Optimized calibration-free algorithm compatible with outputs of various subcutaneous glucose sensors

Abstract

The invention relates to an optimized calibration-free algorithm compatible with various subcutaneous glucose sensor outputs, and belongs to the technical field of data processing, the method comprises the following steps: obtaining a normal session message code, a calibration message code and a background calculation message code according to a message code; inputting the normal session message code into the first data channel to obtain a target noise model, and obtaining effective data according to the normal session message code and the target noise model; inputting the calibration message code into a second data channel and inputting the background calculation message code into a third data channel to obtain a target attenuation model and a target matching model; and inputting the effective data into the target attenuation model and / or the target matching model to obtain a measurement result. The subcutaneous glucose measuring device has the effect of improving the subcutaneous glucose measuring precision.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors. Background Art

[0002] Subcutaneous glucose sensors (continuous glucose monitors, CGM) are electrochemical reactions between glucose oxidase on the electrode and glucose in the interstitial fluid, and then the CGM sensor transmits the current value, which is converted into blood glucose value by processing the current value. In actual use, the actual blood glucose value (glucose value from blood, GB) and the interstitial fluid glucose value (glucose value from interstitial fluid, GIF) measured by the CGM sensor have deviation, lag, nonlinearity, and dynamic problems, which are mainly manifested in: 1. Time dimension: GIF changes lag behind GB changes. The situation where GIF changes delayed or ahead of GB is called the drag phenomenon. During the process of rising blood sugar, the delayed increase of GIF is due to the transfer of glucose from blood to tissues; during the process of falling blood sugar, GIF decreases before GB, which may be due to the effect of insulin on the transfer of glucose from interstitial fluid into peripheral cells.

[0003] 2. Absolute values: There has always been an absolute numerical gap between GIF and GB. The specific difference between GIF and GB may depend on insulin sensitivity and the degree of vascularization of the CGM sensor site. At the same time, factors such as regional blood flow, capillary permeability, and metabolic rate of adjacent cells will affect glucose transport.

[0004] 3. Errors in the CGM sensor itself. Due to differences in the equipment's manufacturing process, the quality of the enzymes used, and the blood sugar calibration technology, the results can also vary.

[0005] In general, there is an error between the actual blood glucose value GB and GIF, which, combined with the error of the sensor, leads to a decrease in the measurement accuracy of the CGM sensor, making the measurement calibration of the CGM sensor particularly important.

[0006] Currently, error correction is performed by calibration with finger blood. The disadvantage of finger blood calibration is that it requires frequent finger blood glucose test for data calibration, which is inconvenient to use and has a poor user experience. In addition, the least squares method is used to calibrate the CGM sensor. The calibration process is: first, a nonlinear compensation model is designed according to the characteristics of the CGM sensor, and then temperature compensation is performed. Then, the data is calibrated according to the compensation data, and the relative error is calculated: , and then use the relative error e to calibrate the CGM sensor.

[0007] However, the above error model requires users to wear the CGM sensor for a long time to obtain a rich training set to ensure the accuracy of the error model. However, under long-term wearing, the current value will tend to change periodically with the increase of time, the irregularity will decrease, the sensor sensitivity will decrease, etc., resulting in a decrease in the accuracy of the error model, making the calibration of the CGM sensor inaccurate.

[0008] It can be seen from this that the current calibration method cannot meet the calibration requirements of the CGM sensor, and the measurement accuracy of the CGM sensor cannot be guaranteed. Summary of the invention

[0009] In order to solve the problem that the measurement accuracy of subcutaneous glucose sensors cannot be guaranteed, the present application provides an optimized calibration-free algorithm that is compatible with the outputs of multiple subcutaneous glucose sensors.

[0010] The present application provides an optimized calibration-free algorithm compatible with the outputs of multiple subcutaneous glucose sensors. The method comprises: According to the message code, a normal conversation message code, a calibration message code and a background calculation message code are obtained; Inputting the normal conversation message code into the first data channel to obtain a target noise model, and obtaining valid data according to the normal conversation message code and the target noise model; Input the calibration message code into the second data channel and input the background calculation message code into the third data channel to obtain a target attenuation model and a target matching model; The valid data is input into the target attenuation model and / or the target matching model to obtain a measurement result.

[0011] By adopting the above technical solution, first, the present application divides the input data into normal session message codes, calibration message codes and background calculation message codes, and then respectively uses different data channels to process the message codes corresponding to the data channels, such as using the first data channel to remove the noise in the normal session message code, and using the second data channel to process the calibration message code and the third data channel to process the background calculation message code to update and obtain the target attenuation model and the target matching model. Finally, the effective data with the noise removed is input into the target attenuation model and / or the target matching model to obtain the measurement result. It can be seen that the present application covers a variety of data processing models, which can optimize the measurement data of different types of CGM sensors, so as to achieve the output of accurate blood glucose values ​​without any calibration, thereby achieving the purpose of improving the measurement accuracy of the subcutaneous glucose sensor.

[0012] In a possible implementation: the normal session message code includes a current value; The step of inputting the normal conversation message code into the first data channel to obtain a target noise model includes: using the current value to update an initial noise model to obtain a target noise model.

[0013] In a possible implementation manner, obtaining valid data according to the normal conversation message code and the target noise model includes: Using the target noise model to evaluate the current value to obtain an evaluation result; Determining whether the evaluation result is normal; If yes, then use the target noise model to remove the noise contained in the current value to obtain valid data; Otherwise, when available data is detected within the preset monitoring time period, the target noise model is used to remove the noise contained in the available data to obtain valid data, and the available data refers to the current value whose noise contained in the current value is lower than the noise threshold.

[0014] In a possible implementation, the determining whether the evaluation result is normal includes: when the current value exceeds the current threshold value for more than n times continuously or the current value increases or decreases sharply, the determination result is that the evaluation result is abnormal.

[0015] In a possible implementation manner: the second data channel and the third data channel have a common end, the common end is configured with an initial deviation model, an initial matching model and an initial attenuation model, the second data channel is configured with a calibration queue on a data segment independent of the third data channel, the third data channel is configured with a database interface on a data segment independent of the second data channel, the database interface is used to trigger the call of data in a database, and when the database interface is triggered by a background calculation message code, the database is called to establish the initial attenuation model; The step of inputting the calibration message code into the second data channel and the step of inputting the background calculation message code into the third data channel to obtain a target attenuation model and a target matching model includes: The calibration queue is used to update the initial deviation model to obtain a target deviation model when receiving the calibration message code; Update the initial matching model according to the calibration queue and the target deviation model to obtain a target matching model; The initial attenuation model is updated according to the calibration queue, the target deviation model, and the target matching model to obtain a target attenuation model.

[0016] In a possible implementation manner: the calibration queue is used to update the initial deviation model to obtain a target deviation model upon receiving the calibration message code, including: The data uploaded by the CGM sensor is used to update the parameters of the initial deviation model to obtain the initial target deviation model; The historical data uploaded by the CGM sensor is retrieved to further update the parameters of the initial target deviation model to obtain the final target deviation model.

[0017] In a possible implementation manner: updating the initial matching model according to the calibration queue and the target deviation model to obtain a target matching model includes: The initial matching model generates a deviation data queue based on historical data uploaded by the CGM sensor; Calculating an average value of the deviation data queue; The target deviation model is used to calculate the adjustment parameters obtained from the data uploaded by the CGM sensor; The initial matching model is updated using the average value and the adjustment parameter to obtain a target matching model.

[0018] In a possible implementation manner: before inputting the valid data into the target attenuation model and / or the target matching model to obtain the measurement result, the method further includes: Determine whether the valid data meets the following conditions: input time < 2h, where the input time refers to the time it takes for the valid data to be input into the target noise model; If yes, inputting the valid data into the target attenuation model; If not, the valid data is input into the target matching model and the target attenuation model in sequence.

[0019] In summary, the present application includes the following beneficial technical effects: First, the application divides the input data into normal session message codes, calibration message codes and background calculation message codes, and then uses different data channels to process the message codes corresponding to the data channels, such as using the first data channel to remove the noise in the normal session message code, and using the second data channel to process the calibration message code and the third data channel to process the background calculation message code to update and obtain the target attenuation model and the target matching model. Finally, the effective data with noise removed is input into the target attenuation model and / or the target matching model to obtain the measurement results. It can be seen that the application covers a variety of data processing models, which can optimize the measurement data of different types of CGM sensors, so as to achieve the output of accurate blood glucose values ​​without any calibration, thereby achieving the purpose of improving the measurement accuracy of subcutaneous glucose sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of an optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to an embodiment of the present application.

[0021] Figure 2This is a flow chart of the first data channel processing a normal session message code in an embodiment of the method of the present application.

[0022] Figure 3 This is a flow chart of updating the initial deviation model to obtain the target deviation model in the method embodiment of the present application.

[0023] Figure 4 This is a flow chart of updating the initial attenuation model to obtain the target attenuation model in the method embodiment of the present application.

[0024] Figure 5 It is a line graph of relative error in the embodiment of the method of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to improve the measurement accuracy of subcutaneous glucose sensors, this application constructs a complete method for processing the current value output by CGM sensors. The method covers a variety of data processing models and can optimize the measurement data of different types of CGM sensors, thereby achieving accurate blood glucose value output without any calibration.

[0027] Figure 1 A flow chart of an optimized calibration-free algorithm compatible with outputs of a variety of subcutaneous glucose sensors according to an embodiment of the present application is shown, and the main process of the method is described as follows.

[0028] Step S101, obtaining a normal conversation message code, a calibration message code and a background calculation message code according to the message code.

[0029] Receive data obtained by the user wearing the CGM sensor for a period of time, including but not limited to the time when the CGM sensor starts measuring blood sugar value and the number of consecutive measurement days, the current value measured by the CGM sensor, and the temperature of the measurement environment where the CGM sensor is located. In this example, the time when the CGM sensor starts measuring blood sugar value is used as the message code and is represented by TD.

[0030] After obtaining the data uploaded by the CGM sensor, first determine whether the message code meets the following conditions: TD < now and now - TD < the number-of-days threshold, where now refers to the current time and the number-of-days threshold is a preset value. In a specific example, the number-of-days threshold is 15 days. It should be noted that when determining whether the message code meets the conditions, the difference between now and TD is compared with the number-of-days threshold, rather than directly comparing the consecutive measurement days with the number-of-days threshold. This is because it is necessary to ensure that the data uploaded by the CGM sensor is recent data, and it is recent and consecutive multi-day data, so as to ensure that the uploaded data can accurately reflect the user's current blood glucose value.

[0031] When the message code meets the above conditions, then divide the data uploaded by the CGM sensor into a normal session message code, a calibration message code, and a background calculation message code. The normal session message code includes the moment when the CGM sensor starts measuring the blood glucose value, the consecutive measurement days, and the current value measured by the CGM sensor. In this application, data channels corresponding to the normal session message code, the calibration message code, and the background calculation message code are respectively set, and the data channel corresponding to the normal session message code is called the first data channel, the data channel corresponding to the calibration message code is called the second data channel, and the data channel corresponding to the background calculation message code is called the third data channel.

[0032] In addition, when the message code does not meet the above conditions, then stop the current measurement and exit the system. The system refers to the computer system used to process the data uploaded by the CGM sensor.

[0033] Step S102: Input the normal session message code into the first data channel to obtain a target noise model, and obtain valid data based on the normal session message code and the target noise model.

[0034] The first data channel is configured with an initial noise model. The initial noise model refers to an untrained noise model or an unupdated noise model. In a specific example, the noise model is one model or a combination of multiple models among a moving average model (MAM), a median filtering model (MFM), a weighted average model (WAM), and a Kalman filtering model (KFM).

[0035] The first data channel is used to process the normal session message code, and the processing process is as follows Figure 2As shown, specifically: first, the current value is used to update the initial noise model to obtain the target noise model, and then the target noise model is used to evaluate the current value to obtain the evaluation result, and then it is determined whether the evaluation result is normal. In a specific example, the abnormal evaluation result includes: the current value exceeds the current threshold value for more than n times continuously or the current value increases or decreases sharply, such as the adjacent current value mutations of more than 5 times, n can be set according to the actual application scenario, in this example, n=9.

[0036] When the evaluation result is normal, the target noise model is used to remove the noise contained in the current value to obtain valid data; otherwise, when the evaluation result is abnormal, it is further determined whether there is available data within the preset monitoring period. Available data refers to the current value whose noise contained in the current value is lower than the noise threshold. If the result of the second determination is that there is available data, the target noise model is used to remove the noise in the available data to obtain valid data, otherwise the session is abnormal and the measurement optimization is stopped.

[0037] Step S103: input the calibration message code into the second data channel and input the background calculation message code into the third data channel to obtain a target attenuation model and a target matching model.

[0038] The second data channel and the third data channel have a common segment, in which an initial deviation model, an initial matching model and an initial attenuation model are configured. A calibration queue is configured on the data segment of the second data channel independently of the third data channel, and the calibration queue is used to store the calibration message code. After the calibration message code enters the calibration queue, the calibration queue automatically corrects the calibration message code, for example, removing the maximum value or minimum value in the calibration message code. Then, for the data in the retained calibration queue, it is used to update the initial deviation model, the initial matching model and the initial attenuation model. At the same time, a database interface is configured on the data segment of the third data channel independently of the second data channel, and the database interface can be triggered by the background calculation message code to call the data in the database. The database is a set of data set in advance to support the establishment of an attenuation model. When the third data channel receives the background calculation message code, the data in the database is retrieved through the database interface to construct an initial attenuation model, and the constructed initial attenuation model is input into the common segment of the second data channel and the third data channel.

[0039] Among them, the initial deviation model is mainly used to automatically update according to the calibration queue to obtain the target deviation model, and then use the target deviation model to process the data uploaded by the CGM sensor, and the processing results are further used in the update of the initial matching model and the update process of the initial attenuation model. Figure 3As shown in the figure, the updating process of the initial deviation model is as follows: firstly, the data uploaded by the CGM sensor is calculated and processed, the parameters of the initial deviation model are updated to obtain the target deviation model, and then the target deviation model is used in the subsequent process of updating the initial matching model. At the same time, when updating the initial deviation model, the historical data uploaded by the CGM sensor needs to be added to further update the parameters of the target deviation model, so as to further correct the obtained target deviation model and finally output the parameters of the target deviation model.

[0040] The initial matching model generates a deviation data queue based on the historical data uploaded by the CGM sensor, and calculates the average value of the deviation data queue during each update process, and uses the average value as a parameter for updating the initial matching model. At the same time, the target deviation model is used to calculate the data uploaded by the CGM sensor to obtain the adjustment parameters, and the adjustment parameters are used to further update the initial matching model to obtain the target matching model. Finally, the target matching model is used to participate in the calculation of the upper and lower bounds of the blood glucose value. The upper bound of the blood glucose value refers to the maximum value of the blood glucose value, and the lower bound of the blood glucose value refers to the minimum value of the blood glucose value. The range between the minimum and maximum values ​​is the ideal range of the blood glucose value. In addition, the target matching model is used to calculate the data uploaded by the CGM sensor, and the calculation results are fed back to the target deviation model to further update the target deviation model. At the same time, the calculation results are also transmitted to the initial attenuation model to support the update of the initial attenuation model.

[0041] The initial attenuation model receives the calibration queue, the parameters of the target deviation model and the parameters of the target matching model to automatically update to obtain the target attenuation model. The target attenuation model is mainly used to calculate the iterative attenuation value. In the process of iterative attenuation value, not only the parameters of the target attenuation model are updated, but also the target deviation algorithm and the target matching model are further called to update the calculation results of the data uploaded by the CGM sensor. In the process of continuous iterative calculation, trend disturbance counting is also performed, and different coefficients are used to further update the parameters of the target attenuation model. The update process of the initial attenuation model is as follows: Figure 4 shown.

[0042] It should be noted that when inputting the data uploaded by the CGM sensor, the iterative attenuation value will be calculated and it will be determined whether the iterative attenuation value exceeds the critical value. If so, the system will enter the state session abnormal mode. The system here also refers to the computer system used to process the data uploaded by the CGM sensor. Otherwise, the system will enter the next step.

[0043] Step S104: input the valid data into the target attenuation model and / or the target matching model to obtain the measurement result.

[0044] For the valid data obtained in step S102, first determine whether the valid data meets the following conditions: input time < 2h, where the input time refers to the time when the valid data is input into the computer system of the present application, specifically the time when the valid data is input into the target noise model. When the valid data meets the above conditions, the valid data is input into the target attenuation model, and the target attenuation model calculates the corrected blood glucose value, blood glucose trend value, algorithm status, noise evaluation, blood glucose upper and lower limits, predicted blood glucose value and other information based on the trained parameters. When the valid data does not meet the above conditions, the valid data is input into the target matching model, and the target matching model is updated with the valid data. After the target attenuation model is updated again by the target matching model, the target attenuation model is used to process the valid data to obtain the corrected blood glucose value, blood glucose trend value, algorithm status, noise evaluation, blood glucose upper and lower limits, predicted blood glucose value and other information.

[0045] This application uses the calculated corrected blood sugar value, blood sugar trend value, algorithm status, noise evaluation, blood sugar upper and lower limits, predicted blood sugar value and other information as measurement results. On the one hand, the measurement results are output to the display for display. The display interface of the measurement results is: Struct LastStepRes { Uint16 Ls1; Uint32 Lf1; Uint8 Lb1; Uint8 Lb2; Uint32 Time; Uint16 Iv1; Uint16 lv2; Uint16 lv3; Uint16 Iv4; Uint8 Ib3; Uint16 pre; } Among them, the initialization value of Uint16 Ls1 is 1, indicating the corrected blood glucose value, the initialization value of Uint32 Lf1 is 127, indicating the blood glucose trend value. Under normal circumstances, the absolute value of the trend value is less than 8, the initialization value of Uint8 Lb1 is 1, indicating the running status of the algorithm model for processing blood glucose values, the initialization value of Uint8 Lb2 is 5, indicating the evaluation status of the noise model, Uint32 Time indicates the current time, the initialization value of Uint16 lv1 is 600, indicating the calculated upper limit of blood glucose, the initialization value of Uint16 lv2 is 20, indicating the calculated lower limit of blood glucose, the initialization value of Uint16 lv3 is 20, and the display content is not defined, the initialization value of Uint16 lv4 is 600, and the display content is not defined, the initialization value of Uint8 lb3 is 0, indicating the measurement result status identifier, and the initialization value of Uint16 pre is 13, indicating the predicted blood glucose value.

[0046] It can be seen that by displaying the measurement results, it is convenient for users to view the data measured by the CGM sensor.

[0047] On the other hand, the computer system will also save the measurement results of this time so that the blood sugar value can be predicted later based on the accumulated measurement results.

[0048] In order to verify that the accuracy of the measurement results obtained by the above method is improved, this application calculates the relative error and average relative error based on the measurement results. The specific calculation formula is: The calculation formula of relative error is shown in Formula 1: (1) The calculation formula of the average relative error is shown in Formula 2: (2) GBn represents the actual blood sugar value at the time of the nth measurement, and GIFn represents the blood sugar value included in the measurement result at the time of the nth measurement.

[0049] It should be noted that the above-mentioned relative error and average relative error are both used as indicators for judging the measurement accuracy of the CGM sensor. The values ​​of MARD1 and MARD2 both represent the error between the actual blood glucose value GB and the blood glucose value GIF measured by the CGM sensor, and are used to measure the accuracy of the measurement results measured by the CGM sensor. The smaller the values ​​of MARD1 and MARD2, the higher the accuracy.

[0050] The following is a specific example of calculating the difference between the actual blood glucose value GB and the blood glucose value GIF measured by the CGM sensor: First, the above measurement optimization method was applied to a CGM sensor on the market. The relative errors of blood glucose values ​​at different time points were obtained over a period of 4 days. The horizontal axis was the number of actual blood glucose tests using finger blood calibration, and the vertical axis was the relative error. Data A is the relative error between the blood glucose value obtained using the measurement optimization method of this application and the actual blood glucose value, where the average relative error MARD(A): 11.78%; Data B is the relative error between the blood glucose value measured without using the measurement optimization method of this application and the actual blood glucose value, where the average relative error MARD(B): 20.24%. Figure 5 shown.

[0051] Depend on Figure 5 It can be seen that when the present application is actually used, ordinary sensors that require finger blood calibration can output relatively accurate blood glucose values ​​without any calibration by the user, and the accuracy is not lower than that of CGM sensors that do not use calibration algorithms.

[0052] Therefore, the optimized calibration-free algorithm of the present application that is compatible with the output of multiple subcutaneous glucose sensors can be applied to any CGM sensor as needed. In this example, the calibration-free algorithm of the present application is compatible with at least three or more different types of CGM sensors, and can also be considered to be compatible with multiple types of CGM sensors, and the current frequencies output by multiple types of CGM sensors can be different, for example, the current output frequency can be 3min, 5min, 15min, and the CGM sensor with a current output frequency of 15min can output a blood glucose value of not less than 3min after the calibration-free algorithm of the present application.

[0053] In addition, after the electrodes of the CGM sensor collect the current value of the human tissue, the calibration-free algorithm of the present application will be optimized, specifically optimized to output a current value per minute, or optimized to output a tighter current value, so as to facilitate continuity analysis and improve the accuracy of the subsequent measurement results. Moreover, in view of the different characteristics of the current values ​​of different human tissues, after the calibration-free algorithm of the present application, the output blood glucose value will achieve maximum difference, which is convenient for analyzing users with similar current values ​​but large differences in actual blood glucose values, so as to timely find people with high blood glucose values, and also facilitate further dynamic updating of the calibration-free algorithm of the present application, improve the subsequent optimization accuracy, so that the CGM sensor that originally requires fingertip blood calibration can also abandon the fingertip blood calibration and rely on the calibration-free algorithm of the present application for optimization, so as to achieve more accurate blood glucose value output without calibration.

[0054] In order to better execute the procedure of the above method, the present application also provides an optimized calibration-free device compatible with the outputs of multiple subcutaneous glucose sensors, and the device includes a memory and a processor.

[0055] The memory may be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the above-mentioned optimized calibration-free algorithm compatible with multiple subcutaneous glucose sensor outputs, etc.; the data storage area may store data involved in the above-mentioned optimized calibration-free algorithm compatible with multiple subcutaneous glucose sensor outputs, etc.

[0056] The processor may include one or more processing cores. The processor calls the data stored in the memory by running or executing the instructions, programs, code sets or instruction sets stored in the memory, performs various functions of the present application and processes data. The processor may be at least one of a special purpose integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field programmable gate array, a central processing unit, a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor function can also be other, and the embodiments of the present application are not specifically limited.

[0057] The present application also provides a computer-readable storage medium, for example, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc., which can store program codes. The computer-readable storage medium stores a computer program that can be loaded by a processor and execute the above-mentioned optimized calibration-free algorithm compatible with the outputs of multiple subcutaneous glucose sensors.

[0058] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. An optimized calibration-free algorithm compatible with multiple subcutaneous glucose sensor outputs, characterized in that: include: According to the message code, a normal conversation message code, a calibration message code and a background calculation message code are obtained; Inputting the normal conversation message code into the first data channel to obtain a target noise model, and obtaining valid data according to the normal conversation message code and the target noise model; Input the calibration message code into the second data channel and input the background calculation message code into the third data channel to obtain a target attenuation model and a target matching model; The valid data is input into the target attenuation model and / or the target matching model to obtain a measurement result.

2. The optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to claim 1, characterized in that: The normal session message code includes a current value; The step of inputting the normal conversation message code into the first data channel to obtain a target noise model includes: using the current value to update an initial noise model to obtain a target noise model.

3. The optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to claim 2, characterized in that: The obtaining valid data according to the normal conversation message code and the target noise model includes: Using the target noise model to evaluate the current value to obtain an evaluation result; Determining whether the evaluation result is normal; If yes, then use the target noise model to remove the noise contained in the current value to obtain valid data; Otherwise, when available data is detected within the preset monitoring time period, the target noise model is used to remove the noise contained in the available data to obtain valid data, and the available data refers to the current value whose noise contained in the current value is lower than the noise threshold.

4. The optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to claim 3, characterized in that: The determining whether the evaluation result is normal includes: when the current value exceeds the current threshold value for more than n times continuously or the current value increases or decreases sharply, the determining result is that the evaluation result is abnormal.

5. The optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to claim 1, characterized in that: The second data channel and the third data channel have a common end, the common end is configured with an initial deviation model, an initial matching model and an initial attenuation model, the second data channel is configured with a calibration queue on a data segment independent of the third data channel, the third data channel is configured with a database interface on a data segment independent of the second data channel, the database interface is used to trigger the call of data in a database, and when the database interface is triggered by a background calculation message code, the database is called to establish the initial attenuation model; The step of inputting the calibration message code into the second data channel and the step of inputting the background calculation message code into the third data channel to obtain a target attenuation model and a target matching model includes: The calibration queue is used to update the initial deviation model to obtain a target deviation model when receiving the calibration message code; Update the initial matching model according to the calibration queue and the target deviation model to obtain a target matching model; The initial attenuation model is updated according to the calibration queue, the target deviation model, and the target matching model to obtain a target attenuation model.

6. The optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to claim 5, characterized in that: The calibration queue is used to update the initial deviation model to obtain a target deviation model when receiving the calibration message code, including: The data uploaded by the CGM sensor is used to update the parameters of the initial deviation model to obtain the initial target deviation model; The historical data uploaded by the CGM sensor is retrieved to further update the parameters of the initial target deviation model to obtain the final target deviation model.

7. The optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to claim 1, characterized in that: The updating of the initial matching model according to the calibration queue and the target deviation model to obtain a target matching model includes: The initial matching model generates a deviation data queue based on historical data uploaded by the CGM sensor; Calculating an average value of the deviation data queue; The target deviation model is used to calculate the adjustment parameters obtained from the data uploaded by the CGM sensor; The initial matching model is updated using the average value and the adjustment parameter to obtain a target matching model.

8. The optimized calibration-free algorithm compatible with outputs of multiple subcutaneous glucose sensors according to claim 1, characterized in that: Before inputting the valid data into the target attenuation model and / or the target matching model to obtain the measurement result, the method further includes: Determine whether the valid data meets the following conditions: input time < 2h, where the input time refers to the time it takes for the valid data to be input into the target noise model; If yes, inputting the valid data into the target attenuation model; If not, the valid data is input into the target matching model and the target attenuation model in sequence.

Images