Intelligent blood fat management method, device, medium and equipment
By establishing a blood lipid risk management model and generating a joint intervention plan, the problem of difficult real-time monitoring and personalized management of blood lipids in the existing technology is solved, and intelligent management of blood lipids and cardiovascular disease prevention is achieved.
Patent Information
- Application Number
- CN202510014180.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-06
- Publication Date
- 2025-05-06
AI Technical Summary
The prior art is difficult to realize real-time monitoring and personalized management of blood lipids, making it difficult to provide targeted blood lipid management plans.
By obtaining the basic information and vascular health status of the target object, collecting non-invasive blood lipid data and establishing a blood lipid risk management model, and generating a joint intervention plan based on machine learning methods to achieve intelligent management of blood lipids.
Real-time and personalized management of blood lipids is achieved, helping users improve blood lipid levels and prevent cardiovascular diseases.
Smart Images
Figure CN119943342A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of health management, and in particular to a blood lipid intelligent management method, device, medium and equipment. Background Art
[0002] With the continuous improvement of living standards, people's dietary choices are becoming more and more diverse. Excessive intake of high-calorie and high-fat foods, coupled with an unhealthy lifestyle, has caused many people to have abnormal blood lipid metabolism. If not controlled, atherosclerosis may occur in the long run, leading to various cardiovascular and cerebrovascular diseases.
[0003] Existing technologies usually use invasive methods to measure users' blood lipids, such as collecting venous blood or fingertip blood, and then testing the total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and other indicators in the blood. However, this data needs to be obtained by laboratory testing in the hospital, and the number of tests is relatively limited. It is not only difficult to achieve real-time monitoring of users' blood lipids, but also impossible to understand the effectiveness of different intervention measures on different users, so it is difficult to provide targeted blood lipid management plans. Summary of the invention
[0004] The present invention provides a blood lipid intelligent management method, device, medium and equipment, which solve the above-mentioned technical problems.
[0005] A first aspect of an embodiment of the present invention provides a blood lipid intelligent management method, comprising the following steps:
[0006] Step 1, obtaining basic information and current vascular health status of the target object, and generating a blood lipid management target for the target object;
[0007] Step 2, collecting blood lipid impact data and corresponding non-invasive blood lipid data of the target object at a preset time node, and establishing a blood lipid risk management model for the target object based on a machine learning method;
[0008] Step 3, set a management cycle for the target object, obtain the invasive blood lipid data of the target object after the current management cycle ends, compare the invasive blood lipid data with the blood lipid management target, and generate a joint intervention plan for the target object in the next management cycle based on the comparison result and the blood lipid risk management model.
[0009] A second aspect of an embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned intelligent blood lipid management method.
[0010] A third aspect of an embodiment of the present invention provides a blood lipid intelligent management device, comprising a computer-readable storage medium and a processor, wherein the processor implements the steps of the above-mentioned blood lipid intelligent management method when executing a computer program on the computer-readable storage medium.
[0011] A fourth aspect of the embodiments of the present invention provides a blood lipid intelligent management device, including a target generation module, a model building module, and a management module.
[0012] The target generation module is used to obtain the basic information and current vascular health status of the target object, and generate the blood lipid management target of the target object;
[0013] The model building module is used to collect blood lipid impact data and corresponding non-invasive blood lipid data of the target object at a preset time node, and establish a blood lipid risk management model for the target object based on a machine learning method;
[0014] The management module is used to set a management cycle for the target object, obtain the invasive blood lipid data of the target object after the current management cycle ends, compare the invasive blood lipid data with the blood lipid management target, and generate a joint intervention plan for the target object in the next management cycle based on the comparison result and the blood lipid risk management model.
[0015] The beneficial effects of the present invention are as follows: the present invention provides a method, device, medium and equipment for intelligent blood lipid management, which collects a large amount of continuous non-invasive blood lipid data of the target object at a preset time node, and analyzes the correlation between the non-invasive blood lipid data and the corresponding blood lipid influencing factors, thereby establishing a corresponding blood lipid risk management model, and providing the target object with a multi-faceted and personalized blood lipid management plan including exercise, diet, and medication, so that the target object's invasive blood lipid data meets its blood lipid management goals, helping users improve blood lipid levels and prevent cardiovascular diseases.
[0016] In order to make the above-mentioned objects, features and advantages of the invention more obvious and easy to understand, the preferred embodiments of the present invention are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.
[0018] Figure 1 is a schematic diagram of the process of the intelligent blood lipid management method provided in Example 1;
[0019] Figure 2 is a schematic diagram of the structure of the blood lipid intelligent management device provided in Example 2;
[0020] Figure 3 This is a schematic diagram of the structure of the intelligent blood lipid management device provided in Example 3. DETAILED DESCRIPTION
[0021] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0022] It should be noted that, if there is no conflict, the various features in the embodiments of the present invention can be combined with each other, all within the scope of protection of the present invention. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order from the module division in the device or the flow chart. Furthermore, the words "first", "second", "third", etc. used in the present invention do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.
[0023] Figure 1 1 is a flow chart of a blood lipid intelligent management method provided in Example 1. Figure 1 As shown, the following steps are included:
[0024] Step 1, obtaining basic information and current vascular health status of the target object, and generating a blood lipid management target for the target object;
[0025] Step 2, collecting blood lipid impact data and corresponding non-invasive blood lipid data of the target object at a preset time node, and establishing a blood lipid risk management model for the target object based on a machine learning method;
[0026] Step 3, set a management cycle for the target object, obtain the invasive blood lipid data of the target object after the current management cycle ends, compare the invasive blood lipid data with the blood lipid management target, and generate a joint intervention plan for the target object in the next management cycle based on the comparison result and the blood lipid risk management model.
[0027] The above embodiments provide a method for intelligent blood lipid management, which collects a large amount of continuous non-invasive blood lipid data of the target object at preset time nodes, and analyzes the correlation between the non-invasive blood lipid data and the corresponding blood lipid influencing data, thereby establishing a blood lipid risk management model for the target object, and managing the target user's blood lipids based on the blood lipid risk management model, including making suggestions on the target user's blood lipid medication, diet, exercise, lifestyle, etc., so that the target object's more accurate invasive blood lipid data meets its blood lipid management goals, thereby providing users with a comprehensive and personalized blood lipid management plan to help users improve blood lipid levels and prevent cardiovascular diseases.
[0028] Each step of the above method is described in detail below using specific embodiments.
[0029] Existing technologies usually set different blood lipid risk levels for different users. Specifically, first understand whether the user has had serious ASCVD events (including a history of ischemic stroke, previous myocardial infarction, etc.) and the number of serious ASCVD events, and then comprehensively assess the user's risk level based on the judgment results and other high-risk factors (including diabetes, hypertension, smoking, etc.), so as to determine the user's blood lipid management indicators and management target values based on the user's risk level, such as the management target value of low-density lipoprotein cholesterol LDL-C or the management target value of high-density lipoprotein cholesterol HDL-C, etc.
[0030] In one embodiment of the present application, based on the above technical solution, a more reasonable blood lipid management target is set for the target object according to the basic information of the target object and the current vascular health status. Specifically, the following steps are included:
[0031] S101, generating an initial risk level of the target object according to the basic information, and specifically generating the initial risk level with reference to the recommendations of the "Chinese Blood Lipid Management Guidelines".
[0032] S102, obtaining the target object's vascular examination data, and generating the target object's current vascular health status according to the vascular examination data. Exemplarily, advanced imaging technologies such as ultrasound, CT scanning or MRI can be used to collect the target object's vascular image, and the vascular image can be identified according to a preset neural network model to obtain the current vascular health status, including the degree of vascular stenosis corresponding to the cerebral blood vessels, coronary arteries and / or lower limb veins.
[0033] S103, judging whether the current blood vessel health status meets a preset condition, and adjusting the initial risk level according to the judgment result to generate a target risk level.
[0034] For example, the preset condition includes whether the degree of vascular stenosis of cerebral blood vessels, coronary arteries and / or lower limb veins is less than the corresponding preset value. It is understandable that the more the number of blood vessels and / or types of blood vessels whose degree of vascular stenosis is less than the corresponding preset value, the higher the target risk level.
[0035] Then, S104 is executed to generate the blood lipid management target of the target object according to the target risk level. Here, the corresponding blood lipid management target can also be generated by referring to the recommendations of the "Chinese Blood Lipid Management Guidelines".
[0036] Exemplarily, in a specific embodiment, the blood lipid impact data in step 2 mainly refers to various parameter values that may cause blood lipid fluctuations in the target object. It is understandable that blood lipid medication data, exercise data, and diet data will affect the blood lipids of the target user. The target diet data includes effective diet data, such as green leafy vegetables, low-sugar fruits, grains, etc., as well as harmful diet data, including tobacco, alcohol, high sugar and high fat intake, etc.
[0037] Existing technologies usually use invasive methods to measure the user's blood lipids, such as collecting venous blood or fingertip blood, and then detecting the total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and other indicators in the blood. However, this data needs to be obtained by testing in the hospital, and the number of tests is relatively limited, making it difficult to achieve real-time, continuous, and multiple tests of the target object's blood lipids, thereby fitting the relationship between blood lipid indicators and blood lipid impact data. In recent years, with the deepening of research, some non-invasive blood lipid detection methods have also been proposed, such as collecting multi-wavelength light signals from fingertips or behind ears, and then combining convolutional neural networks to estimate blood lipid content. Although there is a gap in accuracy compared with invasive blood lipid measurement data, if the measurement conditions are kept consistent, the non-invasive blood lipid measurement data can reflect the real-time and long-term changes in the target object's blood lipids, thereby creating a personal blood lipid risk management model for the target object's blood lipid change state and blood lipid impact data, so as to predict the target object's future blood lipids or manage the target object's current blood lipids.
[0038] In a specific example, multiple time nodes can be set according to the target object's living habits, and then the target object's blood lipid impact data and corresponding non-invasive blood lipid data at the preset time nodes are collected, which specifically includes the following steps:
[0039] First, the blood lipid medication data of the target object is collected, including the drug name, medication time and medication dosage, and the first non-invasive blood lipid data of the target object is collected at the corresponding time nodes before and after medication, such as the drug onset time point, so as to measure the impact of the blood lipid drug on the target object's blood lipids.
[0040] Second, the motion data of the target object is collected, including exercise duration, exercise time, exercise steps, exercise distance, exercise type, exercise intensity, calories consumed, etc., and the second non-invasive blood lipid data of the target object is collected at preset nodes, such as before going to bed, before and after exercise, or in a preset period, so as to measure the impact of exercise on the target object's blood lipids.
[0041] Third, the target diet data of the target object is collected, including the effective diet data and harmful diet data mentioned above, and the third non-invasive blood lipid data of the target object is collected at the corresponding time nodes before and after the diet, so as to measure the impact of the diet on the blood lipids of the target object.
[0042] It is understandable that after collecting a large amount of exercise data, dietary data, blood lipid medication data and related non-invasive blood lipid data, a data set can be established, and a machine learning algorithm or statistical method can be used, such as a regression model based on multiple linear regression method, logistic regression method, etc. to fit the above blood lipid influence data and non-invasive blood lipid data, and establish a blood lipid risk management model for the target object, so as to predict whether the target object will have high blood lipids in the future and manage blood lipids. The specific model creation method is described in the prior art and will not be repeated here.
[0043] Optionally, in a preferred embodiment of the present application, before establishing the blood lipid risk management model of the target object based on the machine learning method, especially before generating the regression model, a blood lipid impact data screening step is also included to obtain blood lipid impact data with stronger correlation, more significant and effective. Specifically, the following steps are included:
[0044] Collect invasive lipid data of target subjects at the end of each management cycle and establish a reference sequence;
[0045] Collect the target subject's total blood lipid medication data, total exercise data, and total diet data in each management cycle, and establish a corresponding comparison sequence;
[0046] A grey correlation analysis method is used to obtain at least one target-related factor from the total blood lipid medication data, the total diet data, and the total exercise data, respectively, so as to establish a blood lipid risk management model for the target object based on the value of the at least one target-related factor at a preset time node and the corresponding non-invasive blood lipid data.
[0047] It can be understood that grey correlation analysis is a multi-factor statistical analysis method used to evaluate the relative influence of a certain indicator on other factors. The basic process is to judge the degree of association between the reference sequence (parent sequence) and the comparison sequence (subsequence) by the degree of similarity of their geometric shapes. If the trends of the changes in the two factors are consistent, that is, the degree of synchronous change is high, then the degree of association between the two factors is considered to be high; otherwise, it is low. This embodiment uses the invasive blood lipid data that needs to be measured after each management cycle as the reference sequence, and the total blood lipid medication data, total exercise data and total diet data of each management cycle, including the total medication amount, total exercise time, total exercise distance, total calories consumed and / or total tobacco and alcohol intake, total effective food intake, etc. of each management cycle as the comparison sequence, so as to sort the above-mentioned influencing factors according to the degree of association, and select the target number of target correlation factors from each category to obtain more effective blood lipid impact data and blood lipid risk management model.
[0048] Finally, step 3 is performed to obtain the invasive blood lipid data of the target object after the current management cycle ends, compare the invasive blood lipid data with the blood lipid management target, and manage the blood lipid of the target object according to the comparison result and the blood lipid risk management model. Exemplarily, in a preferred embodiment, a joint intervention plan for the target object in the next management cycle is generated, which specifically includes the following:
[0049] S301, comparing the invasive blood lipid data with the blood lipid management target, and generating a risk level of the target object in the next management cycle according to the comparison result;
[0050] S301, generating a joint intervention plan corresponding to the risk level through the blood lipid risk management model. Specifically, the blood lipid risk management model can pre-set basic blood lipid management plans of different risk levels according to the recommendations of the "Chinese Blood Lipid Management Guidelines", and then optimize the medication, exercise, diet and other recommendations in the basic blood lipid management plan according to the fitted regression model of the target object, such as ranking the importance of drugs, exercise, and diet and / or adjusting the periodic management goals of different influencing parameters, etc., so as to generate a more targeted joint intervention plan and improve the blood lipid management effect of the target user.
[0051] For example, in a specific embodiment of the present application, the blood lipid impact data of the target object at a preset time node can be obtained through some intelligent methods or intelligent devices, such as collecting the target object's exercise data through the target object's intelligent wearable device.
[0052] In a preferred embodiment, it also includes a smart medicine box connected by wireless and / or Bluetooth, and an identification device is provided on the shell of the smart medicine box to authenticate the identity of the target object. Optionally, the smart medicine box has a multi-layer structure for storing medicines for different diseases, each layer is divided into a plurality of medicine grids, and each medicine grid can store a day's dosage of medicine. The blood lipid medication data of the target object can be automatically collected by the smart medicine box, which specifically includes the following steps:
[0053] Sending the target identity information and target disease information of the target object to the smart medicine box;
[0054] The blood lipid medication data returned by the smart medicine box is received, where the blood lipid medication data is generated by matching the target identity information with the target disease information.
[0055] Exemplarily, the smart medicine box is circular and has a multi-layer structure for storing medicines for different diseases, such as blood pressure medicines on the first layer, blood sugar medicines on the second layer, blood lipid medicines on the third layer, and so on. At the same time, each layer of the structure is divided into multiple medicine grids, and the number of medicine grids can match the management cycle. If the management cycle is set to one week, each layer is divided into 7 medicine grids. For example, each medicine grid on the third layer can hold one day's blood lipid medicine. Of course, in other embodiments, the number of medicine grids may not match the management cycle. First, the user's identity is identified by the identity recognition device set at the shell, such as fingerprint, voiceprint, face recognition and matching, etc. Then, if the disease medicine is to be taken out, the user can rotate the medicine grid of this layer or control the medicine grid of this layer to open or pop up. At this time, the smart medicine box stores and records the user's identity information, the number of layers, and the time of taking medicine. In this way, after receiving the target identity information and the target disease information, it can be matched with the stored record data to obtain the blood lipid medication data of the target object in the current management cycle. In a preferred embodiment, the smart medicine box can also be provided with a weight sensor, and the weight of the medicine box is measured once before and after each layer of the smart medicine box is opened and closed, and whether the medicine in the medicine box is reduced is determined based on the weight change, thereby optimizing and updating the stored record data.
[0056] In a specific embodiment, a smart device may be used to collect target dietary data of a target object. The smart device is provided with a camera and a storage medium, and a preset image recognition model is stored in the storage medium. The collection method specifically includes the following steps:
[0057] A pre-meal image and a post-meal image are collected, wherein the pre-meal image and the corresponding post-meal image have the same code, thereby establishing an association between the pre-meal image and the post-meal image.
[0058] Recognize the pre-meal image and the post-meal image based on a preset image recognition model, and generate total dietary data corresponding to all diners according to the recognition results, wherein the total dietary data includes the number of diners, types of ingredients, cooking methods, and ingredient consumption ratios;
[0059] The target dietary data corresponding to the target object is generated according to the total dietary data, so that when the target object cannot accurately measure his current dietary data, the above-mentioned intelligent method is used to automatically generate it, thereby enriching the use scenarios of the method of the present invention.
[0060] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.
[0061] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned intelligent blood lipid management method.
[0062] Figure 2 is a schematic diagram of the structure of the blood lipid intelligent management device provided in Example 2. Figure 2 As shown, it includes a target generation module 100, a model building module 200 and a management module 300.
[0063] The target generation module 100 is used to obtain basic information and current vascular health status of the target object, and generate a blood lipid management target for the target object;
[0064] The model building module 200 is used to collect blood lipid impact data and corresponding non-invasive blood lipid data of the target object at a preset time node, and establish a blood lipid risk management model for the target object based on a machine learning method;
[0065] The management module 300 is used to set a management cycle for the target object, obtain the invasive blood lipid data of the target object after the current management cycle ends, compare the invasive blood lipid data with the blood lipid management target, and generate a joint intervention plan for the target object in the next management cycle based on the comparison result and the blood lipid risk management model.
[0066] The above embodiments provide an intelligent blood lipid management device, which collects a large amount of continuous non-invasive blood lipid data of the target object at a preset time node, and analyzes the correlation between the non-invasive blood lipid data and the corresponding blood lipid influencing data, thereby establishing a blood lipid risk management model for the target object, and managing the target user's blood lipids based on the blood lipid risk management model, including making suggestions on the target user's blood lipid medication, diet, exercise, lifestyle, etc., so that the target object's more accurate invasive blood lipid data meets its blood lipid management goals, thereby providing users with a comprehensive and personalized blood lipid management plan to help users improve blood lipid levels and prevent cardiovascular diseases.
[0067] In a preferred embodiment, the target generation module 100 includes:
[0068] A first generating unit, configured to generate an initial risk level of a target object according to the basic information;
[0069] a second generating unit, configured to obtain blood vessel examination data of the target object, and generate a current blood vessel health status of the target object according to the blood vessel examination data;
[0070] a third generating unit, configured to determine whether the current blood vessel health status satisfies a preset condition, and adjust the initial risk level according to the determination result to generate a target risk level;
[0071] A fourth generating unit is used to generate a blood lipid management target for the target object according to the target risk level.
[0072] In a preferred embodiment, the model building module 200 includes:
[0073] A first collection unit is used to collect blood lipid medication data of the target object, and collect first non-invasive blood lipid data of the target object at corresponding time nodes before and after medication;
[0074] A second collection unit, used to collect the motion data of the target object, and collect the second non-invasive blood lipid data of the target object at a preset node;
[0075] The third collection unit is used to collect the target diet data of the target object, and collect the third non-invasive blood lipid data of the target object at corresponding time nodes before and after the diet.
[0076] In a preferred embodiment, the model building module 200 further includes a data screening unit, and the data screening unit specifically includes:
[0077] A first setting unit is used to collect invasive blood lipid data of the target subject at the end of each management cycle and establish a reference sequence;
[0078] The second setting unit is used to collect the total blood lipid medication data, total exercise data and total diet data of the target object in each management period, and establish a corresponding comparison sequence;
[0079] A grey correlation analysis unit is used to obtain at least one target correlation factor from the total blood lipid medication data, the total diet data and the total exercise data respectively using a grey correlation analysis method, so as to establish a blood lipid risk management model for the target object based on the value of the at least one target correlation factor at a preset time node and the corresponding non-invasive blood lipid data.
[0080] In a preferred embodiment, the management module 300 specifically includes:
[0081] A comparison unit, used for comparing the invasive blood lipid data with the blood lipid management target, and generating a risk level of the target object in the next management cycle according to the comparison result;
[0082] A management unit is used to generate a joint intervention plan corresponding to the risk level through the blood lipid risk management model.
[0083] It should be noted that the aforementioned explanation of the embodiment of the intelligent blood lipid management method is also applicable to the intelligent blood lipid management device of this embodiment, and will not be repeated here.
[0084] An embodiment of the present invention further provides a blood lipid intelligent management device, comprising a computer-readable storage medium and a processor, wherein the processor implements the steps of the above-mentioned blood lipid intelligent management method when executing a computer program on the computer-readable storage medium.
[0085] Figure 3 Schematic diagram of the structure of the blood lipid intelligent management device provided in Example 3 of the present invention. Figure 3 As shown, the blood lipid intelligent management device 8 of this embodiment includes: a processor 80, a readable storage medium 81, and a computer program 82 stored in the readable storage medium 81 and executable on the processor 80. When the processor 80 executes the computer program 82, the steps in the above-mentioned various method embodiments are implemented, for example Figure 1 Alternatively, when the processor 80 executes the computer program 82, the functions of the modules in the above-mentioned device embodiments are realized, for example Figure 2 Functionality of the modules shown.
[0086] Exemplarily, the computer program 82 may be divided into one or more modules, which are stored in the readable storage medium 81 and executed by the processor 80 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 82 in the blood lipid intelligent management device 8.
[0087] The blood lipid intelligent management device 8 may include, but is not limited to, a processor 80 and a readable storage medium 81. Those skilled in the art will understand that Figure 3 It is only an example of the intelligent blood lipid management device 8 and does not constitute a limitation of the intelligent blood lipid management device 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the intelligent blood lipid management device may also include a power management module, an operation processing module, input and output devices, a network access device, a bus, etc.
[0088] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
[0089] The readable storage medium 81 may be an internal storage unit of the blood lipid intelligent management device 8, such as a hard disk or memory of the blood lipid intelligent management device 8. The readable storage medium 81 may also be an external storage device of the blood lipid intelligent management device 8, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the blood lipid intelligent management device 8. Further, the readable storage medium 81 may also include both the internal storage unit of the blood lipid intelligent management device 8 and an external storage device. The readable storage medium 81 is used to store the computer program and other programs and data required by the blood lipid intelligent management device. The readable storage medium 81 may also be used to temporarily store data that has been output or is to be output.
[0090] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.
[0091] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0092] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0093] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0095] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0096] The present invention is not limited to what is described in the specification and implementation modes, and therefore additional advantages and modifications can be easily realized by those skilled in the art. Therefore, without departing from the spirit and scope of the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details, representative devices, and illustrative examples shown and described herein.
Claims
1. A blood lipid intelligent management method, characterized in that: The following steps are involved: Step 1, obtaining basic information and current vascular health status of the target object, and generating a blood lipid management target for the target object; Step 2, collecting blood lipid impact data and corresponding non-invasive blood lipid data of the target object at a preset time node, and establishing a blood lipid risk management model for the target object based on a machine learning method; Step 3, set a management cycle for the target object, obtain the invasive blood lipid data of the target object after the current management cycle ends, compare the invasive blood lipid data with the blood lipid management target, and generate a joint intervention plan for the target object in the next management cycle based on the comparison result and the blood lipid risk management model.
2. The intelligent blood lipid management method according to claim 1, characterized in that: The blood lipid impact data includes blood lipid medication data, exercise data and target diet data; the blood lipid impact data of the target object at a preset time node and the corresponding non-invasive blood lipid data specifically include: Collecting blood lipid medication data of the target subject, and collecting first non-invasive blood lipid data of the target subject at corresponding time points before and after medication; Collecting the motion data of the target object, and collecting the second non-invasive blood lipid data of the target object at a preset node; The target diet data of the target subject is collected, and the third non-invasive blood lipid data of the target subject is collected at corresponding time points before and after the diet.
3. The blood lipid intelligent management method according to claim 2, characterized in that: The blood lipid risk management model of the target object is established based on the machine learning method, and also includes the step of screening blood lipid impact data, specifically: Collect invasive lipid data of target subjects at the end of each management cycle and establish a reference sequence; Collect the target subject's total blood lipid medication data, total exercise data, and total diet data in each management cycle, and establish a corresponding comparison sequence; A grey correlation analysis method is used to obtain at least one target-related factor from the total blood lipid medication data, the total diet data, and the total exercise data, respectively, so as to establish a blood lipid risk management model for the target object based on the value of the at least one target-related factor at a preset time node and the corresponding non-invasive blood lipid data.
4. The blood lipid intelligent management method according to claim 2, characterized in that: Also included is a smart medicine box connected by wireless and / or Bluetooth, wherein an identification device is provided at the shell of the smart medicine box, and the smart medicine box is provided with a multi-layer structure for storing medicines for different diseases, and each layer is divided into a plurality of medicine compartments; Collecting blood lipid medication data of the target object, specifically including: Sending the target identity information and target disease information of the target object to the smart medicine box; The blood lipid medication data returned by the smart medicine box is received, where the blood lipid medication data is generated by matching the target identity information with the target disease information.
5. The blood lipid intelligent management method according to claim 2, characterized in that: Collecting the target diet data of the target object specifically includes: Collecting a pre-meal image and a post-meal image, wherein the pre-meal image and the corresponding post-meal image have the same code; Recognize the pre-meal image and the post-meal image based on a preset image recognition model, and generate total dietary data corresponding to all diners according to the recognition results, wherein the total dietary data includes the number of diners, types of ingredients, cooking methods, and ingredient consumption ratios; Generate target dietary data corresponding to the target object according to the total dietary data.
6. The intelligent blood lipid management method according to any one of claims 1 to 5, characterized in that: Generate the blood lipid management target of the target object, specifically: generating an initial risk level of the target object based on the basic information; Acquire blood vessel examination data of the target object, and generate a current blood vessel health status of the target object according to the blood vessel examination data; Determining whether the current blood vessel health status meets a preset condition, and adjusting the initial risk level according to the determination result to generate a target risk level; A blood lipid management target for the target object is generated according to the target risk level.
7. The intelligent blood lipid management method according to claim 6, characterized in that: A joint intervention plan for the target object in the next management cycle is generated according to the comparison results and the blood lipid risk management model, specifically: Comparing the invasive blood lipid data with the blood lipid management target, and generating a risk level of the target object in the next management cycle according to the comparison result; A joint intervention plan corresponding to the risk level is generated through the blood lipid risk management model.
8. A blood lipid intelligent management device, characterized in that: It includes target generation module, model building module and management module. The target generation module is used to obtain the basic information and current vascular health status of the target object, and generate the blood lipid management target of the target object; The model building module is used to collect blood lipid impact data and corresponding non-invasive blood lipid data of the target object at a preset time node, and establish a blood lipid risk management model for the target object based on a machine learning method; The management module is used to set a management cycle for the target object, obtain the invasive blood lipid data of the target object after the current management cycle ends, compare the invasive blood lipid data with the blood lipid management target, and generate a joint intervention plan for the target object in the next management cycle based on the comparison result and the blood lipid risk management model.
9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the intelligent blood lipid management method described in any one of claims 1 to 7 above is implemented.
10. A blood lipid intelligent management device, comprising a computer-readable storage medium and a processor, characterized in that: When the processor executes the computer program on the computer-readable storage medium, the steps of the blood lipid intelligent management method described in any one of claims 1-7 are implemented.