Method and system for dynamic recipe updating based on chronic disease patients

Abstract

The application provides a dynamic recipe updating method and system based on a chronic disease patient, and the method comprises the following steps: establishing core indexes of dietary restrictions and dietary recommendations based on the chronic disease type, specific disease stage and clinical indexes of the chronic disease patient; collecting the occupation type and working environment of the chronic disease patient, and identifying recipe execution obstacle elements; determining the dietary differences of the chronic disease patient based on the dietary habits, taste preferences, economic capacity and cooking ability of the chronic disease patient; integrating the core indexes, recipe execution obstacle elements and dietary differences to obtain a recipe which comprehensively considers nutrition and actual operability; and obtaining the disease state changes, occupation state adjustments and recipe execution feedback information of the chronic disease patient in real time, and dynamically updating the recipe information of the chronic disease patient. The application formulates an actually executable recipe, and dynamically updates the recipe, so that the development of the disease and the body changes of the chronic disease patient can be adapted, and the physical health of the chronic disease patient can be ensured.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically a method and system for updating dynamic diets for patients with chronic diseases. Background Technology

[0002] Personalized meal plan updates should play a crucial role in chronic disease management, but they currently face numerous challenges. Firstly, the cost of these services remains high. Chronic disease patients often require long-term health management, and the high service fees undoubtedly increase their financial burden. Many patients, unable to afford the cost, are forced to forgo this service, unable to adjust their diets in a timely manner according to changes in their condition. Secondly, while some meal plans exist for chronic disease patients, these plans tend to be theoretical. In practice, there is often a disconnect between the theoretical plans and the actual implementation by chronic disease patients. Furthermore, these theoretical plans cannot be dynamically updated. The inability to adapt to the progression of the disease and changes in the patient's body makes it difficult to adjust to these changes, impacting the effectiveness of chronic disease management and potentially posing a threat to the patient's health. Therefore, overcoming these technical problems and shortcomings is a key issue that needs to be addressed. Summary of the Invention

[0003] To overcome the aforementioned problems in the existing technology, this application provides a method and system for updating dynamic diets for patients with chronic diseases, which adopts the following technical solution:

[0004] Firstly, this application provides a method for updating a dynamic diet plan for patients with chronic diseases, including:

[0005] Based on the chronic disease type, specific disease stage, and clinical indicators of patients with chronic diseases, core indicators for dietary restrictions and dietary recommendations are established.

[0006] Collect information on the occupational type and work environment of patients with chronic diseases to identify factors that hinder adherence to dietary plans;

[0007] Based on the dietary habits, taste preferences, economic capacity, and cooking abilities of patients with chronic diseases, we determined the dietary differences among patients with chronic diseases.

[0008] By integrating core indicators, factors hindering the implementation of recipes, and dietary differences, we can obtain recipes that comprehensively consider nutrition and practicality.

[0009] It can obtain real-time information on changes in the disease status, occupational status adjustments, and diet implementation feedback of patients with chronic diseases, and dynamically update the diet information of patients with chronic diseases.

[0010] Furthermore, based on the type of chronic disease, specific stage of the disease, and clinical indicators of patients with chronic diseases, core indicators for dietary restrictions and recommendations are established, including:

[0011] The process involves transforming the chronic disease type, specific disease stage, and clinical indicators of patients with chronic diseases into structured factual information; constructing a rule set based on medical guidelines, where each rule includes a condition part and a conclusion part. The condition part is associated with the threshold range of the chronic disease type, specific disease stage, and clinical indicators, while the conclusion part includes the corresponding dietary restriction parameters and dietary recommendation parameters; matching the structured factual information with the rules in the rule set, triggering the conclusion part of a rule when the structured factual information meets the condition part of that rule; and converting the triggered conclusion part into dietary restriction and recommendation schemes in natural language form.

[0012] Furthermore, data on the occupational types and work environments of patients with chronic diseases were collected to identify factors hindering adherence to the diet plan, including:

[0013] We collect occupational type information from patients with chronic diseases, obtain work environments related to occupational types, analyze the impact of occupational types and work environments on the dietary behavior of patients with chronic diseases, identify obstacles that may lead to difficulties in diet implementation, and transform these obstacles into structured descriptions.

[0014] Furthermore, the study analyzed the impact of occupational type and work environment on the dietary behavior of patients with chronic diseases, identified potential obstacles to diet adherence, and transformed these obstacles into structured descriptions, including:

[0015] For unstructured text data, natural language preprocessing is used to extract keywords and form preliminary obstacle descriptions. For structured data, feature encoding is performed, and an obstacle factor dataset is constructed based on the preliminary obstacle descriptions and feature encodings. High-frequency co-occurring obstacle factor combinations in the obstacle factor dataset are identified through association rules. The association rules of obstacle factors that meet the preset reliability and lift thresholds among the high-frequency co-occurring obstacle factor combinations are selected, and the obstacle factor association rules are transformed into structured descriptions containing label, category, and degree of influence fields.

[0016] Furthermore, based on the dietary habits, taste preferences, economic capacity, and cooking abilities of patients with chronic diseases, the dietary differences among them are determined, including:

[0017] The collected data on dietary habits, taste preferences, economic capacity, and cooking skills are feature-coded and quantified to construct a multidimensional feature vector for patients with chronic diseases. Principal component analysis is used to reduce the dimensionality of the multidimensional features to obtain the core features that reflect dietary differences. In a pre-set reference group database, distance measurement and statistical tests are used to determine the degree of deviation between the core features of patients with chronic diseases and the dietary characteristics of the reference group, thus identifying dietary differences.

[0018] Furthermore, by integrating core indicators, factors hindering recipe implementation, and dietary differences, a comprehensive recipe that considers both nutritional value and practical feasibility is obtained, including:

[0019] By integrating core indicators, dietary implementation barriers, and dietary differences into a multi-dimensional feature set, a unified personalized dietary feature description is formed. This personalized dietary feature description constrains a pre-set multi-objective optimization function to generate a dietary plan that comprehensively considers nutrition and practical operability. The core indicators determine the nutritional needs and dietary limitations of patients with chronic diseases; dietary implementation barriers screen dietary plan elements to ensure the practical operability of the plan; and dietary difference information adjusts the taste preferences and economic practicality of the plan.

[0020] Furthermore, it acquires real-time information on changes in the disease status of patients with chronic diseases, adjustments to their occupational status, and feedback on diet adherence, and dynamically updates the diet information of patients with chronic diseases, including:

[0021] By integrating information on changes in disease status, adjustments in occupational status, and feedback on diet plan implementation with core indicators, factors hindering diet plan implementation, and dietary differences, a dynamic comprehensive status dataset for chronic disease patients is constructed. Based on time series data, the trajectory of status changes in the dynamic comprehensive status dataset is obtained, and the current health and behavioral status of chronic disease patients is analyzed. Based on the current health and behavioral status and combined with preset diet plan adjustment rules, when a chronic disease patient needs to adjust their diet plan, a personalized diet plan that matches the patient's real-time status is dynamically generated.

[0022] Secondly, this application also provides a dynamic diet update system for patients with chronic diseases, including:

[0023] The core indicator establishment module is used to establish core indicators for dietary restrictions and dietary recommendations based on the type of chronic disease, specific disease stage, and clinical indicators of patients with chronic diseases.

[0024] The diet plan execution obstacle identification module is used to collect information on the occupational type and work environment of patients with chronic diseases to identify diet plan execution obstacle elements;

[0025] The dietary difference determination module is used to determine the dietary differences among patients with chronic diseases based on their dietary habits, taste preferences, economic capacity, and cooking abilities.

[0026] The recipe framework determination module is used to integrate core indicators, recipe implementation barriers, and dietary differences to obtain recipes that comprehensively consider nutrition and practical operability.

[0027] The recipe dynamic update module is used to obtain real-time information on changes in the disease status, occupational status adjustments, and recipe execution feedback of patients with chronic diseases, and to dynamically update the recipe information of patients with chronic diseases.

[0028] Thirdly, this application provides an electronic device, comprising:

[0029] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0030] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0031] Fifthly, this application provides a computer program that, when executed by a computer, performs the method described in the first aspect.

[0032] In one possible design, the program in the fifth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor.

[0033] This application has the following beneficial effects:

[0034] 1. This application establishes core indicators for dietary restrictions and recommendations based on the chronic disease type, specific disease stage, and clinical indicators of chronic disease patients; collects information on the occupational type and work environment of chronic disease patients to identify factors hindering diet adherence; identifies dietary differences among chronic disease patients based on their eating habits, taste preferences, economic capacity, and cooking abilities; and integrates the core indicators, factors hindering diet adherence, and dietary differences to obtain a comprehensive diet plan that considers both nutrition and practicality. This application accurately identifies obstacles for diet adherents in chronic disease patients with different occupations and work environments, thereby designing personalized diet plans for chronic disease patients and improving the operability of dietary programs.

[0035] 2. This application dynamically updates the dietary information of chronic disease patients by acquiring real-time information on changes in their disease status, adjustments to their occupational status, and feedback on dietary plan implementation. By dynamically adjusting and controlling the dietary plan, this application achieves precise and personalized dietary management for chronic disease patients, improving their adherence to the dietary plan and adapting to their disease progression and physical changes while safeguarding their health. Attached Figure Description

[0036] Figure 1 This is an exemplary system architecture diagram to which embodiments of this application can be applied;

[0037] Figure 2 This is a schematic diagram illustrating the execution of the dynamic diet update logic for patients with chronic diseases according to an embodiment of this application;

[0038] Figure 3 This is a flowchart of a dynamic diet update method for patients with chronic diseases according to an embodiment of this application;

[0039] Figure 4 This is a flowchart illustrating the acquisition restrictions and recommended diet in an embodiment of this application;

[0040] Figure 5 This is a system flowchart of an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0043] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0044] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0045] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0046] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0047] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0048] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0049] It should be noted that the dynamic diet update method for patients with chronic diseases provided in this application is generally executed by a server / terminal device, and correspondingly, the dynamic diet update system for patients with chronic diseases is generally set up in the server / terminal device.

[0050] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0051] Reference Figure 2 and Figure 3 , Figure 2 This is a logic execution diagram of a dynamic diet update method for patients with chronic diseases provided in an embodiment of this disclosure. Figure 3 This is an optional flowchart of a dynamic diet update method for patients with chronic diseases provided in this embodiment of the present disclosure. The dynamic diet update method for patients with chronic diseases can be executed by a terminal, executed by a server, or executed by a combination of a terminal and a server. The dynamic diet update method for patients with chronic diseases includes, but is not limited to, the following steps 301 to 305.

[0052] Step 301: Based on the chronic disease type, specific disease stage, and clinical indicators of patients with chronic diseases, establish core indicators for dietary restrictions and dietary recommendations.

[0053] In this embodiment, core indicators for dietary restrictions and recommendations are established based on the chronic disease type, specific disease stage, and clinical indicators of patients with chronic diseases. Please refer to [reference needed]. Figure 4 The specific content includes:

[0054] Step 41: Transform the chronic disease type, specific disease stage, and clinical indicators of patients with chronic diseases into structured factual information.

[0055] Step 42: Construct a rule set based on medical guidelines. Each rule in the rule set includes a condition part and a conclusion part. The condition part is associated with the type of chronic disease, the specific stage of the disease, and the threshold range of clinical indicators. The conclusion part includes the corresponding dietary restriction parameters and dietary recommendation parameters.

[0056] Step 43: Match the structured fact information with the rules in the rule set. When the structured fact information meets the condition of a certain rule, trigger the conclusion of that rule.

[0057] Step 44: Transform the triggered conclusion into a dietary restriction and recommendation plan in natural language form, which includes a specific food list, intake amount and implementation instructions for the individual patient with chronic disease.

[0058] It is important to note that the occurrence and development of chronic diseases are closely related to diet, and unreasonable dietary habits can exacerbate the pathological state of chronic diseases. For diabetic patients, excessive carbohydrate intake can cause elevated blood sugar, increasing the burden on the pancreas; for hypertensive patients, excessive sodium intake can increase the metabolic burden on the kidneys; and for patients with chronic diseases, excessive protein intake can increase the metabolic burden on the kidneys. Dietary restrictions vary depending on the stage of the disease. Clinical indicators

[0059] The types of chronic diseases covered in this application include diabetes, hypertension, and coronary heart disease, and the specific stages of the disease include the initial stage, the stable stage, and the exacerbation stage. Different chronic diseases have different nutritional needs and reasonable intake ranges, and different chronic diseases have different foods to avoid. Based on the type of chronic disease, the specific stage of the disease, and clinical indicators, by understanding the recommended and prohibited foods for chronic disease patients, this application aims to avoid excessive dietary control caused by patients not knowing what to eat or what foods to avoid, which could negatively impact their health.

[0060] Based on the type of chronic disease, the specific stage of the disease, and clinical indicators, this application establishes core indicators for dietary restrictions and recommendations, enabling the determination of the direction and intensity of dietary intervention for chronic disease patients from three dimensions: the nature of the disease, the degree of development, and the real-time status.

[0061] Step 302: Collect information on the occupational type and work environment of patients with chronic diseases to identify factors that hinder the implementation of the diet plan.

[0062] It should be noted that people in different occupational environments may have the same type and stage of disease, but their energy requirements from food will differ due to differences in actual energy consumption.

[0063] In this embodiment of the application, the occupational type and work environment of patients with chronic diseases are collected to identify factors that hinder adherence to the diet plan, including:

[0064] We collect occupational type information from patients with chronic diseases, obtain work environments related to occupational types, analyze the impact of occupational types and work environments on the dietary behavior of patients with chronic diseases, identify obstacles that may lead to difficulties in diet implementation, and transform these obstacles into structured descriptions.

[0065] In this embodiment, occupational type information of patients with chronic diseases is collected, which may include categories such as sedentary office work, outdoor manual labor, shift work in factories, or other types of outdoor work. Work environment information related to occupational type is obtained, including work schedule, dining conditions, restrictions on meal times, and work stress levels. The impact of occupational type and work environment on the dietary behavior of patients with chronic diseases is analyzed, including irregular meals, limited food choices, inability to cook, and high work stress levels. These obstacles are transformed into structured descriptions, and through these structured descriptions, feasible recipes are recommended for patients with chronic diseases.

[0066] In this embodiment of the application, occupational type information of patients with chronic diseases is collected, and work environments related to these occupational types are obtained. This includes obtaining the occupational types and work environments of patients with chronic diseases based on conversations with them, questionnaires, etc. The occupational type information of patients with chronic diseases includes structured data and unstructured text data.

[0067] In this application embodiment, the impact of occupational type and work environment on the dietary behavior of patients with chronic diseases is analyzed, obstacles that may lead to difficulties in diet adherence are identified, and these obstacles are transformed into structured descriptions, including:

[0068] For unstructured text data, natural language preprocessing is used to extract keywords and form preliminary obstacle descriptions. For structured data, feature encoding is performed, and an obstacle factor dataset is constructed based on the preliminary obstacle descriptions and feature encodings. High-frequency co-occurring obstacle factor combinations in the obstacle factor dataset are identified through association rules. The association rules of obstacle factors that meet the preset reliability and lift thresholds among the high-frequency co-occurring obstacle factor combinations are selected, and the obstacle factor association rules are transformed into structured descriptions containing label, category, and degree of influence fields.

[0069] For example, suppose the collected data on chronic disease patients includes irregular meal times, limited dietary choices, insufficient cooking time, excessive psychological stress, shift work, and lack of dietary knowledge. Association rules are used to identify frequently co-occurring combinations of obstacle factors in the obstacle factor dataset. These include irregular meal times and insufficient cooking time (35% support), limited dietary choices and shift work (40% support), and excessive psychological stress and irregular meal times (32% support). Association rules are then selected from the frequently co-occurring obstacle factor combinations that meet pre-set confidence and lift thresholds. Rule 1: Irregular meal times – shift work (80% confidence, 1.6 lift); limited dietary choices – limited food resources at work location (85% confidence, 1.8 lift); insufficient cooking time (70% confidence, 1.7 lift); excessive psychological stress (75% confidence, 1.5 lift). Therefore, the obstacle factors include irregular meal times, limited dietary choices, insufficient cooking time, and excessive psychological stress.

[0070] The association rules for obstacle factors are transformed into a structured description containing fields for label, category, and degree of impact, as shown in the table below:

[0071]

[0072] Step 303: Based on the dietary habits, taste preferences, economic capacity, and cooking ability of patients with chronic diseases, determine the dietary differences among patients with chronic diseases.

[0073] In this embodiment of the application, the dietary differences among patients with chronic diseases are determined based on their dietary habits, taste preferences, economic capacity, and cooking abilities, including:

[0074] Data on dietary habits, taste preferences, economic capacity, and cooking skills were collected and quantified to construct a multidimensional feature vector for patients with chronic diseases. Principal component analysis was used to reduce the dimensionality of these multidimensional features, revealing the core features reflecting dietary differences. In a pre-defined reference group database, distance metrics and statistical tests were used to determine the degree of deviation between the core features of chronic disease patients and the dietary characteristics of the reference group, thus identifying dietary differences.

[0075] In this embodiment, dietary habit data of patients with chronic diseases is acquired, including but not limited to food types, meal frequency, and eating habits. Taste preference data of patients with chronic diseases is acquired, including their preference for salty, sweet, spicy, and sour flavors. Economic capacity-related data is acquired, covering information such as average monthly disposable income and food budget. Cooking ability-related data is acquired, including cooking skill level, frequency of self-cooking, and available kitchen equipment. Qualitative descriptions are quantified through coding, and quantitative data such as economic capacity and cooking ability are numericalized. Principal component analysis is used to extract key potential factors reflecting dietary differences among patients with chronic diseases, reducing feature dimensionality and highlighting key variables. Distance metrics can include Euclidean distance, cosine similarity, etc.

[0076] Step 304: Integrate the core indicators, factors hindering the implementation of the recipe, and dietary differences to obtain a recipe that comprehensively considers nutrition and practical operability.

[0077] In this embodiment, core indicators, factors hindering recipe implementation, and dietary differences are integrated to obtain a recipe that comprehensively considers nutrition and practical feasibility, including:

[0078] By integrating core indicators, dietary implementation barriers, and dietary differences into a multi-dimensional feature set, a unified personalized dietary feature description is formed. This personalized dietary feature description constrains a pre-set multi-objective optimization function to generate a dietary plan that comprehensively considers nutrition and practical operability. The core indicators determine the nutritional needs and dietary limitations of patients with chronic diseases; dietary implementation barriers screen dietary plan elements to ensure the practical operability of the plan; and dietary difference information adjusts the taste preferences and economic practicality of the plan.

[0079] It should be noted that this application embodiment constrains the multi-objective optimization function through personalized dietary feature description. The multi-objective optimization function generates and outputs optimized personalized meal plan schemes under multiple objectives, including meeting nutritional needs, feasibility of execution, and personalized preferences. Core indicators determine the nutritional needs and limitations of patients with chronic diseases, such as the range of calorie, protein, fat, and carbohydrate intake, as well as disease-related dietary restrictions. Meal plan execution barriers assess the operability limitations of patients with chronic diseases, removing or adjusting dietary plan elements that are not suitable for the current barriers. Dietary difference data adjusts taste and dietary preferences to ensure that the meal plan matches individual tastes and economic capabilities.

[0080] Step 305: Obtain real-time information on changes in the disease status of patients with chronic diseases, adjustments to their occupational status, and feedback on diet implementation, and dynamically update the diet information of patients with chronic diseases.

[0081] In this embodiment of the application, real-time acquisition of disease status changes, occupational status adjustments, and diet execution feedback information of patients with chronic diseases is used to dynamically update the diet information of patients with chronic diseases, including:

[0082] By integrating information on changes in disease status, adjustments in occupational status, and feedback on diet plan implementation with core indicators, factors hindering diet plan implementation, and dietary differences, a dynamic comprehensive status dataset for chronic disease patients is constructed. Based on time series data, the trajectory of status changes in the dynamic comprehensive status dataset is obtained, and the current health and behavioral status of chronic disease patients is analyzed. Based on the current health and behavioral status and combined with preset diet plan adjustment rules, when a chronic disease patient needs to adjust their diet plan, a personalized diet plan that matches the patient's real-time status is dynamically generated.

[0083] In this embodiment, the disease status changes, occupational status adjustments, and diet execution feedback information of chronic disease patients are acquired in real time. The dynamic status dataset is updated in chronological order based on the real-time collected data to form a historical trajectory of the chronic disease patient's status changes.

[0084] In the embodiments of this application, in the process of dynamically generating personalized diet plans that conform to the real-time status of patients, the dietary differences and impairment factors of patients with chronic diseases are taken into account. This application effectively supports the health maintenance and quality of life improvement of patients with chronic diseases by making real-time dynamic adjustments.

[0085] In this embodiment of the application, the recipe is dynamically adjusted and updated when at least one of the following data changes: the patient's disease status changes, occupational status is adjusted, or the recipe execution feedback information.

[0086] In this embodiment of the application, based on a preset multi-dimensional fusion data structure, the changes in disease status, adjustments in occupational status, and feedback information and core indicators of diet execution, as well as factors hindering diet execution and dietary differences, are uniformly integrated.

[0087] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0088] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0089] Continued reference Figure 5 As a response to the above Figure 2 The implementation of the method shown in this application provides an embodiment of a dynamic diet update system for patients with chronic diseases. This system embodiment is similar to... Figure 2 Corresponding to the method embodiment shown, this system can be specifically applied to various electronic devices, including: a core indicator establishment module 501, a recipe execution obstacle identification module 502, a dietary difference determination module 503, a recipe framework determination module 504, and a recipe dynamic update module 505, wherein:

[0090] The core indicator establishment module 501 is used to establish core indicators for dietary restrictions and dietary recommendations based on the chronic disease type, specific disease stage and clinical indicators of patients with chronic diseases.

[0091] The recipe execution obstacle identification module 502 is used to collect information on the occupational type and work environment of patients with chronic diseases and identify recipe execution obstacle elements.

[0092] The dietary difference determination module 503 is used to determine the dietary differences among patients with chronic diseases based on their dietary habits, taste preferences, economic capacity, and cooking abilities.

[0093] The recipe framework determination module 504 is used to integrate core indicators, recipe implementation barriers, and dietary differences to obtain a recipe that comprehensively considers nutrition and practical operability.

[0094] The recipe dynamic update module 505 is used to obtain real-time information on changes in the disease status, occupational status adjustments, and recipe execution feedback of patients with chronic diseases, and to dynamically update the recipe information of patients with chronic diseases.

[0095] This application establishes core indicators for dietary restrictions and recommendations based on the chronic disease type, specific disease stage, and clinical indicators of chronic disease patients; collects information on the occupational type and work environment of chronic disease patients to identify factors hindering diet implementation; identifies dietary differences among chronic disease patients based on their eating habits, taste preferences, economic capacity, and cooking abilities; integrates the core indicators, factors hindering diet implementation, and dietary differences to obtain a comprehensive diet that considers both nutrition and practical operability; and dynamically updates the diet information of chronic disease patients by acquiring real-time information on changes in their disease status, adjustments to their occupational status, and feedback on diet implementation. By developing practically executable diets and dynamically updating them, this application adapts to the development of chronic disease conditions and changes in the patient's body, while ensuring the patient's health.

[0096] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0097] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are interconnected via a system bus. It should be noted that only the computer device 6 with components 6a-6c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0098] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0099] The memory 6a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 6a may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 6a may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 6a may also include both the internal storage unit and its external storage device of the computer device 6. In this embodiment, the memory 6a is typically used to store the operating system and various application software installed on the computer device 6, such as program code for a dynamic diet update method based on chronic disease patients. In addition, the memory 6a can also be used to temporarily store various types of data that have been output or will be output.

[0100] In some embodiments, the processor 6b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 6b is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to run program code stored in the memory 6a or process data, for example, to run the program code for the dynamic diet update method based on patients with chronic diseases.

[0101] The network interface 6c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 6 and other electronic devices.

[0102] This application also provides another embodiment, namely, a non-volatile computer-readable storage medium storing a program for a dynamic recipe update method based on patients with chronic diseases, wherein the dynamic recipe update based on patients with chronic diseases can be executed by at least one processor to perform the steps of the dynamic recipe update method based on patients with chronic diseases as described above.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0104] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for updating dynamic diet plans based on patients with chronic diseases, characterized in that, include: Based on the chronic disease type, specific disease stage, and clinical indicators of patients with chronic diseases, core indicators for dietary restrictions and dietary recommendations are established. Collect information on the occupational type and work environment of patients with chronic diseases to identify factors that hinder adherence to dietary plans; Based on the dietary habits, taste preferences, economic capacity, and cooking abilities of patients with chronic diseases, we determined the dietary differences among patients with chronic diseases. By integrating core indicators, factors hindering the implementation of recipes, and dietary differences, we can obtain recipes that comprehensively consider nutrition and practicality. It can obtain real-time information on changes in the disease status, occupational status adjustments, and diet implementation feedback of patients with chronic diseases, and dynamically update the diet information of patients with chronic diseases.

2. The method for updating dynamic diets for patients with chronic diseases according to claim 1, characterized in that, Based on the type of chronic disease, specific stage of the disease, and clinical indicators of patients with chronic diseases, core indicators for dietary restrictions and recommendations are established, including: Transform the chronic disease type, specific disease stage, and clinical indicators of patients with chronic diseases into structured factual information; A rule set is constructed based on medical guidelines. Each rule in the rule set includes a condition part and a conclusion part. The condition part is associated with the type of chronic disease, the specific stage of the disease, and the threshold range of clinical indicators. The conclusion part includes the corresponding dietary restriction parameters and dietary recommendation parameters. The structured fact information is matched with the rules in the rule set. When the structured fact information meets the condition of a certain rule, the conclusion part of that rule is triggered. The triggered conclusions will be translated into dietary restrictions and recommendations in natural language.

3. The method for updating dynamic diets for patients with chronic diseases according to claim 1, characterized in that, Collect information on the occupational type and work environment of patients with chronic diseases to identify factors that hinder diet adherence, including: We collect occupational type information from patients with chronic diseases, obtain work environments related to occupational types, analyze the impact of occupational types and work environments on the dietary behavior of patients with chronic diseases, identify obstacles that may lead to difficulties in diet implementation, and transform these obstacles into structured descriptions.

4. The method for updating dynamic diets for patients with chronic diseases according to claim 3, characterized in that, This study analyzes the impact of occupational type and work environment on the dietary behavior of patients with chronic diseases, identifies potential obstacles to diet adherence, and transforms these obstacles into structured descriptions, including: Keyword extraction is performed on unstructured text data to form a preliminary obstacle description; feature encoding is performed on structured data, and an obstacle factor dataset is constructed based on the preliminary obstacle description and feature encoding; high-frequency co-occurring obstacle factor combinations in the obstacle factor dataset are identified through association rules; obstacle factor association rules that meet the preset reliability and lift thresholds are selected from the high-frequency co-occurring obstacle factor combinations, and the obstacle factor association rules are transformed into a structured description containing label, category and degree of influence fields.

5. The method for updating dynamic diets for patients with chronic diseases according to claim 1, characterized in that, Based on the dietary habits, taste preferences, economic capacity, and cooking abilities of patients with chronic diseases, dietary differences among them are determined, including: The collected data on dietary habits, taste preferences, economic capacity, and cooking skills are feature-coded and quantified to construct a multidimensional feature vector for patients with chronic diseases. Principal component analysis was used to reduce the dimensionality of multidimensional features and obtain the core features that reflect dietary differences. In a pre-defined reference group database, the degree of deviation between the core characteristics of chronic disease patients and the dietary characteristics of the reference group is determined by distance measurement and statistical tests, thereby identifying dietary differences.

6. The method for updating dynamic diets for patients with chronic diseases according to claim 1, characterized in that, By integrating core indicators, factors hindering recipe implementation, and dietary differences, we can obtain recipes that comprehensively consider both nutritional value and practical feasibility, including: By integrating core indicators, dietary implementation barriers, and dietary differences into a multi-dimensional feature set, a unified personalized dietary feature description is formed. This personalized dietary feature description constrains a pre-set multi-objective optimization function to generate a dietary plan that comprehensively considers nutrition and practical operability. The core indicators determine the nutritional needs and dietary limitations of patients with chronic diseases; dietary implementation barriers screen dietary plan elements to ensure the practical operability of the plan; and dietary difference information adjusts the taste preferences and economic practicality of the plan.

7. The method for updating dynamic diets for patients with chronic diseases according to claim 1, characterized in that, Real-time acquisition of information on changes in disease status, occupational status adjustments, and diet plan implementation feedback for patients with chronic diseases; dynamic updating of diet plan information for patients with chronic diseases, including: By integrating information on changes in disease status, adjustments in occupational status, and feedback on diet plan implementation with core indicators, factors hindering diet plan implementation, and dietary differences, a dynamic comprehensive status dataset for chronic disease patients is constructed. Based on time series data, the trajectory of status changes in the dynamic comprehensive status dataset is obtained, and the current health and behavioral status of chronic disease patients is analyzed. Based on the current health and behavioral status and combined with preset diet plan adjustment rules, when a chronic disease patient needs to adjust their diet plan, a personalized diet plan that matches the patient's real-time status is dynamically generated.

8. A dynamic diet update system for patients with chronic diseases, used to implement the dynamic diet update method for patients with chronic diseases according to claims 1-7, characterized in that, include: The core indicator establishment module is used to establish core indicators for dietary restrictions and dietary recommendations based on the type of chronic disease, specific disease stage, and clinical indicators of patients with chronic diseases. The diet plan execution obstacle identification module is used to collect information on the occupational type and work environment of patients with chronic diseases to identify diet plan execution obstacle elements; The dietary difference determination module is used to determine the dietary differences among patients with chronic diseases based on their dietary habits, taste preferences, economic capacity, and cooking abilities. The recipe framework determination module is used to integrate core indicators, recipe implementation barriers, and dietary differences to obtain recipes that comprehensively consider nutrition and practical operability. The recipe dynamic update module is used to obtain real-time information on changes in the disease status, occupational status adjustments, and recipe execution feedback of patients with chronic diseases, and to dynamically update the recipe information of patients with chronic diseases.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

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