n, and if the absolute value of the difference value is larger than n, determining that the current data is abnormal and removing the current data; step C, if one or more current data before the current data are eliminated due to abnormality, comparing the current data with the previous effective current data, wherein the number of the interval eliminated current data is k; if the current data is positive after the previous current data is subtracted, comparing the difference value with an accumulated rising threshold value m (k +1), and if the difference value is larger than m (k +1), considering that the current data is abnormal and removing the current data; if the current data is negative after subtracting the previous current data, comparing the difference value with an accumulated descending threshold value n (k +1), and if the absolute value of the difference value is greater than n (k +1), considering that the current data is abnormal and eliminating the current data; step D, calculating the arithmetic mean value of the current data in each data packet as a packet current value, and corresponding to the time data of the data packet; if all the current data in the data packet are abandoned, taking the average value of the previous effective packet current value and the next effective packet current value as the packet current value; if the data in two continuous data packets are empty, the monitoring receiving terminal drives the monitoring prompt alarm module to send an alarm of the sensor abnormity; step E, calculating the value of the envelope current multiplied by the conversion coefficient of the transaminase to be used as the value of the transaminase, using the time data as an abscissa and the value of the transaminase as an ordinate to generate a transaminase change curve, and displaying the transaminase change curve through the monitoring data display module; and step F, the monitoring receiving terminal sends the time data and the corresponding transaminase value to the cloud server through the first wireless network module, the cloud server sends the time data and the corresponding transaminase value to the monitoring receiving terminal, and the monitoring data display module synchronously displays a transaminase change curve.
Therefore, when the sensor is worn by a human body, the environment is complex, besides human body transaminase, a plurality of factors for disturbing current exist, the factors are mainly shown in sudden rising or falling of current data, the normal human body transaminase value changes continuously, so a rising threshold value m and a falling threshold value n are set, when the current data change difference value exceeds the rising threshold value m or the falling threshold value n, the current data are judged to be abnormal, and the influence brought by abnormal current data can be reduced by eliminating the abnormal current data and averaging a plurality of current data, so that the transaminase value is more accurate, and the subsequent algorithm is more accurate.
Preferably, the reference correction algorithm comprises the following calculation steps: step A, inputting a reference transaminase value SC from the reference input module of the monitoring receiving terminal; step B, comparing the reference transaminase value SC with the transaminase value SG of the latest data packet, setting a reference difference value threshold q, and if the absolute value SG-SC is less than or equal to q, executing step C; if the absolute value of SG-SC > q is greater than the reference value of the transaminase, the monitoring receiving terminal drives the monitoring prompt alarm module to send an alarm for carrying out blood-indicating measurement again and inputting the reference transaminase value, if the absolute value of SG-SC is less than or equal to q after the SC is measured again, the reference value SC is updated and the step C is executed, and if the absolute value of SG-SC > q is measured again, the monitoring receiving terminal drives the monitoring prompt alarm module to send an alarm that the loss of the sensor is large and the sensor needs to be replaced; step C, calculating an updated transaminase value CG and a reference transaminase conversion coefficient CF according to the original latest transaminase value SG, the latest transaminase conversion coefficient SF, the reference transaminase value SC and the reference weight value r to obtain CG = SC r + SG (1-r), and CF = SF CG/SG, wherein r is more than or equal to 0.6 and less than or equal to 0.95; CG is assigned to SG and CF is assigned to SF.
Therefore, the reference transaminase value SC is measured by a finger-type transaminase meter, usually a more accurate transaminase value can be measured, but the situation that the finger-type transaminase value is inaccurate to measure due to errors in the measurement process is not excluded, if the deviation between the finger-type transaminase value and the transaminase value measured by the sensor is large, the finger-type transaminase value is considered to have problems in the finger-type transaminase measurement process, the finger-type transaminase value needs to be measured again, and if the deviation between the finger-type transaminase value and the transaminase value measured by the sensor is still large, the sensor is considered to have large loss, and the sensor is prompted to be replaced; the accurate measurement means that a larger weight proportion is used for the blood transaminase value, and a smaller weight proportion is used for the transaminase value measured by the sensor, so that the updated transaminase value and the transaminase conversion coefficient are accurate.
Preferably, the transaminase value segmentation algorithm comprises the following calculation steps: step A, the cloud server receives time data and a corresponding transaminase value sent by the monitoring receiving terminal; step B, comparing the transaminase value corresponding to each time data with the transaminase value corresponding to the previous time data respectively, setting a transaminase rise threshold q, comparing the difference between the next transaminase value and the previous transaminase value with the transaminase rise threshold q, recording a first transaminase value SG0 and time data t0 if 5 continuous differences are greater than q, comparing the transaminase value after t0 with SG0 until the next transaminase value lower than SG0 is received, recording the transaminase value SG1 and the corresponding time data t1, and repeating the calculation step from t 1; and step C, dividing the transaminase value at t0 and t1, storing the transaminase value and time data beginning at t0 and ending at t1 into a historical ascending transaminase interval database, and storing the transaminase value and time data beginning at t1 and ending at t0 into a historical normal transaminase interval database.
Therefore, the difference between the next transaminase value and the previous transaminase value is positive, namely, the transaminase rises, if the difference of 5 continuous differences is greater than the transaminase rise threshold q, the user is considered to be in the transaminase rise period after eating, the transaminase value is recovered to the initial level, the transaminase change value which rises to the period of descending and recovering is segmented, the more stable transaminase change value is also segmented and is respectively stored in different historical transaminase value databases, and therefore the subsequent transaminase prediction alarm algorithm is more accurate.
Preferably, the transaminase prediction alert algorithm comprises the following calculation steps: step A, calculating a difference value DSt between the maximum transaminase value of each section of curve in a historical rising transaminase interval database and SG0, and storing each difference value DSt and the starting time t0 into a two-dimensional array; calculating the difference DZt between the maximum transaminase value and the minimum transaminase value of each curve in the historical normal transaminase interval database, and storing each difference DZt and the starting time t1 in a two-dimensional array; b, the cloud server receives time data t sent by the monitoring receiving terminal and a corresponding transaminase value SG; step C, setting a time threshold Dt, an upper transaminase limit SGmax and a lower transaminase limit SGmin; comparing the transaminase value SG corresponding to each time data t with a transaminase difference value DSt of the time difference in Dt in the historical rising transaminase interval database, if the value is | t-t0| SGmax, sending high transaminase early warning information to the monitoring receiving terminal and/or the monitoring receiving terminal, and the monitoring prompt alarm module and/or the monitoring prompt alarm module sending high transaminase early warning; comparing the transaminase value SG corresponding to each time data t with the transaminase difference value DZt of the time difference in Dt in the database of the historical normal transaminase interval, if | t-t1 |)
Thus, by calculating the difference DSt between the maximum transaminase value and SG0 for each curve in the historical ascending transaminase interval database, and the difference DZt between the maximum transaminase value and the minimum transaminase value for each curve in the historical normal transaminase interval database, it is possible to obtain the potential increase in transaminase values near time t0 and the potential decrease at time t 1; the high transaminase early warning is carried out by comparing a predicted value obtained by adding a transaminase rise difference value DSt with a current transaminase value within the time threshold Dt to the transaminase upper limit SGmax, and sending out early warning when the predicted value exceeds the transaminase upper limit SGmax, so that a user can adjust diet and avoid high transaminase; and the low transaminase early warning is implemented by comparing a predicted value obtained by subtracting a transaminase reduction difference value DZt with a time difference within the time threshold Dt from a current transaminase value with the transaminase lower limit SGmin, and giving an early warning when the predicted value is lower than the transaminase lower limit SGmin, so that a wearing user can avoid the low transaminase by taking food and the like.
Preferably, the time threshold Dt is 15 min-60 min.
Therefore, the time threshold Dt can be adjusted according to the wearing time of a user, the data volume of the historical transaminase data stored in the historical ascending transaminase interval database and the historical normal transaminase interval database, and the larger the data volume is, the smaller the time threshold Dt is set, and the setting is generally 15 min-60 min.
The monitoring method of transaminase data communication comprises the steps that a transaminase data monitoring system comprises a sensor, a transmitter, a monitoring receiving terminal and a cloud server, wherein the cloud server can receive data of the monitoring receiving terminal and transmit the data to the monitoring receiving terminal; the monitoring receiving terminals are at least two and are respectively provided with at least two levels of priorities.
Therefore, the monitoring receiving terminal can be used by multiple persons, and different pushing conditions are set according to the priority; the parent of a general wearer can use the monitoring receiving terminal with higher priority, and medical care personnel can use the monitoring receiving terminal with lower priority because the medical care personnel need to monitor a plurality of wearers simultaneously, so that the transaminase early warning information is preferentially pushed to the parent of the wearer and then to the medical care personnel in emergency, and the working pressure of the medical care personnel can be reduced.
Preferably, the monitoring receiving terminal comprises at least a primary monitoring terminal and a secondary monitoring terminal; the cloud server sets an upper transaminase limit SGmax, a lower transaminase limit SGmin and a priority descending proportion w, calculates a difference value DSt between the maximum transaminase value and SG0 of each section of curve in a historical ascending transaminase interval database, and calculates a difference value DZt between the maximum transaminase value and the minimum transaminase value of each section of curve in a historical normal transaminase interval database; the early warning condition of the primary monitoring terminal is SG + DSt > (1-2w) SGmax or SG-DZt < (1+2w) SGmin, and the early warning condition of the secondary monitoring terminal is SG + DSt > (1-w) SGmax or SG-DZt < (1+ w) SGmin.
Therefore, the early warning condition of the primary monitoring terminal is prior to the early warning condition of the secondary monitoring terminal by the priority descending proportion w.
Preferably, the priority descending proportion w is 5% -20%.
Thus, the priority decreasing proportion w is preferably 10% to 15%.
The transaminase data monitoring system comprises a sensor, a transmitter, a monitoring receiving terminal and a cloud server, wherein the monitoring receiving terminal at least comprises a primary monitoring terminal and a secondary monitoring terminal; the monitoring receiving terminal is used by following the sensor and the transmitter wearer, and the monitoring receiving terminal is used by the relatives of the wearer, community workers or hospital medical personnel; and the wearer uses the primary monitoring terminal by relatives, community workers or hospital medical personnel.
Therefore, the monitoring receiving terminal can be used by a plurality of people to monitor the diabetes patients and set different monitoring levels, thereby achieving better monitoring effect and enhancing the use experience of users.
In conclusion, the invention has the following beneficial effects: 1. according to the transaminase data monitoring system, the monitoring receiving terminal is connected with the emitter through Bluetooth, the monitoring receiving terminal is connected with the cloud server through a wireless network, and the monitoring receiving terminal is connected with the cloud server through the wireless network, so that a sensor wearer is ensured to receive transaminase data without being influenced by a network environment, data transmission is stable, and data display timeliness is high.
2. The monitoring receiving terminal adopts calculation methods such as an abnormal data exclusion algorithm, an initial wearing algorithm, a reference correction algorithm and the like; when the current value of the sensor jumps due to human rejection and other reasons, abnormal current data can be eliminated through an abnormal data elimination algorithm, so that the transaminase value is more practical; the electrode has a polarization process at the initial wearing time of the sensor, and the polarization end time can be calculated through an initial wearing algorithm, so that the monitoring receiving terminal starts to calculate the transaminase value; in order to ensure the accuracy of transaminase data in the wearing process of the sensor, the correction of the transaminase value of blood is required for a plurality of times, and the transaminase value and the transaminase reference can be corrected in time by a reference correction algorithm, so that the accuracy of the transaminase data is ensured.
3. The cloud server adopts calculation methods such as a transaminase value segmentation algorithm, a transaminase prediction alarm algorithm and the like; the transaminase value of a human body can obviously rise after ingestion and then slowly fall, and because ingestion has certain randomness, the transaminase value prediction is not accurate according to the integral historical transaminase data; the transaminase value segmentation algorithm distinguishes the transaminase value in an empty stomach state from the transaminase value of the transaminase which is increased after meals, so that the transaminase prediction alarm algorithm carries out prediction calculation according to the distinguished transaminase values, and prediction is more accurate.
4. The monitoring method for transaminase data communication adopts a plurality of monitoring receiving terminals for a plurality of people to use, and sets different pushing conditions according to the priority, thereby ensuring the monitoring effect and simultaneously enhancing the use experience of users.
5. The transaminase data communication application method enables the monitoring receiving terminal to be used by relatives of wearers, community workers or hospital medical staff, the relatives of the wearers use the primary monitoring terminal, and the community workers or the hospital medical staff use the secondary monitoring terminal; the transaminase early warning information is preferably pushed to the primary monitoring terminal, and then pushed to the secondary monitoring terminal in an emergency, so that the working pressure of medical staff or community workers can be relieved.
Drawings
FIG. 1 is a schematic diagram of the transaminase data monitoring system of the present invention.
Detailed Description
The invention will be further explained by means of specific embodiments with reference to the drawings.
Example 1: as shown in fig. 1, the transaminase data monitoring system includes a sensor, a transmitter, a monitoring receiving terminal, and a cloud server; the sensor is provided with an electrode and a first conductive contact which is conducted with the electrode; the transmitter comprises a battery module, a memory module, a first Bluetooth module and a second conductive contact, the sensor and the transmitter are electrically connected through the first conductive contact and the second conductive contact to realize data transmission, the battery module supplies power for the transmitter and the electrode, and the memory module receives micro-current data of the electrode and transmits the micro-current data to the monitoring receiving terminal through the first Bluetooth module; the monitoring receiving terminal comprises a second Bluetooth module, a first wireless network module and monitoring software, the second Bluetooth module and the first Bluetooth module are paired to realize data transmission between the monitoring receiving terminal and the transmitter, the first wireless network module can realize data transmission between the monitoring receiving terminal and the cloud server, and the monitoring software comprises a data processing module, a monitoring data display module, a reference input module and a monitoring prompt alarm module; the monitoring receiving terminal comprises a second wireless network module and monitoring software, the second wireless network module can realize data transmission with the cloud server, and the monitoring software comprises a monitoring data display module and a monitoring prompt alarm module; the cloud server can receive the data of the monitoring receiving terminal and transmit the data to the monitoring receiving terminal.
Therefore, the monitoring receiving terminal is connected with the transmitter through the Bluetooth, and data can still be transmitted in a network poor environment; after receiving the data, the monitoring receiving terminal firstly calculates through the data processing module and displays the transaminase data through the monitoring data display module, so that the delay is reduced as much as possible; the cloud server synchronizes the data to the monitoring receiving terminal after receiving the data of the monitoring receiving terminal, performs predictive calculation, and feeds back the prediction result to the monitoring receiving terminal and the monitoring receiving terminal to remind a user of attention to transaminase control.
The data processing module comprises an abnormal data exclusion algorithm, an initial wearing algorithm and a reference correction algorithm.
Therefore, when the current value of the sensor jumps due to human rejection reaction and other reasons, abnormal current data can be eliminated through an abnormal data elimination algorithm, so that the transaminase value is more practical; the electrode has a polarization process at the initial wearing time of the sensor, and the polarization end time can be calculated through an initial wearing algorithm, so that the monitoring receiving terminal starts to calculate the transaminase value; in order to ensure the accuracy of transaminase data in the wearing process of the sensor, the correction of the transaminase value of blood is required for a plurality of times, and the transaminase value and the transaminase reference can be corrected in time by a reference correction algorithm, so that the accuracy of the transaminase data is ensured.
The cloud server comprises a transaminase value segmentation algorithm and a transaminase prediction alarm algorithm.
Therefore, the transaminase value of a human body can obviously rise after ingestion and then slowly fall, and because ingestion has certain randomness, the transaminase value prediction is not accurate according to the overall historical transaminase data; the transaminase value segmentation algorithm distinguishes the transaminase value in an empty stomach state from the transaminase value of the transaminase which is increased after meals, so that the transaminase prediction alarm algorithm carries out prediction calculation according to the distinguished transaminase values, and prediction is more accurate.
The initial wear algorithm comprises the following calculation steps: step A, the transmitter receives current data of the sensor once at intervals of t and stores the current data into a memory module, and after the current data are received for a times, a current data are packaged into a data packet to be sent to a monitoring receiving terminal; step B, after the monitoring receiving terminal receives a data packet of the transmitter, time data are added by the data processing module, a current data in the data packet are respectively compared with the previous current data, wherein the first current data in the data packet is compared with the last current data in the previous data packet; setting a polarization threshold value p, comparing the difference value of the latter current data minus the former current data with the polarization threshold value p, and considering that the polarization time is ended and calculating the transaminase value when 5 continuous difference values are less than p.
Therefore, the general time interval t is set to be 1min, the receiving times a are set to be 3 times, namely, the transmitter sends a data packet at an interval of 3 minutes, and therefore the Bluetooth power consumption can be reduced; in the polarization process, the current data is in a slowly increasing process, the difference value of the current data after the current data is judged to be subtracted from the current data before the current data is judged to be distinguished, and when 5 continuous difference values are smaller than p, the current value is shown to be restored to a more stable state, namely, the polarization is completed.
The abnormal data exclusion algorithm comprises the following calculation steps: step A, the transmitter receives current data of the sensor once every interval time t and stores the current data into a memory module, and after the current data are received for a times, a current data are packaged into a data packet to be sent to a monitoring receiving terminal; step B, after the monitoring receiving terminal receives a data packet of the transmitter, time data are added by the data processing module, a current data in the data packet are respectively compared with the previous current data, wherein the first current data in the data packet is compared with the last current data in the previous data packet; setting a current data rising threshold value m and a current data falling threshold value n, if the current data after the current data is subtracted from the current data before the current data is positive, comparing a difference value with the rising threshold value m, and if the difference value is larger than m, determining that the current data is abnormal and removing the current data; if the current data is negative after the current data is subtracted from the current data before the current data is negative, comparing the difference value with a drop threshold value n, and if the absolute value of the difference value is larger than n, determining that the current data is abnormal and removing the current data; step C, if one or more current data before the current data are eliminated due to abnormality, comparing the current data with the previous effective current data, wherein the number of the interval eliminated current data is k; if the current data is positive by subtracting the previous current data, comparing the difference value with an accumulated rising threshold value m (k +1), and if the difference value is larger than m (k +1), considering that the current data is abnormal and removing the current data; if the current data is negative after subtracting the previous current data, comparing the difference value with an accumulated descending threshold value n (k +1), and if the absolute value of the difference value is greater than n (k +1), considering that the current data is abnormal and eliminating the current data; step D, calculating the arithmetic mean value of the current data in each data packet as a packet current value, and corresponding to the time data of the data packet; if all the current data in the data packet are abandoned, taking the average value of the previous effective packet current value and the next effective packet current value as the packet current value; if the data in two continuous data packets are empty, the monitoring receiving terminal drives the monitoring prompt alarm module to send out an alarm of sensor abnormity; step E, calculating the value of the envelope current multiplied by the conversion coefficient of the transaminase to be used as the value of the transaminase, using the time data as an abscissa and the value of the transaminase as an ordinate to generate a transaminase change curve, and displaying the curve through a monitoring data display module; and step F, the monitoring receiving terminal sends the time data and the corresponding transaminase value to the cloud server through the first wireless network module, the cloud server sends the time data and the corresponding transaminase value to the monitoring receiving terminal, and the monitoring data display module synchronously displays a transaminase change curve.
Therefore, when the sensor is worn by a human body, the environment is complex, besides human body transaminase, a plurality of factors for disturbing current exist, the factors are mainly shown in sudden rising or falling of current data, the normal human body transaminase value changes continuously, so a rising threshold value m and a falling threshold value n are set, when the current data change difference value exceeds the rising threshold value m or the falling threshold value n, the current data are judged to be abnormal, and the influence brought by abnormal current data can be reduced by eliminating the abnormal current data and averaging a plurality of current data, so that the transaminase value is more accurate, and the subsequent algorithm is more accurate.
The reference correction algorithm comprises the following calculation steps: step A, inputting a reference transaminase value SC from a reference input module of a monitoring receiving terminal; step B, comparing the reference transaminase value SC with the transaminase value SG of the latest data packet, setting a reference difference threshold value q, and executing the step C if | SG-SC | is less than or equal to q; if the absolute value SG-SC is greater than q, the monitoring receiving terminal drives the monitoring and prompting alarm module to send an alarm for carrying out finger blood measurement again and inputting a reference transaminase value, if the absolute value SG-SC is less than or equal to q after the SC is measured again, the reference value SC is updated and the step C is executed, and if the absolute value SG-SC is greater than q after the SC is measured again, the monitoring receiving terminal drives the monitoring and prompting alarm module to send an alarm that the loss of the sensor is large and the sensor needs to be replaced; step C, calculating an updated transaminase value CG and a reference transaminase conversion coefficient CF according to the original latest transaminase value SG, the latest transaminase conversion coefficient SF, the reference transaminase value SC and the reference weight value r to obtain CG = SC r + SG (1-r), and CF = SF CG/SG, wherein r is more than or equal to 0.6 and less than or equal to 0.95; CG is assigned to SG and CF is assigned to SF.
Therefore, the reference transaminase value SC is measured by a finger-type transaminase meter, usually a more accurate transaminase value can be measured, but the condition that the finger-type transaminase value is inaccurate to measure due to errors in the measurement process is not excluded, if the deviation between the finger-type transaminase value and the transaminase value measured by a sensor is large, the finger-type transaminase value is considered to have problems in the finger-type transaminase measurement process, the finger-type transaminase value needs to be measured again, and if the deviation between the finger-type transaminase value and the transaminase value measured by the sensor is still large, the sensor is considered to have large loss, and the sensor is prompted to be replaced; the accurate measurement means that a larger weight proportion is used for the blood transaminase value, and a smaller weight proportion is used for the transaminase value measured by the sensor, so that the updated transaminase value and the transaminase conversion coefficient are accurate.
The transaminase value segmentation algorithm comprises the following calculation steps: a, a cloud server receives time data and a corresponding transaminase value sent by a monitoring receiving terminal; step B, comparing the transaminase value corresponding to each time data with the transaminase value corresponding to the previous time data respectively, setting a transaminase rise threshold q, comparing the difference between the next transaminase value and the previous transaminase value with the transaminase rise threshold q, recording a first transaminase value SG0 and time data t0 if 5 continuous differences are greater than q, comparing the transaminase value after t0 with SG0 until the next transaminase value lower than SG0 is received, recording the transaminase value SG1 and the corresponding time data t1, and repeating the calculation step from t 1; and step C, dividing the transaminase value at t0 and t1, storing the transaminase value and time data beginning at t0 and ending at t1 into a historical ascending transaminase interval database, and storing the transaminase value and time data beginning at t1 and ending at t0 into a historical normal transaminase interval database.
Therefore, the difference between the next transaminase value and the previous transaminase value is positive, namely, the transaminase rises, if the difference of 5 continuous differences is greater than a transaminase rise threshold q, the user is considered to be in the transaminase rise period after eating, and then the transaminase value is recovered to the initial level, the transaminase change value which rises to the period of descending and recovering is segmented, the more stable transaminase change value is also segmented and is respectively stored in different historical transaminase value databases, so that the subsequent transaminase prediction alarm algorithm is more accurate.
The transaminase prediction alert algorithm comprises the following calculation steps: step A, calculating a difference value DSt between the maximum transaminase value of each section of curve in a historical rising transaminase interval database and SG0, and storing each difference value DSt and the starting time t0 into a two-dimensional array; calculating the difference DZt between the maximum transaminase value and the minimum transaminase value of each curve in the historical normal transaminase interval database, and storing each difference DZt and the starting time t1 in a two-dimensional array; b, the cloud server receives time data t sent by the monitoring receiving terminal and a corresponding transaminase value SG; step C, setting a time threshold Dt, an upper transaminase limit SGmax and a lower transaminase limit SGmin; the transaminase value SG corresponding to each time data t is compared with a transaminase difference value DSt of the time difference within Dt in a historical rising transaminase interval database respectively, if the value is | t-t0| SGmax, high transaminase early warning information is sent to a monitoring receiving terminal and/or a monitoring receiving terminal, and a monitoring prompt alarm module and/or a monitoring prompt alarm module sends out high transaminase early warning; comparing the transaminase value SG corresponding to each time data t with the transaminase difference value DZt within Dt of the time difference in the historical normal transaminase interval database respectively, if | t-t1 |)
Thus, by calculating the difference DSt between the maximum transaminase value and SG0 for each curve in the historical ascending transaminase interval database, and the difference DZt between the maximum transaminase value and the minimum transaminase value for each curve in the historical normal transaminase interval database, it is possible to obtain the potential increase in transaminase values near time t0 and the potential decrease at time t 1; the high transaminase early warning is carried out by comparing a predicted value obtained by adding a transaminase rise difference value DSt of the current transaminase value and the time difference within the time threshold Dt with a transaminase upper limit SGmax, and an early warning is sent out when the predicted value exceeds the transaminase upper limit SGmax, so that a user can adjust diet and avoid high transaminase; the low transaminase early warning is characterized in that a predicted value obtained by subtracting a transaminase reduction difference value DZt with a time difference within a time threshold Dt from a current transaminase value is compared with a transaminase lower limit SGmin, an early warning is sent out when the predicted value is lower than the transaminase lower limit SGmin, and a user wearing the low transaminase early warning can avoid the low transaminase by taking food and the like.
The time threshold Dt is 15 min-60 min.
Therefore, the time threshold Dt can be adjusted according to the data volume of the historical transaminase data stored in the user wearing time, the historical ascending transaminase interval database and the historical normal transaminase interval database, and the larger the data volume is, the smaller the time threshold Dt is, and the time threshold Dt is generally set to be 15-60 min.
Example 2: the monitoring method of transaminase data communication comprises the steps that a transaminase data monitoring system comprises a sensor, a transmitter, a monitoring receiving terminal and a cloud server, wherein the cloud server can receive data of the monitoring receiving terminal and transmit the data to the monitoring receiving terminal; the monitoring receiving terminals are at least two, and are respectively provided with at least two levels of priority.
Therefore, the monitoring receiving terminal can be used by multiple people, and different pushing conditions are set according to the priority; the parent of a general wearer can use the monitoring receiving terminal with higher priority, and medical care personnel can use the monitoring receiving terminal with lower priority because the medical care personnel need to monitor a plurality of wearers simultaneously, so that the transaminase early warning information is preferentially pushed to the parent of the wearer and then to the medical care personnel in emergency, and the working pressure of the medical care personnel can be reduced.
The monitoring receiving terminal at least comprises a primary monitoring terminal and a secondary monitoring terminal; the cloud server sets an upper transaminase limit SGmax, a lower transaminase limit SGmin and a priority descending proportion w, calculates a difference value DSt between the maximum transaminase value and SG0 of each section of curve in a historical ascending transaminase interval database, and calculates a difference value DZt between the maximum transaminase value and the minimum transaminase value of each section of curve in a historical normal transaminase interval database; the early warning condition of the primary monitoring terminal is SG + DSt > (1-2w) SGmax or SG-DZt < (1+2w) SGmin, and the early warning condition of the secondary monitoring terminal is SG + DSt > (1-w) SGmax or SG-DZt < (1+ w) SGmin.
Therefore, the early warning condition of the primary monitoring terminal is prior to the early warning condition of the secondary monitoring terminal by the priority descending proportion w.
The descending proportion w of the priority is 5% -20%.
Thus, the priority decreasing ratio w is preferably 10% to 15%.
Example 3: the transaminase data communication application method is characterized in that a transaminase data monitoring system comprises a sensor, a transmitter, a monitoring receiving terminal and a cloud server, wherein the monitoring receiving terminal at least comprises a primary monitoring terminal and a secondary monitoring terminal; the monitoring receiving terminal is used by a wearer following the sensor and the transmitter, and the monitoring receiving terminal is used by relatives of the wearer, community workers or hospital medical staff; the first-level monitoring terminal is used by the parent of the wearer, and the second-level monitoring terminal is used by community workers or hospital medical personnel.
Therefore, the monitoring receiving terminal can be used by a plurality of people to monitor the diabetes patients and set different monitoring levels, thereby achieving better monitoring effect and enhancing the use experience of users.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention and do not limit the spirit and scope of the present invention. Various modifications and improvements of the technical solutions of the present invention may be made by those skilled in the art without departing from the design concept of the present invention, and the technical contents of the present invention are all described in the claims.
Claims (9)
1. Transaminase data monitoring system, characterized by: the system comprises a sensor, a transmitter, a monitoring receiving terminal and a cloud server; the sensor is provided with an electrode and a first conductive contact which is conducted with the electrode; the transmitter comprises a battery module, a memory module, a first Bluetooth module and a second conductive contact, data transmission is realized between the sensor and the transmitter through the electric connection of the first conductive contact and the second conductive contact, the battery module supplies power for the transmitter and the electrode, and the memory module receives micro-current data of the electrode and transmits the micro-current data to the monitoring receiving terminal through the first Bluetooth module; the monitoring receiving terminal comprises a second Bluetooth module, a first wireless network module and monitoring software, the second Bluetooth module and the first Bluetooth module are paired to realize data transmission between the monitoring receiving terminal and the transmitter, the first wireless network module can realize data transmission between the monitoring receiving terminal and the cloud server, and the monitoring software comprises a data processing module, a monitoring data display module, a reference input module and a monitoring prompt alarm module; the monitoring receiving terminal comprises a second wireless network module and monitoring software, the second wireless network module can realize data transmission with the cloud server, and the monitoring software comprises a monitoring data display module and a monitoring prompt alarm module; the cloud server can receive the data of the monitoring receiving terminal and transmit the data to the monitoring receiving terminal.
2. The transaminase data monitoring system of claim 1, characterized in that: the data processing module comprises an abnormal data exclusion algorithm, an initial wearing algorithm and a reference correction algorithm.
3. The transaminase data monitoring system of claim 1, characterized in that: the cloud server comprises a transaminase value segmentation algorithm and a transaminase prediction alarm algorithm.
4. The transaminase data monitoring system of claim 2, wherein the initial wear algorithm comprises the following computational steps: step A, the transmitter receives current data of the sensor once every interval time t and stores the current data into the memory module, and after a times of reception, a current data are packaged into a data packet to be sent to the monitoring receiving terminal; step B, after the monitoring receiving terminal receives the data packet of the transmitter, the data processing module adds time data, and compares a current data in the data packet with the previous current data respectively, wherein the first current data in the data packet is compared with the last current data in the previous data packet; setting a polarization threshold value p, comparing the difference value of the latter current data minus the former current data with the polarization threshold value p, and considering that the polarization time is ended and calculating the transaminase value when 5 continuous difference values are less than p.
5. The transaminase data monitoring system of claim 2, wherein the abnormal data exclusion algorithm includes the following computational steps: step A, the transmitter receives current data of the sensor once every interval time t and stores the current data into the memory module, and after a times of reception, a current data are packaged into a data packet to be sent to the monitoring receiving terminal; step B, after the monitoring receiving terminal receives the data packet of the transmitter, the data processing module adds time data, and compares a current data in the data packet with the previous current data respectively, wherein the first current data in the data packet is compared with the last current data in the previous data packet; setting a current data rising threshold value m and a current data falling threshold value n, if the current data after the current data is subtracted from the current data before the current data is positive, comparing a difference value with the rising threshold value m, and if the difference value is larger than m, determining that the current data is abnormal and removing the current data; if the current data is negative after the current data is subtracted from the current data before the current data is negative, comparing the difference value with the drop threshold value n, and if the absolute value of the difference value is larger than n, determining that the current data is abnormal and removing the current data; step C, if one or more current data before the current data are eliminated due to abnormality, comparing the current data with the previous effective current data, wherein the number of the interval eliminated current data is k; if the current data is positive by subtracting the previous current data, comparing the difference value with an accumulated rising threshold value m (k +1), and if the difference value is larger than m (k +1), considering that the current data is abnormal and removing the current data; if the current data is negative after subtracting the previous current data, comparing the difference value with an accumulated descending threshold value n (k +1), and if the absolute value of the difference value is greater than n (k +1), considering that the current data is abnormal and eliminating the current data; step D, calculating the arithmetic mean value of the current data in each data packet as a packet current value, and corresponding to the time data of the data packet; if all the current data in the data packet are abandoned, taking the average value of the previous effective packet current value and the next effective packet current value as the packet current value; if the data in two continuous data packets are empty, the monitoring receiving terminal drives the monitoring prompt alarm module to send an alarm of the sensor abnormity; step E, calculating the value of the envelope current multiplied by the conversion coefficient of the transaminase to be used as the value of the transaminase, using the time data as an abscissa and the value of the transaminase as an ordinate to generate a transaminase change curve, and displaying the transaminase change curve through the monitoring data display module; and step F, the monitoring receiving terminal sends the time data and the corresponding transaminase value to the cloud server through the first wireless network module, the cloud server sends the time data and the corresponding transaminase value to the monitoring receiving terminal, and the monitoring data display module synchronously displays a transaminase change curve.
6. The transaminase data monitoring system of claim 2, wherein the reference correction algorithm includes the following calculation steps: step A, inputting a reference transaminase value SC from the reference input module of the monitoring receiving terminal; step B, comparing the reference transaminase value SC with the transaminase value SG of the latest data packet, setting a reference difference threshold value q, and executing the step C if | SG-SC | is less than or equal to q; if the absolute value of SG-SC > q is greater than the reference value of the transaminase, the monitoring receiving terminal drives the monitoring prompt alarm module to send an alarm for carrying out blood-indicating measurement again and inputting the reference transaminase value, if the absolute value of SG-SC is less than or equal to q after the SC is measured again, the reference value SC is updated and the step C is executed, and if the absolute value of SG-SC > q is measured again, the monitoring receiving terminal drives the monitoring prompt alarm module to send an alarm that the loss of the sensor is large and the sensor needs to be replaced; step C, calculating an updated transaminase value CG and a reference transaminase conversion coefficient CF according to the original latest transaminase value SG, the latest transaminase conversion coefficient SF, the reference transaminase value SC and the reference weight value r to obtain CG = SC r + SG (1-r), and CF = SF CG/SG, wherein r is more than or equal to 0.6 and less than or equal to 0.95; CG is assigned to SG and CF is assigned to SF.
7. The transaminase data monitoring system of claim 3, wherein the transaminase value segmentation algorithm includes the following calculation steps: step A, the cloud server receives time data and a corresponding transaminase value sent by the monitoring receiving terminal; step B, comparing the transaminase value corresponding to each time data with the transaminase value corresponding to the previous time data respectively, setting a transaminase increasing threshold q, comparing the difference between the next transaminase value minus the previous transaminase value with the transaminase increasing threshold q, recording the first transaminase value SG0 and time data t0 if 5 continuous differences are greater than q, comparing the transaminase value after t0 with SG0 until the next transaminase value lower than SG0 is received, recording the transaminase value SG1 and the corresponding time data t1, and repeating the calculation step from t 1; and step C, dividing the transaminase value at t0 and t1, storing the transaminase value and time data beginning at t0 and ending at t1 into a historical ascending transaminase interval database, and storing the transaminase value and time data beginning at t1 and ending at t0 into a historical normal transaminase interval database.
8. The transaminase data monitoring system of claim 7, wherein the transaminase prediction alert algorithm includes the following computational steps: step A, calculating a difference value DSt between the maximum transaminase value of each section of curve in a historical rising transaminase interval database and SG0, and storing each difference value DSt and the starting time t0 into a two-dimensional array; calculating the difference DZt between the maximum transaminase value and the minimum transaminase value of each curve in the historical normal transaminase interval database, and storing each difference DZt and the starting time t1 in a two-dimensional array; b, the cloud server receives time data t sent by the monitoring receiving terminal and a corresponding transaminase value SG; step C, setting a time threshold Dt, an upper transaminase limit SGmax and a lower transaminase limit SGmin; comparing the transaminase value SG corresponding to each time data t with a transaminase difference value DSt of the time difference in Dt in the historical ascending transaminase interval database, if | t-t0| < Dt, calculating a predicted ascending transaminase value SG + DSt, if SG + DSt > SGmax, sending high transaminase early warning information to the monitoring receiving terminal and/or the monitoring receiving terminal, and the monitoring prompt alarm module and/or the monitoring prompt alarm module sending out high transaminase early warning; and respectively comparing the transaminase value SG corresponding to each time data t with a transaminase difference value DZt of the time difference in Dt in the historical normal transaminase interval database, if | t-t1| < Dt, calculating a predicted descending transaminase value SG-DZt, if SG-DZt < SGmin exists, sending low transaminase early warning information to the monitoring receiving terminal and/or the monitoring receiving terminal, and the monitoring prompt alarm module and/or the monitoring prompt alarm module sending low transaminase early warning.
9. The transaminase data monitoring system of claim 8, wherein: the time threshold Dt is 15-60 min.
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