User health state assessment method and system based on intelligent wearable device

Abstract

The invention discloses a user health state assessment method and system based on an intelligent wearable device, and the method comprises the steps: firstly, transmitting near-infrared light with a preset wavelength to a user according to a preset period through an optical sensor disposed on the intelligent wearable device, and receiving a feedback light signal; the feedback light signals are input into a pre-trained blood glucose state evaluation model to obtain a blood glucose state evaluation result of the user, and the blood glucose state evaluation result is used as an important basis for evaluating the health state of the user. By means of the design, real-time and non-intrusive monitoring of the health state of the user is achieved, and the accuracy of health state evaluation and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to a field, and in particular to a method and system for evaluating the health status of a user based on a smart wearable device. Background Art

[0002] Traditional user health status assessment methods mostly rely on physiological data actively provided by users or regular physical examinations, which not only have a low frequency of data acquisition, but also have a poor user experience. With the popularization of smart wearable devices and the development of optical sensor technology, it has become possible to use smart wearable devices for real-time, non-invasive health monitoring. However, in the prior art, there is still a lack of systematic solutions for how to effectively use optical sensors on smart wearable devices to assess user health status, especially blood sugar status. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for evaluating the health status of a user based on a smart wearable device.

[0004] In a first aspect, an embodiment of the present invention provides a method for evaluating a user's health status based on a smart wearable device, comprising:

[0005] activating a user health status assessment instruction of a smart wearable device according to a preset period, wherein the smart wearable device is equipped with an optical sensor;

[0006] According to the user health status assessment instruction, near-infrared light of a preset wavelength is emitted to the current user through the optical sensor to obtain a target near-infrared light feedback optical signal;

[0007] Inputting the target near-infrared light feedback light signal into a pre-trained blood sugar status assessment model to obtain a blood sugar status assessment result of the target near-infrared light feedback light signal;

[0008] The blood sugar status assessment result is used as the user health status assessment result of the current user.

[0009] In a second aspect, an embodiment of the present invention provides a server system, including a server, wherein the server is used to execute the method described in the first aspect.

[0010] Compared with the prior art, the beneficial effects provided by the present invention include: the present invention discloses a user health status assessment method and system based on a smart wearable device, including: first, using an optical sensor configured on the smart wearable device to emit near-infrared light of a preset wavelength to the user according to a preset period, and receiving a feedback light signal. By inputting these feedback light signals into a pre-trained blood sugar status assessment model, the user's blood sugar status assessment result is obtained, and it is used as an important basis for the user's health status assessment. With this design, real-time, non-invasive monitoring of the user's health status is achieved, and the accuracy of health status assessment and user experience are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of the steps of a method for evaluating a user's health status based on a smart wearable device provided by an embodiment of the present invention;

[0013] Figure 2 A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0015] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0016] In order to solve the technical problems in the aforementioned background technology, Figure 1 This is a flow chart of a method for evaluating the health status of a user based on a smart wearable device provided in an embodiment of the present disclosure. The method for evaluating the health status of a user based on a smart wearable device is introduced in detail below.

[0017] Step S201, activating a user health status assessment instruction of a smart wearable device according to a preset period, wherein the smart wearable device is equipped with an optical sensor;

[0018] Step S202, according to the user health status assessment instruction, emit near-infrared light of a preset wavelength to the current user through the optical sensor to obtain a target near-infrared light feedback optical signal;

[0019] Step S203, inputting the target near-infrared light feedback light signal into a pre-trained blood sugar status assessment model to obtain a blood sugar status assessment result of the target near-infrared light feedback light signal;

[0020] Step S204: Using the blood sugar status assessment result as the user health status assessment result of the current user.

[0021] In an embodiment of the present invention, for example, the preset cycle is set at 8 am and 8 pm every day. On the server side, there is a timed task scheduling system, which sends activation instructions to the smart wearable device connected to the server at these two time points every day according to the preset cycle. For example, user Xiao Ming wears this smart wearable device, such as a smart bracelet. When 8 o'clock in the morning arrives, the server sends an activation instruction on time. This instruction is transmitted to Xiao Ming's smart bracelet through a network (such as Bluetooth, Wi-Fi, etc.). After receiving the instruction, the smart bracelet is ready to start the user health status assessment process, and its built-in optical sensor is also on standby. After receiving the activation instruction from the server, the optical sensor in Xiao Ming's smart bracelet starts working. For example, the preset wavelength of near-infrared light is 940nm, and the optical sensor will emit this specific wavelength of near-infrared light to Xiao Ming's wrist. When near-infrared light irradiates the skin, blood vessels and tissues at Xiao Ming's wrist, optical phenomena such as reflection, scattering and absorption will occur. These interacting lights will form feedback light signals, and the optical sensor of the smart bracelet will capture and receive these feedback light signals, and use them as target near-infrared light feedback light signals for subsequent processing. For example, when near-infrared light penetrates the skin, part of the light is absorbed by the glucose molecules in the blood, and the rest is reflected back. What the optical sensor receives is the feedback light signal containing these blood sugar-related information. The smart bracelet transmits the acquired target near-infrared light feedback light signal to the server through the network. After receiving this signal, the server inputs it into a pre-trained blood sugar status assessment model. For example, this blood sugar status assessment model is trained based on a large amount of sample data and deep learning algorithms. These sample data include near-infrared light feedback light signal characteristics and corresponding blood sugar values ​​at different blood sugar levels. After the server inputs Xiao Ming's target near-infrared light feedback light signal into the model, the model will extract and analyze the signal. For example, the model will analyze the signal's intensity, wavelength change, phase and other features, and compare and calculate them with the features in the training data. Finally, the model gives a blood sugar status assessment result, such as judging whether Xiao Ming's current blood sugar is in the normal range, high or low. After the server obtains Xiao Ming's blood sugar status assessment result, it will be regarded as Xiao Ming's current user health status assessment result. If the assessment result shows that Xiao Ming's blood sugar is normal, the server will mark it as "good" in Xiao Ming's health data record, and may send a notification to Xiao Ming's mobile app to inform him that his current health is good and recommend that he continue to maintain good living habits. If the assessment result shows that Xiao Ming's blood sugar is high, in addition to marking it as "high" in the health data, the server may also send more detailed prompts to Xiao Ming's mobile app, such as suggesting that he pay attention to diet control and increase exercise in the near future, and reminding him to measure his blood sugar again at an appropriate time to observe changes.For example, Xiao Ming is a diabetic patient. The server will synchronize the high blood sugar assessment result to the system of Xiao Ming's medical team so that the medical team can keep abreast of Xiao Ming's condition changes and provide him with more accurate treatment suggestions and adjust the treatment plan. Through the continuous cycle and execution of the above four steps, the server can regularly provide users with accurate health status assessments, help users understand their physical condition in a timely manner, and take appropriate measures to stay healthy.

[0022] In the embodiment of the present invention, the aforementioned step S203 can be implemented through the following example execution.

[0023] Acquire a near-infrared light feedback signal instance and a plurality of pending blood sugar states, wherein the near-infrared light feedback signal instance is configured with a plurality of actual blood sugar states;

[0024] Obtaining a pending blood glucose state vector of each pending blood glucose state based on a blood glucose state vector model, classifying a plurality of pending blood glucose states according to the pending blood glucose state vector to obtain a plurality of pending blood glucose state classes, and obtaining a state class identifier of each pending blood glucose state class based on the actual blood glucose state;

[0025] Importing the near-infrared light feedback signal instance into a blood glucose state assessment model, obtaining a predicted blood glucose state class from a plurality of pending blood glucose state classes based on the blood glucose state assessment model, and then obtaining the pending blood glucose state corresponding to the near-infrared light feedback signal instance from the predicted blood glucose state class;

[0026] Obtaining a first cost parameter according to the evaluation result of the predicted blood sugar state class and the state class identifier, and obtaining a second cost parameter according to the evaluation result of the pending blood sugar state corresponding to the near-infrared light feedback light signal instance and the actual blood sugar state;

[0027] performing collaborative learning on the blood glucose state vector model and the blood glucose state assessment model according to the first cost parameter and the second cost parameter;

[0028] Acquire a target near-infrared light feedback signal, import the target near-infrared light feedback signal into the trained blood sugar status assessment model, and obtain a blood sugar status assessment result of the target near-infrared light feedback signal based on the trained blood sugar status assessment model.

[0029] In an embodiment of the present invention, exemplarily, a large number of near-infrared light feedback signal instances of users and the corresponding actual blood sugar states are stored in the database of the server. For example, there is a near-infrared light feedback signal instance of user A, whose actual blood sugar states measured at different time points are 7.8mmol / L 2 hours after breakfast, 5.2mmol / L before lunch, and 8.5mmol / L after dinner. At the same time, the server sets a plurality of pending blood sugar states, such as 4.0-5.0mmol / L, 5.1-6.0mmol / L, 6.1-7.0mmol / L, etc. The blood sugar state vector model in the server converts each pending blood sugar state into a specific vector representation. For example, the pending blood sugar state 4.0-5.0mmol / L is converted into a vector [0.1, 0.2, 0.3], and 5.1-6.0mmol / L is converted into a vector [0.4, 0.5, 0.6]. Then, according to the similarity of these vectors, the plurality of pending blood sugar states are classified. For example, the vectors [0.1, 0.2, 0.3] and [0.2, 0.3, 0.4] are similar, so they are classified into one category to obtain multiple pending blood sugar state categories. Based on the actual blood sugar state of user A, such as 7.8mmol / L, determine the pending blood sugar state category to which it belongs, and assign a state category identifier to this category, such as "high blood sugar category". The server imports the near-infrared light feedback optical signal instance of user A into the blood sugar state evaluation model. The model first analyzes and processes the signal to calculate the first estimated score for each pending blood sugar state category. For example, if the first estimated score of a pending blood sugar state category is high, and the first estimated probability obtained after data normalization exceeds the pre-configured first preset probability threshold, such as 0.8, then this pending blood sugar state category is determined as a predicted blood sugar state category. After determining the predicted blood sugar state category, the server further determines the pending blood sugar state corresponding to the near-infrared light feedback optical signal instance in this category. For example, traverse each pending blood sugar state in the predicted blood sugar state class, calculate the vector distance between the near-infrared light feedback light signal instance and each pending blood sugar state, and perform data normalization to obtain a second estimated probability. If the second estimated probability of a pending blood sugar state exceeds a pre-configured second preset probability threshold, such as 0.9, then the pending blood sugar state is determined to be the pending blood sugar state corresponding to the near-infrared light feedback light signal instance. For the predicted blood sugar state class, the server calculates the first cost parameter based on its evaluation results, that is, the first estimated probability of each pending blood sugar state class, and the state class identifier. For example, the state class identifier is "high blood sugar class", and the first estimated probability is 0.7. By calculating the KL divergence cost parameter between the two, multiple blood sugar state class cost parameters are obtained, and they are summed to obtain the first cost parameter.For the pending blood sugar state corresponding to the near-infrared light feedback light signal instance, the server calculates the second cost parameter based on the evaluation result, that is, the second estimated probability of each pending blood sugar state, and the actual blood sugar state. For example, the actual blood sugar state is 7.8mmol / L, and the corresponding second estimated probability of the pending blood sugar state is 0.8. By calculating the KL divergence cost parameter between the two, multiple blood sugar state cost parameters are obtained, and they are summed to obtain the second cost parameter. The server comprehensively considers the first cost parameter and the second cost parameter and assigns corresponding weights to them. For example, the weight of the first cost parameter is 0.4, and the weight of the second cost parameter is 0.6, and the weighted sum is used as the target cost parameter. Then, according to this target cost parameter, the parameters of the blood sugar state vector model and the blood sugar state evaluation model are adjusted and optimized at the same time to improve the accuracy and generalization ability of the model. When a new user, such as user B, generates a target near-infrared light feedback light signal, the server obtains this signal. It is imported into the trained blood sugar state evaluation model. The model processes and analyzes this signal, and ultimately provides an assessment result of user B's blood sugar status, such as judging it to be within the "normal blood sugar range."

[0030] In an embodiment of the present invention, the blood sugar state assessment model includes a feature extraction unit and a discrimination unit; the near-infrared light feedback optical signal instance is imported into the blood sugar state assessment model, and a predicted blood sugar state class is obtained from a plurality of pending blood sugar state classes based on the blood sugar state assessment model, which can be implemented through the following examples.

[0031] Importing the near-infrared light feedback optical signal instance into the feature extraction unit to obtain an instance feature vector;

[0032] Importing the instance feature vector into the discrimination unit to obtain a first estimated score for each of the pending blood sugar state classes;

[0033] Performing data standardization on each of the first estimated scores to obtain a first estimated probability of each of the pending blood sugar state classes;

[0034] When the first estimated likelihood is higher than a pre-configured first preset likelihood threshold, the undetermined blood glucose state class corresponding to the first estimated likelihood is determined as a predicted blood glucose state class.

[0035] In an embodiment of the present invention, exemplarily, the server receives a near-infrared light feedback optical signal instance of a user, such as the feedback optical signal of user C. The feature extraction unit starts to process the signal. First, a grouping operation is performed on the near-infrared light feedback optical signal instance. For example, it is divided into 10 optical signal segments to form an optical signal segment array. A start segment flag, such as "start", is added to the start segment of the optical signal segment array, and an end segment flag, such as "end", is added to the end segment to obtain a pending segment array. Then, the pending segment array is subjected to optical signal conversion processing to convert it into a blood glucose state array vector optical signal vector array. For example, each optical signal segment is converted into a numerical vector of a specific length. Next, based on dynamic convolution, the feature extraction unit performs feature extraction on the blood glucose state array vector optical signal vector array. For example, the size of the dynamic convolution kernel is 3, the step size is 1, and a vector representation array is obtained through a convolution operation. This array includes the vector representation of each optical signal segment in the pending segment array. Finally, based on dynamic convolution, an integration operation is performed on each vector representation. First, according to the pre-configured dynamic convolution operation and vector representation, the feature correlation measure of each vector representation is calculated. For example, the feature correlation measure of vector representation A is 0.7. Then, according to the pre-configured activation function, such as the Sigmoid function, and the feature correlation measure, the convolution kernel weight of each vector representation is calculated. For example, the convolution kernel weight of vector representation A is 0.8. Based on these convolution kernel weights, a weighted operation is performed on each vector representation to finally obtain the instance feature vector. The server imports the instance feature vector obtained in the previous step into the discriminant unit. The discriminant unit contains a series of parameters and calculation logic to evaluate the degree of match between the instance feature vector and each pending blood sugar state class. For example, three pending blood sugar state classes are set: hypoglycemia state class (<4.5mmol / L), normal blood sugar state class (4.5-6.0mmol / L) and hyperglycemia state class (>6.0mmo l / L). The discriminant unit will calculate the correlation between the instance feature vector and these three categories respectively, and give the corresponding first estimated scores. For example, for the hypoglycemia state class, the first estimated score calculated by the discrimination unit is 20; for the normal blood sugar state class, the first estimated score is 70; for the hyperglycemia state class, the first estimated score is 10. After the server receives the first estimated score of each pending blood sugar state class given by the discrimination unit, it performs data normalization. For example, the common Soft max function is used for normalization. For the above three first estimated scores (20, 70, 10), after calculation by the Softmax function, the first estimated probability of the hypoglycemia state class is 0.1, the first estimated probability of the normal blood sugar state class is 0.7, and the first estimated probability of the hyperglycemia state class is 0.2. The server pre-configured a first preset probability threshold, for example, 0.5.In the above example, the first estimated probability of the normal blood sugar state class is 0.7, which is higher than the threshold value of 0.5. Therefore, the server determines the normal blood sugar state class as the predicted blood sugar state class. This means that based on the current near-infrared light feedback light signal instance and the evaluation of the model, user C is more likely to be in a normal blood sugar state. Based on such prediction results, the server can further provide users with relevant health advice or share this information with medical institutions to achieve more accurate health management and medical services.

[0036] In an embodiment of the present invention, the evaluation result of the predicted blood sugar state class includes a first estimated probability of each of the pending blood sugar state classes; the first cost parameter is obtained based on the evaluation result of the predicted blood sugar state class and the state class identifier, which can be implemented through the following example.

[0037] Obtaining a target blood glucose state class probability rate for each of the pending blood glucose state classes according to the state class identifier of each of the pending blood glucose state classes;

[0038] Calculating a KL divergence cost parameter between each of the target blood sugar state class likelihoods and the corresponding first estimated likelihood to obtain a plurality of blood sugar state class cost parameters;

[0039] The sum of all the blood sugar status cost parameters is determined as the first cost parameter.

[0040] In the embodiment of the present invention, exemplarily, a large amount of blood sugar state data and corresponding state class identifiers are stored in the server. For example, for the hypoglycemia state class, its state class identifier is "Low"; for the normal blood sugar state class, it is identified as "Normal"; for the hyperglycemia state class, it is identified as "High". The server determines the target blood sugar state class probability corresponding to each state class identifier based on historical data and medical knowledge. For example, in a large-scale study of healthy people, it was found that the proportion of people in hypoglycemia state was about 5%, the proportion of people in normal blood sugar state was about 80%, and the proportion of people in hyperglycemia state was about 15%. Then, the server sets the target blood sugar state class probability of the hypoglycemia state class to 0.05, the target blood sugar state class probability of the normal blood sugar state class to 0.8, and the target blood sugar state class probability of the hyperglycemia state class to 0.15. For example, the server obtains the first estimated probability of the predicted blood sugar state class of a certain user through the previous steps, wherein the first estimated probability of the hypoglycemia state class is 0.03, the first estimated probability of the normal blood sugar state class is 0.75, and the first estimated probability of the hyperglycemia state class is 0.22. Then, the server starts to calculate the KL divergence cost parameter. For the hypoglycemic state class, the KL divergence is calculated as: KL(0.05||0.03)=0.05*log(0.05 / 0.03)+(1-0.05)*log((1-0.05) / (1-0.03)). Similarly, for the normal blood sugar state class: KL(0.8||0.75)=0.8*log(0.8 / 0.75)+(1-0.8)*log((1-0.8) / (1-0.75)). For the hyperglycemic state class: KL(0.15||0.22)=0.15*log(0.15 / 0.22)+(1-0.15)*log((1-0.15) / (1-0.22)). Through the above calculations, the server obtains the KL divergence cost parameters of the hypoglycemia state class, the normal blood sugar state class, and the hyperglycemia state class. The server adds the KL divergence cost parameters of the hypoglycemia state class, the normal blood sugar state class, and the hyperglycemia state class obtained by the above calculation to obtain the first cost parameter. For example, the calculated KL divergence cost parameter of the hypoglycemia state class is 0.02, that of the normal blood sugar state class is 0.05, and that of the hyperglycemia state class is 0.03. Then the first cost parameter = 0.02 + 0.05 + 0.03 = 0.1. By calculating this first cost parameter, the server can evaluate the accuracy and degree of deviation of the model for predicting blood sugar state classes. A smaller first cost parameter indicates that the model's prediction result is closer to the probability of the target blood sugar state class, and the model has better performance; a larger first cost parameter indicates that the model has a large deviation and needs further optimization and adjustment.In actual applications, the server will continuously repeat the above steps and process a large amount of user data to continuously improve and optimize the blood sugar status assessment model, improve its accuracy and reliability, and provide users with more accurate blood sugar status assessment services.

[0041] In the embodiment of the present invention, the obtaining of the pending blood sugar state corresponding to the near-infrared light feedback optical signal instance from the predicted blood sugar state class can be implemented through the following examples.

[0042] Traversing each of the pending blood glucose states in the predicted blood glucose state class, and calculating a vector distance between the instance feature vector and the pending blood glucose state vector of the pending blood glucose state;

[0043] Performing data normalization on the vector distance to obtain a second estimated probability of the pending blood sugar state;

[0044] When the second estimated likelihood is higher than a preconfigured second preset likelihood threshold, the pending blood glucose state corresponding to the second estimated likelihood is determined as the pending blood glucose state corresponding to the near-infrared light feedback optical signal instance.

[0045] In an embodiment of the present invention, illustratively, for example, the server has determined that the predicted blood glucose state class is a normal blood glucose state class, which includes multiple specific pending blood glucose states, such as 4.5-5.0mmol / L, 5.0-5.5mmol / L, 5.5-6.0mmol / L, etc. The server obtains the instance feature vector of the near-infrared light feedback light signal instance. For example, this vector can be expressed as [0.2, 0.3, 0.4, 0.5, 0.6]. For each pending blood glucose state, there is a corresponding pending blood glucose state vector. For example, the pending blood glucose state vector corresponding to 4.5-5.0mmol / L is [0.1, 0.2, 0.3, 0.4, 0.5]. The server uses common vector distance calculation methods, such as the Euclidean distance formula, to calculate the distance between the instance feature vector and each pending blood glucose state vector. For the above example, the distance between the instance feature vector and the 4.5-5.0mmol / L pending blood glucose state vector is calculated as:

[0046]

[0047] Then, the server repeats this calculation process for other pending blood sugar states such as 5.0-5.5mmol / L, 5.5-6.0mmol / L, etc. After the server completes the calculation of all distances, it performs data standardization on these distances. For example, the standardization method used is to convert the distance to a value between 0 and 1. Taking the distance 0.2236 calculated above as an example, for example, the minimum value of all calculated distances is 0.1 and the maximum value is 0.5. The standardized distance is: The server then uses a function to convert the normalized distance into a second estimated probability. For example, the function used is the Sigmoid function, i.e. Where x is the normalized distance. For the normalized distance 0.309, the second estimated probability is calculated as:

[0048]

[0049] The server performs such standardization and conversion on the distance of each pending blood sugar state to obtain the respective second estimated probability. The server pre-configures a second preset probability threshold, for example, 0.6. For the second estimated probability of 0.577 of the pending blood sugar state of 4.5-5.0mmol / L calculated above, since it is less than the threshold value 0.6, the server does not temporarily determine it as a pending blood sugar state corresponding to the near-infrared light feedback light signal instance. However, for another pending blood sugar state, such as 5.0-5.5mmol / L, for example, the second estimated probability calculated is 0.75, and since 0.75 is greater than the threshold value 0.6, the server determines the pending blood sugar state of 5.0-5.5mmol / L as a pending blood sugar state corresponding to the near-infrared light feedback light signal instance. Through such steps, the server can accurately determine the pending blood sugar state that best matches the near-infrared light feedback light signal instance from the predicted blood sugar state class, thereby providing users with more accurate blood sugar state evaluation results. This helps users better understand their blood sugar status and take corresponding health management measures. When processing large amounts of user data, the server repeats these steps over and over again to continually optimize and improve the accuracy and reliability of the assessment.

[0050] In an embodiment of the present invention, the evaluation result of the pending blood sugar state corresponding to the near-infrared light feedback light signal instance includes a second estimated probability of each of the pending blood sugar state in the predicted blood sugar state class; the second cost parameter is obtained based on the evaluation result of the pending blood sugar state corresponding to the near-infrared light feedback light signal instance and the actual blood sugar state, which can be implemented through the following example.

[0051] According to the actual blood sugar state, obtaining the target blood sugar state probability of each of the pending blood sugar states in the predicted blood sugar state class;

[0052] Calculating a KL divergence cost parameter between each of the target blood sugar state likelihood and the corresponding second estimated likelihood to obtain a plurality of blood sugar state cost parameters;

[0053] The sum of all the blood sugar status cost parameters is determined as the second cost parameter.

[0054] In an embodiment of the present invention, illustratively, for example, the server receives the user's actual blood sugar state of 5.2mmol / L, and the predicted blood sugar state class includes three pending blood sugar state ranges: 4.5-5.0mmol / L, 5.0-5.5mmol / L, and 5.5-6.0mmol / L. Based on medical knowledge and statistical data, the server determines the target blood sugar state probability of each pending blood sugar state under this actual blood sugar state. For example, in a large-scale sample of similar blood sugar states, the probability of being in the range of 4.5-5.0mmol / L is about 0.2, the probability of being in the range of 5.0-5.5mmol / L is about 0.7, and the probability of being in the range of 5.5-6.0mmol / L is about 0.1. For example, the second estimated probability of each pending blood sugar state calculated by the previous steps is: the second estimated probability of 4.5-5.0mmol / L is 0.15, the second estimated probability of 5.0-5.5mmol / L is 0.75, and the second estimated probability of 5.5-6.0mmol / L is 0.1. Then the server starts to calculate the KL divergence cost parameter. For the 4.5-5.0mmol / L range: KL(0.2||0.15)=0.2*log(0.2 / 0.15)+(1-0.2)*log((1-0.2) / (1-0.15)). For the 5.0-5.5mmol / L range: KL(0.7||0.75)=0.7*log(0.7 / 0.75)+(1-0.7)*log((1-0.7) / (1-0.75)). For the 5.5-6.0mm ol / L range: KL(0.1||0.1)=0.1*log(0.1 / 0.1)+(1-0.1)*log((1-0.1) / (1-0.1))=0 (because the two are the same). Through the above calculations, the server obtains the KL divergence cost parameters for the three blood sugar states. The server adds the three KL divergence cost parameters calculated above to obtain the second cost parameter. For example, the KL divergence cost parameter in the range of 4.5-5.0mmol / L is 0.03, in the range of 5.0-5.5mmol / L is 0.02, and in the range of 5.5-6.0mmol / L is 0, then the second cost parameter = 0.03 + 0.02 + 0 = 0.05. By calculating this second cost parameter, the server can evaluate the prediction accuracy of the model for the actual blood glucose state of a specific user. A smaller second cost parameter indicates that the prediction results of the model are closer to the actual situation and the model performance is better; a larger second cost parameter indicates that the model has deviations and needs further optimization and adjustment. In actual large-scale user data processing, the server will continue to repeat these steps, calculate and analyze the actual blood glucose state and prediction results of each user, so as to continuously improve the performance of the blood glucose state evaluation model and provide users with more accurate and reliable blood glucose evaluation services.For example, for a large number of users whose actual blood glucose status is 5.8mmol / L, the server calculates the KL divergence cost parameters of the target blood glucose state probability and the second estimated probability of each pending blood glucose state in the predicted blood glucose state class corresponding to each user, and sums them up to obtain the second cost parameter. Through comprehensive analysis and comparison of these second cost parameters, the server can find that the model may over-estimate or underestimate the prediction of certain blood glucose ranges, so as to adjust the parameters and algorithms of the model in a targeted manner to improve the accuracy and stability of the overall prediction. In addition, the server can also combine other relevant health data and characteristics, such as the user's age, weight, eating habits, exercise level, etc., to further optimize the prediction ability of the model. For example, for young and regular exercise users, their blood glucose fluctuations may be relatively small. When calculating the second cost parameter, the server will consider the impact of these factors on the prediction results to more accurately evaluate the performance of the model in different user groups and make corresponding optimization adjustments. At the same time, the server will regularly update and expand the training data set to include more diverse and representative data to ensure that the model can adapt to different blood glucose state changes and user characteristics. After updating the data set, the second cost parameter is recalculated to evaluate the improvement effect of the model, and iterative optimization is continuously performed so that the blood glucose status assessment model can better serve the majority of users and provide strong support and guidance for their health management.

[0055] In the embodiment of the present invention, the step of importing the near-infrared light feedback optical signal instance into the feature extraction unit to obtain the instance feature vector may be implemented through the following example.

[0056] Performing a grouping operation on the near-infrared light feedback optical signal instance to obtain an optical signal segment array, wherein the optical signal segment array includes a plurality of optical signal segments;

[0057] Adding a start segment mark to the start segment of the optical signal segment array, and adding an end segment mark to the end segment of the optical signal segment array, to obtain a pending segment array;

[0058] Performing optical signal conversion processing on the undetermined segment array to obtain a blood sugar state array vector optical signal vector array;

[0059] Based on dynamic convolution, the feature extraction unit is used to perform feature extraction on the blood sugar state array vector light signal vector array to obtain a vector representation array, wherein the vector representation array includes the vector representation of each light signal segment in the to-be-determined segment array;

[0060] Based on dynamic convolution, an integration operation is performed on each of the vector representations to obtain an instance feature vector.

[0061] In an embodiment of the present invention, for example, the server receives an instance of a near-infrared light feedback optical signal, which is a continuous time series signal. In order to facilitate processing and analysis, the server needs to perform grouping operations on it. For example, the time length of the entire signal is evenly divided into 10 time periods, and the signal data in each time period constitutes an optical signal segment. In this way, an optical signal segment array containing 10 optical signal segments is obtained. For example, the first optical signal segment contains signal data from 0 seconds to 1 second, the second optical signal segment contains signal data from 1 second to 2 seconds, and so on. After obtaining the optical signal segment array, the server adds a start segment mark to its start segment, for example, this mark is "START", and adds an end segment mark to the end segment, for example, this mark is "END". In this way, the original optical signal segment array becomes a pending segment array. For example, the first element of the pending segment array becomes a combination of "START" and the data of the first optical signal segment, and the tenth element becomes a combination of the data of the tenth optical signal segment and "END". The server performs optical signal conversion processing on the pending segment array. This involves converting the characteristics of the optical signal such as the intensity and wavelength into a numerical vector representation. For example, the conversion rule is to use the average intensity value, peak intensity value, and wavelength variation range of each optical signal segment as three elements of the vector. Such conversion is performed for each optical signal segment, and finally a blood glucose state array vector optical signal vector array is obtained. For example, the average intensity value of the first optical signal segment is 10, the peak intensity value is 15, and the wavelength variation range is 5, then its corresponding vector is [10,15,5]. Dynamic convolution kernels are configured in the feature extraction unit. The server uses these convolution kernels to perform convolution operations on the blood glucose state array vector optical signal vector array. For example, the size of the convolution kernel is 3 and the step size is 1. For each optical signal segment vector, its corresponding vector representation is obtained by calculation with the convolution kernel. For example, for the vector [10,15,5] of the first optical signal segment, after calculation with the convolution kernel, its vector representation is obtained as [8,12,6]. Such processing is performed on the vectors of all optical signal segments, and finally a vector representation array is obtained. The server again performs integration operations on each vector representation based on dynamic convolution. First, based on the pre-configured dynamic convolution operation and vector representation, the feature relevance measure of each vector representation is calculated. For example, for a certain vector representation [8,12,6], its feature relevance measure is calculated to be 0.7. Then, based on the pre-configured activation function (such as the Sigmoid function) and the feature relevance measure, the convolution kernel weight of each vector representation is calculated. For example, for the above feature relevance measure of 0.7, the calculated convolution kernel weight is 0.8. Finally, based on these convolution kernel weights, a weighted operation is performed on each vector representation. All weighted vector representations are added together to obtain the instance feature vector.For example, there are 10 vector representations, and their convolution kernel weights are calculated respectively and added after weighted operation to obtain the final instance feature vector, such as [50, 60, 70]. Through the above series of processing steps, the server extracts the instance feature vector that can effectively represent its characteristics from the near-infrared light feedback light signal instance, providing strong data support for the subsequent blood sugar status evaluation and analysis. In actual application scenarios, the server may need to process a large number of users' near-infrared light feedback light signal instances at the same time. For each instance, accurate feature extraction and integration are performed according to the above steps to ensure the accuracy and reliability of the evaluation results. At the same time, the server will continuously optimize and adjust the parameters and methods of feature extraction and integration according to new data and evaluation results to improve the performance and adaptability of the entire system. In addition, the server will also consider individual differences between different users, such as age, gender, physical condition and other factors, and make appropriate adjustments and optimizations to the feature extraction and integration process. For example, for the elderly or users with specific diseases, a more sensitive or specific feature extraction method will be used to better reflect their changes in blood sugar status. Moreover, the server will interact and integrate data with other medical devices and systems, and integrate information from multiple data sources to further improve and accurately evaluate the user's blood sugar status. For example, by combining the user's diet records, exercise data, and medication usage, it can provide more references and constraints for feature extraction and integration, thereby improving the clinical value and practicality of the evaluation results.

[0062] In the embodiment of the present invention, the dynamic convolution-based integration operation is performed on each of the vector representations to obtain an instance feature vector, which can be implemented through the following examples.

[0063] Calculating a feature correlation measure for each of the vector representations based on a preconfigured dynamic convolution operation and the vector representations;

[0064] Calculate the convolution kernel weight of each vector representation according to the preconfigured activation function and the feature correlation measure;

[0065] Based on the convolution kernel weight, a weighted operation is performed on each of the vector representations to obtain an instance feature vector.

[0066] In an embodiment of the present invention, illustratively, for example, the server receives a series of vector representations, such as vector representations V1 = [5, 10, 15], V2 = [8, 12, 16], V3 = [3, 9, 18], and so on. The pre-configured dynamic convolution operation is a specific convolution kernel whose parameters have been set in the server. For the vector representation V1, the server uses the convolution kernel to perform a convolution operation with V1. For example, the result of the convolution operation is a numerical value, such as 25. This numerical value reflects the degree of match between V1 and the convolution kernel, and the server defines it as a feature correlation measure. Such a convolution operation is performed on each vector representation to obtain their respective feature correlation measures. For V2, the result of the convolution operation may be 30; for V3, it may be 18. The server pre-configured activation functions, such as the common Sigmoid function. For the feature correlation measure obtained in the previous step, it is input into the activation function for calculation. Taking the feature correlation measure 25 of vector representation V1 as an example, it is input into the Sigmoid function: Sig moid(25) = 1 / (1+e^(-25)), and a value between 0 and 1 is obtained after calculation, for example, 0.95. This value is the convolution kernel weight of V1. Similarly, for the feature correlation measure 30 of V2, its convolution kernel weight is calculated, for example, 0.98; for the feature correlation measure 18 of V3, its convolution kernel weight is calculated, for example, 0.85. After obtaining the convolution kernel weight of each vector representation, the server starts weighted operation. For vector representation V1 = [5, 10, 15], its convolution kernel weight is 0.95. Then the weighted operation is: [5*0.95, 10*0.95, 15*0.95] = [4.75, 9.5, 14.25]. For the vector representation V2 = [8, 12, 16], the convolution kernel weight is 0.98, and the weighted operation is: [8*0.98, 12*0.98, 16*0.98] = [7.84, 11.76, 15.68]. For the vector representation V3 = [3, 9, 18], the convolution kernel weight is 0.85, and the weighted operation is: [3*0.85, 9*0.85, 18*0.85] = [2.55, 7.65, 15.3]. Add all the weighted vector representations to get the instance feature vector. For example, there are only three vector representations, V1, V2, and V3, and their weighted results are added together: [4.75+7.84+2.55, 9.5+11.76+7.65, 14.25+15.68+15.3] = [15.14, 28.91, 45.23]. This result is the final instance feature vector. In actual large-scale data processing, the server will process thousands of vector representations at the same time. For example, in a health monitoring system, the server will receive near-infrared light feedback signal instances from many users every day, and each instance will generate multiple vector representations after processing.For each user's data, the server strictly follows the above steps to calculate. For example, on a certain day, the server received data from 1,000 users. For each user's vector representation, the server accurately calculates the feature correlation measure, convolution kernel weight, and performs weighted operations. This requires the server to have strong computing power and efficient algorithm implementation to ensure that a large amount of data can be processed in a short time and provide accurate blood sugar status assessment results for each user in a timely manner. In addition, the server will continue to optimize these calculation processes, such as adjusting the parameters of the convolution kernel, selecting a more appropriate activation function, or using technical means such as parallel computing to improve processing speed and accuracy. At the same time, the server will store and analyze the processed data to discover potential health trends and abnormalities. For example, if it is found that users in a certain area generally have a certain vector representation pattern, the server will further analyze it and conduct correlation research with local environmental factors, living habits, etc. to provide support for public health decision-making. In addition, the server will interact and share data with the database of medical institutions. When it is found that the instance feature vectors of some users show abnormal blood sugar status, the server will promptly transmit the relevant information to the medical institution so that medical personnel can intervene in time and provide users with more professional diagnosis and treatment suggestions. Through such continuous optimization and collaborative work, the server can provide better and more accurate services for users' health management.

[0067] In the embodiment of the present invention, the collaborative learning of the blood glucose state vector model and the blood glucose state assessment model based on the first cost parameter and the second cost parameter can be implemented through the following examples.

[0068] Assigning weights to the first cost parameter and the second cost parameter to obtain a target cost parameter;

[0069] According to the target cost parameter, the blood glucose state vector model, the feature extraction unit and the discrimination unit are collaboratively learned.

[0070] In the embodiment of the present invention, for example, the server obtains the first cost parameter of 0.2 and the second cost parameter of 0.3 in the previous calculation. The server pre-sets the weight assigned to the first cost parameter as 0.4 and the weight assigned to the second cost parameter as 0.6. Then, the process of calculating the target cost parameter is as follows: target cost parameter = 0.2*0.4+0.3*0.6=0.08+0.18=0.26. After the server obtains the target cost parameter of 0.26, it starts to collaboratively learn the blood glucose state vector model, the feature extraction unit and the discrimination unit. For the blood glucose state vector model, the server adjusts the parameters of the model according to the target cost parameter. For example, if there are some coefficients related to the blood glucose state mapping in the model, the server will decide whether to increase or decrease these coefficients according to the size and direction of the target cost parameter. For example, there is currently a coefficient representing the correlation strength between the blood glucose level and a certain physiological feature. The server determines by calculating the gradient and other methods that if reducing this coefficient can reduce the target cost parameter, it will be appropriately reduced and adjusted. For the feature extraction unit, the server will adjust parameters such as the size, number or step length of the convolution kernel. For example, if the current convolution kernel cannot effectively extract key features, resulting in a large target cost parameter, the server will try to increase the number of convolution kernels or adjust their size to improve the ability of feature extraction. For the discriminant unit, the server will adjust its internal weights and bias parameters. For example, if the discriminant unit does not accurately discriminate certain blood sugar state classes, resulting in an increase in the target cost parameter, the server will update these parameters according to optimization algorithms such as gradient descent to improve the accuracy of the discriminant unit. For example, in an actual application scenario, the server is processing a large number of users' blood sugar data. For the near-infrared light feedback light signal example of user A, the calculated target cost parameter is high, indicating that the current model has a large deviation in the assessment of user A's blood sugar state. The server first analyzes the blood sugar state vector model and finds that the vector mapping of certain blood sugar ranges may be inaccurate, so it appropriately adjusts the relevant mapping coefficients. For the feature extraction unit, the server finds that some subtle features may be ignored due to the current convolution kernel step size being too large, so it reduces the step size to obtain more refined features. For the discrimination unit, the server analyzes the weight distribution and finds that the weight distribution of certain blood sugar status categories is unreasonable, resulting in discrimination errors, and then readjusts the weight value. The server performs similar processing and analysis on the data of multiple users, and continuously iterates and optimizes the parameters of the model. In this process, the server also considers the individual differences between different users and the data change trends in different time periods. For example, for young users and elderly users, due to differences in physiological characteristics, the server will make different adjustments to the parameters of the model.At the same time, if it is found that the blood sugar status of users generally fluctuates greatly within a certain period of time, the server will further analyze whether it is a problem with the model itself or the influence of external factors, and make corresponding adjustments accordingly. By continuously conducting collaborative learning based on the target cost parameters, the server can gradually improve the accuracy and reliability of the blood sugar status assessment model and provide users with more accurate blood sugar status assessment services.

[0071] In the embodiment of the present invention, the obtaining of the undetermined blood glucose state vector of each undetermined blood glucose state based on the blood glucose state vector model can be implemented through the following examples.

[0072] Based on a pre-configured grouping strategy, performing a grouping operation on the pending blood sugar status to obtain at least one blood sugar status array;

[0073] Importing the blood glucose state array into a blood glucose state vector model to obtain a blood glucose state array vector of the blood glucose state array;

[0074] For any of the pending blood sugar states, a averaging operation is performed on all the blood sugar state array vectors corresponding to the pending blood sugar state to obtain a pending blood sugar state vector.

[0075] In an embodiment of the present invention, for example, the grouping strategy pre-configured in the server is to group according to the range of blood glucose values, for example, to divide the pending blood glucose status into the following groups: hypoglycemia group (<4.0mmol / L), normal blood glucose group (4.0-6.0mmol / L), and hyperglycemia group (>6.0mmol / L). For example, the current pending blood glucose status values ​​are 3.5mmol / L, 4.5mmol / L, 5.5mmol / L, and 7.0mmol / L. The server performs grouping operations according to the grouping strategy and obtains the following blood glucose status arrays: hypoglycemia array [3.5], normal blood glucose array [4.5,5.5], and hyperglycemia array [7.0]. The blood glucose status vector model in the server has been trained and can convert the input blood glucose status array into a corresponding vector representation. For the hypoglycemia array [3.5], after inputting it into the model, a specific vector is obtained, such as [0.1, 0.2, 0.3]. For the normal blood glucose array [4.5, 5.5], the vector output by the model may be [0.4, 0.5, 0.6]. For the high blood sugar array [7.0], the vector given by the model may be [0.7, 0.8, 0.9]. Take the vector [0.4, 0.5, 0.6] corresponding to the normal blood sugar array as an example. The server calculates the average value of the two vector elements: (0.4+0.5+0.6) / 2=0.5. The vector element obtained after averaging is 0.5. For other blood sugar state arrays, the server also performs the same averaging operation. For example, the vector corresponding to the low blood sugar array is 0.2 after averaging, and the vector corresponding to the high blood sugar array is 0.8 after averaging. In this way, the server obtains the low blood sugar state vector [0.2], the normal blood sugar state vector [0.5] and the high blood sugar state vector [0.8]. In actual large-scale data processing, the server will process thousands of pending blood sugar state values ​​at the same time. For example, in a large health monitoring system, a large amount of blood sugar data from many users is collected every day. For each user's pending blood sugar state value, the server strictly follows the above steps to perform grouping, vector conversion and averaging operations. For example, on a certain day, the server received the blood sugar status values ​​of 1,000 users. The server first quickly groups these data according to the grouping strategy to form multiple blood sugar status arrays. Then, these arrays are input into the blood sugar status vector model in turn to obtain the corresponding vectors. Finally, the vectors of each array are averaged to obtain the final vector representation of each pending blood sugar status. In this process, the server needs to efficiently manage computing resources to ensure the accuracy and timeliness of data processing. At the same time, the server will continue to optimize algorithms and models to improve processing speed and accuracy. For example, the server will use parallel computing technology to process data from multiple users at the same time, or update and adjust the model based on historical data to adapt to new data features and changing trends.In addition, the server will also conduct a comprehensive analysis of the processed pending blood sugar state vector and other relevant data (such as the user's age, gender, living habits, etc.) to provide medical personnel with more comprehensive and accurate health assessment information. For example, if it is found that users of a certain age group generally have a certain blood sugar state vector pattern, the server will conduct further in-depth analysis and prompt medical personnel to pay attention to specific health problems of people in this age group and formulate corresponding prevention and treatment measures. Through such continuous processing and optimization, the server can provide users and medical personnel with more valuable blood sugar status analysis services to assist in health management and medical decision-making.

[0076] In the embodiment of the present invention, after taking the blood sugar status assessment result as the user health status assessment result of the current user, the following implementation manner is also provided.

[0077] Acquire basic physiological data of the current user, and fuse the basic physiological data and the blood sugar status assessment result as blood sugar-related physiological data;

[0078] Acquire the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion in a preset blood sugar control suggestion database;

[0079] According to the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion, a blood sugar-related physiological data expansion operation is performed by presetting a first NLP model to determine the expanded blood sugar-related physiological data of the current user and the expanded blood sugar-related physiological data of each preset blood sugar control suggestion;

[0080] Perform feature conversion processing according to the extended blood sugar-associated physiological data of the current user and the extended blood sugar-associated physiological data of each preset blood sugar control suggestion, and determine a vector matching the extended blood sugar-associated physiological data of the current user and a vector matching the extended blood sugar-associated physiological data of each preset blood sugar control suggestion;

[0081] Determining the degree of matching of the current user with respect to each preset blood sugar control suggestion through matching degree calculation according to the matching vector of the expanded blood sugar associated physiological data of the current user and the matching vector of the expanded blood sugar associated physiological data of each preset blood sugar control suggestion;

[0082] According to each matching degree, a target blood sugar control suggestion is determined to be displayed to the current user, and the preset blood sugar control suggestion database includes the target blood sugar control suggestion.

[0083] In an embodiment of the present invention, for example, the current user is Xiao Li, and the server obtains Xiao Li's basic physiological data, including age 30, weight 70 kg, height 175 cm, daily exercise volume is moderate, and the diet structure is mainly carbohydrates, and the blood sugar status assessment result just obtained is "high blood sugar". The server merges these basic physiological data with the blood sugar status assessment result to form blood sugar-related physiological data. For example, this information is organized into a structured data record: [age: 30, weight: 70 kg, height: 175 cm, exercise volume: moderate, diet structure: mainly carbohydrates, blood sugar status: high]. The blood sugar control suggestion database preset in the server contains blood sugar control suggestions and corresponding blood sugar-related physiological data in various situations. For example, one of the preset blood sugar control suggestions is for people aged 25-35, overweight, high blood sugar and insufficient exercise, and their blood sugar-related physiological data is [age: 28-32, weight: >75 kg, exercise volume: low, blood sugar status: high]. The server also obtains Xiao Li's blood sugar-related physiological data [age: 30, weight: 70 kg, height: 175 cm, amount of exercise: moderate, diet structure: mainly carbohydrates, blood sugar status: high], as well as multiple similar blood sugar-related physiological data of preset blood sugar control suggestions in the database. The server inputs Xiao Li's blood sugar-related physiological data and the blood sugar-related physiological data of each preset blood sugar control suggestion into the preset first NLP model. For example, the first NLP model will expand his possible carbohydrate intake, digestion and absorption and other related data based on the information that Xiao Li's diet structure is mainly carbohydrates. For the overweight information in the preset blood sugar control suggestion, specific eating habits, lifestyles and other factors that may cause overweight will be expanded. After the expansion operation, Xiao Li's expanded blood sugar-related physiological data is determined, such as [age: 30, weight: 70 kg, height: 175 cm, amount of exercise: moderate, diet structure: mainly carbohydrates, carbohydrate intake: high, digestion and absorption: general, blood sugar status: high]. At the same time, the extended blood sugar-related physiological data of each preset blood sugar control suggestion is also determined. For example, the extended data of a certain suggestion is [age: 28-32, weight: >75 kg, amount of exercise: low, proportion of high-calorie food in the diet: high, blood sugar status: high]. The server performs feature conversion processing on Xiao Li's extended blood sugar-related physiological data and the extended blood sugar-related physiological data of each preset blood sugar control suggestion. For example, a certain feature extraction algorithm is used to convert Xiao Li's extended blood sugar-related physiological data into a vector form, such as [0.5, 0.3, 0.2, 0.1, 0.4, 0.6, 0.7]. Similar conversion is also performed on the extended blood sugar-related physiological data of each preset blood sugar control suggestion to obtain the corresponding vector. The server uses a specific algorithm to calculate the matching degree between Xiao Li's vector and each preset blood sugar control suggestion vector.For example, the degree of match is determined by calculating indicators such as cosine similarity or Euclidean distance between vectors. For example, the degree of match with a preset blood sugar control suggestion is 0.8, and the degree of match with another is 0.6. The server sorts according to the calculated degree of match. If the matching degree threshold is set to 0.7, the control suggestion with a matching degree higher than 0.7 will be selected as the target blood sugar control suggestion. For example, according to the sorting and threshold, the target blood sugar control suggestion displayed to Xiao Li is finally determined to be "increase exercise to at least 150 minutes of moderate intensity exercise per week, adjust the diet structure, reduce carbohydrate intake, and increase the proportion of vegetables and protein". In actual applications, the server may need to process a large number of user requests at the same time. For example, on a health management platform, thousands of users upload their physiological data and blood sugar assessment results every day. For each user, the server repeats the above steps to quickly and accurately determine personalized blood sugar control suggestions for them. At the same time, the server will continuously update and optimize the preset blood sugar control suggestion database, and improve the NLP model and matching algorithm to improve the accuracy and pertinence of the suggestions. In addition, the server will also consider user feedback information. If the user's blood sugar status improves after following the suggestions, the server will incorporate these success stories into the model's training data to further optimize the model. Conversely, if the user's feedback suggests that the suggestions are not applicable, the server will analyze the reasons and adjust the relevant parameters and algorithms to provide management and control suggestions that are more in line with the user's actual situation. Through continuous optimization and improvement, the server can provide users with better and more effective blood sugar management services, helping users better control blood sugar and stay healthy.

[0084] In an embodiment of the present invention, based on the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion, a blood sugar-related physiological data expansion operation is performed by presetting a first NLP model to determine the expanded blood sugar-related physiological data of the current user and the expanded blood sugar-related physiological data of each preset blood sugar control suggestion. This can be implemented through the following examples.

[0085] According to the blood sugar-related physiological data of the current user and the preset first guiding parameter, a blood sugar-related physiological data expansion operation is performed by a preset first NLP model to determine the expanded blood sugar-related physiological data of the current user;

[0086] According to the blood sugar-associated physiological data of each preset blood sugar control suggestion and the preset second guiding parameter, a blood sugar-associated physiological data expansion operation is performed through the first NLP model to determine the expanded blood sugar-associated physiological data of each preset blood sugar control suggestion.

[0087] In an embodiment of the present invention, for example, the current user is Xiao Zhang, and the server obtains Xiao Zhang's blood sugar-related physiological data, including age 25, weight 60 kg, height 165 cm, less daily exercise, high recent blood sugar test results, and dietary preference for sweets. The preset first guide parameter may include some common physiological factors related to blood sugar and the expansion direction of living habits. For example, the first guide parameter may prompt to consider the potential impact of factors such as family history, sleep quality, and psychological pressure on blood sugar. The server inputs Xiao Zhang's blood sugar-related physiological data and the first guide parameter into the preset first NLP model. The first NLP model first analyzes and understands the input data. For example, it will identify that Xiao Zhang's low exercise and dietary preference for sweets may be factors that lead to high blood sugar. Then, the model starts to perform expansion operations based on the prompts in the first guide parameter. The model will query the relevant medical knowledge base and big data analysis results to obtain more information related to Xiao Zhang's situation. For example, the model found that among people aged 20-30 with similar living habits, the proportion of those with a family history of diabetes is relatively high, and poor sleep quality and high psychological stress may also have adverse effects on blood sugar. Through these extensions, it is determined that Xiao Zhang's extended blood sugar-related physiological data may include: [age: 25 years old, weight: 60 kg, height: 165 cm, amount of exercise: little, blood sugar test results: high, dietary preference: sweets, family history: family history of diabetes, sleep quality: poor, psychological stress: high]. The preset blood sugar control suggestion database in the server contains a variety of different control suggestions, and each suggestion has corresponding blood sugar-related physiological data. For example, one of the suggestions is for overweight hyperglycemic patients, and their blood sugar-related physiological data is [weight: >70 kg, blood sugar level: high, exercise habits: irregular]. The preset second guiding parameter may include a specific extension direction for the control suggestion, such as considering the impact of factors such as the patient's work nature and daily work and rest time on the blood sugar control effect. The server inputs the blood sugar-related physiological data and the second guiding parameter of this preset blood sugar control suggestion into the first NLP model. The model will analyze the current blood sugar-related physiological data to understand the relationship between overweight, high blood sugar, and irregular exercise habits. Then, it will be expanded according to the prompt of the second guiding parameter. For example, the model may find that people who work in sedentary jobs and have irregular work and rest schedules may need additional precautions when implementing this control recommendation. After expansion, the extended blood sugar-related physiological data of this preset blood sugar control recommendation may become: [Weight: >70 kg, blood sugar level: high, exercise habits: irregular, nature of work: sedentary, work and rest time: irregular]. For other preset blood sugar control recommendations in the database, the server will also process them in the same way to obtain their respective extended blood sugar-related physiological data. In actual application scenarios, the server may need to process a large number of similar requests at the same time.For example, on an online health consultation platform, many users upload their blood sugar-related physiological data every day, and the server needs to quickly and accurately perform expansion operations for each user and each preset management suggestion. In order to improve processing efficiency and accuracy, the server will adopt a distributed computing architecture and optimized algorithms. At the same time, the server will continue to update and improve the first guiding parameter and the second guiding parameter to adapt to the ever-changing medical research results and user needs. In addition, the server will also perform quality assessment and verification on the expanded blood sugar-related physiological data. For example, compare with authoritative medical databases to ensure that the expanded data is reasonable and scientific. Through such fine processing and continuous optimization, the server can provide users with more comprehensive and personalized blood sugar control suggestions to help users better manage blood sugar and maintain health.

[0088] In an embodiment of the present invention, based on the blood sugar-related physiological data of the current user and the preset first guiding parameter, a blood sugar-related physiological data expansion operation is performed through a preset first NLP model to determine the expanded blood sugar-related physiological data of the current user, which can be implemented through the following examples.

[0089] Writing the blood sugar-related physiological data of the current user into the preset first guiding parameter to obtain the optimized first guiding parameter;

[0090] Importing the tuned first guiding parameter into a preset first NLP model, and determining the extended blood sugar-related physiological data of the current user through context-aware learning;

[0091] The preset first guidance parameter includes the user's blood glucose management tendency, the user's blood glucose monitoring history and the first blood glucose management request for the first NLP model, the first blood glucose management request includes blood glucose management requirement details, and the blood glucose management requirement details are used to indicate that the first NLP model outputs the extended blood glucose-related physiological data of the current user that matches the blood glucose management requirement details.

[0092] In the embodiment of the present invention, for example, the current user is Xiao Wang, and the server obtains Xiao Wang's blood sugar-related physiological data, including age 40, weight 80 kg, height 180 cm, mild fatty liver, large recent blood sugar fluctuations, unbalanced daily diet, etc. The preset first guide parameter originally contains some general information, such as common blood sugar management tendencies (such as tending to manage blood sugar through diet control, exercise or drug treatment), blood sugar monitoring history patterns of different user groups, and the basic framework of the first blood sugar management request for the first NLP model. The server writes Xiao Wang's specific blood sugar-related physiological data into the preset first guide parameter. For example, in the blood sugar management tendency part, it is supplemented that Xiao Wang is more inclined to control blood sugar through diet and exercise; in the blood sugar monitoring history, the specific blood sugar measurement values ​​of Xiao Wang in the past month are recorded in detail; in the blood sugar management requirement details of the first blood sugar management request, it is clearly pointed out that it is necessary to obtain more in-depth blood sugar-related information related to Xiao Wang's current physical condition and living habits. After writing and supplementing, the first guide parameter after tuning is obtained to make it more suitable for Xiao Wang's individual situation. The server imports the tuned first guidance parameter into the preset first NLP model. The first NLP model understands and analyzes the tuned guidance parameter through context-aware learning. The model will first identify Xiao Wang's basic situation, such as age, weight, blood sugar fluctuations and other key information. Then, combined with Xiao Wang's blood sugar management tendency and monitoring history, the model can perceive Xiao Wang's specific situation. For example, the model finds that although Xiao Wang tends to control blood sugar through diet and exercise, he has limited exercise time due to work reasons, and his unbalanced diet is mainly reflected in insufficient vegetable intake. Based on this contextual awareness, the model performs extended analysis. It queries large-scale medical databases and research reports to obtain relevant information similar to Xiao Wang's situation. Through in-depth learning and analysis, Xiao Wang's extended blood sugar-related physiological data is determined. For example, it is found that Xiao Wang's fatty liver may affect insulin sensitivity, high work pressure may cause hormone imbalance and thus affect blood sugar regulation, and insufficient vegetable intake may lead to a lack of certain vitamins and minerals, thereby indirectly affecting blood sugar metabolism. The final extended blood sugar-related physiological data may be: [age: 40 years old, weight: 80 kg, height: 180 cm, mild fatty liver, recent blood sugar fluctuations, unbalanced diet (low vegetable intake), limited exercise time, high work pressure, possible reduced insulin sensitivity, high possibility of hormone imbalance, and lack of certain vitamins and minerals]. In an actual large-scale health management system, the server may handle many user requests like Xiao Wang at the same time. For each user, the server will accurately perform the above steps. For example, in an enterprise's employee health management project, the server receives blood sugar-related physiological data from hundreds of employees in a day. For each employee, the server carefully writes their data into the preset first guidance parameter, performs tuning, and imports it into the first NLP model for extended analysis.To ensure efficient processing and accuracy, the server can use advanced parallel computing technology and optimized algorithms. At the same time, the server will continue to update and improve the first NLP model to adapt to new medical research results and changes in user needs. In addition, the server will also strictly control and verify the expanded blood sugar-related physiological data. For example, in cooperation with professional medical institutions, some data are manually reviewed and verified to ensure the reliability and validity of the data. Through such refined processing and continuous optimization, the server can provide users with a more comprehensive, personalized and accurate expansion of blood sugar-related physiological data, providing a solid foundation for subsequent health management and recommendations.

[0093] In an embodiment of the present invention, based on the blood sugar-related physiological data of each preset blood sugar control suggestion and the preset second guiding parameter, the blood sugar-related physiological data expansion operation is performed through the first NLP model to determine the expanded blood sugar-related physiological data of each preset blood sugar control suggestion, which can be implemented through the following examples.

[0094] Writing the blood sugar-related physiological data of each preset blood sugar control suggestion into the preset second guiding parameter to obtain the adjusted second guiding parameter;

[0095] Importing the tuned second guiding parameter into the first NLP model, and determining the extended blood sugar-related physiological data of each preset blood sugar control suggestion through context-aware learning;

[0096] The preset second guiding parameter includes a second blood glucose management request for the first NLP model, and the second blood glucose management request includes blood glucose management requirement details, and the blood glucose management requirement details are used to represent the extended blood glucose-related physiological data of each preset blood glucose control suggestion that matches the blood glucose management requirement details output by the first NLP model.

[0097] In an embodiment of the present invention, illustratively, for example, the server has the following preset blood sugar control suggestions and their corresponding blood sugar-related physiological data: Suggestion 1: For prediabetic patients with overweight, the blood sugar-related physiological data is [weight: >80 kg, fasting blood sugar: 6.1-6.9 mmol / L, 2-hour postprandial blood sugar: 7.8-11.0 mmol / L, high-calorie diet]. Suggestion 2: For elderly diabetic patients, the blood sugar-related physiological data is [age: >65 years old, large blood sugar fluctuations, combined with cardiovascular disease, and weak exercise ability]. The preset second guidance parameter contains some basic frameworks and information. The server writes the blood sugar-related physiological data of each preset blood sugar control suggestion in detail into the preset second guidance parameter. For suggestion one, the specific weight value (such as 90 kg), detailed diet composition (such as excessive intake of high-sugar and high-fat foods), and the patient's living habits (such as sitting for a long time) and other information are supplemented in the second guidance parameter to obtain the adjusted second guidance parameter. For suggestion 2, specific cardiovascular disease types (such as coronary heart disease), the amplitude and frequency of blood sugar fluctuations, the type and duration of daily exercise (such as walking, 15 minutes each time), etc. are added to form the second guidance parameter after tuning for this suggestion. The server imports the tuned second guidance parameters into the first NLP model one by one. The first NLP model processes these tuned guidance parameters through context-aware learning. For suggestion 1, the model first understands key information such as overweight, blood sugar in prediabetes, and bad diet and lifestyle habits. Then, based on context awareness, the model will query relevant medical research and case data and find that if overweight prediabetic patients also have sleep apnea problems, the risk of diabetes will be further increased. At the same time, long-term psychological stress may also affect blood sugar control. Therefore, the determined extended blood sugar-related physiological data may be: [weight: 90 kg, fasting blood sugar: 6.5mmol / L, blood sugar 2 hours after meal: 9.0mmol / L, high-calorie diet (high sugar, high fat), sedentary, high risk of sleep apnea, and high psychological pressure]. For suggestion two, the model understands the basic conditions of elderly patients, including blood sugar fluctuations, cardiovascular diseases, and poor exercise capacity. Through situational perception and learning, the model may find that if such patients also have impaired renal function, they need to pay special attention to the dosage of certain hypoglycemic drugs. Moreover, fewer social activities may have an impact on mental health, which in turn indirectly affects blood sugar control. Therefore, the determined expanded blood sugar-related physiological data may be: [Age: 70 years old, large blood sugar fluctuations (specific amplitude and frequency), concomitant coronary heart disease, poor exercise capacity (walking, 15 minutes each time), impaired renal function, few social activities, and affected mental health] In a large online medical service platform, the server has to process a large number of such preset blood sugar control suggestions every day.For example, on a busy working day, the server receives dozens of preset blood sugar control suggestions from different medical institutions and health management agencies. For each suggestion, the server strictly follows the above steps to process. The server first writes detailed blood sugar-related physiological data into the preset second guidance parameters to ensure the integrity and accuracy of the information. Then, the tuned guidance parameters are quickly and accurately imported into the first NLP model for analysis and expansion. In order to improve processing efficiency, the server can use distributed computing and caching technology to reduce data transmission and calculation time. At the same time, the server will continue to monitor the performance and accuracy of the model and adjust and optimize the model according to the actual effect. In addition, the server will work closely with medical institutions and experts to timely update and improve the preset blood sugar control suggestions and related guidance parameters to ensure that the information provided to users is always the latest and most effective. Through such continuous and efficient processing, the server can provide users and medical professionals with more comprehensive and in-depth blood sugar control suggestions and expanded blood sugar-related physiological data, promote the prevention and management of diabetes, and improve the health level of the public.

[0098] In an embodiment of the present invention, feature conversion processing is performed based on the extended blood sugar-associated physiological data of the current user and the extended blood sugar-associated physiological data of each preset blood sugar control suggestion to determine a vector that matches the extended blood sugar-associated physiological data of the current user and a vector that matches the extended blood sugar-associated physiological data of each preset blood sugar control suggestion. This can be implemented through the following examples.

[0099] Determine, through pre-processing, a basic vector array of the current user and a basic vector array of each preset blood sugar control suggestion according to the extended blood sugar associated physiological data of the current user and the extended blood sugar associated physiological data of each preset blood sugar control suggestion;

[0100] According to the basic vector array of the current user and the basic vector array of each preset blood sugar control suggestion, feature conversion processing is performed by presetting a second NLP model to determine the vector matching the extended blood sugar associated physiological data of the current user and the vector matching the extended blood sugar associated physiological data of each preset blood sugar control suggestion.

[0101] In an embodiment of the present invention, for example, the current user is Xiao Zhao, and his extended blood sugar-related physiological data include: age 35 years old, weight 75 kg, family history of diabetes, recent work pressure leading to insufficient sleep, irregular diet and preference for high-sugar foods, less daily exercise, and the most recent blood sugar test value is 7.0mmol / L. The extended blood sugar-related physiological data of each preset blood sugar control suggestion stored in the server, such as suggestion one: suitable for people aged 30-40 years old and overweight, who need to control their diet and increase their exercise, and the data is [age: 32-38 years old, weight: >70 kg, unbalanced diet, lack of exercise]; suggestion two: for people with a family history of diabetes and high blood sugar, they need to adjust their work and rest and conduct regular blood sugar monitoring, and the data is [family history of diabetes, blood sugar: 6.5-7.5mm ol / L, irregular work and rest]. During the pre-processing process, the server will clean and standardize these data. For example, numerical data such as age and weight are normalized, and text description information (such as irregular diet, lack of exercise, etc.) is converted into corresponding codes. For Xiao Zhao's extended blood sugar-related physiological data, after processing, a basic vector array may be obtained, such as [0.7, 1.5, 1, 0, 0.2, 0.1, 0.8]. For the extended blood sugar-related physiological data of suggestion one, the basic vector array obtained after processing may be [0.6, 1.2, 1, 1, 0]. For the extended blood sugar-related physiological data of suggestion two, the basic vector array may be [1, 0.7, 0]. The server inputs Xiao Zhao's basic vector array and the basic vector arrays of each preset blood sugar control suggestion into the preset second NLP model. After receiving these inputs, the second NLP model first performs a deep analysis of these vectors to extract key features. For example, for Xiao Zhao's vector, the model will focus on the combination of features such as weight, blood sugar value and exercise status. Then, the model performs feature conversion processing. This involves using complex neural network layers and activation functions to convert the original basic vector array into a more representative and discriminative vector. For Xiao Zhao, after the conversion process, the vector matching the extended blood sugar-related physiological data obtained may be [0.8, 0.3, 0.6, 0.1, 0.7]. For suggestion one, the vector of the converted extended blood sugar-related physiological data match may be [0.5, 0.8, 0.2, 0.4, 0.6]. For suggestion two, the possible matching vector is [0.9, 0.4, 0.1]. In a large health management system, the server processes a large amount of user data like Xiao Zhao every day. For example, on a certain day, the server received extended blood sugar-related physiological data of hundreds of users and extended data of multiple preset blood sugar control suggestions at the same time. For each user, the server converts the complex and diverse physiological data and control suggestion data into a clear and standardized basic vector array according to the pre-processing steps.Then, through the efficient operation of the second NLP model, these basic vector arrays are accurately transformed to generate corresponding matching vectors. In order to ensure the accuracy and efficiency of the processing, the server will continuously optimize the algorithm of the pre-processing and the structure and parameters of the second NLP model. At the same time, the server will also use a large amount of real data to train and verify the model to improve the adaptability and generalization ability of the model to different user situations. In addition, the server will also cooperate with medical institutions and experts to update and improve the preset blood sugar control recommendations based on the latest medical research results and clinical experience, so as to provide users with more scientific and personalized blood sugar management solutions. Through such a rigorous and sophisticated processing flow, the server can provide users with more accurate and effective blood sugar management recommendations to help users better control blood sugar and improve their health.

[0102] In an embodiment of the present invention, the basic vector array of the current user and the basic vector array of the preset blood sugar control suggestions are determined through pre-processing based on the extended blood sugar-associated physiological data of the current user and the extended blood sugar-associated physiological data of the preset blood sugar control suggestions, which can be implemented through the following examples.

[0103] According to the extended blood sugar-associated physiological data of the current user and the preset blood sugar-associated physiological data unit set, determine the blood sugar-associated physiological data units that match each character in the extended blood sugar-associated physiological data of the current user, and according to the blood sugar-associated physiological data units that match each character in the extended blood sugar-associated physiological data of the current user, determine the basic vector array of the current user, the blood sugar-associated physiological data units in the blood sugar-associated physiological data unit set being represented by One-Hot Encoding;

[0104] According to the extended blood sugar-associated physiological data and the preset blood sugar-associated physiological data unit set of each preset blood sugar control suggestion, the blood sugar-associated physiological data unit that matches each character in the extended blood sugar-associated physiological data of each preset blood sugar control suggestion is determined; and according to the blood sugar-associated physiological data unit that matches each character in the extended blood sugar-associated physiological data of each preset blood sugar control suggestion, the basic vector array of each preset blood sugar control suggestion is determined.

[0105] In an embodiment of the present invention, for example, the current user is Xiao Qian, and his extended blood sugar-related physiological data is: "Age 32 years old, weight 68 kg, no family history of diabetes, sufficient sleep but less exercise recently, relatively balanced diet, fasting blood sugar 5.8mmol / L". The set of blood sugar-related physiological data units preset in the server includes various possible description units, such as "age [specific value]", "weight [specific value]", "family history of diabetes", "no family history of diabetes", "sufficient sleep", "inadequate sleep", "more exercise", "less exercise", "balanced diet", "unbalanced diet", "fasting blood sugar [specific value]", etc. Each data unit is represented by One-Hot Encoding (one-bit effective encoding). The server first analyzes Xiao Qian's extended blood sugar-related physiological data. For example, "age 32 years old" matches "age [specific value]" in the data unit set, "weight 68 kg" matches "weight [specific value]", "no family history of diabetes" matches the corresponding data unit, "less exercise" also matches the corresponding unit, "relatively balanced diet" matches "balanced diet", and "fasting blood sugar 5.8 mmol / L" matches "fasting blood sugar [specific value]". Then, based on the matched data units, determine Xiao Qian's basic vector array. For example, the One-Hot Encoding of "age [specific value]" is [1,0,0,0,0,0,0,0,0], "weight [specific value]" is [0,1,0,0,0,0,0,0,0], "no family history of diabetes" is [0,0,1,0,0,0,0,0,0,0], and so on. Then Xiao Qian's basic vector array may be: [1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]. For example, one of the extended blood sugar-related physiological data of the preset blood sugar control suggestion is: "Suitable for people aged 25-35 years old, weighing 60-70 kg, with a habit of light exercise, occasional unbalanced diet, and fasting blood sugar of 5.5-6.0 mmol / L". The server matches the extended blood sugar-related physiological data of the preset control suggestion with the preset blood sugar-related physiological data unit set in the same way. For example, "age 25-35 years old" matches "age [range value]", "weight 60-70 kg" matches "weight [range value]", "mild exercise habits" matches "exercise [mild]", "occasionally unbalanced diet" matches "unbalanced diet", and "fasting blood sugar 5.5-6.0 mmol / L" matches "fasting blood sugar [range value]". According to the One-Hot Encoding of the matched data unit, the basic vector array of the preset blood sugar control recommendation is determined.For example, the One-Hot Encoding of "age [range value]" is [0,0,0,1,0,0,0,0,0], "weight [range value]" is [0,0,0,0,1,0,0,0,0], "exercise [light]" is [0,0,0,0,0,1,0,0,0], and so on. Then the basic vector array of the preset blood sugar control suggestion may be: [0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]. In a large health management system, the server needs to process a large amount of user and preset blood sugar control suggestion data every day. For example, on a certain day, the server received the extended blood sugar-related physiological data of thousands of users and the extended data of dozens of preset blood sugar control suggestions. For each user and each preset suggestion, the server strictly follows the matching and encoding steps above. In order to improve processing efficiency, the server can use parallel computing and distributed storage technology to process and store multiple data at the same time. In addition, the server will continuously update and optimize the preset blood sugar-related physiological data unit set to adapt to new medical research results and changes in clinical practice. In an embodiment of the present invention, the basic vector array of the current user and the basic vector array of each preset blood sugar control suggestion are used to perform feature conversion processing through a preset second NLP model to determine the matching vector of the current user's extended blood sugar-related physiological data and the matching vector of the extended blood sugar-related physiological data of each preset blood sugar control suggestion, which can be implemented through the following examples.

[0106] Importing the basis vector array of the current user into a preset second NLP model, and determining a plurality of feature mapping vectors matching the basis vector array of the current user through feature mapping processing;

[0107] According to the multiple feature mapping vectors matched by the basic vector array of the current user, determining the vector matched by the extended blood glucose-related physiological data of the current user through feature merging processing;

[0108] Importing the basic vector arrays of the preset blood sugar control suggestions into the second NLP model, and determining a plurality of feature mapping vectors matching the basic vector arrays of the preset blood sugar control suggestions through feature mapping processing;

[0109] According to the multiple feature mapping vectors matched by the basic vector arrays of the preset blood sugar control suggestions, the vectors matched by the extended blood sugar associated physiological data of the preset blood sugar control suggestions are determined through feature merging processing.

[0110] In an embodiment of the present invention, illustratively, for example, the basic vector array of the current user Xiao Sun is [0.2, 0.5, 0.3, 0.1, 0.4, 0.6, 0.7, 0.8, 0.9]. The server imports this basic vector array into the preset second NLP model. After receiving this input, the feature mapping layer in the second NLP model starts feature mapping processing. For example, a layer of neural network in the model will multiply this basic vector with a set of preset weight matrices to obtain multiple intermediate results. For example, after this layer of calculation, three intermediate result vectors are obtained, namely [0.15, 0.25, 0.35], [0.45, 0.55, 0.65] and [0.75, 0.85, 0.95]. These intermediate result vectors are the feature mapping vectors matched by Xiao Sun's basic vector array. For the above three feature mapping vectors of Xiao Sun, the server performs feature merging processing. One possible processing method is to sum or average these three vectors. For example, the three feature mapping vectors mentioned above are added together to obtain [1.35, 1.65, 1.95] by summing up. This result vector is the vector that matches Xiao Sun's extended blood sugar-related physiological data. For example, there are two preset blood sugar control suggestions, and their basic vector arrays are: Suggestion 1: [0.3, 0.4, 0.2, 0.1, 0.5, 0.6, 0.8, 0.7, 0.9]. Suggestion 2: [0.1, 0.2, 0.7, 0.6, 0.3, 0.4, 0.5, 0.9, 0.8]. The server imports these two basic vector arrays into the second NLP model respectively. For the basic vector array of suggestion one, after feature mapping processing, three feature mapping vectors such as [0.2, 0.3, 0.4], [0.5, 0.6, 0.7] and [0.8, 0.9, 1.0] are obtained. For the basic vector array of suggestion 2, feature mapping processing results in three feature mapping vectors: [0.15, 0.25, 0.35], [0.45, 0.55, 0.65], and [0.75, 0.85, 0.95]. For the three feature mapping vectors of suggestion 1, the summation results in [1.5, 1.8, 2.1], which is the vector matching the extended blood glucose-related physiological data of suggestion 1. For the three feature mapping vectors of suggestion 2, the summation results in [1.35, 1.65, 1.95], which is the vector matching the extended blood glucose-related physiological data of suggestion 2. In a widely used health management platform, the server processes a large amount of similar user and preset blood glucose control suggestions every day. For example, in one day, the server receives basic vector arrays of hundreds of users from different regions and basic vector arrays of dozens of preset blood glucose control suggestions. For each user and each preset suggestion, the server strictly follows the above feature mapping and merging steps for processing. To ensure the efficiency and accuracy of processing, the server uses advanced hardware acceleration technology and optimized algorithms.At the same time, the server will continue to monitor and evaluate the performance of the second NLP model, and adjust and improve the model according to the actual processing results and user feedback. In addition, the server will work with medical institutions and experts to interpret and verify the clinical significance of the generated vectors matching the extended blood sugar-related physiological data. If it is found that certain processing results are inconsistent with medical common sense or there are deviations, the server will promptly investigate the reasons, which may be data anomalies, model parameter setting problems or algorithm defects, and take corresponding measures to correct and optimize. Through such continuous optimization and improvement, the server can provide users with more accurate and targeted blood sugar control suggestions to help users better manage their health.

[0111] In an embodiment of the present invention, the matching degree of the current user for each preset blood sugar control suggestion is determined by matching degree calculation based on the vector matching the extended blood sugar associated physiological data of the current user and the vector matching the extended blood sugar associated physiological data of each preset blood sugar control suggestion, which can be implemented through the following examples.

[0112] The vector matching the extended blood sugar-associated physiological data of the current user and the vector matching the extended blood sugar-associated physiological data of each preset blood sugar control suggestion are imported into a preset matching model, and the matching degree of the current user for each preset blood sugar control suggestion is determined through matching degree calculation. The matching degree for each preset blood sugar control suggestion is characterized by a matching indicator set, and the matching indicator set includes multiple matching indicators.

[0113] In an embodiment of the present invention, illustratively, for example, the current user is Xiao Li, and the server has determined the vector of Xiao Li's extended blood sugar-related physiological data matching and the vector of the extended blood sugar-related physiological data matching of multiple preset blood sugar control suggestions. The vector of Xiao Li's extended blood sugar-related physiological data matching is: [0.3, 0.5, 0.2, 0.1, 0.4]. The vector of the extended blood sugar-related physiological data matching of the preset blood sugar control suggestion one is: [0.2, 0.4, 0.3, 0.1, 0.5]. The vector of the extended blood sugar-related physiological data matching of the preset blood sugar control suggestion two is: [0.4, 0.3, 0.1, 0.2, 0.5]. The server imports these vectors into the preset matching model. This preset matching model contains a series of algorithms and parameters for calculating the degree of matching. First, the model compares and calculates Xiao Li's vector and the vector of the preset suggestion one element by element. For example, it calculates multiple matching indicators such as the Euclidean distance and cosine similarity between the two vectors. For example, through calculation, the Euclidean distance is 0.2 and the cosine similarity is 0.8. These indicators will be combined into a matching indicator set. For preset suggestion 2, the same calculation and indicator generation are performed. For example, the Euclidean distance is 0.3 and the cosine similarity is 0.7. Then, the matching degree of Xiao Li for each preset blood sugar control suggestion is determined based on these matching indicator sets. For example, if the server sets the evaluation criteria for the matching degree as follows: the Euclidean distance is less than 0.2 and the cosine similarity is greater than 0.7 for high matching, the Euclidean distance is between 0.2-0.3 and the cosine similarity is between 0.6-0.7 for moderate matching, and the other cases are low matching. Then for preset suggestion 1, since the Euclidean distance is 0.2 and the cosine similarity is 0.8, which meets the standard of high matching, it is determined that Xiao Li's matching degree for preset suggestion 1 is high matching. For preset suggestion 2, the Euclidean distance is 0.3 and the cosine similarity is 0.7, which meets the standard of moderate matching, so it is determined that Xiao Li's matching degree for preset suggestion 2 is moderate matching. In a large online health management system, the server processes a large amount of user data like Xiao Li every day. For example, in one day, the server received the matching vectors of the extended blood sugar-related physiological data of 1,000 users and the matching vectors of the extended blood sugar-related physiological data of 50 preset blood sugar control suggestions. For each user and each preset suggestion, the server will perform the matching calculation according to the above steps. In order to improve computing efficiency, the server will adopt a distributed computing architecture to distribute computing tasks to multiple computing nodes at the same time. At the same time, the server will continue to optimize the algorithms and parameters of the preset matching model to improve the accuracy and reliability of the matching calculation. For example, the model is trained and adjusted based on a large amount of historical data, or new matching indicators and calculation methods are introduced. In addition, the server will also monitor and verify the quality of the calculation results.For example, a portion of the calculation results are randomly selected and manually evaluated and compared by professional medical personnel to ensure that the results of the matching calculation are consistent with medical common sense and actual conditions. If deviations or errors are found in certain calculation results, the server will promptly trace back to find the cause, which may be data input errors, model failures, or calculation logic problems, and make corresponding corrections and improvements. Through such a rigorous and efficient processing process, the server can accurately determine the blood sugar control recommendations that best match their physiological data for users, thereby providing users with more personalized and effective health management services.

[0114] In the embodiment of the present invention, determining the target blood sugar control suggestion displayed to the current user according to each matching degree can be implemented through the following example.

[0115] Determining the matching scores of the matching degrees according to the matching indicator sets of the matching degrees in the matching degrees and the importance factors of the multiple matching indicators in the matching indicator sets of the matching degrees;

[0116] A preset number of preset blood sugar control suggestions determined from high to low according to the matching scores are determined as target blood sugar control suggestions displayed to the current user.

[0117] In an embodiment of the present invention, for example, the current user is Xiao Zhang, and the server has determined the degree of matching of Xiao Zhang for multiple preset blood sugar control suggestions, and each degree of matching has a corresponding matching indicator set. For example, there are three preset blood sugar control suggestions: suggestion A, suggestion B, and suggestion C. The matching indicator set corresponding to the degree of matching between Xiao Zhang and suggestion A includes a Euclidean distance of 0.15, a cosine similarity of 0.85, and an importance factor of the Euclidean distance of 0.4 and an importance factor of the cosine similarity of 0.6. First, the server determines the matching score based on the matching indicator set of each matching degree and the importance factors of multiple matching indicators. For suggestion A, the matching score is calculated as follows: the score of the Euclidean distance = 0.15 × 0.4 = 0.06. The score of the cosine similarity = 0.85 × 0.6 = 0.51. The matching score of suggestion A = 0.06 + 0.51 = 0.57. For example, the matching metric set between Xiao Zhang and suggestion B includes a Euclidean distance of 0.2, a cosine similarity of 0.7, an importance factor of Euclidean distance of 0.4, and an importance factor of cosine similarity of 0.6. For suggestion B, the matching score is calculated as follows: the score of Euclidean distance = 0.2 × 0.4 = 0.08. The score of cosine similarity = 0.7 × 0.6 = 0.42. The matching score of suggestion B = 0.08 + 0.42 = 0.5. For example, the matching metric set between Xiao Zhang and suggestion C includes a Euclidean distance of 0.1, a cosine similarity of 0.9, an importance factor of Euclidean distance of 0.4, and an importance factor of cosine similarity of 0.6. For suggestion C, the matching score is calculated as follows: the score of Euclidean distance = 0.1 × 0.4 = 0.04. The score of cosine similarity = 0.9 × 0.6 = 0.54. The matching score of suggestion C = 0.04 + 0.54 = 0.58. Next, the server will sort these suggestions from high to low according to the matching scores. The sorting result is: Suggestion C (0.58) > Suggestion A (0.57) > Suggestion B (0.5). For example, the preset number is 2, and the server will determine that Suggestion C and Suggestion A are the target blood sugar control suggestions displayed to Xiao Zhang. In a large health management platform, the server has to process a large amount of user data like Xiao Zhang every day. For example, in one day, the server will process the matching degree calculation and determination of target blood sugar control suggestions for 500 users. For each user, the server will perform precise calculations and sorting according to the above steps. In order to ensure the accuracy and efficiency of the calculation, the server will use optimized algorithms and data structures. At the same time, the server will continuously adjust the importance factor of the matching indicator based on user feedback and actual results to improve the rationality and effectiveness of the target blood sugar control suggestions. In addition, the server will also consider other factors, such as the user's health history, living habits, medication, etc., to further screen and optimize the target blood sugar control suggestions.

[0118] In an embodiment of the present invention, before obtaining the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion in a preset blood sugar control suggestion database, the following implementation manner is also provided.

[0119] Obtaining a vector matching the extended blood sugar-associated physiological data of the training user and a vector matching the extended blood sugar-associated physiological data of each training blood sugar control suggestion in the training blood sugar control suggestion set;

[0120] Importing the matching vector of the expanded blood sugar-associated physiological data of the training user and the matching vector of the expanded blood sugar-associated physiological data of each training blood sugar control suggestion into the initial matching model, and determining the matching degree of the training user for each training blood sugar control suggestion by matching degree calculation;

[0121] Determine the parameters of the classification cost function according to the matching degree of each training blood sugar control suggestion and the preset true matching degree;

[0122] According to the parameters of the classification cost function, the initial matching model is tuned until the training termination state is reached according to the parameters of the classification cost function, and the initial matching model is tuned to obtain a preset matching model.

[0123] In an embodiment of the present invention, illustratively, for example, in a health management system, the server is performing a model training and optimization process. First, the server obtains the vector of the extended blood sugar-associated physiological data matching of the training user. For example, the vector of the extended blood sugar-associated physiological data matching of the training user Xiao Wang is [0.2, 0.3, 0.4, 0.1, 0.5]. At the same time, the server also obtains the vector of the extended blood sugar-associated physiological data matching of each training blood sugar control suggestion in the training blood sugar control suggestion set. For example, there are three training blood sugar control suggestions, the vector of the extended blood sugar-associated physiological data matching of suggestion one is [0.1, 0.2, 0.3, 0.4, 0.5], the vector of the extended blood sugar-associated physiological data matching of suggestion two is [0.3, 0.2, 0.1, 0.5, 0.4], and the vector of the extended blood sugar-associated physiological data matching of suggestion three is [0.5, 0.4, 0.3, 0.2, 0.1]. Next, the server imports the vector of the extended blood sugar-associated physiological data matching of Xiao Wang and the vector of the extended blood sugar-associated physiological data matching of each training blood sugar control suggestion into the initial matching model. The initial matching model determines the matching degree of Xiao Wang for each training blood sugar control suggestion through a series of calculations and analyses. For example, it is calculated that Xiao Wang's matching degree with suggestion one is 0.6, with suggestion two is 0.7, and with suggestion three is 0.4. At the same time, the server presets the real matching degree. For example, for Xiao Wang's case, the real matching degree is 0.5 with suggestion one, 0.8 with suggestion two, and 0.3 with suggestion three. Then, the server determines the parameters of the classification cost function based on the matching degree for each training blood sugar control suggestion and the preset real matching degree. For example, using the common mean square error as the classification cost function, for suggestion one, the calculated error is (0.6-0.5)^2=0.01; for suggestion two, the error is (0.7-0.8)^2=0.01; for suggestion three, the error is (0.4-0.3)^2=0.01. Combining these errors, the parameters of the classification cost function are determined. After that, the server tunes the initial matching model according to the parameters of the classification cost function. This involves adjusting parameters such as weights and biases in the model to reduce the error between the predicted degree of match and the actual degree of match. The server repeats this process until the training termination state is reached according to the parameters of the classification cost function. For example, when the result of the classification cost function is less than a certain threshold (such as 0.05), or after a certain number of training iterations (such as 1000 times), the training termination state is considered to be reached. Finally, the server tunes the initial matching model to obtain the preset matching model. In actual large-scale training scenarios, the server may process data from hundreds or thousands of training users at the same time. For example, in one training session, the server obtained the matching vectors of the extended blood sugar-associated physiological data of 100 training users and the corresponding matching vectors of the extended blood sugar-associated physiological data of multiple training blood sugar control suggestions.For each training user, the above steps are followed to calculate the degree of matching, determine the parameters of the classification cost function, and tune the model. In order to improve training efficiency, the server can adopt a distributed computing framework to distribute training tasks to multiple computing nodes for parallel execution. At the same time, the server will also monitor various indicators during the training process, such as accuracy, recall rate, etc., to evaluate the performance of the model and the training effect. If the model is found to be overfitting or underfitting during the training process, the server will take corresponding regularization measures or adjust the model structure. Through continuous optimization and improvement, the server can obtain a preset matching model with good performance, accuracy and reliability, laying a solid foundation for providing users with accurate blood sugar management recommendations in the future.

[0124] In an embodiment of the present invention, before obtaining the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion in the preset blood sugar control suggestion database, the following implementation manner is also provided.

[0125] Obtaining the extended blood sugar-related physiological data of the training user and the extended blood sugar-related physiological data of each training blood sugar control suggestion in the training blood sugar control suggestion set;

[0126] According to the extended blood sugar-related physiological data of the training user and the extended blood sugar-related physiological data of each training blood sugar control suggestion, the initial second NLP model and the initial matching model are trained to obtain the preset second NLP model and the preset matching model.

[0127] In an embodiment of the present invention, illustratively, for example, in a professional health management system, the server is responsible for training and optimizing the model. First, the server obtains a large number of extended blood sugar-related physiological data of training users. For example, the extended blood sugar-related physiological data of training user Xiao Li includes information such as age 30, weight 70 kg, slight exercise habits, recent blood sugar fluctuations within a certain range, and a high proportion of carbohydrates in the diet structure. At the same time, the server also obtains the extended blood sugar-related physiological data of each training blood sugar control suggestion in the training blood sugar control suggestion set. For example, one of the training blood sugar control suggestions is for people aged 25-35, moderate weight, less exercise and less blood sugar fluctuations. Its extended blood sugar-related physiological data includes specific dietary adjustment suggestions, exercise planning, and blood sugar monitoring frequency. Next, the server uses these acquired data to train the initial second NLP model and the initial matching model. During the training process, the initial second NLP model learns how to extract key features from the extended blood sugar-related physiological data of the training user and convert them into effective vector representations. For example, for Xiao Li's extended blood glucose-related physiological data, the model may learn the importance of features such as age, weight, exercise habits, and blood glucose fluctuations, and convert them into a specific vector, such as [0.3, 0.4, 0.2, 0.1]. The initial matching model learns how to determine the degree of matching based on the similarity or correlation between the extended blood glucose-related physiological data of the training user and the extended blood glucose-related physiological data of each training blood glucose control suggestion. For example, when Xiao Li's extended blood glucose-related physiological data is matched with the extended blood glucose-related physiological data of the training blood glucose control suggestions for a specific population mentioned above, the model gives an initial matching degree evaluation by calculating the feature similarity, data distribution difference, etc. between the two. The server continuously adjusts the parameters of the model so that the output of the model gradually approaches the expected result. For example, if the initial matching model gives a too low matching degree between Xiao Li and a training blood glucose control suggestion, but in fact it should be higher based on professional knowledge, the server will adjust the parameters of the model according to this deviation, such as weight value, threshold, etc., to improve the accuracy of the matching degree evaluation. After multiple iterative training and parameter adjustment, the server finally obtains the preset second NLP model and the preset matching model.

[0128] For example, after 1,000 training iterations, the preset second NLP model can more accurately convert the newly input user's extended blood glucose-related physiological data into a representative vector, while the preset matching model can more accurately evaluate the degree of match between the user and different blood glucose control suggestions. In an actual large-scale training scenario, the server may simultaneously process the extended blood glucose-related physiological data from thousands of training users and the extended blood glucose-related physiological data of many different training blood glucose control suggestions. In order to improve the training efficiency, the server can use parallel computing technology to process multiple data samples at the same time. In addition, the server will regularly evaluate the performance of the model, such as using cross-validation, accuracy, recall and other indicators to measure the quality of the model. If it is found that the model performs poorly on certain specific types of user data or control suggestions, the server will further optimize the data collection and preprocessing process, or adjust the structure and algorithm of the model to improve the generalization ability and adaptability of the model. Through continuous training and optimization, the server can ensure that the preset second NLP model and the preset matching model can provide accurate and useful blood glucose control suggestion matching services for actual users, helping users to better manage blood glucose and maintain health.

[0129] The embodiment of the present invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the user health status assessment method based on the smart wearable device. Figure 2 As shown, Figure 2 The block diagram of the computer device 100 provided in the embodiment of the present invention. The computer device 100 includes a memory 111, a processor 112 and a communication unit 113. To achieve data transmission or interaction, the memory 111, the processor 112 and the communication unit 113 are directly or indirectly electrically connected to each other. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0130] For illustrative purposes, the foregoing description is made with reference to specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. Numerous modifications and variations are possible in accordance with the above teachings. These embodiments are selected and described in order to best illustrate the principles of the present disclosure and its practical application, so that those skilled in the art can best utilize the present disclosure and utilize various embodiments with different modifications to suit the intended specific application.

Claims

1. A user health status assessment method based on smart wearable devices, characterized in that: include: activating a user health status assessment instruction of a smart wearable device according to a preset period, wherein the smart wearable device is equipped with an optical sensor; According to the user health status assessment instruction, near-infrared light of a preset wavelength is emitted to the current user through the optical sensor to obtain a target near-infrared light feedback optical signal; Inputting the target near-infrared light feedback light signal into a pre-trained blood sugar status assessment model to obtain a blood sugar status assessment result of the target near-infrared light feedback light signal; The blood sugar status assessment result is used as the user health status assessment result of the current user.

2. The method according to claim 1, characterized in that The step of inputting the target near-infrared light feedback light signal into a pre-trained blood sugar status assessment model to obtain a blood sugar status assessment result of the target near-infrared light feedback light signal includes: Acquire a near-infrared light feedback signal instance and a plurality of pending blood sugar states, wherein the near-infrared light feedback signal instance is configured with a plurality of actual blood sugar states; Based on a pre-configured grouping strategy, performing a grouping operation on the pending blood sugar status to obtain at least one blood sugar status array; Importing the blood glucose state array into a blood glucose state vector model to obtain a blood glucose state array vector of the blood glucose state array; For any pending blood sugar state, performing a averaging operation on all the blood sugar state array vectors corresponding to the pending blood sugar state to obtain a pending blood sugar state vector; Classify the plurality of pending blood sugar states according to the pending blood sugar state vector to obtain a plurality of pending blood sugar state classes, and obtain a state class identifier of each pending blood sugar state class based on the actual blood sugar state; Importing the near-infrared light feedback signal instance into a blood glucose state assessment model, obtaining a predicted blood glucose state class from a plurality of pending blood glucose state classes based on the blood glucose state assessment model, and then obtaining the pending blood glucose state corresponding to the near-infrared light feedback signal instance from the predicted blood glucose state class; Obtaining a first cost parameter according to the evaluation result of the predicted blood sugar state class and the state class identifier, and obtaining a second cost parameter according to the evaluation result of the pending blood sugar state corresponding to the near-infrared light feedback light signal instance and the actual blood sugar state; performing collaborative learning on the blood glucose state vector model and the blood glucose state assessment model according to the first cost parameter and the second cost parameter; Acquire a target near-infrared light feedback signal, import the target near-infrared light feedback signal into the trained blood sugar status assessment model, and obtain a blood sugar status assessment result of the target near-infrared light feedback signal based on the trained blood sugar status assessment model.

3. The method according to claim 2, characterized in that The blood sugar state evaluation model comprises a feature extraction unit and a discrimination unit; the near-infrared light feedback light signal instance is introduced into the blood sugar state evaluation model, and a predicted blood sugar state class is obtained from a plurality of pending blood sugar state classes based on the blood sugar state evaluation model, comprising: Performing a grouping operation on the near-infrared light feedback optical signal instance to obtain an optical signal segment array, wherein the optical signal segment array includes a plurality of optical signal segments; Adding a start segment mark to the start segment of the optical signal segment array, and adding an end segment mark to the end segment of the optical signal segment array, to obtain a pending segment array; Performing optical signal conversion processing on the undetermined segment array to obtain a blood sugar state array vector optical signal vector array; Based on dynamic convolution, the feature extraction unit is used to perform feature extraction on the blood sugar state array vector light signal vector array to obtain a vector representation array, wherein the vector representation array includes the vector representation of each light signal segment in the to-be-determined segment array; Calculating a feature correlation measure for each of the vector representations based on a preconfigured dynamic convolution operation and the vector representations; Calculate the convolution kernel weight of each vector representation according to the preconfigured activation function and the feature correlation measure; Based on the convolution kernel weight, performing a weighted operation on each of the vector representations to obtain an instance feature vector; Importing the instance feature vector into the discrimination unit to obtain a first estimated score for each of the pending blood sugar state classes; Performing data standardization on each of the first estimated scores to obtain a first estimated probability of each of the pending blood sugar state classes; When the first estimated likelihood is higher than a pre-configured first preset likelihood threshold, the undetermined blood glucose state class corresponding to the first estimated likelihood is determined as a predicted blood glucose state class.

4. The method according to claim 3, characterized in that The evaluation result of the predicted blood sugar state class includes a first estimated probability of each of the pending blood sugar state classes; the first cost parameter is obtained according to the evaluation result of the predicted blood sugar state class and the state class identifier, including: Obtaining a target blood glucose state class probability rate for each of the pending blood glucose state classes according to the state class identifier of each of the pending blood glucose state classes; Calculating a KL divergence cost parameter between each of the target blood sugar state class likelihoods and the corresponding first estimated likelihood to obtain a plurality of blood sugar state class cost parameters; The sum of all the blood sugar status cost parameters is determined as the first cost parameter.

5. The method according to claim 3, characterized in that: The step of obtaining the undetermined blood sugar state corresponding to the near-infrared light feedback light signal instance from the predicted blood sugar state class includes: Traversing each of the pending blood glucose states in the predicted blood glucose state class, and calculating a vector distance between the instance feature vector and the pending blood glucose state vector of the pending blood glucose state; Performing data normalization on the vector distance to obtain a second estimated probability of the pending blood sugar state; When the second estimated likelihood is higher than a preconfigured second preset likelihood threshold, the pending blood glucose state corresponding to the second estimated likelihood is determined as the pending blood glucose state corresponding to the near-infrared light feedback optical signal instance.

6. The method according to claim 5, characterized in that The evaluation result of the pending blood sugar state corresponding to the near-infrared light feedback light signal instance includes a second estimated probability of each of the pending blood sugar states in the predicted blood sugar state class; the second cost parameter is obtained according to the evaluation result of the pending blood sugar state corresponding to the near-infrared light feedback light signal instance and the actual blood sugar state, including: According to the actual blood sugar state, obtaining the target blood sugar state probability of each of the pending blood sugar states in the predicted blood sugar state class; Calculating a KL divergence cost parameter between each of the target blood sugar state likelihood and the corresponding second estimated likelihood to obtain a plurality of blood sugar state cost parameters; The sum of all the blood sugar status cost parameters is determined as the second cost parameter.

7. The method according to claim 3, characterized in that The step of collaboratively learning the blood glucose state vector model and the blood glucose state evaluation model according to the first cost parameter and the second cost parameter includes: Assigning weights to the first cost parameter and the second cost parameter to obtain a target cost parameter; According to the target cost parameter, the blood glucose state vector model, the feature extraction unit and the discrimination unit are collaboratively learned.

8. The method according to claim 1, characterized in that: After taking the blood sugar status assessment result as the user health status assessment result of the current user, the method further includes: Acquire basic physiological data of the current user, and fuse the basic physiological data and the blood sugar status assessment result as blood sugar-related physiological data; Acquire the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion in a preset blood sugar control suggestion database; Writing the blood sugar-related physiological data of the current user into the preset first guiding parameter to obtain the optimized first guiding parameter; Importing the tuned first guidance parameter into a preset first NLP model, and determining the extended blood glucose-related physiological data of the current user through context-aware learning; the preset first guidance parameter includes the user's blood glucose management tendency, the user's blood glucose monitoring history, and a first blood glucose management request for the first NLP model, the first blood glucose management request includes blood glucose management requirement details, and the blood glucose management requirement details are used to indicate that the first NLP model outputs the extended blood glucose-related physiological data of the current user that matches the blood glucose management requirement details; Writing the blood sugar-related physiological data of each preset blood sugar control suggestion into the preset second guiding parameter to obtain the adjusted second guiding parameter; The tuned second guiding parameter is imported into the first NLP model, and the extended blood glucose-related physiological data of each preset blood glucose control suggestion is determined through context-aware learning; the preset second guiding parameter includes a second blood glucose management request for the first NLP model, and the second blood glucose management request includes blood glucose management requirement details, and the blood glucose management requirement details are used to indicate that the first NLP model outputs the extended blood glucose-related physiological data of each preset blood glucose control suggestion that matches the blood glucose management requirement details; According to the extended blood sugar-associated physiological data of the current user and the preset blood sugar-associated physiological data unit set, determine the blood sugar-associated physiological data unit that matches each character in the extended blood sugar-associated physiological data of the current user, and according to the blood sugar-associated physiological data unit that matches each character in the extended blood sugar-associated physiological data of the current user, determine the basic vector array of the current user, wherein the blood sugar-associated physiological data units in the blood sugar-associated physiological data unit set are represented by One-Hot Encoding; Determine the blood sugar associated physiological data unit that matches each character in the extended blood sugar associated physiological data of each preset blood sugar control suggestion according to the extended blood sugar associated physiological data of each preset blood sugar control suggestion and determine the basic vector array of each preset blood sugar control suggestion according to the blood sugar associated physiological data unit that matches each character in the extended blood sugar associated physiological data of each preset blood sugar control suggestion; Importing the basis vector array of the current user into a preset second NLP model, and determining a plurality of feature mapping vectors matching the basis vector array of the current user through feature mapping processing; According to the multiple feature mapping vectors matched by the basic vector array of the current user, determining the vector matched by the extended blood glucose-related physiological data of the current user through feature merging processing; Importing the basic vector arrays of each preset blood sugar control suggestion into the second NLP model, and determining a plurality of feature mapping vectors matching the basic vector arrays of each preset blood sugar control suggestion through feature mapping processing; According to the plurality of feature mapping vectors matched by the basic vector arrays of the preset blood sugar control suggestions, determining the vectors matched by the extended blood sugar-associated physiological data of the preset blood sugar control suggestions through feature merging processing; Importing the matching vector of the extended blood sugar-associated physiological data of the current user and the matching vector of the extended blood sugar-associated physiological data of each preset blood sugar control suggestion into a preset matching model, and determining the matching degree of the current user for each preset blood sugar control suggestion by matching degree calculation, wherein the matching degree for each preset blood sugar control suggestion is characterized by a matching indicator set, and the matching indicator set includes multiple matching indicators; Determining the matching scores of the matching degrees according to the matching indicator sets of the matching degrees in the matching degrees and the importance factors of the multiple matching indicators in the matching indicator sets of the matching degrees; A preset number of preset blood sugar control suggestions determined from high to low according to the matching scores are determined as target blood sugar control suggestions displayed to the current user, and the preset blood sugar control suggestion database includes the target blood sugar control suggestions.

9. The method according to claim 8, characterized in that Before obtaining the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion in the preset blood sugar control suggestion database, the method further includes: Obtaining a vector matching the extended blood sugar-associated physiological data of the training user and a vector matching the extended blood sugar-associated physiological data of each training blood sugar control suggestion in the training blood sugar control suggestion set; Importing the matching vector of the expanded blood sugar-associated physiological data of the training user and the matching vector of the expanded blood sugar-associated physiological data of each training blood sugar control suggestion into the initial matching model, and determining the matching degree of the training user for each training blood sugar control suggestion by matching degree calculation; Determine the parameters of the classification cost function according to the matching degree of each training blood sugar control suggestion and the preset true matching degree; According to the parameters of the classification cost function, the initial matching model is tuned until the training termination state is reached according to the parameters of the classification cost function, and the initial matching model is tuned to obtain a preset matching model; Before obtaining the blood sugar-related physiological data of the current user and the blood sugar-related physiological data of each preset blood sugar control suggestion in the preset blood sugar control suggestion database, the method further includes: Obtaining the extended blood sugar-related physiological data of the training user and the extended blood sugar-related physiological data of each training blood sugar control suggestion in the training blood sugar control suggestion set; According to the extended blood sugar-related physiological data of the training user and the extended blood sugar-related physiological data of each training blood sugar control suggestion, the initial second NLP model and the initial matching model are trained to obtain the preset second NLP model and the preset matching model.

10. A server system, characterized in that: The method comprises a server, wherein the server is used to execute the method described in any one of claims 1 to 9.

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