Non-invasive personalized dynamic blood glucose trend monitoring and early warning method and device

Abstract

The application relates to the technical field of health management, and discloses a non-invasive personalized dynamic blood glucose trend monitoring and early warning method and device.The method comprises the following steps: collecting physiological characteristic information and personal basic information of a user to be monitored in real time; sending the physiological characteristic information and the personal basic information to a health information analysis module, so that the health information analysis module restores a current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information; dynamically adjusting the current blood glucose value according to current behavior information of the user to be monitored to obtain a target blood glucose value; generating a blood glucose atlas of the user to be monitored through the target blood glucose value, and managing the health of the user to be monitored based on the blood glucose atlas; compared with the blood glucose meter used in the prior art to monitor the blood glucose value at a certain time point, the application can obtain continuous and multi-point blood glucose data in a non-invasive manner, effectively improves the accuracy of monitoring dynamic blood glucose, and realizes non-invasive personalized dynamic monitoring and early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of health management, in particular to a non-invasive personalized dynamic blood glucose trend monitoring and early warning method and device. BACKGROUND

[0002] With the continuous development of social economy and the continuous improvement of people's living standards, the number of patients with hyperglycemia in China is increasing year by year, and at present it has reached about 10% of the population. An important factor causing this situation is the cumbersome blood glucose monitoring method. At present, the blood glucose monitoring method on the market uses a blood glucose meter to obtain blood from the vein or fingertip for blood glucose monitoring through two invasive methods of venous blood and finger prick. Due to the minimally invasive and invasive blood glucose monitoring method, users have a psychological and physiological rejection, thereby ignoring the monitoring of blood glucose. Moreover, the above-mentioned method can only monitor the blood glucose value at a certain time point. Since the user's physical condition is changing, the blood glucose value is also changing, which ultimately results in low accuracy of the monitored blood glucose value and inability to predict the trend of blood glucose change.

[0003] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a non-invasive personalized dynamic blood glucose trend monitoring and early warning method and device, which aims to solve the technical problems of low accuracy of monitoring dynamic blood glucose in the prior art and inability to realize non-invasive personalized dynamic monitoring and early warning.

[0005] To achieve the above-mentioned purpose, the present application provides a non-invasive personalized dynamic blood glucose trend monitoring and early warning method, which comprises the following steps:

[0006] Real-time collection of physiological characteristic information of a user to be monitored, and acquisition of personal basic information;

[0007] Sending the physiological characteristic information and personal basic information to a health information analysis module, so that the health information analysis module restores the current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information;

[0008] Dynamic adjustment of the current blood glucose value according to the current behavior information of the user to be monitored to obtain a target blood glucose value;

[0009] Generating a blood glucose profile of the user to be monitored through the target blood glucose value, and managing the health of the user to be monitored based on the blood glucose profile.

[0010] Optionally, the physiological characteristic information of the to-be-monitored user is collected in real time, and the personal basic information is acquired, including:

[0011] The physiological information of the to-be-monitored user is collected in real time.

[0012] The physiological characteristic information of the to-be-monitored user is obtained by acquiring the physiological characteristic parameters and performing feature extraction on the physiological information according to the physiological characteristic parameters, and the personal basic information is acquired.

[0013] Optionally, the physiological characteristic information and the personal basic information are sent to a health information analysis module, so that the health information analysis module restores the current blood glucose value of the to-be-monitored user according to the physiological characteristic information and the personal basic information, including:

[0014] The physiological characteristic information and the personal basic information are sent to a health information analysis module, so that the health information analysis module determines a corresponding mass-differentiated blood glucose model according to the personal basic information, restores the physiological characteristic information into a physiological medical waveform graph, and calculates the physiological medical waveform graph through the mass-differentiated blood glucose model to restore the current blood glucose value of the to-be-monitored user.

[0015] Optionally, the current blood glucose value is dynamically adjusted according to the current behavior information of the to-be-monitored user to obtain a target blood glucose value, including:

[0016] The blood glucose influence data set is obtained according to the current behavior information of the to-be-monitored user.

[0017] The fluctuation range and the weight value of each blood glucose influence data are obtained according to the blood glucose influence data set.

[0018] The fluctuation range of each blood glucose influence data is sorted according to the weight value to obtain a corresponding blood glucose influence data sorting result.

[0019] The current blood glucose value is dynamically adjusted according to the blood glucose influence data sorting result to obtain a target blood glucose value.

[0020] Optionally, after the current blood glucose value is dynamically adjusted according to the blood glucose influence data sorting result to obtain a target blood glucose value, the method further includes:

[0021] The target blood glucose value is sent to a health state analysis module, so that the health state analysis module queries and feeds back the current health state of the to-be-monitored user according to the mapping relationship between the blood glucose parameter and the health state.

[0022] Optionally, the blood glucose graph of the to-be-monitored user is generated through the blood glucose parameter, and the to-be-monitored user is managed based on the blood glucose graph, including:

[0023] acquisition time of physiological feature information of the to-be-monitored user is acquired;

[0024] The acquisition time is matched with the blood glucose parameters to obtain a paired blood glucose parameter set;

[0025] The paired blood glucose parameter set is sequentially input to a target health management model according to the acquisition time, so that the target management model draws and feeds back a full-time blood glucose change curve according to the paired blood glucose parameter set;

[0026] A blood glucose atlas of the to-be-monitored user is generated according to the full-time blood glucose change curve, and the to-be-monitored user is managed based on the blood glucose atlas.

[0027] Optionally, the management of the to-be-monitored user based on the blood glucose atlas comprises:

[0028] A blood glucose change trend range of the to-be-monitored user within a preset time is determined according to the blood glucose atlas;

[0029] An initial blood glucose threshold value is dynamically adjusted according to current behavior information and a current health state of the to-be-monitored user to obtain a preset blood glucose threshold value;

[0030] When a maximum value of the blood glucose change trend range reaches the preset blood glucose threshold value, a frequency of reaching the preset blood glucose threshold value is counted;

[0031] When the frequency of reaching the preset blood glucose threshold value is greater than or equal to a target frequency threshold value, a preset alarm prompt information is sent.

[0032] In addition, to achieve the above-mentioned purpose, the application further provides a non-invasive personalized dynamic blood glucose trend monitoring and early warning device, which comprises:

[0033] An acquisition module is configured to acquire physiological feature information of a to-be-monitored user in real time and acquire personal basic information;

[0034] An analysis module is configured to send the physiological feature information and the personal basic information to a health information analysis module, so that the health information analysis module restores a current blood glucose value of the to-be-monitored user according to the physiological feature information and the personal basic information;

[0035] A determination module is configured to dynamically adjust the current blood glucose value according to current behavior information of the to-be-monitored user to obtain a target blood glucose value;

[0036] A generation module is configured to generate a blood glucose atlas of the to-be-monitored user through the target blood glucose value, and manage the health of the to-be-monitored user based on the blood glucose atlas.

[0037] In addition, to achieve the above object, the present application also provides a non-invasive personalized dynamic blood glucose trend monitoring and early warning device, comprising a memory, a processor and a non-invasive personalized dynamic blood glucose trend monitoring and early warning program stored on the memory and executable on the processor, wherein the non-invasive personalized dynamic blood glucose trend monitoring and early warning program is configured to implement the non-invasive personalized dynamic blood glucose trend monitoring and early warning method as described above.

[0038] In addition, to achieve the above object, the present application also provides a storage medium, wherein the storage medium stores a non-invasive personalized dynamic blood glucose trend monitoring and early warning program, and the non-invasive personalized dynamic blood glucose trend monitoring and early warning program is executed by a processor to implement the non-invasive personalized dynamic blood glucose trend monitoring and early warning method as described above.

[0039] The non-invasive personalized dynamic blood glucose trend monitoring and early warning method provided by the present application can acquire continuous and multi-point blood glucose data in a non-invasive manner, effectively improve the accuracy of monitoring dynamic blood glucose, and realize non-invasive personalized dynamic monitoring and early warning, compared with the prior art which uses a blood glucose meter to acquire blood from veins or fingertips in a non-invasive or minimally invasive manner to monitor blood glucose values at a certain time point. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 FIG. 1 is a structural schematic diagram of a non-invasive personalized dynamic blood glucose trend monitoring and early warning device related to a hardware running environment of an embodiment scheme of the present application;

[0041] Figure 2 FIG. 2 is a flowchart of a first embodiment of a non-invasive personalized dynamic blood glucose trend monitoring and early warning method of the present application;

[0042] Figure 3 FIG. 3 is a flowchart of a second embodiment of a non-invasive personalized dynamic blood glucose trend monitoring and early warning method of the present application;

[0043] Figure 4 FIG. 4 is a flowchart of a third embodiment of a non-invasive personalized dynamic blood glucose trend monitoring and early warning method of the present application;

[0044] Figure 5 Figure 1 is a schematic diagram of a function module of a first embodiment of the non-invasive personalized dynamic blood glucose trend monitoring and early warning device of the present application.

[0045] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are merely illustrative of the present application and do not limit the present application.

[0047] Reference Figure 1 , Figure 1 Figure 2 is a schematic diagram of a structure of the non-invasive personalized dynamic blood glucose trend monitoring and early warning device related to the hardware operating environment of the embodiment of the present application.

[0048] As Figure 1 shown, the non-invasive personalized dynamic blood glucose trend monitoring and early warning device can include a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 can include a display, an input unit such as a keyboard, and can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (Wi-Fi) interface). The memory 1005 can be a high-speed random access memory (RAM) memory, or a stable non-volatile memory (NVM), such as a disk memory. The memory 1005 can also be a storage device independent of the aforementioned processor 1001.

[0049] Those skilled in the art can understand Figure 1 that the structure shown in the figure does not constitute a limitation on the non-invasive personalized dynamic blood glucose trend monitoring and early warning device, and can include more or fewer components than the figure, or combine certain components, or different component arrangements.

[0050] As Figure 1 shown, the memory 1005 as a storage medium can include an operating system, a network communication module, a user interface module, and a non-invasive personalized dynamic blood glucose trend monitoring and early warning program.

[0051] In Figure 1The network interface 1004 shown in the non-invasive personalized dynamic blood glucose trend monitoring and early warning device is mainly used for data communication with the network integrated platform workstation; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the non-invasive personalized dynamic blood glucose trend monitoring and early warning device can be arranged in the non-invasive personalized dynamic blood glucose trend monitoring and early warning device, and the non-invasive personalized dynamic blood glucose trend monitoring and early warning device calls the non-invasive personalized dynamic blood glucose trend monitoring and early warning program stored in the memory 1005 through the processor 1001, and executes the non-invasive personalized dynamic blood glucose trend monitoring and early warning method provided in the embodiment of the application.

[0052] Based on the above hardware structure, the non-invasive personalized dynamic blood glucose trend monitoring and early warning method embodiment of the application is proposed.

[0053] Reference Figure 2 , Figure 2 The flowchart of the first embodiment of the non-invasive personalized dynamic blood glucose trend monitoring and early warning method of the application is shown.

[0054] In the first embodiment, the non-invasive personalized dynamic blood glucose trend monitoring and early warning method comprises the following steps:

[0055] Step S10, real-time collection of physiological characteristic information of a user to be monitored, and acquisition of personal basic information.

[0056] It should be noted that the execution subject of the present embodiment is a non-invasive personalized dynamic blood glucose trend monitoring and early warning device, and can also be other devices that can achieve the same or similar functions, such as a blood glucose monitoring smart bracelet, etc., and the present embodiment does not limit this. In the present embodiment, a blood glucose monitoring smart bracelet is taken as an example for description.

[0057] It should be understood that the physiological characteristic information refers to information capable of representing the physiological characteristics of the user to be monitored, and the physiological characteristic information includes but is not limited to information such as the shape, flow rate, heart rate variation rhythm, body temperature, respiratory rate, systolic pressure frequency, diastolic pressure frequency, and blood oxygen of blood, etc. The above physiological characteristic information can be collected by various sensors arranged on the blood glucose monitoring smart bracelet, such as green light sensors and red light sensors. The personal basic information includes but is not limited to information such as age, gender, height, and weight, etc.

[0058] Step S20, sending the physiological characteristic information and the personal basic information to a health information analysis module, so that the health information analysis module restores the current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information.

[0059] It can be understood that the current blood glucose value refers to the blood glucose value of the user to be monitored at the current time, specifically the glucose concentration in the blood in the user to be monitored, specifically the health information analysis module restores the physiological characteristic information into a physiological medical waveform diagram, and then calculates the physiological medical waveform diagram according to a mass of differential blood glucose models corresponding to the personal basic information, so as to restore the current blood glucose value. The personal basic information can reflect the individualization of blood glucose monitoring, and the input of the mass of differential blood glucose models is the physiological medical waveform diagram, and the output is the current blood glucose value of the user to be monitored.

[0060] Further, step S20 comprises: sending the physiological characteristic information and the personal basic information to the health information analysis module, so that the health information analysis module determines a corresponding mass of differential blood glucose models according to the personal basic information, restores the physiological characteristic information into a physiological medical waveform diagram, and calculates the physiological medical waveform diagram through the mass of differential blood glucose models to restore the current blood glucose value of the user to be monitored.

[0061] It should be understood that the mass of differential blood glucose models corresponds to the personal basic information, for example, the input to the personal basic information is age 18 years old, weight 65 kg, height 175 cm, and gender male. At this time, the mass of differential blood glucose models determined is the blood glucose model of age 18 years old, weight 65 kg, height 175 cm, and gender male, so as to reflect the individualization of blood glucose monitoring in the embodiment. The physiological medical waveform diagram refers to a waveform diagram constructed by a plurality of types of physiological characteristic information. After obtaining the physiological medical waveform diagram, the physiological medical waveform diagram is input to the mass of differential blood glucose models, so that the mass of differential blood glucose models calculates the physiological medical waveform diagram to restore the current blood glucose value of the user to be monitored.

[0062] Step S30, dynamically adjusting the current blood glucose value according to the current behavior information of the user to be monitored to obtain a target blood glucose value.

[0063] It can be understood that since the current behavior of the user to be monitored also affects the change of blood glucose in the body, after determining the current blood glucose value, the current behavior information needs to be considered to obtain the most accurate blood glucose value of the user to be monitored at the current time. The blood glucose influence data set refers to a set composed of various data that affect the change of blood glucose, for example, food, fitness exercise, emotion, and medicine all affect the change of blood glucose. After obtaining the current blood glucose value, each blood glucose influence data in the blood glucose influence data set is used to dynamically adjust the current blood glucose value to obtain a target blood glucose data.

[0064] Further, the step S30 comprises: obtaining a blood glucose influence data set according to the current behavior information of the to-be-monitored user; obtaining a fluctuation range and a weight value of each blood glucose influence data according to the blood glucose influence data set; sorting the fluctuation range of each blood glucose influence data according to the weight value to obtain a corresponding blood glucose influence data sorting result; and dynamically adjusting the current blood glucose value according to the blood glucose influence data sorting result to obtain a target blood glucose value of the to-be-monitored user.

[0065] It should be understood that the fluctuation range refers to the range of fluctuation of each blood glucose influence data on the current blood glucose value, and the weight value refers to the weight value corresponding to the degree of influence on the current blood glucose value, that is, the greater the degree of influence, the greater the corresponding weight value, and the blood glucose influence data sorting result is the result of sorting the fluctuation range of each blood glucose influence data in descending order, and then the current blood glucose value is dynamically adjusted according to the blood glucose influence data sorting result to obtain the target blood glucose value of the to-be-monitored user.

[0066] Further, after the current blood glucose value is dynamically adjusted according to the blood glucose influence data sorting result to obtain the target blood glucose value, the method further comprises: sending the target blood glucose value to a health state analysis module, so that the health state analysis module determines and feeds back the current health state of the to-be-monitored user according to the mapping relationship between the blood glucose parameter and the health state.

[0067] It should be understood that the current health state refers to the health state of the to-be-monitored user at the current time, and the level of the current health state is divided into excellent, good, medium and poor, and the current health state of the to-be-monitored user is determined by the health state analysis module according to the mapping relationship between the blood glucose parameter and the health state.

[0068] The step S40 generates a blood glucose map of the to-be-monitored user through the blood glucose parameter, and manages the health of the to-be-monitored user based on the blood glucose map.

[0069] It should be understood that the blood glucose map refers to a map recording the blood glucose value of the to-be-monitored user at each time, and the blood glucose map is generated by the blood glucose parameter and the collection time, and the health of the to-be-monitored user is managed based on the blood glucose map after the blood glucose map is generated.

[0070] The embodiment collects physiological characteristic information and personal basic information of a user to be monitored in real time, sends the physiological characteristic information and the personal basic information to a health information analysis module, so that the health information analysis module restores a current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information, dynamically adjusts the current blood glucose value according to current behavior information of the user to be monitored to obtain a target blood glucose value, generates a blood glucose map of the user to be monitored through the target blood glucose value, and manages health of the user to be monitored based on the blood glucose map. Compared with the prior art that uses a blood glucose meter to obtain blood of a vein or a fingertip through a destructive or minimally invasive way to monitor a blood glucose value at a time point, the present application can obtain continuous and multi-point blood glucose data through a non-destructive way, effectively improve the accuracy of monitoring dynamic blood glucose, and realize non-destructive personalized dynamic monitoring and early warning.

[0071] In an embodiment, as Figure 3 The second embodiment of the non-invasive personalized dynamic blood glucose trend monitoring and early warning method is based on the first embodiment, and the step S10 includes:

[0072] In step S101, physiological information of a user to be monitored is collected in real time, and personal basic information is obtained.

[0073] It should be understood that the physiological information refers to data related to the physiology of the user to be monitored. When the user to be monitored wears a blood glucose monitoring smart bracelet on the wrist, the blood glucose monitoring smart bracelet collects multiple types of physiological information of the user to be monitored in real time, and obtains basic information such as age, gender, height, and weight.

[0074] In step S102, a physiological characteristic parameter is obtained, the physiological information is feature-extracted according to the physiological characteristic parameter, and physiological characteristic information of the user to be monitored is obtained.

[0075] It can be understood that the physiological characteristic parameter refers to blood glucose-related data in physiological data, and the physiological characteristic parameter is used to feature-extract the physiological data to obtain the physiological characteristic information of the user to be monitored.

[0076] The embodiment collects physiological information of a user to be monitored in real time, and obtains personal basic information. A physiological characteristic parameter is obtained, the physiological information is feature-extracted according to the physiological characteristic parameter, and physiological characteristic information of the user to be monitored is obtained. In this way, the physiological information of the user to be monitored is obtained, the physiological characteristic information of the user to be monitored is extracted from the physiological information according to the physiological characteristic parameter, and the accuracy of obtaining the physiological characteristic information is effectively improved.

[0077] In an embodiment, as Figure 4The third embodiment of the non-invasive personalized dynamic blood glucose trend monitoring and early warning method is proposed based on the first embodiment, and the step S40 comprises:

[0078] In step S401, the acquisition time of the physiological characteristic information of the user to be monitored is obtained.

[0079] It should be understood that the acquisition time refers to the time of real-time acquisition of the physiological characteristic information of the user to be monitored, for example, the time of first acquisition of the physiological characteristic information is 08:08:22, and the time of second acquisition of the physiological characteristic information is 08:08:32.

[0080] In step S402, the acquisition time is matched with the blood glucose parameter to obtain a paired blood glucose parameter set.

[0081] It can be understood that the blood glucose parameter set refers to a paired set composed of matched time and blood glucose data, for example, blood glucose data A is obtained by collecting physiological characteristic information through a bracelet at 08:08:22 and restoring in a mass differentiated blood glucose model, blood glucose data B is obtained by collecting physiological characteristic information through a bracelet at 08:08:32 and restoring in a mass differentiated blood glucose model, and at this time, the paired blood glucose parameter set comprises blood glucose data A->08:08:22 and blood glucose data B->08:08:32.

[0082] In step S403, the paired blood glucose parameter set is sequentially input into the target health management model according to the acquisition time, so that the target management model draws and feeds back a full-time blood glucose change curve according to the paired blood glucose parameter set.

[0083] It should be understood that the target health management model refers to a model for managing the health of a user, which can be obtained by training historical health data through a neural network model. The neural network model can be a convolutional neural network model, or other network models that can achieve the same or similar functions.

[0084] It should be understood that the full-time blood glucose change curve is composed of blood glucose values at different times. After obtaining the paired blood glucose parameter set, the paired data in the paired blood glucose parameter set are sequentially input into the target health management model according to the chronological relationship of the acquisition time. After receiving the target paired data, the target health management model connects the position points corresponding to the dynamic blood glucose data in sequence according to the relationship of the acquisition time, so as to draw a full-time blood glucose change curve, and feeds back the full-time blood glucose change curve to a user terminal. The user terminal can be a blood glucose monitoring smart bracelet, or a mobile terminal or PC terminal installed with an application APP, and the present embodiment does not limit this.

[0085] In step S404, a blood glucose profile of the user to be monitored is generated according to the blood glucose change curve in the whole time period, and the user to be monitored is managed based on the blood glucose profile.

[0086] Further, in step S404, the following steps are included: determining a blood glucose change trend range of the user to be monitored in a preset time period according to the blood glucose profile; dynamically adjusting an initial blood glucose threshold value based on current behavior information and a current health state of the user to be monitored to obtain a preset blood glucose threshold value; when a maximum value of the blood glucose change trend range reaches the preset blood glucose threshold value, counting a frequency of reaching the preset blood glucose threshold value; and when the frequency of reaching the preset blood glucose threshold value is greater than or equal to a target frequency threshold value, issuing a preset alarm prompt information.

[0087] It can be understood that the blood glucose change trend range refers to a range of blood glucose change of the user to be monitored in a preset time period, which is determined by a maximum blood glucose value and a minimum blood glucose value in the preset time period. The preset blood glucose value refers to a minimum blood glucose value causing an alarm, which is obtained by dynamically adjusting an initial blood glucose threshold value based on current behavior information and a current health state. For example, the initial blood glucose threshold value is m, but when the user to be monitored is in a running state, the initial blood glucose threshold value is increased to n, n > m, and when the current health state of the user to be monitored is low, in order to ensure the safety of the user to be monitored, the initial blood glucose threshold value is reduced to p, p < n. After the blood glucose change range is obtained, it is determined whether a maximum value of the blood glucose change range reaches the preset blood glucose threshold value. If yes, it is further determined whether the frequency of reaching the preset blood glucose threshold value is greater than or equal to a target frequency threshold value. If yes, it indicates that the user to be monitored may be in danger in the preset range, that is, the user to be monitored is reminded through the preset alarm information to achieve the management of the health of the user to be monitored. The preset alarm information includes text warning information and voice warning information, and the text warning information can be color change of a corresponding part in a display interface.

[0088] The embodiment obtains the collection time of physiological characteristic information of the to-be-monitored user, matches the collection time with the blood glucose parameter to obtain a paired blood glucose parameter set, sequentially inputs the paired blood glucose parameter set into a target health management model according to the collection time, so that the target management model draws and feeds back a full-time blood glucose change curve according to the paired blood glucose parameter set, generates a blood glucose atlas of the to-be-monitored user according to the full-time blood glucose change curve, and manages the to-be-monitored user based on the blood glucose atlas. In the above manner, the paired blood glucose parameter set is obtained, the paired blood glucose parameter set is sequentially input into the target health management model according to the collection time, the full-time blood glucose change curve is output by the target health management model, and the to-be-monitored user is managed according to the blood glucose atlas generated according to the full-time blood glucose change curve, so that non-invasive personalized monitoring of blood glucose trend can be realized, the to-be-monitored user is timely reminded when a danger is predicted to occur, and the experience of the to-be-monitored user is improved.

[0089] In addition, the embodiment of the present application also provides a storage medium, wherein the storage medium stores a non-invasive personalized dynamic blood glucose trend monitoring and early warning program, and the non-invasive personalized dynamic blood glucose trend monitoring and early warning program is executed by a processor to realize the steps of the non-invasive personalized dynamic blood glucose trend monitoring and early warning method as described above.

[0090] Since the storage medium adopts all the technical solutions of the above-mentioned embodiments, it at least has all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be repeated here.

[0091] In addition, with reference to Figure 5 , the embodiment of the present application also provides a non-invasive personalized dynamic blood glucose trend monitoring and early warning device, which comprises:

[0092] The acquisition module 10 is configured to acquire physiological characteristic information of a to-be-monitored user in real time and obtain personal basic information.

[0093] The analysis module 20 is configured to send the physiological characteristic information and the personal basic information to a health information analysis module, so that the health information analysis module restores a current blood glucose value of the to-be-monitored user according to the physiological characteristic information and the personal basic information.

[0094] The determination module 30 is configured to dynamically adjust the current blood glucose value according to current behavior information of the to-be-monitored user to obtain a target blood glucose value.

[0095] The generation module 40 is configured to generate a blood glucose atlas of the to-be-monitored user through the target blood glucose value, and manage the health of the to-be-monitored user based on the blood glucose atlas.

[0096] The embodiment restores the current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information by collecting the physiological characteristic information and the personal basic information of the user to be monitored in real time, sending the physiological characteristic information and the personal basic information to a health information analysis module, and adjusting the current blood glucose value according to the current behavior information of the user to be monitored to obtain a target blood glucose value, generates the blood glucose map of the user to be monitored through the target blood glucose value, and manages the health of the user to be monitored based on the blood glucose map. Compared with the prior art that uses a blood glucose meter to obtain blood of a vein or a fingertip through a destructive or minimally invasive way to monitor the blood glucose value at a certain time point, the embodiment can obtain continuous and multi-point blood glucose data through a non-destructive way, effectively improves the accuracy of monitoring dynamic blood glucose, and realizes non-destructive personalized dynamic monitoring and early warning.

[0097] It should be noted that the above-described workflow is only illustrative and does not limit the protection scope of the present application. In actual application, a person skilled in the art can select part or all of the above-described workflow to achieve the purpose of the embodiment according to actual needs, which is not limited herein.

[0098] In addition, technical details not described in detail in the embodiment can be referred to the non-invasive personalized dynamic blood glucose trend monitoring and early warning method provided by any embodiment of the present application, which will not be described herein.

[0099] Other embodiments of the non-invasive personalized dynamic blood glucose trend monitoring and early warning device of the present application or the implementation method can refer to the above-described method embodiments, which will not be described herein.

[0100] In addition, it should be noted that in this document, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or system. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of another identical element in the process, method, article or system including the element.

[0101] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0102] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application essentially or say the part of the prior art contribution can be embodied in the form of software products, the computer software product is stored in a storage medium (such as read only memory (Read Only Memory, ROM) / RAM, disk, optical disk), including a number of instructions to make a terminal device (may be a mobile phone, computer, integrated platform workstation, or network equipment, etc.) executes the method described in various embodiments of the present application.

[0103] The above is only the preferred embodiment of the present application, not the patent scope of the present application, any equivalent structure or equivalent flow transformation using the content of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A non-invasive personalized dynamic blood glucose trend monitoring and early warning method, characterized in that, The non-invasive personalized dynamic blood glucose trend monitoring and early warning method comprises the following steps: Real-time collection of physiological characteristic information of a user to be monitored and acquisition of personal basic information; The physiological characteristic information and the personal basic information are sent to a health information analysis module, so that the health information analysis module restores a current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information; The current blood glucose value is dynamically adjusted according to current behavior information of the user to be monitored to obtain a target blood glucose value; A blood glucose graph of the user to be monitored is generated through the target blood glucose value, and health of the user to be monitored is managed based on the blood glucose graph; The current blood glucose value is dynamically adjusted according to the current behavior information of the user to be monitored to obtain a target blood glucose value, comprising: Blood glucose influence data sets are obtained according to the current behavior information of the user to be monitored; The fluctuation ranges and weight values of each blood glucose influence data are obtained according to the blood glucose influence data sets; The fluctuation ranges of each blood glucose influence data are sorted according to the weight values to obtain corresponding blood glucose influence data sorting results; The current blood glucose value is dynamically adjusted according to the blood glucose influence data sorting results to obtain a target blood glucose value.

2. The non-invasive personalized dynamic blood glucose trend monitoring and alerting method as claimed in claim 1, wherein, The real-time collection of physiological characteristic information of a user to be monitored and the acquisition of personal basic information comprise: Real-time collection of physiological information of a user to be monitored and acquisition of personal basic information; Physiological characteristic parameters are acquired, and the physiological information is feature extracted according to the physiological characteristic parameters to obtain physiological characteristic information of the user to be monitored.

3. The non-invasive personalized dynamic blood glucose trend monitoring and alerting method as claimed in claim 1, wherein, The physiological characteristic information and the personal basic information are sent to a health information analysis module, so that the health information analysis module restores a current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information, comprising: The physiological characteristic information and the personal basic information are sent to a health information analysis module, so that the health information analysis module determines corresponding mass differentiated blood glucose models according to the personal basic information, restores physiological medical waveform graphs from the physiological characteristic information, calculates the physiological medical waveform graphs through the mass differentiated blood glucose models, and restores a current blood glucose value of the user to be monitored.

4. The non-invasive personalized dynamic blood glucose trend monitoring and alerting method as claimed in claim 1, wherein, After the current blood glucose value is dynamically adjusted according to the blood glucose influence data sorting results to obtain a target blood glucose value, the method further comprises: The target blood glucose value is sent to a health state analysis module, so that the health state analysis module determines and feeds back a current health state of the user to be monitored according to a mapping relationship between blood glucose parameters and health states.

5. The non-invasive personalized dynamic blood glucose trend monitoring and alerting method as claimed in claim 1, wherein, The blood glucose graph of the user to be monitored is generated through the blood glucose parameters, and the user to be monitored is managed based on the blood glucose graph, comprising: An acquisition time of the physiological characteristic information of the user to be monitored is acquired; The acquisition time is matched with the blood glucose parameters to obtain a set of paired blood glucose parameters; The set of paired blood glucose parameters is sequentially input into a target health management model according to the acquisition time, so that the target health management model draws and feeds back a full-time blood glucose change curve according to the set of paired blood glucose parameters; According to the full-time blood glucose change curve, a blood glucose profile of the user to be monitored is generated, and the user to be monitored is managed based on the blood glucose profile.

6. The non-invasive personalized dynamic blood glucose trend monitoring and alerting method as claimed in claim 5, wherein, The management of the user to be monitored based on the blood glucose profile comprises: determining a blood glucose change trend range of the user to be monitored within a preset time according to the blood glucose profile; dynamically adjusting an initial blood glucose threshold value according to current behavior information and a current health state of the user to be monitored to obtain a preset blood glucose threshold value; when a maximum value of the blood glucose change trend range reaches the preset blood glucose threshold value, counting a frequency of reaching the preset blood glucose threshold value; when the frequency of reaching the preset blood glucose threshold value is greater than or equal to a target frequency threshold value, issuing a preset alarm prompt information.

7. A non-invasive personalized dynamic blood glucose trend monitoring and warning device of the non-invasive personalized dynamic blood glucose trend monitoring and warning method according to any one of claims 1 to 6, characterized in that, The non-invasive personalized dynamic blood glucose trend monitoring and early warning device comprises: a collection module configured to collect physiological characteristic information of a user to be monitored in real time and obtain personal basic information; an analysis module configured to send the physiological characteristic information and the personal basic information to a health information analysis module, so that the health information analysis module restores a current blood glucose value of the user to be monitored according to the physiological characteristic information and the personal basic information; a determination module configured to dynamically adjust the current blood glucose value according to current behavior information of the user to be monitored to obtain a target blood glucose value; a generation module configured to generate a blood glucose profile of the user to be monitored through the target blood glucose value, and manage health of the user to be monitored based on the blood glucose profile.

8. A non-invasive personalized dynamic blood glucose trend monitoring and alerting device, characterized in that, The non-invasive personalized dynamic blood glucose trend monitoring and early warning device comprises a memory, a processor, and a non-invasive personalized dynamic blood glucose trend monitoring and early warning program stored in the memory and executable on the processor, wherein the non-invasive personalized dynamic blood glucose trend monitoring and early warning program is configured to implement the non-invasive personalized dynamic blood glucose trend monitoring and early warning method according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium stores a non-invasive personalized dynamic blood glucose trend monitoring and early warning program, and the non-invasive personalized dynamic blood glucose trend monitoring and early warning program is executed by the processor to implement the non-invasive personalized dynamic blood glucose trend monitoring and early warning method according to any one of claims 1 to 6.

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