CN115064261A

CN115064261A - Blood glucose statistical system and method based on artificial intelligence

Info

Publication number: CN115064261A
Application number: CN202210639987.6A
Authority: CN
Inventors: 白晓苏; 丁贤根; 丁远彤; 肖苑辉; 冼俊芳; 谢宝虹
Original assignee: Harbour Star Health Biology Shenzhen Co ltd; Shenzhen Longhua Peoples Hospital
Current assignee: Harbour Star Health Biology Shenzhen Co ltd; Shenzhen Longhua Peoples Hospital
Priority date: 2022-06-08
Filing date: 2022-06-08
Publication date: 2022-09-16

Abstract

The invention belongs to the field of intelligent medical treatment, and discloses a blood glucose statistical system and a method based on artificial intelligence, which comprises the following steps: the detection module is used for acquiring a blood glucose detection value of a detected person in a preset mode and recording the current detection time; the calibration module is used for receiving the blood glucose detection value obtained by the detection module, establishing a blood flow rate model according to the blood glucose detection value, obtaining a blood glucose fluctuation difference value and obtaining a calibrated blood glucose value according to the blood glucose fluctuation difference value; and the statistic module is used for counting a blood glucose change function through a cloud data center based on the blood glucose detection value and the calibrated blood glucose value, and monitoring the blood glucose change condition of the detected person according to the blood glucose change function. The invention can realize accurate monitoring of the blood sugar change condition of the detector by using the blood sugar values measured in various modes.

Description

Blood glucose statistical system and method based on artificial intelligence

Technical Field

The invention relates to the field of intelligent medical treatment, in particular to a blood glucose statistical system and method based on artificial intelligence.

Background

With the ever-increasing standard of living, diabetes has become one of the most important chronic non-infectious diseases that currently threaten human health worldwide. According to the recent statistics of 2013 of the international diabetes union (IDF), the prevalence rate of global diabetes in adults aged 20-79 years is 8.3%, and the number of patients reaches 3.82 hundred million. In the estimation of the incidence and the incidence trend of each country and region, the number of diabetes mellitus in 2013 in China is 9840 ten thousand, and the diabetes mellitus occupies the first place of the world, and is Indian (6510 ten thousand), American (2440 ten thousand), Brazil (1190 thousand) and Russian (1090 ten thousand). Other countries with less than 1000 million patients but in the top ten include Mexico, Indonesia, Germany, Egypt and Japan. IDF estimates that by 2035 chinese people with diabetes will reach 1.43 billion and remain the first world, while the united states will only reach 2970 million.

Today, people are constantly exploring new technologies and new modes to effectively relieve the illness state of patients and reduce the generation of complications when diabetes has high morbidity. Electronic medicine has gained wide acceptance for the clinical laboratory results of diabetes improvement. The subject of studying whether internet technology can affect the life of diabetic patients originates abroad. The research content comprises a blood sugar data statistical system, a diabetes management information system and the like.

At present, when a blood glucose data statistical system carries out statistics on blood glucose data of a human body, only one of an invasive mode and a non-invasive mode is usually adopted to measure the blood glucose concentration in blood of the human body, but no matter the invasive mode is adopted to measure the blood glucose concentration in the blood, or the non-invasive mode is adopted to measure the blood glucose concentration in the blood, measurement deviation exists, and then the blood glucose statistical result when the blood glucose is counted is inaccurate.

The above-described contents are only for assisting understanding of the technical solution of the present invention, and do not represent an admission that the technical solution is the prior art.

Disclosure of Invention

The invention mainly aims to provide a blood sugar statistical system and a blood sugar statistical method based on artificial intelligence, and aims to solve the technical problem that the statistical result is not accurate due to the fact that a single blood sugar detection mode is adopted to count the blood sugar change condition in the prior art.

In order to achieve the above object, the present invention provides an artificial intelligence based blood glucose statistical system, which comprises a detection module, a calibration module and a statistical module:

the detection module is used for acquiring a blood glucose detection value of a detected person in a preset mode and recording the current detection time, wherein the preset mode comprises a first mode and a second mode;

the calibration module is used for receiving the blood glucose detection value obtained by the detection module, and establishing a blood flow rate model according to the blood glucose detection value to obtain a blood glucose fluctuation difference value; the blood glucose monitoring device is also used for acquiring a calibration blood glucose value according to the blood glucose fluctuation difference value;

the statistic module is used for counting a blood glucose change function through a cloud data center based on the blood glucose detection value and the calibrated blood glucose value, and monitoring the blood glucose change condition of the detected person according to the blood glucose change function.

Optionally, the first mode includes:

after venous blood of a detected person is extracted, detecting a blood glucose detection value of the venous blood by using a biochemical analyzer, and recording the position of a detection point; and/or the presence of a gas in the gas,

after extracting the fingertip blood of a detected person, detecting the blood glucose detection value of the fingertip blood by using a portable blood glucose meter, and recording the position of the detection point; and/or the presence of a gas in the atmosphere,

and detecting the blood sugar detection value of the detected person by using a minimally invasive continuous blood sugar meter, and recording the position of the detection point.

Optionally, the second mode includes:

detecting the blood sugar detection value of the person to be detected by a medical-grade blood sugar detection device, and recording the position of the detection point.

Optionally, the calibration module is further configured to obtain a blood flow rate of the subject according to an average flow rate of venous blood of the subject at a detection point position;

calculating a blood glucose concentration fluctuation function according to the blood glucose detection value and the blood flow rate;

calculating a blood glucose concentration difference value based on a distance difference value and the blood glucose concentration fluctuation function, wherein the distance difference value is a difference value of different detection point positions in the first mode.

Optionally, the calibration module is further configured to calculate a blood glucose difference value based on a first blood glucose detection value and a second blood glucose detection value at the same time, where the first blood glucose detection value is a blood glucose detection value in the first mode, and the second blood glucose detection value is a blood glucose detection value in the second mode;

adjusting the first blood glucose detection value according to the blood glucose difference value to obtain a target blood glucose value;

obtaining a calibrated blood glucose value based on the target blood glucose value and the blood glucose concentration difference value.

Optionally, the obtaining module is further configured to obtain a fasting blood glucose detection value, a first target blood glucose detection value, and a second target blood glucose detection value of the subject in a preset mode, where the first target blood glucose detection value and the second target blood glucose detection value are measured at a first preset time and a second preset time respectively after the subject takes a fixed amount of glucose solution or a fixed amount of food.

Optionally, the obtaining module is further configured to obtain a fasting blood glucose test value, a third target blood glucose test value, and a fourth target blood glucose test value of the subject in a preset mode, where the third target blood glucose test value and the fourth target blood glucose test value are obtained by measuring the fasting blood glucose test value, the third target blood glucose test value, and the fourth target blood glucose test value of the subject at a third preset time and a fourth preset time after the subject takes a fixed amount of insulin solution or a fixed amount of prescription drug.

Optionally, the statistic module is further configured to count a group blood glucose change function through the cloud data center based on the blood glucose detection value and the calibrated blood glucose value of the at least one detected person, and monitor a blood glucose change condition of the group according to the group blood glucose change function.

Optionally, the statistic module is further configured to count a seasonal blood glucose variation function through the cloud data center based on the blood glucose detection value and the calibrated blood glucose value of the at least one detected person with a season as a statistic time point, and monitor a seasonal blood glucose variation condition of the group according to the seasonal blood glucose variation function.

In addition, in order to achieve the above object, the present invention further provides an artificial intelligence based blood glucose statistical method, where the artificial intelligence based blood glucose statistical method includes:

acquiring a blood glucose detection value of a detected person in a preset mode, and recording the current detection time, wherein the preset mode comprises a first mode and a second mode;

establishing a blood flow rate model according to the blood glucose detection value to obtain a blood glucose fluctuation difference value, and acquiring a calibrated blood glucose value according to the blood glucose fluctuation difference value;

and counting a blood glucose change function through a cloud data center based on the blood glucose detection value and the calibrated blood glucose value, and monitoring the blood glucose change condition of the detected person according to the blood glucose change function.

In summary, compared with the prior art, the technical solution provided by the embodiment of the present invention has at least the following beneficial effects:

1. the embodiment of the invention is convenient to detect the blood sugar value of the human body through the first mode, and the detected person can automatically detect the blood sugar concentration value at home and can also detect the blood sugar concentration value of the human body, namely the blood sugar value of the human body through the first mode in community hospitals or hospitals with incomplete medical equipment. The blood glucose detection value of the person to be detected is obtained through the first mode very conveniently and quickly, and the blood glucose concentration value of the human body can be obtained through the first mode at home or in any medical institution. Therefore, the subject can quickly and easily acquire the blood glucose level detection value, i.e., the blood glucose level, in the first mode.

2. In order to acquire a more accurate blood glucose detection value of a detected person, the embodiment of the invention can monitor the blood glucose value by using a second mode, namely medical-grade blood glucose monitoring equipment, and further calibrate the blood glucose detection value acquired by the first mode according to the more accurate blood glucose detection value detected by the second mode. The blood glucose concentration condition that is monitored self by the person who need not to gather blood glucose detection person through the second mode constantly, only need can obtain more accurate blood glucose value according to the blood glucose detection value under the second mode that history was gathered again through the blood glucose detection value that first mode was gathered, the blood glucose concentration value that can detect out through the second mode is that the blood glucose value is used for the human blood glucose concentration value that the calibration was detected out under the first mode, can make the calibration blood glucose value that finally obtains more accurate, the person who is detected need not to gather the blood glucose concentration value through the second mode often and only need can obtain comparatively accurate calibration blood glucose value through the blood glucose concentration value that first mode was gathered.

3. According to the embodiment of the invention, by collecting the blood sugar detection value of the detected person under the multi-dimensional condition, for example, the blood sugar detection value can be detected before and after meals, fasting detection can be performed, and detection can be performed after insulin is injected, so that the data support for generating the blood sugar change function can be more sufficient by measuring the blood sugar value of the detected person from the multi-dimensional condition, and the blood sugar change condition of the detected person can be more accurately monitored by monitoring the blood sugar concentration condition of the detected person from the multi-dimensional condition.

4. In order to effectively utilize the blood sugar data stored in the cloud database, the embodiment of the invention firstly carries out statistical analysis on the blood sugar condition in the group by utilizing the blood sugar detection value and the calibrated blood sugar value of at least one detected person in the cloud data center to obtain the blood sugar change function of the group, and can realize providing data reference for the pathogenesis research of diabetes in the group through the blood sugar change function of the group, thereby realizing providing reference for the current situation of diabetes pathogenesis monitoring, the pathogenesis research and the like of diabetes and the like of the medical and health management department, and also providing accurate data support for relevant diabetes pharmaceutical factories through the blood sugar change function of the group, so that the relevant diabetes pharmaceutical factories can develop medicaments for effectively treating the diabetes according to the data, and simultaneously, the cloud data center saves the sampling work of data statistics, thereby reducing the possibility of manual acquisition errors caused by manual acquisition of data.

5. In order to effectively utilize the blood sugar data stored in the cloud database, the embodiment of the invention firstly acquires the blood sugar detection value and the blood sugar calibration value of at least one detected person by utilizing a cloud data center, and takes seasons as statistical time points to carry out statistical analysis on the blood sugar conditions in a group to obtain a seasonal blood sugar change function, thereby providing reference for monitoring the current condition of the diabetes mellitus and researching the pathogenesis of the diabetes mellitus and the like for a medical health management department, providing data reference for the seasonal pathogenesis research of the diabetes mellitus in the group by the seasonal blood sugar change function, and providing accurate group seasonal data support for relevant diabetes pharmaceutical factories by the seasonal blood sugar change function, so that the relevant diabetes pharmaceutical factories can develop the drugs capable of effectively treating the diabetes mellitus according to the data, meanwhile, sampling work needing data statistics is omitted through the cloud data center, and the possibility of manual acquisition errors caused by manual data acquisition is reduced.

6. The embodiment of the invention obtains the blood sugar detection value of the detected person in the preset mode through the detection module, and records the current detection time, and receives the blood glucose detection value obtained by the detection module through the calibration module, establishing a blood flow rate model according to the blood glucose detection value to obtain a blood glucose fluctuation difference value, obtaining a calibrated blood glucose value according to the blood glucose fluctuation difference value, and finally counting a blood glucose change function through a cloud data center through a counting module, and monitors the blood sugar change condition of the detected person according to the blood sugar change function, and utilizes the blood sugar value in the non-invasive mode to calibrate the blood sugar value in the invasive mode, further obtaining a calibrated blood glucose value, counting a plurality of blood glucose detection values and a plurality of corresponding calibrated blood glucose values through a cloud data center, and a blood sugar change function of the detector is constructed, so that the blood sugar change condition of the detector is accurately monitored.

7. The invention obtains the blood sugar detection value of a detected person in a preset mode through a detection module, records the current detection time, receives the blood sugar detection value obtained by the detection module through a calibration module, establishes a blood flow rate model according to the blood sugar detection value, obtains a blood sugar fluctuation difference value, obtains a calibration blood sugar value according to the blood sugar fluctuation difference value, finally counts a blood sugar change function through a cloud data center through a statistical module, monitors the blood sugar change condition of the detected person according to the blood sugar change function, calibrates the blood sugar value in an invasive mode by using the blood sugar value in a non-invasive mode, further obtains the calibration blood sugar value, counts a plurality of blood sugar detection values and a plurality of corresponding calibration blood sugar values through the cloud data center, and constructs the blood sugar change function of the detected person, the blood sugar change function of a group and the seasonal blood sugar change function of the group, and then the blood sugar change of the testee, the blood sugar change of the group and the seasonal blood sugar change of the group can be accurately monitored.

Drawings

FIG. 1 is a schematic diagram of an interaction between an artificial intelligence-based blood glucose statistics system and a user terminal according to the present invention;

FIG. 2 is a block diagram of a first embodiment of an artificial intelligence based blood glucose statistics system according to the present invention;

FIG. 3 is a flowchart illustrating a first embodiment of an artificial intelligence based blood glucose statistics system according to the present invention.

The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.

The terms "first," "second," and the like in the present invention are used for distinguishing identical items or similar items having substantially the same functions, and it should be understood that the terms "first," "second," and "n" have no logical or temporal dependency, and do not limit the number or execution order.

Referring to fig. 1, an interaction diagram of an artificial intelligence-based blood glucose statistics system 100 and a user terminal 300 according to a preferred embodiment of the present invention is shown. The blood glucose statistic system 100 based on artificial intelligence and the server 200 can establish communication connection through a network for data interaction. The server 200 may establish a communication connection with one or more user terminals 300 through a network for data interaction.

In this embodiment, the user terminal 300 may be, but is not limited to, a smart phone, a Personal Computer (PC), a tablet PC, a personal digital assistant (pda), a Mobile Internet Device (MID), and the like. The server 200 may be, but is not limited to, a cloud server, an integrated server, a distributed server, and the like. Preferably, the network is a wireless network.

Today, people are constantly exploring new technologies and new modes to effectively relieve the illness state of patients and reduce the generation of complications when diabetes has high morbidity. Electronic medicine has gained wide acceptance for the clinical laboratory results of diabetes improvement. The subject of studying whether internet technology can affect the life of diabetic patients originates abroad. The research content comprises a blood sugar data statistical system, a diabetes management information system and the like. However, at present, when a blood glucose data statistics system performs statistics on blood glucose data of a human body, only one of an invasive mode and a non-invasive mode is often adopted to measure a blood glucose concentration in blood of the human body, but no matter the invasive mode is adopted to measure the blood glucose concentration in the blood, or the non-invasive mode is adopted to measure the blood glucose concentration in the blood, a measurement deviation exists, and a blood glucose statistics result during the statistics of blood glucose is inaccurate. In order to solve the above technical problems, an embodiment of the present invention provides an artificial intelligence based blood glucose statistics system, and referring to fig. 2, fig. 2 is a block diagram illustrating a structure of a first embodiment of an artificial intelligence based blood glucose statistics system according to the present invention, and for convenience of description, only parts related to the embodiment of the present invention are shown.

In the embodiment of the present invention, the detection module 10, the calibration module 20, and the statistics module 30, wherein:

the detection module 10 is configured to obtain a blood glucose detection value of a detected person in a preset mode, and record a current detection time, where the preset mode includes a first mode and a second mode.

The first mode refers to a method for obtaining a blood glucose level of a human body in a noninvasive manner, that is, by taking blood or by measuring the blood glucose concentration in the blood of the human body in the case of skin injury, and the second mode refers to a method for obtaining a blood glucose level of a human body in a noninvasive manner, that is, by measuring the blood glucose concentration in the blood of the human body in the case of no skin injury. The blood glucose test value is understood to be the content of glucose in human blood, which can be expressed in terms of millimole/liter, and the blood glucose test value of the tested person can be obtained by a method including, but not limited to, inputting by a user through a user terminal, inputting by a medical staff through a user terminal, transmitting by a network or other collection methods. The current detection time represents the time of collecting blood glucose data of a detected person, for example, when venous blood is extracted to detect the blood glucose concentration in human blood, the current detection time is the time of extracting venous blood, and when the blood glucose concentration in the human blood is detected in a non-invasive mode, the time of obtaining the blood glucose concentration of the detected person is the current detection time.

Wherein, the first mode comprises:

after the fingertip blood of a detected person is extracted, detecting a blood glucose detection value of the fingertip blood by using a portable blood glucose meter, and recording the position of the detection point; and/or the presence of a gas in the gas,

Specifically, the vein blood drawing can be performed by selecting veins at any part of the whole body in principle, and puncture blood drawing is possible. For convenience of operation, the median elbow vein or the basilic vein of the elbow is usually selected clinically for puncture blood drawing. If the vein of the elbow of the child is not obvious, the vein of the foot or the vein of the board barrier of the scalp can be selected for puncture blood drawing. If the old people have angiosclerosis or the critical peripheral blood vessels have obvious collapse and the puncture is difficult, a deep vein can be selected for puncture blood drawing. Such as the internal jugular vein, the subclavian vein or the femoral vein, may be used for puncture blood test. Blood glucose measurements are understood to mean the amount of glucose in human blood expressed in millimoles per liter, and biochemical analyzers, also commonly referred to as biochemicals, are instruments that use the principle of photo-electric colorimetry to measure a particular chemical component in a body fluid. The blood glucose measurement value of the venous blood of the subject, i.e., the glucose content in the venous blood, can be measured by a biochemical meter. After the venous blood of the subject is collected, the position of the test point, i.e., the blood drawing position, of the subject is recorded, and when the venous blood of the subject is collected through the median elbow vein, for example, it is possible to record whether the venous blood is collected from the left hand of the subject or the venous blood is collected from the right hand of the subject.

In particular, a portable blood glucose meter is one of blood glucose meters, and is called a portable blood glucose meter because of small volume, convenient operation and convenient daily use. The portable glucometer mainly comprises a portable glucometer machine, a test strip, a blood sampling pen and a blood sampling needle head, wherein the disposable blood sampling needle head is arranged in the blood sampling pen, the fingertip blood of a detected person is collected through the blood sampling pen, and the blood glucose detection value of the fingertip blood of the detected person, namely the glucose content in the fingertip blood, is detected through the portable glucometer machine. After the fingertip blood of the subject is collected, the position of the detection point of the subject, i.e., the blood drawing position, is recorded, and, for example, it may be recorded whether the fingertip blood is collected from the left hand of the subject or the fingertip blood is collected from the right hand of the subject.

Specifically, the testing method of the minimally invasive continuous blood glucose detector is based on the electrochemical reaction principle that a blood taking needle punctures the surface of skin to collect interphalangeal blood and test the blood glucose concentration, and the minimally invasive continuous blood glucose detector can record whether the blood drawing position is at the left-hand position or the right-hand position when measuring the blood glucose concentration and can also record which finger of a detected person is collected when recording.

In this embodiment, it is more convenient to detect human blood sugar value through first mode, and the person of being detected can detect the blood sugar value at home by oneself, can also go to community's hospital or can all realize examining human blood sugar value through first mode to some incomplete hospitals of medical equipment. The blood glucose detection value of the detected person is obtained through the first mode very conveniently and quickly. In this embodiment, the subject can quickly and conveniently acquire the blood glucose level detection value, i.e., the blood glucose level, in the first mode.

Wherein the second mode comprises:

Specifically, the second mode detects the blood glucose concentration in the blood of the subject using, in particular, a medical-grade blood glucose test device, while recording the test site location. The medical-grade blood sugar detection equipment can achieve noninvasive and accurate measurement of blood sugar concentration in human blood, and blood sugar values measured in the second mode are closer to real blood sugar values of a human body, so that the obtained blood sugar values of the human body can reflect real blood sugar concentration of the human body and real health states of the human body better in the second mode. Each time human blood glucose is collected by the second mode, a test point position may be recorded, for example, whether the test point position is in a left-hand position or a right-hand position. Since the medical facilities of the community hospital or some non-third hospitals are not complete, the community hospital or some non-third hospitals cannot provide the second mode for detecting the blood sugar value for the detected person, and generally, only hospitals above three grades are equipped with the medical-grade blood sugar monitoring device for detecting the blood sugar concentration of the detected person.

In this embodiment, in order to acquire a more accurate blood glucose detection value of a subject, the blood glucose value may be monitored by using a medical-grade blood glucose monitoring device in the second mode of the present invention, and the blood glucose detection value acquired in the first mode may be calibrated according to the more accurate blood glucose detection value detected in the second mode. The person to be detected does not need to collect the blood sugar concentration condition of the person to be detected through the second mode all the time, and the person can obtain a more accurate blood sugar value only through the blood sugar detection value collected through the first mode and then according to the blood sugar detection value collected historically under the second mode.

In a specific implementation, in order to effectively reflect corresponding blood glucose detection values of a detected person in different states, in an embodiment, the obtaining module is further configured to obtain a fasting blood glucose detection value, a first target blood glucose detection value, and a second target blood glucose detection value of the detected person in a preset mode, where the first target blood glucose detection value and the second target blood glucose detection value are obtained by measuring the fasting blood glucose detection value, the first target blood glucose detection value, and the second target blood glucose detection value of the detected person at a first preset time and a second preset time after the detected person takes a fixed amount of glucose solution or a fixed amount of food.

In particular, the blood glucose test values include, but are not limited to, a fasting blood glucose value, a first target blood glucose test value, a second target blood glucose test value. The first predetermined time may be understood as a time required for the normal person to increase blood sugar after taking a certain sugar-elevating substance, and the second predetermined time may be understood as a time required for the normal person to return blood sugar to a normal state after taking a certain sugar-elevating substance. The fasting blood glucose value is that the blood glucose concentration of the blood of a detected person is collected under the fasting condition; after the subject takes the fixed amount of glucose solution, the first preset time can be represented by GT1, the second preset time can be represented by GT2, the first preset time is shorter than the second preset time, the first target blood sugar detection value is the blood sugar detection value measured after GT1 after the subject takes the fixed amount of glucose solution, and the second target blood sugar detection value is the blood sugar detection value measured after GT2 after the subject takes the fixed amount of glucose solution; after the subject intakes the fixed amount of food, the first preset time can be represented by DT1, the second preset time can be represented by DT2, the first preset time is shorter than the second preset time, the first target blood glucose detection value is the blood glucose detection value measured after DT1 after the subject intakes the fixed amount of food, and the second target blood glucose detection value is the blood glucose detection value measured after DT2 after the subject intakes the fixed amount of food. If the subject is a diabetic patient, since the diabetic patient has abnormal insulin secretion throughout the year, blood glucose rises in a short time after a certain amount of carbohydrate is ingested and is difficult to return to a normal blood glucose state without taking a medicine, and therefore, if the subject is a diabetic patient, both the first target blood glucose measurement value and the second target blood glucose measurement value are higher than the fasting blood glucose measurement value.

In this embodiment, only by collecting blood glucose detection values of a detected person under a multi-dimensional condition to monitor a blood glucose concentration condition of the detected person, the finally generated blood glucose change function can monitor the blood glucose change condition of the detected person more accurately.

In a specific implementation, in order to effectively reflect corresponding blood glucose detection values of a subject in different states, in an embodiment, the obtaining module is further configured to obtain a fasting blood glucose detection value, a third target blood glucose detection value, and a fourth target blood glucose detection value of the subject in a preset mode, where the third target blood glucose detection value and the fourth target blood glucose detection value are obtained by measuring the fasting blood glucose detection value, the third target blood glucose detection value, and the fourth target blood glucose detection value of the subject at a third preset time and a fourth preset time after the subject takes a fixed amount of insulin solution or a fixed amount of prescription drug, respectively.

In particular, the blood glucose test values include, but are not limited to, a fasting blood glucose value, a third target blood glucose test value, a fourth target blood glucose test value. The third preset time may be understood as a time required for the diabetic patient to return to a normal state after taking a fixed amount of insulin solution or taking a fixed amount of prescribed drug (a prescribed drug for treating diabetes, which may be, for example, metformin, saxagliptin, sitagliptin, etc., where the prescribed drug for treating diabetes is not limited), and the fourth preset time is twice the third preset time. The fasting blood glucose value is that the blood glucose concentration of the blood of a detected person is collected under the fasting condition; after the subject takes the fixed-amount insulin solution, the third preset time can be represented by IT1, the fourth preset time can be represented by IT2, the third preset time is shorter than the fourth preset time, the third target blood sugar detection value is a blood sugar detection value measured after the subject takes the fixed-amount insulin solution after IT1 time, and the fourth target blood sugar detection value is a blood sugar detection value measured after the subject takes the fixed-amount glucose solution after IT2 time; after the subject takes the fixed amount of the prescription drug, the third preset time can be represented by PT1, the fourth preset time can be represented by PT2, the third preset time is shorter than the fourth preset time, the third target blood glucose detection value is a blood glucose detection value measured after PT1 time after the subject takes the fixed amount of the prescription drug, and the fourth target blood glucose detection value is a blood glucose detection value measured after PT2 time after the subject takes the fixed amount of the prescription drug. If the subject is a diabetic patient, the fasting blood glucose measurement value of the subject may be higher than the normal blood glucose measurement value due to abnormal insulin secretion in the subject, and after the subject takes a certain amount of insulin solution or a prescribed drug (for example, a prescribed drug for diabetes, such as metformin, saxagliptin, and sitagliptin, which is not limited herein), the blood glucose of the subject may return to the normal state due to the drug or the insulin solution, so that, in most cases, the third target blood glucose measurement and the fourth target blood glucose measurement may be lower than the fasting blood glucose measurement value.

The calibration module 20 is configured to receive the blood glucose detection value obtained by the detection module, establish a blood flow rate model according to the blood glucose detection value, obtain a blood glucose fluctuation difference value, and obtain a calibrated blood glucose value according to the blood glucose fluctuation difference value.

Specifically, the blood glucose detection values of the detected person in the first mode and the blood glucose detection values of the detected person in the second mode are received, the blood glucose detection values include but are not limited to fasting blood glucose value, first target blood glucose detection value, second target blood glucose detection value, third target blood glucose detection value and fourth target blood glucose detection value, when the blood movement model is established, the control variable method can be adopted to realize that the multi-factor problem is changed into a plurality of single-factor problems, only one factor is changed, so that the influence of the factor on things is researched, the research is respectively carried out, and finally the solution is comprehensively solved, the control variable method is applied to the invention to establish the blood flow rate model, the fasting blood glucose value of the detected person in the first mode is compared with the fasting blood glucose value in the second mode, the first target blood glucose detection value of the detected person in the first mode is compared with the first target blood glucose detection value in the second mode, comparing a second target blood sugar detection value of the detected person in the first mode with a second target blood sugar detection value of the detected person in the second mode, comparing a third target blood sugar detection value of the detected person in the first mode with a third target blood sugar detection value of the detected person in the second mode, and comparing a fourth target blood sugar detection value of the detected person in the first mode with a fourth target blood sugar detection value of the detected person in the second mode to complete the whole process of establishing a blood flow rate model, obtain blood sugar fluctuation difference values of multiple types of blood sugar detection values, and further obtain a blood sugar calibration value of the blood sugar detection values in the first mode according to the fluctuation difference values of the multiple types of blood sugar detection values.

In a specific implementation, in order to make the blood glucose data stored in the cloud database more accurate, in an embodiment, the calibration module 20 is further configured to obtain a blood flow rate of the subject according to an average flow rate of venous blood of the subject at a detection point; calculating a blood glucose concentration fluctuation function according to the blood glucose detection value and the blood flow rate; calculating a blood glucose concentration difference value based on a distance difference value and the blood glucose concentration fluctuation function, wherein the distance difference value is a difference value of different detection point positions in the first mode.

When the detected person is in a static state, a non-static state or a static state after moving, the average flow rate of venous blood of the detected person changes, the average flow rate of venous blood of the detected person is closely related to the heart rate of the detected person at the current moment, and meanwhile, the blood flow rate of the detected person also causes deviation of the detected blood glucose concentration. Therefore, in order to enable the blood glucose data stored in the cloud database to be more accurate, a blood glucose concentration fluctuation function is introduced to calculate the condition of error when the blood glucose concentration is detected due to the blood flow velocity of the detected person.

Wherein, the blood sugar concentration in the human blood is also influenced by the position of the detection point, namely the blood drawing position, compared with the method of drawing the venous blood in the invasive mode to collect the venous blood of the detected person, the location of the blood draw of the subject may be recorded, such as whether the location of the blood draw is the left or right hand location of the subject, or the elbow or wrist of the tested person, the venous blood collected by the tested person through the elbow of the left hand can be compared with the venous blood collected through the wrist of the left hand, or comparing the venous blood collected by the tested person through the elbow of the right hand with the venous blood collected through the wrist of the right hand, and finally, calculating a blood glucose concentration difference value according to the blood glucose concentration function according to the distance difference value between the elbow and the wrist.

In the embodiment of the present invention, in order to make the monitored blood glucose data stored in the cloud database more accurate, the calibration module 20 determines the blood flow rate of the detected person according to the average venous blood flow rate of the detected person at the detection point, and calculates the blood glucose concentration fluctuation function according to the blood glucose detection value and the blood flow rate, so as to cause the deviation when the blood glucose concentration value is collected due to the difference of the monitored blood flow rates through the blood glucose concentration fluctuation function, and calculate the blood glucose concentration difference value based on the distance difference value and the blood glucose concentration fluctuation function, so as to make the monitored blood glucose data stored in the cloud database more accurate by preventing the deviation when the blood glucose concentration value is collected due to the difference of the detection point positions or the difference of the monitored blood flow rates of the detected person.

It should be noted that, in order to make the monitored blood glucose data stored in the cloud database more accurate, in an embodiment, the calibration module 20 is further configured to calculate a blood glucose difference value based on a first blood glucose detection value and a second blood glucose detection value at the same time, where the first blood glucose detection value is a blood glucose detection value in the first mode, and the second blood glucose detection value is a blood glucose detection value in the second mode; adjusting the first blood glucose detection value according to the blood glucose difference value to obtain a target blood glucose value; obtaining a calibrated blood glucose value based on the target blood glucose value and the blood glucose concentration difference value.

Wherein, the first blood sugar detection value is the blood sugar detection value of the detected person acquired by a first mode, namely an invasive mode, and the second blood sugar detection value is the blood sugar detection value of the detected person acquired by a second mode, namely a non-invasive mode, the method can be realized by adopting a control variable method when calculating the blood sugar difference value, namely, a multi-factor problem is changed into a plurality of single-factor problems, only one factor is changed, thereby researching the influence of the factor on things, and respectively researching the influence, and finally, the method is comprehensively solved, a method for controlling a time variable is adopted when calculating the blood sugar concentration difference value, namely, the blood sugar concentration of the blood of the detected person is acquired by the first mode and the second mode at the same time, the target blood sugar value of the detected person is the blood sugar difference value of the blood sugar detection value of the detected person acquired by the first mode and the second mode at the same time, the blood glucose detection value of the detected person acquired through the first mode acquisition can be adjusted according to the difference value, the adjusted first blood glucose detection value, namely the target blood glucose value, is obtained, finally, the final calibrated blood glucose value is obtained according to the target blood glucose value and the blood glucose concentration difference value, and each first blood glucose detection value corresponds to one calibrated blood glucose value.

In this embodiment, in order to make the monitored blood glucose data stored in the cloud database more accurate, a blood glucose difference value is calculated based on a first blood glucose detection value and a second blood glucose detection value at the same time, and then the first blood glucose detection value is adjusted according to the blood glucose difference value to obtain a target blood glucose value; and finally, obtaining a calibrated blood glucose value based on the target blood glucose value and the blood glucose concentration difference value. Therefore, in order to prevent an increase in calibration error in calibrating the first blood glucose value due to the acquisition of the first blood glucose measurement value and the second blood glucose measurement value at different times. The statistic module 30 is configured to count a blood glucose change function through a cloud data center based on the blood glucose detection value and the calibrated blood glucose value, and monitor a blood glucose change condition of the person to be detected according to the blood glucose change function.

The cloud data center is established, so that the user terminal and the cloud data center are realized through a network, including but not limited to cloud communication, storage of a cloud database, cloud computing and the like. The user terminal uploads the blood sugar detection data through cloud communication, the cloud data center executes cloud computing on the received blood sugar detection data, and then the result of the cloud computing is transmitted back to the blood sugar counting system based on artificial intelligence. It should be noted that, by adopting the cloud mode, the basic data can be stored in the cloud database, which not only reduces the repeated labor of single data statistics, but also improves the sharing of data statistics resources, and further improves the basis of function improvement.

It should be noted that, in order to effectively utilize the blood glucose data stored in the cloud database, and perform statistical analysis according to the monitored blood glucose data to provide data reference for the research on the pathogenesis of diabetes in the group, in an embodiment, the statistical module 30 is further configured to count a group blood glucose change function through the cloud data center based on the blood glucose detection value and the calibrated blood glucose value of at least one detected person, and monitor the blood glucose change condition of the group according to the group blood glucose change function.

In the embodiment of the invention, in order to effectively utilize the blood sugar data stored in the cloud database, the blood sugar condition of the group is statistically analyzed by utilizing the blood sugar detection value and the calibrated blood sugar value of at least one detected person in the cloud data center to obtain the blood sugar change function of the group, and the research on the pathogenesis of the diabetes in the group can be realized by the blood sugar change function of the group to provide data reference, so that the current condition of the pathogenesis of the diabetes is monitored and the research on the pathogenesis of the diabetes is carried out by a medical and health management department, and accurate data support can be provided for relevant diabetes pharmaceutical factories by the blood sugar change function of the group, so that the relevant diabetes pharmaceutical factories can develop medicaments capable of effectively treating the diabetes according to the data, and meanwhile, the sampling work needing data statistics is saved by the cloud data center, thereby reducing the possibility of manual acquisition errors caused by manual acquisition of data.

It should be noted that, in order to effectively utilize the blood glucose data stored in the cloud database, statistical analysis can be performed according to the monitored blood glucose data by taking seasons as statistical time points, so as to provide data references for seasonal pathogenesis research of diabetes in a group, in an embodiment, the statistical module 30 is further configured to take seasons as statistical time points, count a seasonal blood glucose change function through the cloud data center based on a blood glucose detection value and a calibrated blood glucose value of at least one detected person, and monitor a seasonal blood glucose change situation of the group according to the seasonal blood glucose change function.

In the embodiment of the invention, in order to effectively utilize the blood sugar data stored in the cloud database, the blood sugar detection value and the blood sugar value of at least one detected person are collected by utilizing a cloud data center, the blood sugar condition in a group is statistically analyzed by taking seasons as statistical time points, and a seasonal blood sugar change function is obtained, so that reference is provided for monitoring the current condition of the diabetes mellitus and researching the pathogenesis of the diabetes mellitus by a medical health management department, data reference can be provided for the seasonal pathogenesis research of the diabetes mellitus in the group by the seasonal blood sugar change function, accurate group seasonal data support can be provided for relevant diabetes pharmaceutical factories by the seasonal blood sugar change function, and the relevant diabetes pharmaceutical factories can develop medicaments capable of effectively treating the diabetes mellitus according to the data, meanwhile, sampling work needing data statistics is omitted through the cloud data center, and the possibility of manual acquisition errors caused by manual data acquisition is reduced.

In the embodiment of the present invention, the detection module 10, the calibration module 20, and the statistics module 30 are connected in a wireless manner or a wired manner, and the connection relationship between the modules is not limited herein.

In this embodiment, because the blood glucose data statistics system usually only adopts one of the invasive and non-invasive modes to measure the blood glucose concentration in the blood of the human body when performing statistics on the blood glucose data of the human body at present, but there is a measurement deviation no matter the invasive mode is adopted to measure the blood glucose concentration in the blood or the non-invasive mode is adopted to measure the blood glucose concentration in the blood, and further the blood glucose statistics result when performing statistics on the blood glucose is not accurate. In order to solve the technical problems, the invention obtains a blood glucose detection value of a detected person in a preset mode through a detection module, records the current detection time, receives the blood glucose detection value obtained by the detection module through a calibration module, establishes a blood flow rate model according to the blood glucose detection value to obtain a blood glucose fluctuation difference value, obtains a calibrated blood glucose value according to the blood glucose fluctuation difference value, counts a blood glucose change function through a cloud data center through a statistics module, monitors the blood glucose change condition of the detected person according to the blood glucose change function, calibrates the blood glucose value in an invasive mode through the blood glucose value in a non-invasive mode to further obtain the calibrated blood glucose value, counts a plurality of blood glucose detection values and a plurality of corresponding calibrated blood glucose change functions through the cloud data center, and establishes a blood glucose change function, a blood glucose change value, a blood glucose value, a calibration parameter, the blood sugar change function of the group and the seasonal blood sugar change function of the group, so that the blood sugar change of the testee, the blood sugar change of the group and the seasonal blood sugar change of the group can be accurately monitored.

Referring to fig. 3, fig. 3 is a schematic flow chart of a first embodiment of an artificial intelligence based blood glucose statistics method according to the present invention, which is applied to an artificial intelligence based blood glucose statistics apparatus as described above, the artificial intelligence based blood glucose statistics apparatus comprising: the blood sugar statistical method based on artificial intelligence comprises a detection module, a calibration module and a statistical module, wherein the blood sugar statistical method based on artificial intelligence comprises the following steps:

step S10: the method comprises the steps of obtaining a blood glucose detection value of a detected person in a preset mode, and recording the current detection time, wherein the preset mode comprises a first mode and a second mode.

It should be noted that the execution subject of this embodiment is an artificial intelligence-based blood glucose statistics apparatus, and a detection module in the artificial intelligence-based blood glucose statistics apparatus is configured to obtain a blood glucose detection value of a detected person in a preset mode, and record a current detection time, where the preset mode includes a first mode and a second mode; the calibration module is used for receiving the blood glucose detection value obtained by the detection module, establishing a blood flow rate model according to the blood glucose detection value to obtain a blood glucose fluctuation difference value, and obtaining a calibrated blood glucose value according to the blood glucose fluctuation difference value; the statistic module is used for counting a blood glucose change function through a cloud data center based on the blood glucose detection value and the calibrated blood glucose value, and monitoring the blood glucose change condition of the detected person according to the blood glucose change function.

It is understood that the first mode refers to a method for invasively obtaining the blood glucose level of the human body, namely, measuring the blood glucose concentration in the blood of the human body by blood sampling or other methods under the condition of skin injury, and the second mode refers to a method for invasively obtaining the blood glucose level of the human body, namely, measuring the blood glucose concentration in the blood of the human body under the condition of no skin injury. The blood glucose test value is understood to be the content of glucose in human blood, which can be expressed in terms of millimole/liter, and the blood glucose test value of the tested person can be obtained by a method including, but not limited to, inputting by a user through a user terminal, inputting by a medical staff through a user terminal, transmitting by a network or other collection methods. The current detection time represents the time of collecting blood glucose data of a detected person, for example, when venous blood is extracted to detect the blood glucose concentration in human blood, the current detection time is the time of extracting venous blood, and when the blood glucose concentration in the human blood is detected in a non-invasive mode, the time of obtaining the blood glucose concentration of the detected person is the current detection time. Specifically, the vein blood drawing can be performed by selecting veins at any part of the whole body in principle, and puncture blood drawing is possible. For convenience of operation, the median elbow vein of the elbow or the basilic vein is usually selected clinically for puncture blood drawing. If the vein of the elbow of the child is not obvious, the vein of the foot or the vein of the board barrier of the scalp can be selected for puncture blood drawing. If the old people have angiosclerosis or the critical peripheral blood vessels have obvious collapse and the puncture is difficult, a deep vein can be selected for puncture blood drawing. Such as the internal jugular vein, the subclavian vein or the femoral vein, may be used for puncture blood test. Blood glucose measurements are understood to mean the amount of glucose in human blood expressed in millimoles per liter, and biochemical analyzers, also commonly referred to as biochemicals, are instruments that use the principle of photo-electric colorimetry to measure a particular chemical component in a body fluid. The blood glucose measurement value of the venous blood of the subject, i.e., the glucose content in the venous blood, can be measured by a biochemical meter. After the venous blood of the subject is collected, the position of the test point, i.e., the blood drawing position, of the subject is recorded, and when the venous blood of the subject is collected through the median elbow vein, for example, it is possible to record whether the venous blood is collected from the left hand of the subject or the venous blood is collected from the right hand of the subject.

It should be noted that the portable blood glucose meter is a kind of blood glucose meter, and is called as a portable blood glucose meter because of its small volume, easy operation and convenient daily use. The portable glucometer mainly comprises a portable glucometer machine, a test strip, a blood sampling pen and a blood sampling needle head, wherein the disposable blood sampling needle head is arranged in the blood sampling pen, fingertip blood of a detected person is collected through the blood sampling pen, and a blood glucose detection value of the fingertip blood of the detected person, namely the glucose content in the fingertip blood, is detected through the portable glucometer machine. After the fingertip blood of the subject is collected, the position of the detection point of the subject, i.e., the blood drawing position, is recorded, and, for example, it may be recorded whether the fingertip blood is collected from the left hand of the subject or the fingertip blood is collected from the right hand of the subject.

It can be understood that the testing method of the minimally invasive continuous blood glucose detector is based on the electrochemical reaction principle that a blood taking needle punctures the surface of skin to collect interphalangeal blood and test the blood glucose concentration, and the minimally invasive continuous blood glucose detector can record whether the blood drawing position is at the left-hand position or the right-hand position when measuring the blood glucose concentration and can also record which finger of the detected person is collected when recording.

Step S20: and establishing a blood flow rate model according to the blood glucose detection value to obtain a blood glucose fluctuation difference value, and obtaining a calibrated blood glucose value according to the blood glucose fluctuation difference value.

Specifically, receiving a blood glucose detection value of a subject in a first mode and a blood glucose detection value of the subject in a second mode, wherein the blood glucose detection values include but are not limited to fasting blood glucose value, a first target blood glucose detection value, a second target blood glucose detection value, a third target blood glucose detection value and a fourth target blood glucose detection value, when establishing a blood movement model, the blood movement model can be realized by adopting a control variable method, namely, a multi-factor problem is changed into a plurality of single-factor problems, only one factor is changed, so as to research the influence of the factor on things, and the influence is respectively researched, and finally, the method of comprehensively solving is implemented, the control variable method is applied to the invention to establish a blood flow rate model, the fasting blood glucose value of the subject in the first mode is compared with the fasting glucose value of the subject in the second mode, the first target blood glucose detection value of the subject in the first mode is compared with the first target blood glucose detection value of the subject in the second mode, comparing a second target blood sugar detection value of the detected person in the first mode with a second target blood sugar detection value of the detected person in the second mode, comparing a third target blood sugar detection value of the detected person in the first mode with a third target blood sugar detection value of the detected person in the second mode, and comparing a fourth target blood sugar detection value of the detected person in the first mode with a fourth target blood sugar detection value of the detected person in the second mode to complete the whole process of establishing a blood flow rate model, obtain blood sugar fluctuation difference values of multiple types of blood sugar detection values, and further obtain a blood sugar calibration value of the blood sugar detection values in the first mode according to the fluctuation difference values of the multiple types of blood sugar detection values.

In one embodiment, the blood flow rate of the detected person is obtained through the calibration module according to the average flow rate of the venous blood of the detected person at a detection point position; calculating a blood glucose concentration fluctuation function according to the blood glucose detection value and the blood flow rate; calculating a blood glucose concentration difference value based on a distance difference value and the blood glucose concentration fluctuation function, wherein the distance difference value is a difference value of different detection point positions in the first mode.

Specifically, when the subject is in a static state, a non-static state or a static state after exercise, the average flow rate of venous blood of the subject changes, the average flow rate of venous blood of the subject is closely related to the heart rate of the subject at the current moment, and the blood flow rate of the subject also causes the detected blood glucose concentration to deviate. Therefore, in order to enable the blood glucose data stored in the cloud database to be monitored to be more accurate, a blood glucose concentration fluctuation function is introduced to calculate the condition that an error occurs when the blood glucose concentration is detected due to the blood flow velocity of the detected person.

In particular, the blood glucose concentration in the human blood is also influenced by the position of the detection point, namely the blood drawing position, and compared with the method of drawing venous blood in an invasive mode to collect the venous blood of a detected person, the location of the blood draw of the subject may be recorded, such as whether the location of the blood draw is the left or right hand location of the subject, or the elbow or wrist of the tested person, the venous blood collected by the tested person through the elbow of the left hand can be compared with the venous blood collected through the wrist of the left hand, or comparing the venous blood collected by the tested person through the elbow of the right hand with the venous blood collected through the wrist of the right hand, and finally, calculating a blood glucose concentration difference value according to the blood glucose concentration function according to the distance difference value between the elbow and the wrist.

Step S30: and counting a blood glucose change function through a cloud data center based on the blood glucose detection value and the calibrated blood glucose value, and monitoring the blood glucose change condition of the detected person according to the blood glucose change function.

Specifically, a cloud data center is established, so that the user terminal and the cloud data center are realized through a network, including but not limited to cloud communication, storage of a cloud database, cloud computing and the like. The user terminal uploads the blood sugar detection data through cloud communication, the cloud data center executes cloud computing on the received blood sugar detection data, and then the result of the cloud computing is transmitted back to the blood sugar counting system based on artificial intelligence. It should be noted that, by adopting the cloud mode, the basic data can be stored in the cloud database, which not only reduces the repeated labor of single data statistics, but also improves the sharing of data statistics resources, and further improves the basis of function improvement.

In order to effectively utilize the blood glucose data stored in the cloud database, statistical analysis can be performed according to the monitored blood glucose data to provide data reference for the research on the pathogenesis of diabetes in the group, in an embodiment, the statistical module 30 is further configured to count a group blood glucose change function through the cloud data center based on the blood glucose detection value and the calibrated blood glucose value of at least one detected person, and monitor the blood glucose change condition of the group according to the group blood glucose change function.

In order to effectively utilize the blood glucose data stored in the cloud database, statistical analysis can be performed according to the monitored blood glucose data by taking seasons as statistical time points, so as to provide data reference for seasonal pathogenesis research of diabetes in a group, in an embodiment, the statistical module 30 is further configured to take seasons as statistical time points, count a seasonal blood glucose change function through the cloud data center based on a blood glucose detection value and a calibrated blood glucose value of at least one detected person, and monitor a seasonal blood glucose change condition of the group according to the seasonal blood glucose change function.

In this embodiment, because the blood glucose data statistics system usually only adopts one of the invasive and non-invasive modes to measure the blood glucose concentration in the blood of the human body when performing statistics on the blood glucose data of the human body at present, but there is a measurement deviation no matter the invasive mode is adopted to measure the blood glucose concentration in the blood or the non-invasive mode is adopted to measure the blood glucose concentration in the blood, and further the blood glucose statistics result when performing statistics on the blood glucose is not accurate. In order to solve the technical problems, the invention obtains a blood glucose detection value of a detected person in a preset mode through a detection module, records the current detection time, receives the blood glucose detection value obtained by the detection module through a calibration module, establishes a blood flow rate model according to the blood glucose detection value, obtains a blood glucose fluctuation difference value, obtains a calibration blood glucose value according to the blood glucose fluctuation difference value, finally counts a blood glucose change function through a cloud data center through a statistics module, monitors the blood glucose change condition of the detected person according to the blood glucose change function, calibrates the blood glucose value in an invasive mode by using the blood glucose value in a non-invasive mode, further obtains the calibration blood glucose value, counts a plurality of blood glucose detection values and a plurality of corresponding calibration blood glucose values through the cloud data center, and establishes the blood glucose change function of the detected person, and then the blood sugar change condition of the detector can be accurately monitored.

It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited in this respect.

It should be noted that the above-mentioned work flows are only illustrative and do not limit the scope of the present invention, and in practical applications, those skilled in the art may select some or all of them according to actual needs to implement the purpose of the solution of the present embodiment, and the present invention is not limited herein.

Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. The blood glucose statistical system based on artificial intelligence is characterized by comprising a detection module, a calibration module and a statistical module:

the calibration module is used for receiving the blood glucose detection value obtained by the detection module, establishing a blood flow rate model according to the blood glucose detection value to obtain a blood glucose fluctuation difference value, and obtaining a calibrated blood glucose value according to the blood glucose fluctuation difference value;

2. The system of claim 1, wherein the first mode comprises:

after extracting the fingertip blood of a detected person, detecting the blood glucose detection value of the fingertip blood by using a portable blood glucose meter, and recording the position of the detection point; and/or the presence of a gas in the gas,

3. The system of claim 2, wherein the second mode comprises:

4. The system of claim 3, wherein the calibration module is further configured to obtain the blood flow rate of the subject based on the average flow rate of venous blood of the subject at the test point location;

5. The system of claim 4, wherein the calibration module is further configured to calculate a blood glucose difference value based on a first blood glucose measurement value and a second blood glucose measurement value at the same time, wherein the first blood glucose measurement value is a blood glucose measurement value in the first mode, and the second blood glucose measurement value is a blood glucose measurement value in the second mode;

6. The system of claim 3, wherein the obtaining module is further configured to obtain a fasting glucose test value, a first target glucose test value and a second target glucose test value of the subject in a preset mode, wherein the first target glucose test value and the second target glucose test value are measured by the subject at a first preset time and a second preset time after the subject takes a fixed amount of glucose solution or a fixed amount of food, respectively.

7. The system of claim 3, wherein the obtaining module is further configured to obtain a fasting blood glucose test value, a third target blood glucose test value and a fourth target blood glucose test value of the subject in a preset mode, wherein the third target blood glucose test value and the fourth target blood glucose test value are measured by the subject at a third preset time and a fourth preset time after the subject takes a fixed amount of insulin solution or a fixed amount of prescribed medication, respectively.

8. The system of any one of claims 1 to 7, wherein the statistics module is further configured to count a group blood glucose variation function through the cloud data center based on the blood glucose detection value and the calibrated blood glucose value of the at least one subject, and monitor blood glucose variation of the group according to the group blood glucose variation function.

9. The system of any one of claims 1 to 7, wherein the statistical module is further configured to count a seasonal blood glucose variation function through the cloud data center based on the blood glucose detection value and the calibrated blood glucose value of the at least one subject with a season as a statistical time point, and monitor a seasonal blood glucose variation condition of the population according to the seasonal blood glucose variation function.

10. An artificial intelligence based blood glucose statistical method, wherein the artificial intelligence based blood glucose statistical method is applied to the artificial intelligence based blood glucose statistical system according to any one of claims 1 to 9, the method comprising: