Fat reduction strategy generation method and device, electronic equipment and computer program product

Abstract

The invention discloses a fat reduction strategy generation method and device, electronic equipment and a computer program product. The method relates to the technical field of artificial intelligence and comprises the steps that body data and preference data of a target user are collected, and the body data at least comprises metabolism data and daily basic activity consumption data; a candidate fat reduction strategy set is determined according to the body data and the preference data, and each fat reduction strategy comprises an exercise strategy and an energy intake strategy; for each fat reduction strategy, calculating dynamic calorie data according to the body data and the content of the fat reduction strategy, and determining dynamic weight data according to the dynamic calorie data and the current weight of the target user; and according to the dynamic weight data and the preference data associated with each fat-reducing strategy, screening out a target fat-reducing strategy from the candidate fat-reducing strategy set. Through the method and the device, the problem that the fat reduction success rate is affected due to the fact that fat reduction strategies in related technologies are difficult to adapt to the state of the user is solved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and more specifically, to a method, apparatus, electronic device, and computer program product for generating a weight loss strategy. Background Technology

[0002] Weight is an important indicator of human health. As people pay more attention to health, keeping their weight within a reasonable range is a common aspiration.

[0003] Fat-burning exercises are an important part of weight control. Many people follow the fat-burning strategies of fitness apps to achieve weight control. In this process, the formulation and execution of fat-burning strategies are the focus of many users.

[0004] However, the weight loss strategies in related technologies mainly preset fixed target weight loss speeds, which cannot meet the specific needs of different users. In order to solve this problem, some weight loss strategies have emerged in related technologies, which recommend a general weight loss strategy based on the basic information input by the user. However, they do not take into account the changes in the user's state during weight loss exercise training, and the prediction of weight loss effect is inaccurate, which reduces the likelihood of users following the weight loss strategy and the success rate of weight loss.

[0005] There is currently no effective solution to the problem that weight loss strategies in related technologies are difficult to adapt to the user's condition, thus affecting the success rate of weight loss. Summary of the Invention

[0006] The main purpose of this application is to provide a method, device, electronic device and computer program product for generating a weight loss strategy, so as to solve the problem that the weight loss strategy in the related technology is difficult to adapt to the user's status and affects the success rate of weight loss.

[0007] To achieve the above objectives, according to one aspect of this application, a method for generating a weight loss strategy is provided. The method includes: collecting body data and preference data of a target user, wherein the body data includes at least metabolic data and daily basal activity expenditure data; determining a set of candidate weight loss strategies based on the body data and preference data, wherein each weight loss strategy includes an exercise strategy and an energy intake strategy; for each weight loss strategy, calculating dynamic calorie data based on the body data and the content of the weight loss strategy, and determining dynamic weight data based on the dynamic calorie data and the target user's current weight; and selecting a target weight loss strategy from the set of candidate weight loss strategies based on the dynamic weight data associated with each weight loss strategy and the preference data.

[0008] Optionally, determining the candidate fat loss strategy set based on body data and preference data includes: determining an objective function and determining the constraints of the objective function based on body data and preference data; determining an initial solution space set that meets the constraints, wherein each solution space includes a first solution space code and a second solution space code, the first solution space code representing an exercise strategy and the second solution space code representing an energy intake strategy; iteratively solving the initial solution space based on the objective function to obtain a candidate solution space set, and determining the candidate fat loss strategy set based on the candidate solution space set.

[0009] Optionally, iteratively solving the initial solution space based on the objective function to obtain a candidate solution space set includes: selecting multiple solutions whose fitness meets the fitness condition from the initial solution space set, and reorganizing the multiple solutions whose fitness meets the fitness condition to obtain the solution space set for the next round, wherein the fitness of each solution is calculated according to the objective function; if the iteration termination condition is not met, selecting multiple solutions that meet the fitness condition from the solution space set for the next round, and reorganizing the multiple solutions that meet the fitness condition to obtain the solution space set for the next round after that, until the iteration termination condition is met.

[0010] Optionally, selecting multiple solutions whose fitness meets the fitness condition from the initial solution space set and reorganizing these solutions to obtain the solution space set for the next round includes: selecting multiple solutions from the initial solution space set using a competitive selection strategy; calculating the fitness of each selected solution according to the objective function and selecting multiple solutions whose fitness meets the fitness condition from the selected solutions; performing cross-merging on the multiple solutions whose fitness meets the fitness condition to obtain multiple solutions after cross-merging; and performing mutation processing on the multiple solutions after cross-merging to obtain the solution space set for the next round.

[0011] Optionally, the objective function represents: the total training expenditure, penalty, and reward of the fat loss strategy. The penalty includes at least one of the following: energy deficit excess penalty, duration penalty, and exercise course repetition penalty. The reward includes at least one of the following: progressive exercise load reward and weekly training frequency stability reward. The constraints include at least one of the following: daily energy deficit is within a preset energy deficit range, daily training duration is within a preset duration range, training days are fixed within the fat loss strategy cycle, exercise courses are not repeated within the fat loss strategy cycle, and the difficulty of exercise courses increases within the fat loss strategy cycle.

[0012] Optionally, for each weight loss strategy, dynamic calorie data is calculated based on body data and the content of the weight loss strategy. Dynamic weight data is determined based on dynamic calorie data and the target user's current weight, including: calculating the difference between total energy expenditure and intake data daily within the weight loss strategy period to obtain a daily energy deficit, wherein total energy expenditure data includes exercise energy expenditure data and basic activity energy expenditure data; determining the daily cumulative energy deficit based on the daily energy deficit, and determining the daily predicted weight loss data based on the daily cumulative energy deficit and the energy equation; and determining the daily predicted weight based on the current weight and the daily predicted weight loss data to obtain dynamic weight data.

[0013] Optionally, selecting a target weight loss strategy from the candidate weight loss strategy set based on the dynamic weight data and preference data associated with each weight loss strategy includes: selecting a weight loss strategy that achieves the weight loss goal from the candidate weight loss strategy set based on the dynamic weight data, wherein the predicted weight and target weight on the last day of the weight loss strategy that achieves the weight loss goal are less than a preset difference; and selecting a target weight loss strategy from the weight loss strategies that achieve the weight loss goal based on preference data.

[0014] To achieve the above objectives, according to another aspect of this application, a device for generating a weight loss strategy is provided. The device includes: a data collection unit for collecting body data and preference data of a target user, wherein the body data includes at least metabolic data and daily basal activity expenditure data; a first determining unit for determining a set of candidate weight loss strategies based on the body data and preference data, wherein each weight loss strategy includes an exercise strategy and an energy intake strategy; a second determining unit for calculating dynamic calorie data for each weight loss strategy based on the body data and the content of the weight loss strategy, and determining dynamic weight data based on the dynamic calorie data and the target user's current weight; and a filtering unit for filtering a target weight loss strategy from the set of candidate weight loss strategies based on the dynamic weight data associated with each weight loss strategy and the preference data.

[0015] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes a method for generating a fat-loss strategy during runtime.

[0016] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of a method for generating a fat-reduction strategy.

[0017] In this embodiment, body data and preference data of the target user are collected. The body data includes at least metabolic data and daily basic activity expenditure data. A set of candidate weight loss strategies is determined based on the body data and preference data. Each weight loss strategy includes an exercise strategy and an energy intake strategy. For each weight loss strategy, dynamic calorie data is calculated based on the body data and the content of the weight loss strategy, and dynamic weight data is determined based on the dynamic calorie data and the target user's current weight. A target weight loss strategy is selected from the set of candidate weight loss strategies based on the dynamic weight data associated with each weight loss strategy and the preference data. This solves the problem in related technologies where weight loss strategies are difficult to adapt to the user's condition, affecting the success rate of weight loss. By determining a set of candidate weight loss strategies based on body data and preference data, and then selecting a target weight loss strategy from the set based on dynamic weight data and preference data, the target weight loss strategy is correlated with the user's weight changes and preferences, thereby generating a weight loss strategy that is suitable for the user's condition and improving the success rate of weight loss. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 A hardware block diagram of a computer terminal for implementing a method for generating a weight loss strategy is shown.

[0020] Figure 2 This is a flowchart of a method for generating a weight loss strategy according to an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of a device for generating a weight loss strategy according to an embodiment of this application;

[0022] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0023] 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. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding operation entry points for them to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making process. If the user chooses to agree, the user can view the purpose of data use in real time through the authorization interface and has the right to withdraw authorization or delete data at any time. After authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0026] Example 1

[0027] According to an embodiment of this application, a method for generating a weight loss strategy is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a method for generating a weight loss strategy is shown. Figure 1 As shown, computer terminal 10 (or mobile device) may include one or more ( Figure 1The processor 102 (illustrated as 102a, 102b, ..., 102n) may include, but is not limited to, a processing device such as a microprocessor (MCU) or a field-programmable gate array (FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may include: a display, an input / output interface (I / O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0030] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the weight loss strategy generation method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned weight loss strategy generation method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0032] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0033] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for generating the fat loss strategy shown. Figure 2 This is a flowchart of a method for generating a weight loss strategy according to an embodiment of this application.

[0034] Step S201: Collect the target user's body data and preference data, wherein the body data includes at least metabolic data and daily basic activity consumption data.

[0035] The executing entity in this embodiment can be fitness software, which collects the target user's body data and preference data with the user's authorization.

[0036] Among these, the metabolic data in the body data refers to the user's basal metabolic rate (BMR), which is the minimum amount of energy required to maintain basic life functions at rest. The daily basal activity expenditure data in the body data refers to the calories burned during non-exercise activity (NEAT), including calories burned naturally during daily activities such as standing and walking.

[0037] It should be noted that body data is not limited to metabolic data and daily basic activity expenditure data. For example, it can also include baseline daily intake, which is the user's average daily calorie intake without special dietary control.

[0038] The preference data can include various types, such as users' preferences for exercise types, training time, dietary tastes, and training intensity.

[0039] This step lays the foundation for generating personalized weight loss strategies by comprehensively and accurately collecting the target user's body and preference data.

[0040] Step S202: Determine a set of candidate fat loss strategies based on body data and preference data, wherein each fat loss strategy includes an exercise strategy and an energy intake strategy.

[0041] The candidate fat loss strategy set includes a series of programs incorporating exercise strategies and energy intake strategies. The exercise strategy includes training programs over a period of time, such as dividing the week into training days and rest days and determining the training program for each training day. The energy intake strategy may include intake adjustment data over a period of time, such as an intake coefficient vector for each day over 30 days, where the intake coefficient vector represents the adjustment ratio of dietary intake relative to a baseline intake.

[0042] For example, an initial set of weight loss strategies can be generated first, and then weight loss strategies that match the target user's body data and preference data can be selected from it to obtain a set of candidate weight loss strategies.

[0043] Step S203: For each weight loss strategy, calculate dynamic calorie data based on body data and the content of the weight loss strategy, and determine dynamic weight data based on the dynamic calorie data and the target user's current weight.

[0044] Dynamic calorie data can be the difference between total energy expenditure and energy intake for each day within the time period indicated by the predicted weight loss strategy. It reflects the daily calorie surplus or deficit. Since the daily calorie surplus or deficit determines the target user's daily weight, dynamic weight data is obtained by predicting the target user's daily weight based on the target user's current weight and the daily calorie surplus or deficit.

[0045] This step provides a quantitative basis for selecting subsequent target weight loss strategies by converting each candidate weight loss strategy into specific changes in calorie surplus / deficit and corresponding weight changes.

[0046] Step S204: Select the target weight loss strategy from the candidate weight loss strategy set based on the dynamic weight data and preference data associated with each weight loss strategy.

[0047] For example, one can first filter out weight loss strategies that achieve the weight loss goal from a set of candidate weight loss strategies based on dynamic weight data, and then filter out weight loss strategies that match the user's preferences based on preference data to obtain the target weight loss strategy. Alternatively, one can first filter out weight loss strategies that match the user's preferences from a set of candidate weight loss strategies based on preference data, and then filter out weight loss strategies that achieve the weight loss goal based on dynamic weight data to obtain the target weight loss strategy.

[0048] Then, the target exercise strategy and target energy intake strategy from the target fat loss strategy, along with the associated dynamic weight data, are recommended to the target users, enabling them to achieve a balance between diet and training: more exercise and more food, less exercise and less food.

[0049] The method for generating a weight loss strategy provided in this application collects the target user's body data and preference data, wherein the body data includes at least metabolic data and daily basic activity consumption data; a set of candidate weight loss strategies is determined based on the body data and preference data, wherein each weight loss strategy includes an exercise strategy and an energy intake strategy; for each weight loss strategy, dynamic calorie data is calculated based on the body data and the content of the weight loss strategy, and dynamic weight data is determined based on the dynamic calorie data and the target user's current weight; based on the dynamic weight data and preference data associated with each weight loss strategy, a target weight loss strategy is selected from the set of candidate weight loss strategies. This solves the problem in related technologies where weight loss strategies are difficult to adapt to the user's condition, affecting the success rate of weight loss. By determining the set of candidate weight loss strategies based on body data and preference data, and then selecting the target weight loss strategy from the set based on dynamic weight data and preference data, the target weight loss strategy is related to the user's weight changes and preferences, thereby achieving the effect of generating a weight loss strategy adapted to the user's condition and improving the success rate of weight loss.

[0050] Optionally, in the method for generating a fat loss strategy provided in this application embodiment, determining a set of candidate fat loss strategies based on body data and preference data includes: determining an objective function and determining constraints on the objective function based on body data and preference data; determining an initial solution space set that meets the constraints, wherein each solution space includes a first solution space code and a second solution space code, the first solution space code representing an exercise strategy and the second solution space code representing an energy intake strategy; iteratively solving the initial solution space based on the objective function to obtain a set of candidate solution spaces, and determining a set of candidate fat loss strategies based on the set of candidate solution spaces.

[0051] For example, the objective function and constraints are first determined. The objective function balances multiple dimensions of needs during the determination of the candidate fat loss strategy set. For instance, the objective function can be set by combining total training expenditure, penalty terms, and reward terms to balance weight loss effectiveness, health and safety, and user preferences. The constraints of the objective function are the hard boundaries in determining the candidate fat loss strategy set. These constraints may include a range of health energy deficits, upper limits on training duration, and requirements for course diversity, ensuring that the generated fat loss strategies do not adversely affect the user's health.

[0052] Then, the problem of determining the set of candidate fat loss strategies is transformed into a constrained optimization problem. First, an initial solution space set that meets the constraints is determined. The initial solution space set includes multiple solution spaces. The first solution space in each solution space can be encoded as a course schedule matrix, which is determined by the number of days T and the course list C for the day. The second solution space in each solution space can be encoded as a 30-day intake coefficient vector. The intake coefficient vector is the adjustment ratio of dietary intake relative to the baseline intake, which provides dynamic calorie intake guidance for each training day and rest day, matching the exercise strategy and jointly promoting the achievement of weight loss goals.

[0053] Then, the constrained optimization problem is solved iteratively. The search direction is guided by the objective function. In each iteration, a set of high-quality solution spaces is selected. When the iteration termination condition is met, the set of solution spaces obtained in the last iteration is determined as the set of candidate solution spaces. Each candidate solution space in the set of candidate solution spaces represents a candidate weight loss strategy. Multiple candidate weight loss strategies constitute a set of candidate weight loss strategies.

[0054] This embodiment models the problem of formulating a fat loss strategy, sets up a dual-chromosome encoding (exercise strategy and energy intake strategy), determines the initial solution space set, solves the candidate solution space set based on the objective function and constraints, and determines the candidate fat loss strategy set, thus realizing the personalized customization of the fat loss strategy.

[0055] Optionally, in the method for generating a weight loss strategy provided in this application embodiment, iteratively solving the initial solution space based on the objective function to obtain a candidate solution space set includes: selecting multiple solutions whose fitness meets the fitness condition from the initial solution space set, and reorganizing the multiple solutions whose fitness meets the fitness condition to obtain the solution space set for the next round, wherein the fitness of each solution is calculated according to the objective function; if the iteration termination condition is not met, selecting multiple solutions that meet the fitness condition from the solution space set for the next round, and reorganizing the multiple solutions that meet the fitness condition to obtain the solution space set for the next round after that, until the iteration termination condition is met.

[0056] For example, after generating the initial solution space set, at the beginning of each iteration, the fitness value of each solution in the solution space is calculated. This value is derived from the objective function, which comprehensively considers weight loss effect, health constraints, and user preferences. The solution that the user finds a suitable weight loss strategy is a high-quality solution. Then, multiple solutions whose fitness values ​​meet the fitness conditions are selected, for example, multiple solutions with fitness values ​​greater than a preset fitness.

[0057] Next, the selected high-quality solutions are recombined. For example, crossover and mutation operations can be performed. For example, the high-quality solutions are used as parents and two-point crossover is performed to generate the genes of the next generation of solutions. At the same time, in order to maintain the diversity of the solution space and avoid the algorithm from converging to a local optimum too early, the next generation of solutions is randomly mutated.

[0058] By reorganizing the solution space, a new solution space set is obtained for the next round. The solutions in this set are the results of the previous generation's solutions optimized by the algorithm. After reorganizing the solution space, it is checked whether the iteration termination condition is met. If not (for example, the number of iterations has not reached the preset upper limit of X rounds, or the objective function value has not converged to a sufficiently small range of change), the above steps are repeated from the solution space set of the next round: screening, calculating fitness values, and reorganizing, until the iteration termination condition is met. The resulting solution space set is the candidate solution space set, which contains multiple optimized fat loss strategy options.

[0059] This embodiment continuously optimizes the solution space through algorithm optimization. By screening and recombination, a candidate solution space set is selected from the initial solution space set, which is a weight loss strategy tailored for the user (course candidate set and daily intake coefficient set). This not only finds a weight loss strategy that meets the weight loss needs, but also makes the weight loss strategy conform to the user's personal preferences, while meeting health and safety standards.

[0060] Optionally, in the method for generating a weight loss strategy provided in this application embodiment, the process of selecting multiple solutions whose fitness meets the fitness condition from the initial solution space set and recombining the multiple solutions whose fitness meets the fitness condition to obtain the solution space set for the next round includes: selecting multiple solutions from the initial solution space set using a competitive selection strategy; calculating the fitness of each selected solution according to the objective function and selecting multiple solutions whose fitness meets the fitness condition from the selected multiple solutions; performing cross-merging on the multiple solutions whose fitness meets the fitness condition to obtain multiple solutions after cross-merging; and performing mutation processing on the multiple solutions after cross-merging to obtain the solution space set for the next round.

[0061] For example, in the initial solution space set, a competitive selection strategy is first used to select solutions, ensuring that solutions with better performance are retained and further optimized. For instance, a tournament selection strategy can simulate natural competition, randomly selecting several solutions for comparison, with the winning solution being chosen to proceed to the next stage. Competitive selection strategies are not limited to competitive selection strategies; for example, they can also be used for roulette wheel selection.

[0062] Then, for each selected solution, the fitness is calculated using an objective function. This objective function can incorporate factors such as weight loss effectiveness, health constraints, user preferences, and strategy diversity. A high fitness value indicates that the solution is closer to the optimization objective. Based on the calculated fitness values, multiple solutions that meet the fitness criteria are selected, such as solutions with fitness values ​​greater than a fitness threshold. Such solutions not only achieve better weight loss results but also meet health, safety, and personalization requirements, laying the data foundation for subsequent iterations and optimizations.

[0063] Furthermore, a crossover operation is performed on the selected solutions, that is, a portion is selected from each of the two solutions and combined into a new solution. For example, by randomly selecting two points for crossover, multiple solutions are generated after crossover and merging, which promotes the genetic mixing between solutions (the fusion of course scheduling and dietary intake strategies), thereby generating new potential fat loss strategies.

[0064] Then, a mutation operation is performed on the solutions after cross-merging, that is, some genes in the solutions are randomly changed to obtain the solution space set for the next round. For example, the values ​​of courses in the course scheduling matrix or intake coefficient vectors are adjusted, thereby improving the diversity of the solution space, preventing the algorithm from converging to a local optimum too early during the search process, and ensuring the effectiveness of the global search.

[0065] This embodiment employs a competitive selection strategy to select high-quality solutions from the initial solution space set. Then, it generates the next round of solution space through cross-merging and adds a mutation strategy to ensure the diversity of the solution space and avoid getting trapped in local optima. After multiple rounds of iteration, the solution in the last round of the algorithm becomes the candidate solution space set.

[0066] Optionally, in the method for generating a fat loss strategy provided in this application embodiment, the objective function represents: the total training consumption, penalty, and reward of the fat loss strategy. The penalty includes at least one of the following: energy deficit excess penalty, duration penalty, and exercise course repetition penalty. The reward includes at least one of the following: progressive exercise load reward and weekly training frequency stability reward.

[0067] For example, the value of the objective function = the value of the total training expenditure term - the value of the total penalty term + the value of the total reward term. Here, the total training expenditure term represents the expected total calorie expenditure in the fat loss strategy. The value of the total penalty term is the sum of the values ​​of multiple penalty terms, and the value of the total reward term is the sum of the values ​​of multiple reward terms.

[0068] The penalty items, including energy deficit overload penalties and duration penalties, ensure that the weight loss strategy achieves its goals while avoiding the health risks associated with excessive exertion and prolonged training. The repetitive exercise program penalty prevents monotonous repetition and ensures the diversity of the weight loss strategy. The reward items, including progressive exercise load rewards and weekly training frequency stability rewards, guide the weight loss strategy to gradually increase and stabilize training intensity and frequency, helping users adapt to and adhere to the strategy.

[0069] This embodiment combines total training expenditure, penalty term, and reward term to set the objective function, thereby achieving a balance between weight loss effect, health and safety, and user preference in the process of determining the set of candidate fat loss strategies.

[0070] Optionally, in the method for generating a fat loss strategy provided in the embodiments of this application, the constraints include at least one of the following: the daily energy deficit is within a preset energy deficit range, the daily training duration is within a preset duration range, the training days within the cycle of the fat loss strategy are fixed, the exercise courses within the cycle of the fat loss strategy are not repeated, and the difficulty of the exercise courses within the cycle of the fat loss strategy is on an increasing trend.

[0071] For example, constraints may include health constraints, the purpose of which is to ensure the safety and health of the fat loss process. Specific requirements may include: controlling the daily energy deficit within a preset energy deficit range, so as to ensure the fat loss effect while avoiding excessive dieting that could cause physical damage and maintaining metabolic health.

[0072] For example, constraints may include time constraints, the purpose of which is to avoid overtraining. Specific requirements may include: controlling the daily training duration within a preset range, thereby making the fat loss strategy feasible and in line with the time schedule and lifestyle of ordinary users.

[0073] For example, constraints may include physiological constraints, the purpose of which is to ensure full recovery and avoid sports injuries. Specific requirements may include: fixing the training days within the cycle of the fat loss strategy (e.g., fixing the number of training days per week), so that the training days and rest days are scientifically spaced, allowing the body sufficient recovery time, which is in line with the physiological laws of the human body.

[0074] For example, constraints may include training diversity constraints, which aim to keep training fresh and improve user compliance. Specific requirements may include: controlling the exercise courses within the cycle of the fat loss strategy to avoid monotony and repetition, thereby keeping users interested in training and improving completion rate and user experience.

[0075] For example, constraints may include adaptive constraints, the purpose of which is to match the user's physical fitness development in a scientific and gradual manner. Specific requirements may include: controlling the difficulty of exercise courses within the cycle of the fat loss strategy to increase, so that lower difficulty courses are arranged in the early stage, and the training intensity is gradually increased as physical fitness improves, which is in line with scientific training principles and the law of human adaptation.

[0076] In determining the set of candidate weight loss strategies, this embodiment uses a multi-dimensional constraint penalty mechanism (e.g., health deficit, duration, course repetition, training day regularity, gradual difficulty, etc.) to constrain the generated candidate weight loss strategies, ensuring weight loss effectiveness while taking into account health, safety, personalization, and fun.

[0077] Optionally, in the method for generating a weight loss strategy provided in this application embodiment, for each weight loss strategy, dynamic calorie data is calculated based on body data and the content of the weight loss strategy, and dynamic weight data is determined based on the dynamic calorie data and the target user's current weight, including: calculating the difference between total consumption data and intake data daily within the period of the weight loss strategy to obtain a daily energy deficit, wherein the total consumption data includes exercise consumption data and basic activity consumption data; determining the daily cumulative energy deficit based on the daily energy deficit, and determining the daily predicted weight loss data based on the daily cumulative energy deficit and the energy equation; and determining the daily predicted weight based on the current weight and the daily predicted weight loss data to obtain dynamic weight data.

[0078] For example, during the period of a weight loss strategy, the difference between the user's total consumption data (including exercise consumption data involved in the specific content of the weight loss strategy and non-exercise daily activity consumption) and intake data is calculated daily, i.e. the daily energy deficit. By comparing daily consumption and intake, the user's energy surplus or deficit during the strategy execution process can be obtained, providing basic data for subsequent weight prediction.

[0079] Next, based on the daily energy deficit, the cumulative daily energy deficit is calculated, which is the sum of the daily energy deficit and the energy deficit from the previous day, reflecting the overall changes in energy balance throughout the entire weight loss strategy period. Then, the cumulative daily energy deficit is substituted into the energy equation for calculation. The energy equation treats weight change as a continuous function of time and uses the principle of energy balance (energy expenditure - energy intake = weight change) to dynamically predict the predicted weight loss data for each day.

[0080] Then, using the current weight as a starting point and combining it with the daily predicted weight loss data, the predicted weight for each day throughout the entire strategy period is calculated. The predicted weight over multiple days constitutes dynamic weight data, which is dynamically generated based on the energy equation and the user's actual strategy execution. Compared to non-static estimation, it can more accurately reflect the trend of the user's weight change over time.

[0081] In this embodiment, for each weight loss strategy, the energy expenditure on training days, energy expenditure on rest days, and cumulative deficit are calculated daily. The cumulative deficit is then substituted into the energy equation to obtain the predicted weight loss data for each day, thus achieving the goal of accurately predicting the long-term weight loss effect.

[0082] Optionally, in the method for generating a weight loss strategy provided in this application embodiment, selecting a target weight loss strategy from the candidate weight loss strategy set based on the dynamic weight data and preference data associated with each weight loss strategy includes: selecting a weight loss strategy that achieves a weight loss goal from the candidate weight loss strategy set based on the dynamic weight data, wherein the predicted weight and target weight on the last day of the weight loss strategy that achieves the weight loss goal are less than a preset difference; and selecting a target weight loss strategy from the weight loss strategies that achieve the weight loss goal based on preference data.

[0083] For example, for a weight loss strategy in the candidate weight loss strategy set, the predicted weight data for the last day of the weight loss strategy is determined based on dynamic weight data. The predicted weight data for the last day is compared with the weight indicated by the weight loss target. If the difference between the two is small, it means that the weight loss strategy can achieve the expected weight loss target, and the weight loss strategy is determined as the weight loss strategy that achieves the weight loss target.

[0084] Furthermore, a second screening is conducted based on user preference data, which includes, but is not limited to, users' preferences for exercise types, training time arrangements, and dietary habits. The preference data reflects users' personal preferences and feasibility requirements for weight loss strategies.

[0085] By analyzing user preference data, the initially selected weight loss strategies are evaluated and ranked. For example, if a user prefers to train in the morning, weight loss strategies that include morning training are prioritized; if a user wants to include specific foods in their diet, weight loss strategies that meet that preference and also conform to health standards are also selected.

[0086] This embodiment combines dynamic weight data and user preference data to ensure that the determined target weight loss strategy meets the needs of personalization and goal orientation. The use of dynamic weight data avoids the static prediction errors that may exist in the formulation of weight loss strategies in related technologies; the screening of user preference data ensures that the selected target weight loss strategy not only helps users achieve their weight loss goals, but also matches the user's personal preferences, enhancing user experience and compliance.

[0087] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0088] Example 2

[0089] This application also provides an apparatus for generating a weight loss strategy. It should be noted that the apparatus for generating a weight loss strategy in this application can be used to execute the method for generating a weight loss strategy provided in this application. The apparatus for generating a weight loss strategy provided in this application will be described below.

[0090] According to an embodiment of this application, an apparatus for implementing the above-described method for generating a weight loss strategy is also provided. Figure 3 This is a schematic diagram of a fat reduction strategy generation device according to an embodiment of this application, as shown below. Figure 3 As shown, the device includes:

[0091] The data collection unit 301 is used to collect the target user's body data and preference data, wherein the body data includes at least metabolic data and daily basic activity consumption data;

[0092] The first determining unit 302 is used to determine a set of candidate fat loss strategies based on body data and preference data, wherein each fat loss strategy includes an exercise strategy and an energy intake strategy.

[0093] The second determining unit 303 is used to calculate dynamic calorie data based on body data and the content of the fat loss strategy for each fat loss strategy, and to determine dynamic weight data based on the dynamic calorie data and the target user's current weight.

[0094] The filtering unit 304 is used to filter out the target weight loss strategy from the candidate weight loss strategy set based on the dynamic weight data and preference data associated with each weight loss strategy.

[0095] The weight loss strategy generation device provided in this application embodiment collects body data and preference data of the target user through a collection unit 301, wherein the body data includes at least metabolic data and daily basic activity consumption data; a first determining unit 302 determines a set of candidate weight loss strategies based on the body data and preference data, wherein each weight loss strategy includes an exercise strategy and an energy intake strategy; a second determining unit 303 calculates dynamic calorie data for each weight loss strategy based on the body data and the content of the weight loss strategy, and determines dynamic weight data based on the dynamic calorie data and the target user's current weight; a filtering unit 304 filters out the target weight loss strategy from the set of candidate weight loss strategies based on the dynamic weight data and preference data associated with each weight loss strategy, thereby solving the problem in related technologies that weight loss strategies are difficult to adapt to the user's state, affecting the success rate of weight loss. By determining a set of candidate weight loss strategies based on body data and preference data, and then filtering out the target weight loss strategy from the set based on dynamic weight data and preference data, the target weight loss strategy is related to the user's weight changes and preferences, thereby achieving the effect of generating a weight loss strategy adapted to the user's state and improving the success rate of weight loss.

[0096] Optionally, in the fat loss strategy generation device provided in this application embodiment, the first determining unit 302 includes: a first determining module, used to determine an objective function and determine the constraints of the objective function based on body data and preference data; a second determining module, used to determine an initial solution space set that meets the constraints, wherein each solution space includes a first solution space code and a second solution space code, the first solution space code representing an exercise strategy and the second solution space code representing an energy intake strategy; and a solving module, used to iteratively solve the initial solution space based on the objective function to obtain a candidate solution space set, and determine a candidate fat loss strategy set based on the candidate solution space set.

[0097] Optionally, in the fat reduction strategy generation device provided in this application embodiment, the solution module includes: a first screening submodule, used to screen multiple solutions whose fitness meets the fitness condition from the initial solution space set, and to reorganize the multiple solutions whose fitness meets the fitness condition to obtain the solution space set for the next round, wherein the fitness of each solution is calculated according to the objective function; and a second screening submodule, used to select multiple solutions that meet the fitness condition from the solution space set for the next round if the iteration termination condition is not met, and to reorganize the multiple solutions that meet the fitness condition to obtain the solution space set for the next round after that, until the iteration termination condition is met.

[0098] Optionally, in the fat reduction strategy generation device provided in this application embodiment, the second screening submodule includes: a first selection submodule, used to select multiple solutions from the initial solution space set using a competitive selection strategy; a second selection submodule, used to calculate the fitness of each selected solution according to the objective function, and screen multiple solutions whose fitness meets the fitness condition from the multiple selected solutions; and a recombination submodule, used to cross-merge the multiple solutions whose fitness meets the fitness condition to obtain multiple solutions after cross-merging, and to perform mutation processing on the multiple solutions after cross-merging to obtain the solution space set for the next round.

[0099] Optionally, in the fat loss strategy generation device provided in this application embodiment, the objective function represents: the total training consumption, penalty, and reward of the fat loss strategy. The penalty includes at least one of the following: energy deficit excess penalty, duration penalty, and exercise course repetition penalty. The reward includes at least one of the following: exercise load progressive reward and weekly training frequency stability reward. The constraints include at least one of the following: daily energy deficit is within a preset energy deficit range, daily training duration is within a preset duration range, training days are fixed within the fat loss strategy cycle, exercise courses are not repeated within the fat loss strategy cycle, and the difficulty of exercise courses within the fat loss strategy cycle shows an increasing trend.

[0100] Optionally, in the fat loss strategy generation device provided in this application embodiment, the second determining unit 303 includes: a calculation module, used to calculate the difference between total consumption data and intake data daily within the period of the fat loss strategy to obtain a daily energy deficit, wherein the total consumption data includes exercise consumption data and basic activity consumption data; a third determining module, used to determine the daily cumulative energy deficit based on the daily energy deficit, and to determine the daily predicted weight loss data based on the daily cumulative energy deficit and the energy equation; and a fourth determining module, used to determine the daily predicted weight based on the current weight and the daily predicted weight loss data to obtain dynamic weight data.

[0101] Optionally, in the fat loss strategy generation device provided in this application embodiment, the screening unit 304 includes: a first screening module, used to screen fat loss strategies that achieve weight loss goals from a set of candidate fat loss strategies based on dynamic weight data, wherein the predicted weight and target weight of the fat loss strategy that achieves weight loss goals on the last day are less than a preset difference; and a second screening module, used to screen target fat loss strategies from fat loss strategies that achieve weight loss goals based on preference data.

[0102] It should be noted that the above-mentioned units and modules correspond to the steps in Embodiment 1, and the instances and application scenarios implemented by the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above-mentioned modules may also be part of the device and can run in the computer terminal 10 provided in Embodiment 1.

[0103] Example 3

[0104] Embodiments of this application may provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0105] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0106] The processor can access information and applications stored in memory via a transmission device to execute the steps of generating a fat loss strategy.

[0107] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs (tablet computers), and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0108] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0109] Example 4

[0110] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the method for generating the weight loss strategy provided in Embodiment 1.

[0111] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0112] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing the steps of a method for generating a weight loss strategy.

[0113] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0114] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0116] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0119] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating a weight loss strategy, characterized in that, include: Collect target users' body data and preference data, wherein the body data includes at least metabolic data and daily basic activity consumption data; A set of candidate fat loss strategies is determined based on the body data and the preference data, wherein each fat loss strategy includes an exercise strategy and an energy intake strategy; For each weight loss strategy, dynamic calorie data is calculated based on the body data and the content of the weight loss strategy, and dynamic weight data is determined based on the dynamic calorie data and the target user's current weight. Based on the dynamic weight data associated with each weight loss strategy and the preference data, a target weight loss strategy is selected from the set of candidate weight loss strategies.

2. The method according to claim 1, characterized in that, The set of candidate fat loss strategies determined based on the body data and the preference data includes: Determine the objective function, and determine the constraints of the objective function based on the body data and the preference data; Determine an initial set of solution spaces that meet the constraints, wherein each solution space includes a first solution space code and a second solution space code, the first solution space code representing the motion strategy and the second solution space code representing the energy intake strategy; The initial solution space is iteratively solved based on the objective function to obtain a set of candidate solution spaces, and the set of candidate weight loss strategies is determined based on the set of candidate solution spaces.

3. The method according to claim 2, characterized in that, Based on the objective function, the initial solution space is iteratively solved to obtain a set of candidate solution spaces, including: Multiple solutions whose fitness meets the fitness condition are selected from the initial solution space set, and the multiple solutions whose fitness meets the fitness condition are reorganized to obtain the solution space set for the next round, wherein the fitness of each solution is calculated according to the objective function; If the iteration termination condition is not met, select multiple solutions that meet the fitness condition from the solution space set of the next round, and reorganize the multiple solutions that meet the fitness condition to obtain the solution space set of the next round, until the iteration termination condition is met.

4. The method according to claim 3, characterized in that, From the initial solution space set, multiple solutions whose fitness meets the fitness condition are selected, and these solutions are reorganized to obtain the solution space set for the next round, which includes: A competitive selection strategy is employed to select multiple solutions from the initial solution space set; The fitness of each selected solution is calculated according to the objective function, and multiple solutions whose fitness meets the fitness condition are selected from the multiple selected solutions. Multiple solutions whose fitness meets the fitness condition are cross-merged to obtain multiple cross-merged solutions. The cross-merged solutions are then mutated to obtain the solution space set for the next round.

5. The method according to claim 3, characterized in that, The objective function represents the total training expenditure, penalty, and reward of the fat loss strategy. The penalty includes at least one of the following: energy deficit excess penalty, duration penalty, and exercise course repetition penalty. The reward includes at least one of the following: progressive exercise load reward and weekly training frequency stability reward. The constraints include at least one of the following: the daily energy deficit is within a preset energy deficit range, the daily training duration is within a preset duration range, the training days within the fat loss strategy cycle are fixed, the exercise courses within the fat loss strategy cycle are not repeated, and the difficulty of the exercise courses within the fat loss strategy cycle is on an increasing trend.

6. The method according to claim 1, characterized in that, For each weight loss strategy, dynamic calorie data is calculated based on the body data and the content of the weight loss strategy, and dynamic weight data is determined based on the dynamic calorie data and the target user's current weight, including: The difference between total energy expenditure and energy intake is calculated daily during the fat loss strategy period to obtain the daily energy deficit. The total energy expenditure includes exercise expenditure and basic activity expenditure. The daily cumulative energy deficit is determined based on the daily energy deficit, and the daily predicted weight loss data is determined based on the daily cumulative energy deficit and the energy equation. The predicted weight for each day is determined based on the current weight and the predicted weight loss data for each day, thus obtaining the dynamic weight data.

7. The method according to claim 1, characterized in that, Based on the dynamic weight data associated with each weight loss strategy and the preference data, the target weight loss strategy is selected from the candidate weight loss strategy set, including: Based on the dynamic weight data, a weight loss strategy that achieves the weight loss goal is selected from the candidate weight loss strategy set, wherein the predicted weight and target weight on the last day of the weight loss strategy that achieves the weight loss goal are less than a preset difference. The target weight loss strategy is selected from the weight loss strategies for achieving the weight loss goal based on the preference data.

8. A device for generating a weight loss strategy, characterized in that, include: The centralized collection unit is used to collect the target user's body data and preference data, wherein the body data includes at least metabolic data and daily basic activity consumption data; The first determining unit is configured to determine a set of candidate fat loss strategies based on the body data and the preference data, wherein each fat loss strategy includes an exercise strategy and an energy intake strategy. The second determining unit is used to calculate dynamic calorie data based on the body data and the content of the fat loss strategy for each fat loss strategy, and to determine dynamic weight data based on the dynamic calorie data and the current weight of the target user. The filtering unit is used to filter out the target weight loss strategy from the set of candidate weight loss strategies based on the dynamic weight data associated with each weight loss strategy and the preference data.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 7.

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