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

Claim 2:
3. The intelligent real-time dynamic blood glucose monitoring system based on the cloud big data according to claim 1, wherein the cloud big data server is configured to calculate the CF0 and the CFd as follows:
for each user, the cloud big data server is configured to calculate blood glucose value output by the implantable dynamic glucose sensor using a preset CF0 and CFd:

SG=Is×(CF0+CFd×t)=Is×CF0+(Is×t)CFd, 

after the user has used the finger blood glucose meter to measure their own blood glucose, a measured value of the blood glucose meter is collected through the smart phone and uploaded to the cloud big data server, the cloud big data server is configured to calculate the updated CF0 and CFd by a regression equation, as follows:
step A, extract sample data: near each measurement time Tb of the finger blood glucose meter, screen out dynamic glucose sensor data having a time interval |Tb−Ts| less than a threshold value, and extract following data:
1. BG: measured data of the finger blood glucose meter
2. SG: blood glucose calculation data of the implantable dynamic glucose sensor
3. Is: current data of the implantable dynamic glucose sensor
4. Ts: time stamp data of the implantable dynamic glucose sensor
5. T0s: start time stamp data of the implantable dynamic glucose sensor
select data with |SG−BG| less than the threshold value as a valid sample to form a sample record package [BGn Isn Tsn T0sn] for calculation;
use data of last three to six months for a regression, and arrange a subscript number n of a historical data variable in reverse order of TS, that is, the closer to current historical data, the smaller the subscript number, the data of the implantable dynamic glucose sensor come from multiple sensor probes of a same model;
step B, establish a sample matrix:, G
  =
  
   [
   
    
     
      
       BG
       1
      
     
    
    
     
      
       BG
       2
      
     
    
    
     
      
       BG
       3
      
     
    
    
     
      ⋮
     
    
    
     
      
       BG
       n
      
     
    
   
   ]
  
 

 
  I
  =
  
   [
   
    
     
      
       Is
       1
      
     
     
      
       
        Is
        1
       
       ×
       
        t
        1
       
      
     
    
    
     
      
       Is
       2
      
     
     
      
       
        Is
        2
       
       ×
       
        t
        2
       
      
     
    
    
     
      
       Is
       3
      
     
     
      
       
        Is
        3
       
       ×
       
        t
        3
       
      
     
    
    
     
      ⋮
     
     
      ⋮
     
    
    
     
      
       Is
       n
      
     
     
      
       
        Is
        n
       
       ×
       
        t
        n
       
      
     
    
   
   ]
  
 

wherein, tn=Tsn−T0sn;
step C, establish a coefficient matrix:, C
   =
   
    [
    
     
      
       
        CF
        ⁢
        0
       
      
     
     
      
       CFd
      
     
    
    ]
   
  
  ;
 

step D, construct a weight matrix:, W
  =
  
   [
   
    
     
      
       w
       ⁡
       (
       
        Ts
        1
        ′
       
       )
      
     
     
       
     
     
       
     
     
       
     
     
       
     
    
    
     
       
     
     
      
       w
       ⁡
       (
       
        Ts
        2
        ′
       
       )
      
     
     
       
     
     
       
     
     
       
     
    
    
     
       
     
     
       
     
     
      
       w
       ⁢
       
        (
        
         Ts
         3
         ′
        
        )
       
      
     
     
       
     
     
       
     
    
    
     
       
     
     
       
     
     
       
     
     
      ⋱
     
     
       
     
    
    
     
       
     
     
       
     
     
       
     
     
       
     
     
      
       w
       ⁢
       
        (
        
         Ts
         n
         ′
        
        )
       
      
     
    
   
   ]
  
 

wherein, Ts′n=Tcurrent−Tsn, Tcurrent is a current time; w(Ts′n) is a weight coefficient of Ts′n, so that the closer Tsn is to the current time, the greater the weight is;
step E, solve an overdetermined equation G=IC:
using a weighted least square method to solve a matrix equation Ĉ=(ITWI)−1ITWG;
step F, eliminate abnormal data: calculate a residual matrix {circumflex over (ε)}=G− IĈ, remove data items with residuals greater than the threshold value, and then repeating step B to E of regression algorithm to obtain a sum of updated values  and  of CF0 and CFd;
step G, finally, use the updated values  and  calculated by regression to correct the CF0 and CFd with a predetermined correction ratio γ as a conversion coefficient setting of the next dynamic glucose sensor:

CF0:=(1−γ)×CF0+γ×

CFd:=(1−γ)×CFd+γ×

a range of γ values is 0<γ<1.