Fitness plan recommendation method and apparatus, storage medium, and program product

Abstract

The present disclosure relates to the technical field of computers, and relates to a fitness plan recommendation method and apparatus, a storage medium, and a program product. The fitness plan recommendation method comprises: in response to receiving request information of a user for acquiring a fitness plan, determining fitness goal information of the user; on the basis of the fitness goal information and preconfigured attribute information of a plurality of fitness actions, determining at least one target fitness action set, wherein each target fitness action set comprises at least one fitness action among the plurality of fitness actions of which attribute information matches the fitness goal information, and the attribute information of each fitness action comprises content required for performing the fitness action; generating a fitness plan on the basis of the at least one target fitness action set and the attribute information of the at least one fitness action in each target fitness action set; and displaying the fitness plan to the user.

Description

Fitness plan recommendation method and device, storage medium, and program product TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to a fitness plan recommendation method and device, a storage medium, and a program product. BACKGROUND

[0002] With the development of artificial intelligence technology, the application of intelligent agents has penetrated into various aspects of our life, such as intelligent question answering, intelligent voice assistants, intelligent planning, and the like.

[0003] In the related art, after performing intent understanding on a request of a user for obtaining a fitness plan, a fitness plan of the user is directly output; or a plurality of fitness courses are preconfigured, and a fitness course matched with the request of the user is recommended to the user.

[0004] SUMMARY

[0005] This summary is provided to introduce a selection of concepts, which will be described with greater specificity below in the detailed description section. This summary is not intended to identify key or essential features of the claimed technology, nor is it intended to limit the scope of the claimed technology.

[0006] According to a first aspect of some embodiments of the present disclosure, a fitness plan recommendation method is provided, including:

[0007] In response to receiving request information of a user for obtaining a fitness plan, determining fitness target information of the user;

[0008] According to the fitness target information and preconfigured attribute information of a plurality of fitness actions, determining at least one target fitness action set, wherein each target fitness action set includes at least one fitness action in the plurality of fitness actions whose attribute information matches the fitness target information, and the attribute information of each fitness action includes content necessary for performing the each fitness action;

[0009] According to the at least one target fitness action set and the attribute information of at least one fitness action in each target fitness action set, generating a fitness plan;

[0010] Displaying the fitness plan to the user.

[0011] According to a second aspect of some embodiments of the present disclosure, a fitness plan recommendation device is provided, including:

[0012] A first determining module is configured to, in response to receiving request information of a user for obtaining a fitness plan, determine fitness target information of the user;

[0013] a second determining module configured to determine at least one target exercise action set according to the fitness target information and attribute information of a plurality of exercise actions, wherein each target exercise action set comprises at least one exercise action in the plurality of exercise actions whose attribute information matches the fitness target information, and the attribute information of each exercise action comprises content necessary for performing the exercise action;

[0014] a generating module configured to generate a fitness plan according to the at least one target exercise action set and the attribute information of at least one exercise action in each target exercise action set;

[0015] a displaying module configured to display the fitness plan to the user.

[0016] According to a third aspect of some embodiments of the present disclosure, there is provided a fitness plan recommendation device, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute a fitness plan recommendation method of any of the embodiments of the present disclosure based on instructions stored in the memory.

[0017] According to a fourth aspect of some embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, performs a fitness plan recommendation method of any of the embodiments of the present disclosure.

[0018] According to a fifth aspect of some embodiments of the present disclosure, there is provided a computer program product which, when executed on a computer, causes the computer to implement a fitness plan recommendation method of any of the embodiments.

[0019] Other features, aspects, and advantages of the present disclosure will become apparent from the following detailed description of the exemplary embodiments of the present disclosure with reference made to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0020] The preferred embodiments of the present disclosure will be described below with reference to the accompanying drawings. The accompanying drawings used to explain the present disclosure are provided to provide a further understanding of the present disclosure, and together with the specific description of the embodiments below, comprise a part of the specification and serve to explain the present disclosure. It should be understood that the accompanying drawings only relate to some embodiments of the present disclosure, and do not limit the present disclosure. In the drawings:

[0021] FIG. 1 is a flowchart illustrating a fitness plan recommendation method according to some embodiments of the present disclosure;

[0022] FIG. 2 is a flowchart illustrating a process of determining at least one target exercise action set according to some embodiments of the present disclosure;

[0023] FIG. 3 is a schematic diagram illustrating a fitness plan, according to some embodiments of the present disclosure;

[0024] FIG. 4 is a block diagram illustrating a fitness plan recommendation apparatus, according to some embodiments of the present disclosure;

[0025] FIG. 5 is an architectural diagram illustrating a fitness plan recommendation apparatus, according to some embodiments of the present disclosure;

[0026] FIG. 6 is a block diagram illustrating a fitness plan recommendation apparatus, according to some other embodiments of the present disclosure;

[0027] FIG. 7 illustrates a block diagram of an electronic device, according to some embodiments of the present disclosure.

[0028] It should be understood that the dimensions of the various portions shown in the attached drawings are shown schematically and are not necessarily true to scale. Wherever possible, the same or similar reference numerals are used in the drawings to refer to the same or like parts throughout the several views of the drawings. As such, the description is not always be repeated with each identical or similar component. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, but not all the embodiments. The description of the embodiments below is actually only illustrative, and should not be construed as any limitation on the present disclosure and its application or use. It should be understood that the present disclosure can be implemented in various forms, and should not be construed as being limited to the embodiments described herein.

[0030] It should be understood that the various steps recited in the method embodiments of the present disclosure can be executed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present disclosure is not limited in this respect. Unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions, and numerical values set forth in these embodiments should be interpreted as merely exemplary, and not limiting to the scope of the present disclosure.

[0031] The term "comprise" and variations thereof used in the present disclosure means an open term that includes at least the recited elements / features, but does not exclude other elements / features, i.e., "including, but not limited to". In addition, the term "include" and variations thereof used in the present disclosure means an open term that includes at least the recited elements / features, but does not exclude other elements / features, i.e., "including, but not limited to". Therefore, include and comprise are synonymous. The term "based on" means "based at least in part on".

[0032] Reference throughout this specification to "one embodiment", "an embodiment", or "embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, however. As used in this specification, the words "may" and "could" mean one or more possible implementations.

[0033] It should be noted that the terms "first", "second", and so on used in the present disclosure are merely used to distinguish different apparatuses, modules, or units, and are not intended to limit the order or interdependence of the functions performed by these apparatuses, modules, or units. Unless otherwise specified, the terms "first", "second", and so on are not intended to imply a given order or any other manner of given order in time, space, ranking, or any other manner.

[0034] It should be noted that the terms "one", "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that "one" or "multiple" should be understood as "one or more" unless otherwise explicitly specified in the context.

[0035] The names of the messages or information exchanged between the plurality of apparatuses in the embodiments of the present disclosure are merely for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0036] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings, but the present disclosure is not limited to these specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments. In addition, in one or more embodiments, specific features, structures, or characteristics can be combined by any suitable means from the present disclosure that will be clear to those skilled in the art.

[0037] Large models, such as Large Language Models (LLM), refer to models with a large number of parameters and complex structures in the field of machine learning and artificial intelligence. These models usually require a large amount of computing resources and data for training and optimization. With the development of deep learning technology, the size and complexity of models are increasing in order to better capture and represent complex patterns and relationships in input data.

[0038] Large models can achieve higher performance on many tasks by training on vast amounts of data. Compared to small models, large models are generally better at capturing subtle patterns in the data, resulting in better generalization performance on a variety of tasks. However, large models also present some challenges, such as the gradient vanishing / explosion problem, overfitting, optimization difficulty, and increased computational and storage resource requirements.

[0039] In recent years, with the improvement of hardware performance, large models have made significant progress in natural language processing, computer vision, reinforcement learning, and other fields. For example, large models based on the Transformer architecture have achieved unprecedented results on tasks such as natural language understanding, generation, and translation.

[0040] In related technologies, the way the model directly outputs a fitness plan after understanding the intent has many uncertainties and the efficiency of fitness plan recommendation is low, and the user experience is poor. The uncertainties here include that the fitness plan obtained by some users at some time or some users may lack necessary information for fitness, which is not stable enough. By pre-configuring fitness courses, the fitness plan matched for the user is not personalized enough and is not flexible enough, resulting in poor user experience.

[0041] The present disclosure provides a technical solution that can balance the accuracy, efficiency, and personalization of fitness plan recommendation and improve user experience.

[0042] FIG. 1 is a flowchart illustrating a fitness plan recommendation method according to some embodiments of the present disclosure.

[0043] As shown in FIG. 1, the fitness plan recommendation method includes: step S110, in response to receiving a request information of a user for obtaining a fitness plan, determining fitness goal information of the user; step S120, determining at least one target fitness action set according to the fitness goal information and attribute information of a plurality of fitness actions, wherein each target fitness action set includes at least one fitness action in the plurality of fitness actions whose attribute information matches the fitness goal information, and the attribute information of each fitness action includes the content necessary to perform the each fitness action; step S130, generating a fitness plan according to the at least one target fitness action set and the attribute information of at least one fitness action in each target fitness action set; and step S140, presenting the fitness plan to the user. For example, the fitness plan recommendation method is executed by an intelligent agent system, and the intelligent agent can be referred to as a bot. The intelligent agent of the present disclosure can be a chat bot. For example, in the intelligent agent system, the intelligent agent interacts with the server, front end, etc. to complete the process of recommending a fitness plan to a user.

[0044] In the above embodiment, the fitness actions are taken as the matching granularity, from the plurality of pre-configured fitness actions, the target fitness action set matching the fitness target information of the user is determined for the user according to the attribute information of the plurality of fitness actions, and then the fitness plan is generated and displayed according to the determined target fitness action set and the attribute information of the fitness actions in the target fitness action set. The matching process of the fitness action is realized by using the computer program module, which improves the efficiency of the fitness plan recommendation. Taking the fitness action as the matching granularity improves the flexibility of the fitness plan recommendation, and further guarantees the personalized needs of the fitness plan recommendation. The attribute information of the plurality of pre-configured fitness actions includes the necessary content for performing the plurality of fitness actions, reduces the situation that the fitness plan cannot be performed or is difficult to perform, and further guarantees the accuracy and certainty of the fitness plan recommendation. Based on this, the fitness plan recommendation method of the present disclosure can balance the accuracy, efficiency and personalization of the fitness plan recommendation, and improve the user experience. The fitness plan recommendation method of the present disclosure balances the professionalism, feasibility, effectiveness and suitability. Suitability refers to suitability for users.

[0045] In step S110, in response to receiving the request information of the user for obtaining the fitness plan, the fitness target information of the user is determined. For example, the fitness target information can be realized by a user demand and target confirmation function module. The acquisition of the fitness target information is authorized by the user.

[0046] In some embodiments, the determining the fitness target information of the user in response to receiving the request information of the user for obtaining the fitness plan comprises: in response to receiving the request information of the user for obtaining the fitness plan, obtaining the interaction content of the user with the intelligent agent; processing the interaction content by using a target model to obtain the fitness target information. For example, the target model can be a natural language processing model, a generation model, a large language model or a multi-modal large model. In this embodiment, the target model is used to process the interaction content, accurately obtain the fitness target information of the user, and quickly and accurately generate the fitness plan in combination with the matching process.

[0047] In some embodiments, the fitness target information includes fitness preference information, and the obtaining the interaction content of the user with the intelligent agent in response to receiving the request information of the user for obtaining the fitness plan comprises: processing the request information by using a target model to obtain the intention information of the user; generating and sending the first interaction content of the intelligent agent and the user by using the target model according to the intention information of the user, the first interaction content being used to request the user to obtain the fitness preference information; receiving the second interaction content input by the user for the first interaction content, the second interaction content being used to provide the fitness preference information to the intelligent agent.

[0048] In this embodiment, by guiding the user to interact with the agent in the case where the user requests to obtain a fitness plan, the latest fitness preference information of the user can be obtained, and the accuracy of the fitness plan recommendation can be improved.

[0049] In some embodiments, the fitness goal information includes fitness demand information, and the determining the fitness goal information of the user in response to receiving the request information of the user for obtaining a fitness plan includes: obtaining fitness feedback information of the user in a historical time period; processing the request information and the fitness feedback information by using a target model to obtain the fitness demand information of the user; in response to being unable to obtain the fitness demand information of the user by processing the request information and the fitness feedback information by using the target model, obtaining preset fitness demand information as the fitness demand information of the user. For example, the fitness feedback information can be stored in a fitness record library. The obtaining of the fitness feedback information is also authorized by the user.

[0050] In this embodiment, in the process of determining the fitness demand information of the user, the fitness feedback information in the historical time period is referred to, which can improve the accuracy of the determination of the fitness demand information, and further improve the accuracy of the fitness plan recommendation.

[0051] In some embodiments, the determining the fitness goal information of the user in response to receiving the request information of the user for obtaining a fitness plan includes:

[0052] In response to receiving the request information of the user for obtaining a fitness plan, it is determined whether a parameter in the request information meets a preset condition;

[0053] In the case where the parameter in the request information meets the preset condition, the fitness goal information is determined;

[0054] In the case where the parameter in the request information does not meet the preset condition, a prompt information is generated by using a target model and sent to the user, and the prompt information is used to prompt the user to modify the request information so that the parameter in the modified request information meets the preset condition;

[0055] In response to receiving the modified request information, the fitness goal information of the user is determined.

[0056] In this embodiment, by parameter verification, the accuracy of the fitness plan recommendation is improved.

[0057] For example, the user sends a request for formulating a fitness plan to the agent, and the agent calls a plug-in by using a large model to perform parameter verification. The process of parameter verification is performed by a health management server, for example.

[0058] In step S120, at least one target exercise set is determined according to the fitness goal information and preconfigured attribute information of a plurality of exercises, wherein each target exercise set includes at least one exercise of the plurality of exercises whose attribute information matches the fitness goal information, and the attribute information of each exercise includes the content necessary for performing the exercise.

[0059] In some embodiments, the preconfigured attribute information of the plurality of exercises can be pre-stored in a pre-built fitness material library. The fitness material library is, for example, a database. Since the attribute information of each exercise includes the content necessary for performing the exercise, the fitness plan generated by the fitness plan recommendation method of the present disclosure has good execution for the user, i.e., the execution is relatively high, and the generated fitness plan is relatively unified and complete in content for any user, and the certainty is also high.

[0060] Table 1 shows the attribute information of part of the exercises of the fitness material library as an example of the data structure of the fitness material library. As shown in Table 1, the fitness material library includes the name of the exercise, the type of the exercise, the exercise equipment, the time per set, the number per set, the energy consumption per set, the interval time between sets, the exercise location (also referred to as the exercise place), and the action demonstration. For example, the fitness material library can also include the exercise part, the action points, etc. (not shown in Table 1). For example, the action points of jogging include “pay attention to breathing: 3-4 steps per breath”, and the action points of dumbbell incline bird dog include “pinch the latissimus dorsi muscle, and hold the hands horizontally”. The energy consumption is, for example, the heat value that can be consumed by the exercise.

[0061] For example, in order to save the storage space of the fitness material library, the action demonstration field stores the resource link of the action demonstration. For another example, the time per set, the number per set, and the energy consumption per set are of int type, and the action points are of string type. The exercise location includes at least one of home, outdoor, and gym. The type of exercise includes, for example, aerobic and anaerobic.

[0062] The fitness material library in Table 1 is only an example and does not constitute a specific limitation on the fitness material library of the present disclosure or the preconfigured attribute information of the plurality of exercises. The fitness material library stores the attribute information of the plurality of exercises in an enumerated manner.

[0063] Table 1: Example of data structure of fitness material library

[0064] In some embodiments, the fitness goal information includes fitness preference information and fitness demand information, and the above step S120 can be implemented in the manner shown in FIG. 2.

[0065] FIG. 2 is a flowchart illustrating determining at least one target set of exercises, according to some embodiments of the present disclosure.

[0066] As shown in FIG. 2, in step S121, from the plurality of exercises, a plurality of candidate exercises are selected according to the attribute information of the plurality of exercises, which have attribute information matching the exercise preference information.

[0067] In some embodiments, the attribute information of each exercise includes at least one of a type of exercise, a location of exercise, and an exercise equipment, and the exercise preference information includes at least one of a type of exercise preferred by the user, a location of exercise preferred by the user, and an exercise equipment preferred by the user. The selecting, from the plurality of exercises, a plurality of candidate exercises according to the attribute information of the plurality of exercises, which have attribute information matching the exercise preference information, includes at least one of:

[0068] selecting, from the plurality of exercises, a plurality of exercises having a type of exercise same as the type of exercise preferred by the user, as the plurality of candidate exercises;

[0069] selecting, from the plurality of exercises, a plurality of exercises having a location of exercise same as the location of exercise preferred by the user, as the plurality of candidate exercises;

[0070] selecting, from the plurality of exercises, a plurality of exercises having an exercise equipment same as the exercise equipment preferred by the user, as the plurality of candidate exercises.

[0071] In the above embodiments, in the case where the user has no obvious preference for the type of exercise, the step of selecting, from the plurality of exercises, a plurality of exercises having a type of exercise same as the type of exercise preferred by the user, as the plurality of candidate exercises, is not performed. Similarly, the same applies to the location of exercise and the exercise equipment. In this embodiment, in the case where the location of exercise is a gym, the exercise equipment is by default all equipment.

[0072] For example, the process of determining the plurality of candidate exercises can be performed by a health management server.

[0073] In step S122, according to the exercise requirement information and the attribute information of the plurality of candidate exercises, at least one target set of exercises is determined, wherein each target set of exercises includes at least one candidate exercise from the plurality of candidate exercises, which has attribute information matching the exercise requirement information.

[0074] In this embodiment, since the matching process of the fitness requirement is generally more complex than the matching process of the fitness preference, selecting the fitness actions meeting the fitness preference first and then selecting the fitness actions meeting the fitness requirement from the fitness actions meeting the fitness preference can reduce the time of the matching process of the fitness requirement and improve the efficiency of the fitness plan recommendation.

[0075] In some embodiments, the determining of the at least one target fitness action set according to the fitness requirement information and the attribute information of the plurality of candidate fitness actions can be implemented in the following manner.

[0076] Firstly, a plurality of quantity conditions with different priorities are obtained, and the quantity condition with the highest priority is determined as a reference quantity condition.

[0077] Secondly, in the order from high to low of the priorities of the plurality of preset quantity conditions, the following operations are performed:

[0078] It is determined whether there is at least one reference fitness action set such that the quantity of the candidate fitness actions in each reference fitness action set meets the reference quantity condition and the attribute information of each fitness action set meeting the reference quantity condition meets the fitness requirement information.

[0079] In response to that there is the at least one reference fitness action set under the reference quantity condition, the at least one reference fitness action set is determined as the at least one candidate fitness action set.

[0080] In response to that there is no at least one reference fitness action set under the reference quantity condition, the first quantity condition in the plurality of quantity conditions and arranged in the order of priority after the priority of the reference quantity condition is updated as the reference quantity condition, and the determination of whether there is the at least one reference fitness action set is repeatedly performed until the at least one candidate fitness action set is determined.

[0081] From the at least one candidate fitness action set, the at least one target fitness action set is determined.

[0082] For example, the plurality of quantity conditions include a first quantity condition, a second quantity condition and a third quantity condition. The first quantity condition is that the quantity of the candidate fitness actions in the target fitness action set is 3, the second quantity condition is that the quantity of the candidate fitness actions in the target fitness action set is 4, and the third quantity condition is that the quantity of the candidate fitness actions in the target fitness action set is 5. The priority of the first quantity condition is higher than that of the second quantity condition, and the priority of the second quantity condition is higher than that of the third quantity condition. The plurality of quantity conditions and the priorities of the plurality of quantity conditions can be set according to actual application scenarios. The priorities of the plurality of quantity conditions can also be obtained by random sorting.

[0083] Taking the first number condition, the second number condition and the third number condition as examples, a candidate action set including the plurality of candidate actions can be constructed, and the candidate action set is traversed until three candidate fitness actions satisfying the fitness requirement information are obtained, and a target fitness action set is constructed based on the three candidate fitness actions satisfying the fitness requirement information. In the case where there are multiple groups of three candidate actions satisfying the fitness requirement information, one or more groups of three candidate actions satisfying the fitness requirement information can be selected to construct the target fitness action set, wherein each target fitness action set includes three candidate fitness actions satisfying the fitness requirement information. In some embodiments, the target action set can be determined by using an algorithm for solving the classic three-number sum problem. In other embodiments, the target action set can also be determined by using an optimized algorithm for solving the classic three-number sum problem.

[0084] In the case where three candidate fitness actions satisfying the fitness requirement information cannot be obtained, a candidate action set including the plurality of candidate actions can be constructed, and the candidate action set is traversed until four candidate fitness actions satisfying the fitness requirement information are obtained, and a target fitness action set is constructed based on the four candidate fitness actions satisfying the fitness requirement information. In the case where there are multiple groups of four candidate actions satisfying the fitness requirement information, one or more groups of four candidate actions satisfying the fitness requirement information can be selected to construct the target fitness action set, wherein each target fitness action set includes four candidate fitness actions satisfying the fitness requirement information. In some embodiments, the four-number sum problem can be converted into a three-number sum problem by extracting the first number, and the target action set can be determined by using an algorithm for solving the three-number sum problem.

[0085] In the case where four candidate fitness actions satisfying the fitness requirement information cannot be obtained, a candidate action set including the plurality of candidate actions can be constructed, and the candidate action set is traversed until five candidate fitness actions satisfying the fitness requirement information are obtained, and a target fitness action set is constructed based on the five candidate fitness actions satisfying the fitness requirement information. In the case where there are multiple groups of five candidate actions satisfying the fitness requirement information, one or more groups of five candidate actions satisfying the fitness requirement information can be selected to construct the target fitness action set, wherein each target fitness action set includes five candidate fitness actions satisfying the fitness requirement information. In some embodiments, the five-number sum problem can be converted into a four-number sum problem by extracting the first number, and the target action set can be determined by using an algorithm for solving the four-number sum problem.

[0086] The algorithm process of the classic three-number sum is as follows. Create an empty result list and perform the first loop. The first loop includes skipping duplicate elements appropriately, defining the double pointers of the second and third loops, pointing to the next element of the current element and the last element of the array respectively. When the double pointers meet the conditions, add the list of three elements meeting the conditions to the result list, move the double pointers and skip duplicate elements.

[0087] The optimized three-number sum can find the combination with the smallest difference. Let the attribute s of the fitness action corresponding to the fitness requirement information nums[i]+nums[j]+nums[k], in order to judge whether s is the closest number to the variable target corresponding to the fitness requirement information, a variable minDiff is also needed to maintain the minimum value of |s-target|. If s=target, s is the final result, and s is returned directly. If s is greater than target, in the case of s-target being less than minDiff, it means that a number closer to target is found, minDiff is updated to s-target, and the final result is updated to s. As in the classic three-number sum, k is reduced by 1. If s is less than target, in the case of s-target being less than minDiff, it means that a number closer to target is found, minDiff is updated to target-s, and the final result is updated to s. As in the classic three-number sum, j is incremented by 1.

[0088] In some embodiments, the fitness requirement information includes at least one of a fitness time requirement and an energy consumption requirement, and the attribute information of each fitness action includes at least one of a fitness time and an energy consumption. According to the fitness requirement information and the attribute information of the plurality of candidate fitness actions, determining the at least one target fitness action set includes:

[0089] According to the association relationship between the at least one of the fitness time requirement and the energy consumption requirement and the at least one of the fitness time and the energy consumption of each candidate fitness action, determining at least one candidate fitness action set, wherein the fitness time corresponding to each candidate fitness action set satisfies the fitness time requirement and / or the energy consumption satisfies the energy consumption requirement;

[0090] From the at least one candidate fitness action set, determining the at least one target fitness action set.

[0091] In some embodiments, the fitness time requirement is, for example, 1 hour of fitness, and the energy consumption requirement is, for example, 500 calories. In the case where the fitness time requirement and the energy consumption requirement exist at the same time, the candidate fitness action set satisfying the fitness time requirement and the energy consumption requirement can be found respectively, and then the intersection of the two is selected as the final candidate fitness action set.

[0092] For example, the process of determining the at least one target set of fitness actions can be performed by the health management server.

[0093] Returning to FIG. 1, in step S130, a fitness plan is generated according to the at least one target set of fitness actions and attribute information of at least one fitness action in each target set of fitness actions.

[0094] In some embodiments, the attribute information of each fitness action includes fitness time and / or energy consumption of the each fitness action, and the generating the fitness plan according to the at least one target set of fitness actions and attribute information of at least one fitness action in each target set of fitness actions includes:

[0095] determining a target set of fitness actions for generating the fitness plan;

[0096] determining total fitness time and / or total energy consumption corresponding to the target set of fitness actions for generating the fitness plan according to fitness time and / or energy consumption of each fitness action in the target set of fitness actions for generating the fitness plan;

[0097] filling the total fitness time and / or total energy consumption corresponding to the target set of fitness actions for generating the fitness plan and the attribute information of at least one fitness action in the target set of fitness actions for generating the fitness plan into a preset fitness plan template to obtain the fitness plan. For example, the health management server can perform these operations.

[0098] Filling the corresponding information in the manner of the fitness plan template can obtain the fitness plan more quickly. By showing the total fitness time and / or total energy consumption, the experience of user fitness can be further improved.

[0099] The fitness plan generated in the above embodiments can be stored in a fitness plan library.

[0100] In step S140, the fitness plan is shown to the user.

[0101] In some embodiments, the attribute information of each fitness action includes name, execution method and fitness guidance content of the each fitness action, the fitness guidance content including at least one of action tips and action demonstration, and the showing the fitness plan to the user includes: rendering the fitness plan in the form of a card to obtain a fitness plan card; and showing the fitness plan card to the user, wherein the fitness plan card includes name, group number, number of each group and the fitness guidance content of each fitness action in the fitness plan. The action demonstration, for example, includes at least one of motion diagram, dynamic image and video.

[0102] In some embodiments, the total energy consumption can also be shown to the user in the workout plan card.

[0103] In some embodiments, the server sends the workout plan to the client, and the client renders the workout plan in the form of a card to obtain the workout plan card.

[0104] FIG. 3 is a schematic diagram illustrating a workout plan according to some embodiments of the present disclosure.

[0105] As shown in FIG. 3, the workout plan includes 3 workout actions. The names of the 3 workout actions are action 1, action 2 and action 3, respectively. The card showing the workout plan includes the names of the 3 workout actions, and also includes the number of sets, the number of repetitions and the workout guidance content of each workout action. The number of sets of action 1 is 4 sets, the number of repetitions is 12, and the workout guidance content is workout guidance content 1. The number of sets of action 2 is 4 sets, the number of repetitions is 10, and the workout guidance content is workout guidance content 2. The number of sets of action 3 is 3 sets, the number of repetitions is 15, and the workout guidance content is workout guidance content 3. For example, the workout guidance content includes at least one of a motion diagram, a dynamic image (e.g., in Graphics Interchange Format (GIF)) and a demonstration video of the workout action. For example, the card showing the workout plan can also include the total workout time and the total energy consumption (not shown in FIG. 3).

[0106] FIG. 3 is merely an example, and does not constitute a specific limitation on the form of the card of the present disclosure.

[0107] In some embodiments, the workout plan recommendation method further includes, at a preset time, sending, to the user, request information for obtaining workout feedback information for the workout plan; and processing, by using a target model, content input by the user in response to the request information for obtaining workout feedback information, to obtain workout feedback information of the user for the workout plan. By actively asking the user for workout feedback, the workout feedback information of the user can be continuously updated, thereby improving the user experience.

[0108] For example, the workout feedback information includes completion information of the workout plan, such as whether all the workout actions of the workout plan are performed, whether the workout is completed, etc. The workout feedback information also includes workout feeling information. The completion information of the workout plan can be stored in a workout record library, and the workout feeling information can be stored in a personal information library of the user.

[0109] For example, the process of obtaining the workout feedback information can be implemented by a user condition tracking or feedback function module.

[0110] The above is a content publishing method provided by some embodiments of the present disclosure. Next, a fitness plan recommendation device in some embodiments of the present disclosure will be described in conjunction with FIG. 4.

[0111] FIG. 4 is a block diagram illustrating a fitness plan recommendation device according to some embodiments of the present disclosure.

[0112] As shown in FIG. 4, the fitness plan recommendation device 4 includes a first determination module 41, a second determination module 42, a generation module 43, and a presentation module 44.

[0113] The first determination module 41 is configured to determine fitness target information of a user in response to receiving request information of the user to obtain a fitness plan. For example, the first determination module can also be referred to as a user demand and target confirmation function module.

[0114] The second determination module 42 is configured to determine at least one target fitness action set according to the fitness target information and attribute information of a plurality of fitness actions, wherein each target fitness action set includes at least one fitness action in the plurality of fitness actions whose attribute information matches the fitness target information, and the attribute information of each fitness action includes content necessary for performing the fitness action.

[0115] The generation module 43 is configured to generate a fitness plan according to the at least one target fitness action set and the attribute information of at least one fitness action in each target fitness action set. For example, the second determination module and the generation module can jointly constitute a fitness plan formulation module.

[0116] The presentation module 44 is configured to present the fitness plan to the user.

[0117] The fitness plan recommendation device 4 can be used to perform steps S110-S140 of FIG. 1. In some embodiments, the fitness plan recommendation device 4 can also perform any steps in other embodiments of the present disclosure.

[0118] It should be noted that each of the above modules is only a logical module divided according to the specific function implemented thereby, and is not intended to limit the specific implementation manner, for example, it can be implemented in software, hardware, or a combination of software and hardware. In actual implementation, each of the above modules can be implemented as an independent physical entity, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.). In addition, each of the above modules is indicated by a dashed line in the drawing to indicate that these modules can not actually exist, and the operations / functions implemented thereby can be implemented by the processing circuit itself.

[0119] FIG. 5 is an architectural diagram of a fitness plan recommendation device according to some embodiments of the present disclosure.

[0120] As shown in FIG. 5, the architecture of the fitness plan recommendation device includes a bot engine, a workflow including generating a fitness plan, a fitness material library, and a UI (User Interface) display module. The bot engine performs intent understanding on the request information of the user for obtaining a fitness plan, so as to determine the fitness goal information of the user. According to the result of the intent understanding, the bot engine starts the operation of generating a fitness plan in the workflow. The process of generating a fitness plan includes determining at least one target fitness action set according to the fitness goal information and the attribute information of a plurality of pre-configured fitness actions, and generating a fitness plan according to the at least one target fitness action set and the attribute information of at least one fitness action in each target fitness action set. The attribute information of the plurality of pre-configured fitness actions is stored in the fitness material library as shown in FIG. 5, and the workflow accesses the fitness material library to obtain a fitness plan during the process of generating a fitness plan. The fitness material library is a database. The UI display interface module is used to generate a fitness plan card according to the fitness plan generated by the workflow and send it to the user.

[0121] The above is the fitness plan recommendation device in some embodiments of the present disclosure.

[0122] FIG. 6 is a block diagram illustrating a fitness plan recommendation device according to another embodiment of the present disclosure.

[0123] As shown in FIG. 6, the fitness plan recommendation device 6 includes a memory 61 and a processor 62 coupled to the memory 61, the processor 62 being configured to execute the fitness plan recommendation method of any of the preceding embodiments based on instructions stored in the memory 61.

[0124] The memory 61 is used to store one or more computer-readable instructions. The memory 61 can include any combination of various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory, including but not limited to random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), flash memory. The memory 61 may, for example, store an operating system, application programs, a boot loader, a database, and other programs, and can also store various application programs and various data.

[0125] The processor 62 is used to run computer-readable instructions to implement the fitness plan recommendation method of any of the preceding embodiments. For specific implementation of each step of the fitness plan recommendation method, please refer to the above embodiments, and the repeated parts will not be described here.

[0126] The processor 62 and the memory 61 can communicate with each other directly or indirectly. For example, the processor 62 and the memory 61 can communicate through a network. The network can include a wireless network, a wired network, and / or any combination of a wireless network and a wired network. The processor 62 and the memory 61 can also communicate with each other through a system bus, and the present disclosure does not limit this.

[0127] It should be noted that the components of the fitness program recommendation device 6 shown in FIG. 6 are exemplary only, and are not intended to be limiting, and the fitness program recommendation device 6 can have other components according to actual application needs. The processor 62 can combine with other components in the fitness program recommendation device 6 to perform the desired functions.

[0128] The fitness program recommendation device can be implemented in software, firmware, and / or hardware, and can be integrated in an electronic device in which a related application is installed.

[0129] FIG. 7 shows a block diagram of an electronic device according to some embodiments of the present disclosure.

[0130] The electronic device 7 shown in FIG. 7 can be a computer system having a dedicated hardware structure, and can perform corresponding functions when a related application is installed.

[0131] The electronic device includes, but is not limited to, a mobile terminal such as a smartphone, a notebook computer, a Personal Digital Assistant (PDA), a Tablet Personal Computer (Tablet PC), a PMP (Portable Multimedia Player), a vehicle terminal (e.g., a car navigation terminal), a wearable device, and the like, and a stationary terminal such as a digital television, a desktop computer, and the like.

[0132] As shown in FIG. 7, a central processing unit (CPU) 71 performs various processes according to a program stored in a read-only memory (ROM) 72 or a program loaded from a storage portion 78 to a random access memory (RAM) 73. In the RAM 73, data required when the CPU 71 performs various processes and the like is stored as needed. The central processing unit is merely exemplary, and can be other types of processors such as various processors described above. The ROM 72, the RAM 73, and the storage portion 78 can be various forms of computer readable storage media. It should be noted that although the ROM 72, the RAM 73, and the storage portion 78 are shown separately in FIG. 7, one or more of them can be combined or located in the same or different memory or storage module.

[0133] The CPU 71, the ROM 72, and the RAM 73 are connected to each other via the bus 74. The input / output interface 75 is also connected to the bus 74.

[0134] The following components are connected to the input / output interface 75: an input portion 76, such as a touch panel, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, and the like; an output portion 77 including a display, such as a cathode ray tube (CRT), a liquid crystal display (LCD), a speaker, a vibrator, and the like; a storage portion 78 including a hard disk, a magnetic tape, and the like; and a communication portion 79 including a network interface card, such as a LAN card, a modem, and the like. The communication portion 79 allows communication processing to be performed via a network, such as the Internet. It is easily understood that, although the respective devices or modules in the electronic device 7 are shown in FIG. 7 to communicate through the bus 74, they can also communicate through a network or other means, where the network can include a wireless network, a wired network, and / or any combination of a wireless network and a wired network.

[0135] A drive 710 is also connected to the input / output interface 75 as necessary. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like, is attached to the drive 710 as necessary, so that a computer program read therefrom is installed in the storage portion 78 as necessary.

[0136] In the case where the above series of processes are implemented by software, the program constituting the software can be installed from a network or a storage medium 711 or the like.

[0137] According to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product that, when run on a computer, causes the computer to implement the fitness plan recommendation method described in any of the embodiments. The computer program product includes a computer program carried on a computer-readable medium, which contains program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication portion 79, or installed from the storage portion 78, or installed from the ROM 72. When the computer program is executed by the CPU 71, the fitness plan recommendation method of the embodiments of the present disclosure is executed.

[0138] It should be noted that, in the context of the present disclosure, the computer-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0139] The computer readable medium can be a computer readable storage medium or a computer readable signal medium, or any combination thereof.

[0140] The computer readable storage medium can include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. The computer readable storage medium stores a computer program, which is executed by the processor to implement the fitness plan recommendation method of any of the foregoing embodiments.

[0141] The computer readable signal medium can include a data signal that is propagated in baseband or that is propagated as part of a carrier wave. Such propagated data signal can take a variety of forms, including but not limited to electro-magnetic, optical or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a storage medium and that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer readable medium can be transmitted using any suitable medium, including but not limited to wire, cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0142] The computer readable medium described above can be included in the electronic device described above; or can exist separately from the electronic device and be not assembled into the electronic device.

[0143] In some embodiments, a computer program product is also provided, which, when running on a computer, causes the computer to implement the fitness plan recommendation method of any of the foregoing embodiments.

[0144] In some embodiments, a computer program is also provided, which includes instructions that, when executed by a processor, cause the processor to perform the fitness plan recommendation method of any of the foregoing embodiments. For example, the instructions can be embodied as computer program code.

[0145] Computer program code for carrying out operations of the present disclosure can be written in any one or more of a variety of programming languages or combinations of languages including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0146] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0147] The functions described above can be implemented in at least part by one or more hardware logic components. For example, and without limitation, illustrative hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0148] While certain aspects of the present disclosure have been described with reference to one or more particular embodiments thereof, those skilled in the art will understand that many alternative embodiments can be made therefrom. In general, embodiments of the present disclosure are applicable to any suitable electronic device, system, or architecture. In addition, unless otherwise indicated, the functions performed by the various components described herein can be implemented using electronic components, software, firmware, or any suitable combination thereof. Furthermore, from the disclosure herein, one skilled in the art will recognize that the various embodiments described herein can be implemented in a computer program product tangibly embodied in a machine readable storage medium (e.g., memory component) including many instructions that can be used to control the behavior of a computer or other machine.

Claims

1. A fitness plan recommendation method, comprising: determining fitness goal information of a user in response to receiving request information of the user for obtaining a fitness plan; determining at least one target fitness action set according to the fitness goal information and attribute information of a plurality of pre-configured fitness actions, wherein each target fitness action set comprises at least one fitness action of the plurality of fitness actions whose attribute information matches the fitness goal information, and the attribute information of each fitness action comprises content necessary for performing the fitness action; generating a fitness plan according to the at least one target fitness action set and the attribute information of at least one fitness action in each target fitness action set; showing the fitness plan to the user.

2. The exercise program recommendation method according to claim 1, wherein, The fitness goal information comprises fitness preference information and fitness requirement information, and the determining at least one target fitness action set according to the fitness goal information and attribute information of a plurality of pre-configured fitness actions comprises: selecting a plurality of fitness actions whose attribute information matches the fitness preference information from the plurality of fitness actions as a plurality of candidate fitness actions according to the attribute information of the plurality of fitness actions; determining the at least one target fitness action set according to the fitness requirement information and the attribute information of the plurality of candidate fitness actions, wherein each target fitness action set comprises at least one candidate fitness action of the plurality of candidate fitness actions whose attribute information matches the fitness requirement information.

3. The exercise program recommendation method according to claim 2, wherein, The determining the at least one target fitness action set according to the fitness requirement information and the attribute information of the plurality of candidate fitness actions comprises: obtaining a plurality of quantity conditions with different priorities, and determining a quantity condition with the highest priority as a reference quantity condition; in order of priority from high to low of the plurality of pre-set quantity conditions, performing the following operations: determining whether there is at least one reference fitness action set such that the quantity of candidate fitness actions in each reference fitness action set satisfies the reference quantity condition and the attribute information of each fitness action set satisfying the reference quantity condition satisfies the fitness requirement information; in response to there being the at least one reference fitness action set under the reference quantity condition, determining the at least one reference fitness action set as the at least one candidate fitness action set; in response to there being no at least one reference fitness action set under the reference quantity condition, updating a first quantity condition in the plurality of quantity conditions whose priority is arranged after the priority of the reference quantity condition as the reference quantity condition, and repeating the determination of whether there is the at least one reference fitness action set until the at least one candidate fitness action set is determined; determining the at least one target fitness action set from the at least one candidate fitness action set.

4. The fitness plan recommendation method according to any one of claims 2-3, wherein, The attribute information of each exercise action includes at least one of an exercise type, an exercise location, and an exercise equipment, the exercise preference information includes at least one of a preferred exercise type of the user, a preferred exercise location of the user, and a preferred exercise equipment of the user, and the selecting, from the plurality of exercise actions, a plurality of exercise actions with attribute information matching the exercise preference information as a plurality of candidate exercise actions according to the attribute information of the plurality of exercise actions includes at least one of: selecting, from the plurality of exercise actions, a plurality of exercise actions with the same exercise type as the preferred exercise type of the user as the plurality of candidate exercise actions; selecting, from the plurality of exercise actions, a plurality of exercise actions with the same exercise location as the preferred exercise location of the user as the plurality of candidate exercise actions; selecting, from the plurality of exercise actions, a plurality of exercise actions with the same exercise equipment as the preferred exercise equipment of the user as the plurality of candidate exercise actions.

5. The fitness plan recommendation method according to any one of claims 2-3, wherein, The exercise requirement information includes at least one of an exercise time requirement and an energy consumption requirement, the attribute information of each exercise action includes at least one of an exercise time and an energy consumption, and the determining the at least one target exercise action set according to the exercise requirement information and the attribute information of the plurality of candidate exercise actions includes: determining at least one candidate exercise action set according to an association between at least one of the exercise time requirement and the energy consumption requirement and at least one of the exercise time and the energy consumption of each candidate exercise action, wherein the exercise time corresponding to each candidate exercise action set satisfies the exercise time requirement and / or the energy consumption satisfies the energy consumption requirement; determining the at least one target exercise action set from the at least one candidate exercise action set.

6. The exercise program recommendation method according to any one of claims 1 to 3, wherein, The determining the exercise target information of the user in response to receiving the request information for obtaining an exercise plan of the user includes: obtaining interaction content of the user with an intelligent agent in response to receiving the request information for obtaining an exercise plan of the user; processing the interaction content by using a target model to obtain the exercise target information.

7. The exercise program recommendation method according to claim 6, wherein, The exercise target information includes exercise preference information, and the obtaining interaction content of the user with an intelligent agent in response to receiving the request information for obtaining an exercise plan of the user includes: processing the request information by using the target model to obtain intention information of the user; generating and sending first interaction content of the intelligent agent and the user by using the target model according to the intention information of the user, wherein the first interaction content is used to request the user to obtain the exercise preference information; receiving second interaction content input by the user for the first interaction content, wherein the second interaction content is used to provide the exercise preference information to the intelligent agent.

8. The exercise program recommendation method according to any one of claims 1 to 3, wherein, The exercise target information includes exercise requirement information, and the determining the exercise target information of the user in response to receiving the request information for obtaining an exercise plan of the user includes: obtaining exercise feedback information of the user in a historical time period; processing the request information and the fitness feedback information by using a target model to obtain the fitness demand information of the user; in response to being unable to obtain the fitness demand information of the user by processing the request information and the fitness feedback information by using the target model, obtaining preset fitness demand information as the fitness demand information of the user.

9. The exercise program recommendation method according to any one of claims 1-3, wherein, The attribute information of each fitness action includes a name, an execution mode and fitness guidance content of the each fitness action, and the fitness guidance content includes at least one of action tips and action demonstration. The method of displaying the fitness plan to the user includes: rendering the fitness plan in a card form to obtain a fitness plan card; displaying the fitness plan card to the user, wherein the fitness plan card includes the name, the number of groups, the number of each group and the fitness guidance content of each fitness action in the fitness plan.

10. The exercise program recommendation method according to any one of claims 1-3, wherein, The method of determining the fitness target information of the user in response to receiving the request information of the user for obtaining the fitness plan includes: in response to receiving the request information of the user for obtaining the fitness plan, judging whether parameters in the request information meet a preset condition; in a case where the parameters in the request information meet the preset condition, determining the fitness target information; in a case where the parameters in the request information do not meet the preset condition, generating and sending a prompt information to the user by using a target model, the prompt information being used to prompt the user to modify the request information so that parameters in the modified request information meet the preset condition; in response to receiving the modified request information, determining the fitness target information of the user.

11. The exercise program recommendation method according to any one of claims 1-3, wherein, The attribute information of each fitness action includes a fitness time and / or energy consumption of the each fitness action. The method of generating the fitness plan according to the at least one target fitness action set and the attribute information of at least one fitness action in each target fitness action set includes: determining a target fitness action set for generating a fitness plan; determining a total fitness time and / or total energy consumption corresponding to the target fitness action set for generating a fitness plan according to a fitness time and / or energy consumption of each fitness action in the target fitness action set for generating a fitness plan; filling the total fitness time and / or total energy consumption corresponding to the target fitness action set for generating a fitness plan and the attribute information of at least one fitness action in the target fitness action set for generating a fitness plan into a preset fitness plan template to obtain the fitness plan.

12. The exercise program recommendation method according to any one of claims 1-3, wherein, The method further includes: at a preset time, sending request information for obtaining fitness feedback information to the user for the fitness plan; processing content input by the user for the request information for obtaining fitness feedback information by using a target model to obtain fitness feedback information of the user for the fitness plan.

13. A fitness plan recommendation device, comprising: a first determination module configured to determine fitness target information of a user in response to receiving request information of the user for obtaining a fitness plan; a second determining module, configured to determine at least one target exercise set according to the fitness target information and attribute information of a plurality of exercises, wherein each target exercise set comprises at least one exercise of the plurality of exercises whose attribute information matches the fitness target information, and the attribute information of each exercise comprises content necessary for performing the exercise; a generating module, configured to generate a fitness plan according to the at least one target exercise set and the attribute information of at least one exercise in each target exercise set; a presenting module, configured to present the fitness plan to the user.

14. A fitness plan recommendation apparatus, comprising: a memory; and a processor coupled to the memory, configured to execute a fitness plan recommendation method according to any one of claims 1 to 12 based on instructions stored in the memory.

15. A computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the fitness plan recommendation method according to any one of claims 1 to 12.

16. A computer program product, which, when executed on a computer, causes the computer to implement the fitness plan recommendation method according to any one of claims 1 to 12.

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