Patent ID: 11937920
Assignee: MICROTECH MEDICAL (HANGZHOU) CO., LTD.
Field: Medical technology (Instruments)
Classification: CPC A  G  H | IPC A  G  H

Claim 6:
7. An intelligent real-time dynamic blood glucose monitoring method based on cloud big data, comprising the following steps:
step 1: when a smart phone application detects that an implantable dynamic glucose sensor is connected with the smart phone and starts to work, checking whether a user has a history of using a same type of the implantable dynamic glucose sensor;
wherein, when the user doesn't have the history of using the same type of the implantable dynamic glucose sensor, determining the user as a new user, using a conversion coefficient CF0 between a preset sensor signal Is and a blood glucose output value and a conversion coefficient change rate CFd, uploading data to a cloud big data server, and outputting a blood glucose monitoring value according to a preset conversion correlation through the implantable dynamic glucose sensor,
wherein, when the user has the history of using the same type of the implantable dynamic glucose sensor, determining the user as an existing user, and downloading records of a latest CF0 and CFd of the user to the implantable dynamic glucose sensor from the cloud big data server through the smart phone as a parameter for calculating the blood glucose output value;
step 2: detecting whether a current time has reached a new measurement cycle through the implantable dynamic glucose sensor;
when the current time hasn't reached the new measurement cycle through the implantable dynamic glucose sensor, waiting until the system enters the new measurement cycle, then, in each new measurement cycle, calculating the conversion coefficient CF (t) in the new measurement cycle, wherein t is an interval between a current timestamp Ts and a sensor start-up time T0s, obtaining an impedance measurement data Z by the sensor in the current measurement cycle, and communicating with the cloud big data server to obtain updated parameters Ω and CFcal;
a process of calculating and outputting SG includes following steps:
step A, checking whether there is the newly acquired impedance measurement data Z in the current measurement cycle;
when there is the newly acquired impedance measurement data Z in the current measurement cycle, detecting a change of the impedance value from the last measurement and updating CFcal;
wherein, when there is no recent CFcal update and Z changes significantly, updating a compensation local conversion parameter CFcomp and uploading the impedance measurement data Z and timestamp Tz to the cloud big data server,
wherein, when there is a recent CFcal update or there is no significant change in Z, uploading only the impedance measurement data Z and timestamp TZ, and using the current compensation conversion parameter CFcomp;
step B, checking whether the cloud big data server has newly acquired impedance compensation parameter Ω′ and correction conversion parameter CFcal through the smart phone in the current cycle; if the cloud big data server has the newly acquired impedance compensation parameter Ω′ and the correction conversion parameter CFcal through the smart phone in the current cycle, downloading updated Ω′ and CFcal′ from the cloud big data server through the smart phone; if the cloud big data server hasn't the newly acquired impedance compensation parameter Ω′ and the correction conversion parameter CFcal through the smart phone in the current cycle, continuing to use current Ω and CFcal;
step C, calculating a current conversion coefficient CF (t), and a calculation formula for the CF (t) is as follows:, CF
   ⁡
   (
   t
   )
  
  =
  
   
    
     C
     ⁢
     F
     ⁢
     0
    
    +
    
     CFd
     ×
     t
    
    +
    
     α
     ×
     
      CF
      ⁢
      comp
     
    
    +
    
     β
     ×
     CFcal
    
   
   
    1
    +
    α
    +
    β
   
  
 

wherein, α, β are weight coefficients of CFcomp and CFcal, and a value range is 0<α<10, 0<β<10;
step D, obtaining current data of the dynamic glucose sensor Is (t) and calculating an output blood glucose value SG (t) of the sensor, a calculation formula for the SG (t) is SG(t)=Is(t)×CF(t);
step 3: uploading an original output current signal value of the sensor, the calculated SG value, and the corresponding timestamp Ts to the cloud big data server through the smart phone;
step 4: when a smart phone receives a signal that a probe of the implantable dynamic glucose sensor reached a service life, informing a cloud computing module to calculate a new conversion coefficient CF0 and a new conversion coefficient change rate CFd through a regression algorithm, and when the user uses a next new sensor probe, informing the dynamic glucose sensor to update parameters through the smart phone in step 1;
step 5: when the user is using the implantable dynamic glucose sensor, if the user also uses the finger blood glucose meter for blood glucose measurement at the same time, uploading the measured blood glucose value BG and timestamp Tb to a cloud database through the smart phone;
step 6: when the cloud database receives finger blood glucose meter data through the smart phone, informing the cloud computing module to calculate a new impedance compensation parameter Ω′ and a new correction conversion parameter CFcal′ through the regression algorithm, and pushing calculation results to the smart phone, then informing the implantable dynamic glucose sensor to update the parameters through the smart phone, so that the implantable dynamic glucose sensor updates the parameters in step 2.