Motion planning method and device, electronic equipment, storage medium and program

Abstract

The embodiment of the invention discloses an exercise planning method and device, electronic equipment, a storage medium and a program, and the exercise planning method comprises the steps: obtaining a fitness target, exercise physiological data and fitness condition associated data of a target user; analyzing the exercise physiological data of the target user to obtain a comprehensive health portrait of the target user; and generating a target exercise plan of the target user according to the fitness target of the target user, the fitness condition associated data and the comprehensive health portrait. According to the technical scheme, the operability and the execution rate of the exercise plan can be improved, the user experience is enhanced, and the exercise effect is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of sports, and more particularly to a motion planning method, device, electronic device, storage medium, and program. Background Technology

[0002] As the concept of healthy living gains popularity, personalized exercise plans are becoming increasingly common in the fitness industry.

[0003] In the existing technology, exercise planning methods mainly include: (1) a fitness coach manually tailors a training plan according to the user's specific needs; (2) a fitness application based on a fixed template provides general exercise guidance to the user; (3) smart wearable devices provide basic exercise suggestions by monitoring physiological data such as heart rate and steps; (4) some fitness systems will also combine the user's input goals such as fat loss or muscle gain and recommend corresponding training plans to the user through preset algorithms.

[0004] In the process of developing this invention, the inventors discovered the following shortcomings in the existing technology: Most suggestions provided by existing exercise planning methods are too general and fail to deeply integrate with the user's actual scenario. Specifically, these methods do not fully consider practical factors such as the user's equipment, time planning, and venue constraints, resulting in a significant reduction in the operability of the recommended exercise programs during actual implementation. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, storage medium, and program for motion planning, which can improve the operability and execution rate of motion plans, thereby enhancing user experience and improving exercise results.

[0006] According to one aspect of the present invention, a motion planning method is provided, comprising:

[0007] Acquire the target user's fitness goals, exercise physiological data, and related data on fitness conditions;

[0008] The exercise physiological data of the target user are analyzed to obtain a comprehensive health profile of the target user;

[0009] Based on the target user's fitness goals, fitness conditions, related data, and comprehensive health profile, a target exercise plan is generated for the target user.

[0010] According to another aspect of the present invention, a motion planning device is provided, comprising:

[0011] The data acquisition module is used to acquire the target user's fitness goals, exercise physiological data, and fitness condition-related data;

[0012] The comprehensive health profile determination module is used to analyze the exercise physiological data of the target user to obtain a comprehensive health profile of the target user.

[0013] The target exercise plan generation module is used to generate a target exercise plan for the target user based on the target user's fitness goals, fitness condition-related data, and comprehensive health profile.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the motion planning method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the motion planning method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the motion planning method described in any embodiment of the present invention.

[0020] This invention acquires a target user's fitness goals, exercise physiological data, and related fitness condition data. It then analyzes the target user's exercise physiological data to obtain a comprehensive health profile. Based on this profile, a target exercise plan is generated for the target user. This solution fully considers the user's fitness conditions, achieving deep integration with the user's actual scenario. This makes the exercise plan more closely aligned with the user's real-world situation, solving the problem of generic exercise suggestions in existing exercise planning methods. It improves the operability and execution rate of exercise plans, thereby enhancing user experience and improving exercise effectiveness.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a motion planning method provided in Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of a motion planning method provided in Embodiment 2 of the present invention;

[0025] Figure 3 This is a schematic diagram of a motion planning device provided in Embodiment 3 of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "target," etc., in the specification, claims, and accompanying drawings of this invention 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 embodiments of the invention 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.

[0029] Example 1

[0030] Figure 1This is a flowchart of a motion planning method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where personalized motion planning is performed based on a user's fitness conditions. The method can be executed by a motion planning device, which can be implemented in software and / or hardware, and is generally integrated into an electronic device. This electronic device can be a terminal device or a server device, as long as it can execute the motion planning method. The present invention does not limit the specific type of electronic device. Correspondingly, as... Figure 1 As shown, the method includes the following operations:

[0031] S110. Obtain the target user's fitness goals, exercise physiological data, and fitness condition-related data.

[0032] Among them, the target users can be users with exercise planning needs. Fitness goals can be specific results or expected states that the target user hopes to achieve during the fitness process, such as including but not limited to fat loss and body shaping, muscle gain and weight gain, localized body shaping, and maintaining health. This embodiment of the invention does not limit the specific type of fitness goals. Exercise physiological data can be data obtained by monitoring and recording various quantitative indicators related to physiological functions of the target user during exercise and at rest. For example, exercise physiological data can include but is not limited to physiological state data and exercise habit data. This embodiment of the invention does not limit the specific type of exercise physiological data. Physiological state data can be quantitative indicators reflecting the functional state of various physiological systems and the overall health status of the target user, such as including but not limited to body temperature, respiratory rate, blood pressure, weight, body fat percentage, and sleep quality. This embodiment of the invention does not limit the specific type of physiological state data. Exercise habit data can be various quantitative information generated by the target user in exercise behavior through recording and analysis, such as including but not limited to the target user's training frequency, preference for exercise type, exercise intensity, and exercise duration. This embodiment of the invention does not limit the specific type of exercise habit data. Fitness condition-related data can be auxiliary data such as external environment and personal objective conditions that are closely related to the target user's fitness behavior, effects, and sustainability. For example, fitness condition-related data may include, but is not limited to, exercise venue, exercise time, equipment configuration, and whether there is coaching guidance. This embodiment of the invention does not limit the specific type of fitness condition-related data.

[0033] In this embodiment of the invention, users with exercise planning needs can be designated as target users. After identifying the target users, their fitness goals can be determined, and their exercise physiological data can be continuously collected through multi-source devices such as smartwatches or fitness equipment. Simultaneously, by interacting with the target users, fitness-related conditions such as their exercise venue, exercise time, equipment configuration, and whether they have coaching guidance can be determined, providing a more comprehensive reference for generating exercise plans.

[0034] S120. Analyze the exercise physiological data of the target user to obtain a comprehensive health profile of the target user.

[0035] The comprehensive health profile is a systematic and personalized description of a target user's overall health status, potential risks, behavioral characteristics, and health needs, constructed based on the integrated analysis of multi-dimensional data. For example, the comprehensive health profile may include, but is not limited to, a profile of the target user's physiological state, health status, and exercise habits. This embodiment of the invention does not limit the specific content included in the comprehensive health profile.

[0036] Correspondingly, after obtaining the target user's exercise physiological data, machine learning algorithms, such as cluster analysis or time series models, can be used to comprehensively analyze the target user's exercise physiological data in order to construct a comprehensive health profile of the target user.

[0037] S130. Generate the target exercise plan for the target user based on the target user's fitness goals, fitness condition association data, and comprehensive health profile.

[0038] Among them, the target exercise plan can be a personalized exercise program tailored for the target user based on factors such as the target user's fitness goals, fitness conditions, related data, and comprehensive health profile.

[0039] Accordingly, after obtaining the target user's fitness goals, fitness conditions related data, and comprehensive health profile, a systematic and personalized operation plan can be formulated for the target user, guided by the target user's fitness goals and combined with multi-dimensional information such as the target user's fitness conditions related data and comprehensive health profile.

[0040] It should be noted that the target exercise plan is used to display on the exercise device to instruct the target user to perform the exercise process according to the target exercise plan. For example, the exercise device may be a wearable device such as a smartwatch and a fitness tracker, or a smart fitness device equipped with a smart display screen, etc. The embodiments of the present invention do not limit the type of exercise device.

[0041] In summary, the exercise planning method provided by this invention enables a high degree of adaptation between the target exercise plan and the actual conditions of the target user. This adaptability makes it more convenient for users to execute the plan, effectively improving the execution efficiency and actual results of the target exercise plan. Simultaneously, because the target exercise plan fully considers fitness condition-related factors such as equipment, time, and venue, the target user does not need to frequently adjust the plan due to these limitations, thus enhancing the target user's satisfaction with the target exercise plan. Furthermore, personalized exercise plans developed based on the target user's fitness condition-related data and exercise physiological data can more accurately match their fitness goals, thereby significantly improving exercise effectiveness.

[0042] This invention acquires a target user's fitness goals, exercise physiological data, and related fitness condition data. It then analyzes the target user's exercise physiological data to obtain a comprehensive health profile. Based on this profile, a target exercise plan is generated for the target user. This solution fully considers the user's fitness conditions, achieving deep integration with the user's actual scenario. This makes the exercise plan more closely aligned with the user's real-world situation, solving the problem of generic exercise suggestions in existing exercise planning methods. It improves the operability and execution rate of exercise plans, thereby enhancing user experience and improving exercise effectiveness.

[0043] Example 2

[0044] Figure 2 This is a flowchart of a motion planning method provided in Embodiment 2 of the present invention. This embodiment is a specific embodiment based on the above embodiment. In this embodiment, specific optional implementation methods are given for analyzing the target user's exercise physiological data to obtain the target user's comprehensive health profile, and generating the target user's target exercise plan based on the target user's fitness goals, fitness condition correlation data, and comprehensive health profile. Optional implementation operations are also given after generating the target user's target exercise plan based on the target user's fitness goals, fitness condition correlation data, and comprehensive health profile. Correspondingly, as... Figure 2 As shown, the method in this embodiment may include:

[0045] S210. Obtain the target user's fitness goals, exercise physiological data, and fitness condition-related data.

[0046] In an optional embodiment of the present invention, obtaining the fitness goals of the target user may include: obtaining fitness goal description data of the target user; and analyzing the fitness goal description data of the target user using a target big model to determine the fitness goals of the target user.

[0047] Among them, fitness goal description data can be descriptive data about fitness goals and physiological needs input by the target user through text or voice.

[0048] The target large-scale model can be any available large language model, used to analyze fitness goal description data input by target users through text or voice. A large language model (LLM), also called a large-scale language model, refers to a deep learning model trained on a large amount of relevant data (such as text data, voice data, or combined text and image data) capable of processing text sequences. It can generate natural language text or understand the meaning of language text. These models typically have billions of parameters. Large language models can handle various natural language tasks, such as text classification, question answering, and dialogue, and have wide applications. The input to a large language model is data, such as text data, voice data, or combined text and image data. The large language model encodes the input data to obtain corresponding word vector representations, and then decodes the encoded word vectors to automatically complete the input data processing and obtain the corresponding output data. For example, text can be input into a large language model, which will process and predict the input text and output the corresponding response text.

[0049] In this embodiment of the invention, when obtaining the fitness goals of a target user, the first step is to obtain the fitness goal description data input by the target user through text or voice. After obtaining the fitness goal description data, a target big data model can be used to analyze the target user's fitness goal description data, thereby determining the target user's fitness goals.

[0050] In a specific example, assuming the target user inputs a fitness goal description via voice as "create a running weight loss plan," the target big data model can identify the user's intent in the input description and determine that the user's fitness goal is "weight loss." This solution achieves real-time demand identification through intelligent dialogue, improving the user experience and the relevance of exercise plans.

[0051] S220. Analyze the physiological state data of the target user to obtain the physiological state profile and health status of the target user.

[0052] Among these, a physiological state profile can be a dataset used to comprehensively describe and analyze various indicators, characteristics, and states of a target user's physiological health. Health status can reflect the overall physiological health condition of the target user.

[0053] Specifically, machine learning algorithms can be used to comprehensively analyze the physiological status data of target users. Based on the results of this comprehensive analysis, a physiological status profile of the target user can be generated, accurately identifying their health status. This physiological status profile and health status can assess various aspects of the target user, including fitness level, body fat percentage, and muscle fatigue. This approach, through multi-source data fusion and in-depth analysis, significantly improves the accuracy and comprehensiveness of physiological status assessment, providing a reliable basis for generating precise, personalized exercise plans.

[0054] In a specific example, assuming that a comprehensive analysis of physiological data identifies a target user as having a high body fat percentage and osteoporosis, then when developing an exercise plan for that user, high-intensity running or jumping exercises should be avoided, and running time should be kept short. Conversely, assuming that the comprehensive analysis of physiological data identifies a significant increase in the target user's weight from 60 kg in December 2024 to 68 kg in April 2025, along with a noticeable increase in BMI (Body Mass Index) reaching the "obese" range, then the exercise plan for that user needs to consider weight loss.

[0055] S230. Analyze the target user's running habit data to obtain a profile of the target user's running habits.

[0056] Among them, the exercise habit profile can be a comprehensive model that reflects information such as the target user's exercise habits, exercise preferences, exercise frequency, and exercise intensity.

[0057] Similarly, machine learning algorithms can be used to analyze long-term exercise habit data of target users. Based on the comprehensive analysis of this data, an exercise habit profile can be generated for each target user. Furthermore, this profile can be used to determine information such as exercise frequency, exercise preference type, exercise intensity, and exercise time. Based on this profile, personalized exercise plans that closely align with the target user's lifestyle can be generated, thereby improving the sustainability of these plans.

[0058] In a specific example, by collecting long-term exercise habit data from multiple devices and analyzing this data using machine learning algorithms, it was determined that the target user exercises 3-4 times per week, primarily on Fridays and Sundays, with occasional workouts on Mondays. Their exercise time is mainly divided into two sessions: approximately 90 minutes of training on Friday evenings from 6:00 PM to 7:40 PM, and approximately 90 minutes of training on Sunday mornings from 10:30 AM to 12:00 PM. The training content is primarily boxing, a moderate-to-high intensity aerobic exercise, burning between 570 and 1000 kcal per session. In addition, the target user also engages in strength training (such as seated barbell shoulder presses) and jogging, forming a training pattern that combines aerobic and strength training.

[0059] S240. Determine the comprehensive health profile of the target user based on the target user's physiological state profile, health status profile, and exercise habit profile.

[0060] Correspondingly, after obtaining the physiological state profile, health status profile, and exercise habit profile of the target user, a systematic integration and in-depth correlation analysis can be performed on the physiological state profile, health status profile, and exercise habit profile of the target user to generate a more comprehensive and three-dimensional comprehensive health profile of the target user, so as to provide a comprehensive and accurate basis for the subsequent formulation of personalized target exercise plans.

[0061] S250. Compare and analyze the fitness goals of the target user with the comprehensive health profile to obtain the analysis results of the target user's exercise ability.

[0062] Among them, the results of the exercise ability analysis can be a comparative analysis between the ability required to achieve fitness goals and the actual exercise ability of the target user based on a comprehensive health profile.

[0063] Specifically, analysis can be conducted on the fitness goals of the target users to determine the abilities required to achieve those goals. Simultaneously, analysis can be performed based on the target users' comprehensive health profile to determine their actual exercise capabilities. Furthermore, a comparative analysis can be conducted between the abilities required to achieve the fitness goals and the target users' actual exercise capabilities to determine the overall exercise capacity analysis results for the target users.

[0064] In a specific example, if it is determined that the muscle strength required for a target user to achieve their fitness goals is higher than their actual athletic ability, then targeted muscle strength training can be conducted. Through such targeted muscle strength training, the gap between the required ability and actual athletic ability can be gradually narrowed, helping the target user achieve their fitness goals more efficiently.

[0065] S260. Generate the target exercise plan for the target user based on the target user's fitness goals, fitness condition association data, comprehensive health profile, and exercise ability analysis results.

[0066] Accordingly, after obtaining the target user's fitness goals, fitness condition-related data, comprehensive health profile, and exercise ability analysis results, a personalized target exercise plan can be generated based on these data. The target exercise plan can detail the daily exercise activities, duration, intensity, and required equipment usage. This embodiment of the invention does not limit the specific content of the target exercise plan. By accurately adapting to the target user's lifestyle, physical condition, and exercise preferences, the above solution ensures a high degree of alignment between the target exercise plan and the actual scenario.

[0067] For example, a 28-day exercise plan can be generated, divided into 4 phases of 7 days each. Specifically, each week can be divided into 3 aerobic days, 2 strength training days, and 2 rest days. On aerobic days, focus on low-intensity aerobic exercises such as running, swimming, or HIIT (High-Intensity Interval Training). For instance, run for 30 minutes daily, covering 3 kilometers at a pace of 10 minutes per kilometer, aiming to burn 200 kcal. On strength training days, use strength training equipment for 40 minutes, focusing on the chest, shoulders, and arms, aiming to burn 300 kcal. During training, it is recommended to control the pace of movement, maintain proper posture, and avoid excessive weight-bearing to prevent injury. After training, thoroughly stretch the upper body muscles to promote recovery. On rest days, perform 10-15 minutes of light stretching to relax muscles and avoid prolonged sitting.

[0068] Optionally, after the target exercise plan is generated, the target user can adjust the target exercise plan according to their own wishes.

[0069] S270. Obtain the motion feedback information of the target user.

[0070] Among them, motion feedback information can be information obtained by monitoring and analyzing the physiological state data and motion performance data of the human body during exercise.

[0071] In this embodiment of the invention, after generating the target exercise plan, the target user can refer to the target exercise plan for exercise training. During the target user's exercise, physiological state data and exercise performance data of the target user can be collected through multi-source devices as exercise feedback information; at the same time, exercise feelings and problems actively fed back by the target user in the form of conversation can also be collected to form a complete exercise feedback information system, and then the target exercise plan can be dynamically adjusted according to the exercise feedback information.

[0072] S280. Compare and analyze the target user's motion feedback information and the target user's target motion plan, and dynamically adjust the target motion plan based on the comparison and analysis results of the target user's motion feedback information and the target user's target motion plan.

[0073] Accordingly, after obtaining the exercise feedback information from the target users, this feedback information can be compared and analyzed with the target exercise plan to determine the completion status of the target users' target exercise plan. Based on the results of this comparative analysis, the target exercise plan can be dynamically adjusted, and post-exercise precautions, such as post-exercise dietary recommendations, can be provided.

[0074] In an optional embodiment of the present invention, the step of dynamically adjusting the target exercise plan based on the comparative analysis results of the target user's exercise feedback information and the target user's target exercise plan may include: when it is determined that the target user is executing the target exercise plan and that the target user has completed the target exercise plan within a target time, generating an exercise analysis evaluation of the target user based on the target user's exercise feedback information; when it is determined that the target user is executing the target exercise plan and that the target user has not completed the target exercise plan within the target time, generating remaining exercise guidance within the target time based on the comparative analysis results of the target user's exercise feedback information and the target user's target exercise plan; and when it is determined that the target user is not executing the target exercise plan, generating an exercise analysis evaluation of the target user based on the target user's exercise feedback information, and dynamically adjusting the target exercise plan based on the target user's exercise analysis evaluation.

[0075] The target time can be a phased time node of the target exercise plan, such as including but not limited to a day or a week. This embodiment of the invention does not limit the specific duration of the target time. Remaining exercise guidance can be suggestions provided to the target user regarding the remaining exercise time and intensity based on the user's exercise status and target exercise plan within the target time. Exercise analysis and evaluation can be a systematic analysis and comprehensive evaluation of the exercise completed by the target user within the target time.

[0076] In this embodiment of the invention, when dynamically adjusting the target exercise plan based on the comparative analysis results of the target user's exercise feedback information and the target user's target exercise plan, it is first necessary to determine whether the target user has executed the target plan and whether the target user has completed the target plan. If it is determined that the target user has executed the target exercise plan and completed the target exercise plan within the target time, then there is no need to adjust the target exercise plan. It is only necessary to conduct a systematic analysis and comprehensive evaluation based on the target user's exercise feedback information within the target time to help the target user understand the help this exercise provides to their fitness goals. If the target user has executed the target exercise plan but has not completed the target exercise plan within the target time, then it is necessary to conduct a comparative analysis based on the target user's current exercise feedback information and the target exercise plan to generate the remaining exercise guidance for the target user based on the analysis results, so as to guide the target user to complete the target training plan. If it is determined that the target user has not executed the target exercise plan but has chosen other types of exercise for training, then it is possible to analyze the help the target user's exercise provides to their fitness goals based on the target user's exercise feedback information, and adjust the target exercise plan based on the target user's existing exercise volume to achieve the fitness goals.

[0077] This invention, in its embodiments, acquires a target user's fitness goals, exercise physiological data, and fitness condition-related data, and analyzes the target user's physiological state data to obtain a physiological state profile and health status. Simultaneously, it analyzes the target user's exercise habit data to obtain an exercise habit profile. After obtaining the target user's physiological state profile, health status, and exercise habit profile, a comprehensive health profile is determined based on these profiles. Further, the target user's fitness goals are compared and analyzed with the comprehensive health profile to obtain the target user's exercise capacity analysis results. Based on these results, a target exercise plan is generated for the target user. After determining the target user's target exercise plan, the target user's exercise feedback information is acquired, and a comparative analysis is performed between the exercise feedback information and the target exercise plan. Based on the comparative analysis results, the target exercise plan is dynamically adjusted. The above solution fully considers the user's fitness conditions and achieves deep integration with the user's actual scenario, making the exercise plan more in line with the user's real situation. It solves the problem of generalized exercise suggestions in existing exercise planning methods, improves the operability and execution rate of exercise plans, thereby enhancing the user experience and improving exercise results.

[0078] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information (such as physiological state data, exercise habit data, etc.) involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0079] It should be noted that all information (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.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant regions.

[0080] It should be noted that any arrangement or combination of the technical features in the above embodiments also falls within the protection scope of this invention.

[0081] Example 3

[0082] Figure 3 This is a schematic diagram of a motion planning device provided in Embodiment 3 of the present invention, as shown below. Figure 3 As shown, the device includes: a data acquisition module 310, a comprehensive health profile determination module 320, and a target exercise plan generation module 330, wherein:

[0083] The data acquisition module 310 is used to acquire the target user's fitness goals, exercise physiological data, and fitness condition-related data.

[0084] The comprehensive health profile determination module 320 is used to analyze the exercise physiological data of the target user to obtain a comprehensive health profile of the target user.

[0085] The target exercise plan generation module 330 is used to generate a target exercise plan for the target user based on the target user's fitness goals, fitness condition association data, and comprehensive health profile.

[0086] This invention acquires a target user's fitness goals, exercise physiological data, and related fitness condition data. It then analyzes the target user's exercise physiological data to obtain a comprehensive health profile. Based on this profile, a target exercise plan is generated for the target user. This solution fully considers the user's fitness conditions, achieving deep integration with the user's actual scenario. This makes the exercise plan more closely aligned with the user's real-world situation, solving the problem of generic exercise suggestions in existing exercise planning methods. It improves the operability and execution rate of exercise plans, thereby enhancing user experience and improving exercise effectiveness.

[0087] Optionally, the data acquisition module 310 is specifically used to: acquire the fitness goal description data of the target user; analyze the fitness goal description data of the target user using a target big model, and determine the fitness goal of the target user.

[0088] Optionally, the exercise physiological data includes physiological state data and exercise habit data; the comprehensive health profile determination module 320 is specifically used to: analyze the physiological state data of the target user to obtain the physiological state profile and health status of the target user; analyze the exercise habit data of the target user to obtain the exercise habit profile of the target user; and determine the comprehensive health profile of the target user based on the physiological state profile, health status and exercise habit profile of the target user.

[0089] Optionally, the target exercise plan generation module 330 is specifically used to: compare and analyze the fitness goals of the target user with the comprehensive health profile to obtain the exercise ability analysis results of the target user; and generate the target exercise plan of the target user based on the fitness goals, fitness condition association data, comprehensive health profile and exercise ability analysis results of the target user.

[0090] Optionally, the above device may further include an exercise plan adjustment module, used to acquire the exercise feedback information of the target user; compare and analyze the exercise feedback information of the target user and the target exercise plan of the target user; and dynamically adjust the target exercise plan based on the comparison and analysis results of the exercise feedback information of the target user and the target exercise plan of the target user.

[0091] Optionally, the exercise plan adjustment module is specifically used for: when it is determined that the target user is executing the target exercise plan and that the target user has completed the target exercise plan within the target time, generating an exercise analysis evaluation of the target user based on the target user's exercise feedback information; when it is determined that the target user is executing the target exercise plan and that the target user has not completed the target exercise plan within the target time, generating remaining exercise guidance within the target time based on a comparative analysis of the target user's exercise feedback information and the target user's target exercise plan; and when it is determined that the target user is not executing the target exercise plan, generating an exercise analysis evaluation of the target user based on the target user's exercise feedback information, and dynamically adjusting the target exercise plan based on the target user's exercise analysis evaluation.

[0092] The motion planning device described above can execute the motion planning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the motion planning method provided in any embodiment of the present invention.

[0093] Since the motion planning device described above is a device capable of executing the motion planning method in the embodiments of the present invention, those skilled in the art can understand the specific implementation methods and various variations of the motion planning device in this embodiment based on the motion planning method described in the embodiments of the present invention. Therefore, how the motion planning device implements the motion planning method in the embodiments of the present invention will not be described in detail here. Any device used by those skilled in the art to implement the motion planning method in the embodiments of the present invention falls within the scope of protection of this application.

[0094] Example 4

[0095] Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0096] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0097] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0098] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as motion planning methods.

[0099] In some embodiments, the motion planning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the motion planning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the motion planning method by any other suitable means (e.g., by means of firmware).

[0100] Optionally, the exercise planning method may include: acquiring the target user's fitness goals, exercise physiological data, and fitness condition-related data; analyzing the target user's exercise physiological data to obtain the target user's comprehensive health profile; and generating the target user's target exercise plan based on the target user's fitness goals, fitness condition-related data, and comprehensive health profile.

[0101] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0103] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0105] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0106] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0107] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0108] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A motion planning method, characterized in that, include: Acquire the target user's fitness goals, exercise physiological data, and related data on fitness conditions; The exercise physiological data of the target user are analyzed to obtain a comprehensive health profile of the target user; Based on the target user's fitness goals, fitness conditions, related data, and comprehensive health profile, a target exercise plan is generated for the target user.

2. The method according to claim 1, characterized in that, The process of obtaining the target user's fitness goals includes: Obtain the fitness goal description data of the target user; The fitness goal description data of the target user is analyzed using a target big model to determine the target user's fitness goal.

3. The method according to claim 1, characterized in that, The exercise physiological data includes physiological state data and exercise habit data; the analysis of the exercise physiological data of the target user to obtain a comprehensive health profile of the target user includes: Analyze the physiological state data of the target user to obtain a physiological state profile and health status of the target user; Analyze the target user's operational habit data to obtain a profile of the target user's operational habits; A comprehensive health profile of the target user is determined based on the target user's physiological state profile, health status profile, and exercise habit profile.

4. The method according to claim 1, characterized in that, The step of generating a target exercise plan for the target user based on the target user's fitness goals, fitness condition-related data, and comprehensive health profile includes: The fitness goals of the target user are compared and analyzed with the comprehensive health profile to obtain the analysis results of the target user's exercise ability; Based on the target user's fitness goals, fitness condition-related data, comprehensive health profile, and exercise ability analysis results, a target exercise plan is generated for the target user.

5. The method according to claim 1, characterized in that, After generating the target exercise plan for the target user based on the target user's fitness goals, fitness condition association data, and comprehensive health profile, the method further includes: Obtain the motion feedback information of the target user; The target user's exercise feedback information and the target user's target exercise plan are compared and analyzed, and the target exercise plan is dynamically adjusted based on the comparison and analysis results.

6. The method according to claim 5, characterized in that, The step of dynamically adjusting the target exercise plan based on the comparative analysis results of the target user's exercise feedback information and the target user's target exercise plan includes: If it is determined that the target user executes the target exercise plan and that the target user completes the target exercise plan within the target time, an exercise analysis and evaluation of the target user is generated based on the target user's exercise feedback information. If it is determined that the target user is executing the target exercise plan, and it is determined that the target user has not completed the target exercise plan within the target time, then the remaining exercise guidance within the target time is generated based on the comparative analysis results of the target user's exercise feedback information and the target user's target exercise plan. If it is determined that the target user has not executed the target exercise plan, an exercise analysis evaluation of the target user is generated based on the target user's exercise feedback information, and the target exercise plan is dynamically adjusted based on the target user's exercise analysis evaluation.

7. A motion planning device, characterized in that, include: The data acquisition module is used to acquire the target user's fitness goals, exercise physiological data, and fitness condition-related data; The comprehensive health profile determination module is used to analyze the exercise physiological data of the target user to obtain a comprehensive health profile of the target user. The target exercise plan generation module is used to generate a target exercise plan for the target user based on the target user's fitness goals, fitness condition-related data, and comprehensive health profile.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the motion planning method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the motion planning method according to any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, wherein, When the computer program / instructions are executed by the processor, they implement the motion planning method according to any one of claims 1-6.

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