Health suggestion generation method and device, medium and program product

Abstract

The invention discloses a health suggestion generation method and device, a medium and a program product, and relates to the technical field of sports health data calculation and intelligent evaluation. The health suggestion generation method comprises the following steps: calculating the physical ability of a user on that day according to the training load of the user on that day and the potential physical ability of the user, and calculating the fatigue of the user on that day according to the fatigue of the user on that day and the training load of the user on that day; determining a user training state value and a fusion coefficient according to the ratio of the user's physical ability on the day to the user's fatigue on the day and the user portrait; summing a product value of the user sleep influence value and the fusion coefficient with a user training state value to obtain a user physical state evaluation value; and displaying a health suggestion generated based on the user physical state evaluation value. According to the technical scheme provided by the embodiment of the invention, the health suggestions accurately fitting the actual body state of the user can be given, and the user satisfaction is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of sports and health data calculation and intelligent assessment technology, and in particular to a method, device, medium and program product for generating health recommendations. Background Technology

[0002] Existing health management applications or smart devices can typically only record users' physiological indicators and exercise data, and perform simple analysis based on this data to provide corresponding health advice.

[0003] However, health advice is often general and static, and cannot be dynamically adjusted according to the user's real-time physical condition. This makes the advice less scientific and accurate, and unable to truly help the user. Summary of the Invention

[0004] This invention provides a method, device, medium, and program product for generating health advice, in order to solve the problem that current methods cannot accurately provide health advice that matches the user's actual physical condition.

[0005] According to one aspect of the present invention, a method for generating health recommendations is provided, comprising:

[0006] Calculate the user's physical fitness for the day based on the user's training load and potential physical fitness, and calculate the user's fatigue level for the day based on the user's fatigue level from the day before yesterday and the user's training load for the day.

[0007] Based on the ratio of the user's daily physical fitness to the user's daily fatigue level, and the user profile, determine the user's training status value and fusion coefficient;

[0008] The sum of the product of the user's sleep impact value and the fusion coefficient, and the user's training state value, yields the user's physical state assessment value.

[0009] Display health recommendations generated based on the user's physical condition assessment values.

[0010] According to another aspect of the present invention, a health advice generation apparatus is provided, comprising:

[0011] The first data calculation module is used to calculate the user's physical fitness for the day based on the user's training load and potential physical fitness, and to calculate the user's fatigue level for the day based on the user's fatigue level the day before yesterday and the user's training load for the day.

[0012] The second data calculation module is used to determine the user's training status value and fusion coefficient based on the ratio of the user's physical fitness to the user's fatigue level on the same day, as well as the user profile.

[0013] The physical condition assessment value calculation module is used to sum the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical condition assessment value.

[0014] The health advice display module is used to display health advice generated based on the user's physical condition assessment values.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and a memory communicatively connected to said at least one processor;

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the health recommendation generation 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 health recommendation generation method according to any embodiment of the present invention.

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

[0020] The technical solution of this invention calculates the user's daily physical fitness based on the user's daily training load and potential physical fitness, and calculates the user's daily fatigue level based on the user's fatigue level from the previous day and the user's daily training load. Then, based on the ratio of the user's daily physical fitness to the user's daily fatigue level, and the user profile, the user's training status value and fusion coefficient are determined. Finally, the product of the user's sleep impact value and the fusion coefficient is summed with the user's training status value to obtain the user's physical status assessment value, which then displays health recommendations generated based on the user's physical status assessment value. This solution assesses the user's physical status from multiple dimensions to accurately predict the user's physical function, thereby providing targeted and personalized health recommendations. It solves the problem of current methods that cannot accurately provide health recommendations tailored to the user's actual physical condition, and can provide health recommendations that are precisely aligned with the user's actual physical condition, greatly improving user satisfaction.

[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 A flowchart of a health advice generation method provided in Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of a health advice generation method provided in Embodiment 2 of the present invention;

[0025] Figure 3 This is a schematic diagram of a health advice generation device provided in Embodiment 3 of the present invention;

[0026] Figure 4 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. 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 "first," "second," 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 the 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 a 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 illustrating a health advice generation method according to Embodiment 1 of the present invention. This embodiment is applicable to situations where precise health advice is provided to users. The method can be executed by a health advice generation device, which can be implemented in hardware and / or software and can be configured in an electronic device. The electronic device may include, but is not limited to, user fitness equipment, smartwatches, and other user communication terminals, such as mobile phones. Figure 1 As shown, the method includes:

[0031] Step 110: Calculate the user's physical fitness for the day based on the user's training load and potential physical fitness, and calculate the user's fatigue level for the day based on the user's fatigue level from the day before yesterday and the user's training load for the day.

[0032] Among these, the user's daily training load can be the user's training load for that day. The user's potential physical fitness can be the user's physical fitness from the previous day. The user's fatigue level from the day before yesterday can be the user's fatigue level from the previous day. The user's fatigue level for the day can be the user's fatigue level for that day.

[0033] In this embodiment of the invention, the user's daily training load can be calculated based on existing training load calculation methods, and the user's potential physical fitness can be determined based on existing user physical fitness assessment methods. Then, the user's daily training load and potential physical fitness are weighted according to preset corresponding weights to obtain the user's daily physical fitness. Simultaneously, the user's fatigue level from the day before yesterday can be determined based on existing human fatigue assessment methods. Then, the user's fatigue level from the day before yesterday and the user's daily training load are weighted according to preset corresponding weights to obtain the user's daily fatigue level.

[0034] Step 120: Determine the user's training status value and fusion coefficient based on the ratio of the user's daily physical fitness to the user's daily fatigue level and the user profile.

[0035] User profiles can be used to describe the attributes of user groups or individuals. User training status values ​​can be numerical values ​​representing a user's training load and recovery status. For example, the range of user training status values ​​is [0, ∞). The fusion coefficient can be the influence coefficient of user sleep factors when fused with user training status values.

[0036] In this embodiment of the invention, the user's age group can be identified from the user profile, and the correlation coefficient of the user's training state that matches the user's age group can be determined. Then, the ratio of the user's physical fitness to the user's fatigue level on the day can be calculated, and the ratio can be divided with the correlation coefficient of the user's training state to obtain the user's training state value. Then, the fusion coefficient that matches the state value interval to which the user's training state value falls can be determined.

[0037] For example, the correlation coefficients for user training status calculations differ for different user age groups. For instance, if the current user's age group is young adult, the correlation coefficient for the current user's training status calculation is 1. The correlation coefficients for user training status calculations adapted to user age groups can be adjusted as needed.

[0038] For example, the fusion coefficients will be different when the user's training state values ​​fall into different state value ranges.

[0039] Step 130: Sum the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical state assessment value.

[0040] The user's sleep impact value can be determined based on the user's sleep duration the previous day, and it can also be considered an impact value on the user's physical condition. For example, there is a mapping relationship between the user's sleep duration the previous day and the user's sleep impact value. The user's physical condition assessment value can be a numerical value representing the user's physical functional state. The range of the user's physical condition value is [0,∞).

[0041] In this embodiment of the invention, the product of the user's sleep impact value and the fusion coefficient can be calculated, and then the sum of the product value and the user's training state value can be calculated, and the calculated sum can be used as the user's physical state assessment value.

[0042] For example, it can be based on the formula Calculate the user's physical condition assessment value. Among them, This represents the user's training state value. Represents the fusion coefficient. This indicates the impact of the user's sleep.

[0043] Step 140: Display health recommendations generated based on the user's physical condition assessment values.

[0044] In this embodiment of the invention, suitable health advice can be selected from a health advice strategy library based on the user's physical condition value, and then displayed through a smart terminal in at least one form such as text or voice.

[0045] Optionally, health advice may include a title and explanatory text. The title may be a short statement indicating the preferred exercise activity. The explanatory text may explain the reasons for the recommended exercise activity. Users can directly understand the recommended exercise activity from the title and understand the reasons for the recommendation from the explanatory text.

[0046] The technical solution of this invention calculates the user's daily physical fitness based on the user's daily training load and potential physical fitness, and calculates the user's daily fatigue level based on the user's fatigue level from the previous day and the user's daily training load. Then, based on the ratio of the user's daily physical fitness to the user's daily fatigue level, and the user profile, the user's training status value and fusion coefficient are determined. Finally, the product of the user's sleep impact value and the fusion coefficient is summed with the user's training status value to obtain the user's physical status assessment value, which then displays health recommendations generated based on the user's physical status assessment value. This solution assesses the user's physical status from multiple dimensions to accurately predict the user's physical function, thereby providing targeted and personalized health recommendations. It solves the problem of current methods that cannot accurately provide health recommendations tailored to the user's actual physical condition, and can provide health recommendations that are precisely aligned with the user's actual physical condition, greatly improving user satisfaction.

[0047] Example 2

[0048] Figure 2 This is a flowchart of a health suggestion generation method provided in Embodiment 2 of the present invention. This embodiment is a specific embodiment based on the above embodiment, and provides specific optional implementation methods for calculating the user's daily physical fitness based on the user's daily training load and potential physical fitness, and calculating the user's daily fatigue level based on the user's fatigue level from the previous day and the user's daily training load. Figure 2 As shown, the method includes:

[0049] Step 210: Determine the physical fitness weight, fatigue weight, first training load weight, and second training load weight.

[0050] The physical fitness weight can be a weighting coefficient based on the user's training load for the day. The fatigue weight can be a weighting coefficient based on the user's fatigue level from the previous day. The first training load weight and the second training load weight can be two weighting coefficients used to calculate the user's physical fitness and fatigue level for the day, respectively.

[0051] In this embodiment of the invention, a physical fitness weight and a first training load weight are preset for calculating the user's potential physical fitness for the day, and a fatigue weight and a second training load weight are preset for calculating the user's fatigue level for the day.

[0052] Step 220: Calculate the user's daily physical fitness based on the user's daily training load, potential physical fitness, physical fitness weight, and first training load weight.

[0053] In this embodiment of the invention, the user's potential physical fitness can be multiplied by the physical fitness weight, and the user's daily training load can be multiplied by the first training load weight, and the product values ​​can be summed to obtain the user's daily physical fitness.

[0054] Step 230: Calculate the user's fatigue level for the day based on the user's fatigue level from the day before yesterday, the user's training load for the day, the fatigue level weight, and the second training load weight.

[0055] In this embodiment of the invention, the user's fatigue level from the day before yesterday can be multiplied by the fatigue level weight, and the user's training load for the day can be multiplied by the second training load weight, and the calculated product values ​​can be summed as the user's fatigue level for the day.

[0056] For example, it can be based on Calculates the user's daily physical fitness based on the formula. Calculate the user's fatigue level for the day. Among them, Indicates the user's potential physical fitness. This indicates the user's training load for the day, with physical fitness as the weighting factor. The first training load weight is , This is the user's physical fitness level for the day. This indicates the user's fatigue level from the day before yesterday, with a fatigue level weight of [weight missing]. The second training load weight is , This indicates the user's fatigue level for the day. It should be noted that the user's potential physical fitness and the user's fatigue level from the previous day can be calculated recursively using the above formula, and initial user physical fitness and fatigue levels can be set, which are equivalent to the user's physical fitness and fatigue levels on the first day.

[0057] Step 240: Based on the ratio of the user's daily physical fitness to the user's daily fatigue level, and the user profile, determine the user's training status value and fusion coefficient.

[0058] In an optional embodiment of the present invention, determining the user training state value and fusion coefficient based on the ratio of the user's daily physical fitness to the user's daily fatigue level and the user profile may include: determining the user's age mapping parameter based on the user profile; dividing the ratio of the user's daily physical fitness to the user's daily fatigue level by the user's age mapping parameter to obtain the user training state value; and determining the fusion coefficient based on the matching result between the user training state value and the target training state interval.

[0059] The user age mapping parameter can be a proportional coefficient configured for the age group corresponding to the user profile. The target training state interval can be a pre-set range of training state values ​​used to determine the fusion coefficients.

[0060] In this embodiment of the invention, user age mapping parameters can be determined based on the age range of the user group according to the user profile. Then, after calculating the ratio of the user's daily physical fitness to the user's daily fatigue, the ratio is divided by the user age mapping parameters to obtain the user training state value. Further identification is made as to whether the user training state value falls into the target training state interval. If the user training state value falls into the target training state interval, it indicates that the user training state value matches the target training state interval. Otherwise, it indicates that the user training state value does not match the target training state interval, so as to further determine the fusion coefficient corresponding to the matching result.

[0061] In an optional embodiment of the present invention, determining the fusion coefficient based on the matching result between the user training state value and the target training state interval may include: when the user training state value and the target training state interval are successfully matched, using the first coefficient as the fusion coefficient; when the user training state value and the target training state interval fail to match, using the second coefficient as the fusion coefficient.

[0062] The first coefficient can be the fusion coefficient when the user's training state value successfully matches the target training state interval. The second coefficient can be the fusion coefficient when the user's training state value fails to match the target training state interval. For example, the first coefficient can be -1, and the second coefficient can be 1. The target training state interval can be (0, 0.8).

[0063] In this embodiment of the invention, if the user's training state value successfully matches the target training state interval, the first coefficient is used as the fusion coefficient; if the user's training state value fails to match the target training state interval, the second coefficient is used as the fusion coefficient.

[0064] Step 250: Sum the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical state assessment value.

[0065] In an optional embodiment of the present invention, before summing the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical state assessment value, the method may further include: obtaining the user's sleep duration and determining the sleep state impact function corresponding to the user's sleep duration; and determining the user's sleep impact value based on the sleep state impact function corresponding to the user's sleep duration.

[0066] Among them, the sleep state influence function can be a piecewise function that calculates the user's sleep influence value.

[0067] In this embodiment of the invention, the sleep state influence function corresponding to different sleep duration intervals of the user can be obtained, thereby determining the sleep state influence function corresponding to the user's sleep duration, and then substituting the user's sleep duration into the sleep state influence function corresponding to the user's sleep duration to obtain the user's sleep influence value.

[0068] For example, the sleep state influence function can be Where d represents the user's sleep duration.

[0069] In an optional embodiment of the present invention, after summing the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical state assessment value, the method may further include: performing boundary clipping processing on the user's physical state assessment value according to the boundary protection rules of the user's physical state assessment value.

[0070] Boundary protection rules can be rules that adjust the boundary values ​​of user physical status assessments. Boundary clipping can be used to impose boundary constraints on user physical status assessments according to boundary protection rules.

[0071] In this embodiment of the invention, in order to ensure that the user's physical condition assessment value is positive, a boundary protection rule for the user's physical condition assessment value is set, thereby truncating the lower limit of the user's physical condition assessment value to 0 based on the user's physical condition assessment value, so as to realize the boundary pruning processing of the user's physical condition assessment value.

[0072] For example, if a user's physical condition assessment value falls within (0, 0.8], the health advice could be: "Your body is not active enough; please continue exercising." If the user's physical condition assessment value falls within (0.8, 1.3], the health advice could be: "Your recent training intensity is exactly what you need; your body is ready for a bigger challenge." If the user's physical condition assessment value falls within (1.3, 1.5], the health advice could be: "Your recent intensity has been a bit too high; don't do this kind of intensity more than twice a week." If the user's physical condition assessment value falls within (1.5, ∞], the health advice could be: "Your body is too fatigued recently; please rest."

[0073] In an optional embodiment of the present invention, after performing boundary clipping processing on the user's physical condition assessment value, the method may further include: obtaining user exercise suggestion association data; and generating health suggestions based on the boundary-clipped user physical condition assessment value, user exercise suggestion association data, and health suggestion strategy library.

[0074] The user exercise suggestion associated data can be data related to the user's exercise behavior. This data may include, but is not limited to, the user's training status value, the user's daily training load, the user's daily exercise type, and the user's historical exercise data. The health suggestion strategy library can be a collection of health suggestions. This library may include, but is not limited to, dietary suggestions and exercise suggestions.

[0075] In this embodiment of the invention, user exercise suggestion association data can be obtained, and then health suggestions that match the user's physical condition assessment value and user exercise suggestion association data after boundary clipping can be selected from the health suggestion strategy library.

[0076] Optionally, for each interval divided by the range of user's physical status values, for the main filtering process of a health advice strategy library, once the interval in which the user's physical status value falls is determined, the corresponding main filtering process can be triggered, and then the corresponding sub-process of the triggered main filtering process that matches the data associated with the user's exercise advice can be determined, so as to determine the final target exercise advice from the health advice strategy library according to the sub-process.

[0077] Specifically, each interval defined by the range of user's physical condition values ​​can correspond to a physical condition cue word. Similarly, the user's previous exercise expenditure, represented by data related to exercise suggestions, can correspond to an exercise expenditure cue word. Then, based on these cue words, exercise suggestions matching the chosen cue word are selected from the health suggestion strategy library. Optionally, the same set of cue words can correspond to exercise suggestions with different descriptions but consistent intensity, enriching the diversity of exercise suggestions.

[0078] Optionally, if the main filtering process is determined based on the range in which the user's physical condition value falls, and the sub-process cannot be adapted based on the user's exercise suggestion association data, then the fallback exercise suggestion in the health suggestion strategy library corresponding to the range in which the user's physical condition value falls will be used as the exercise suggestion.

[0079] Step 260: Display health recommendations generated based on the user's physical condition assessment values.

[0080] For example, the explanation can continue based on the above example, assuming... , User's training load for the day Then it can be calculated , Thus, calculate , If today's sleep duration ,but , , .like ,but , This leads to a lower limit of 0. This solution accurately assesses daily training readiness and, based on training and sleep patterns, issues risk warnings for high-intensity training days with insufficient sleep.

[0081] The technical solution of this invention determines physical fitness weight, fatigue weight, first training load weight, and second training load weight. Based on the user's daily training load, potential physical fitness, physical fitness weight, and first training load weight, the user's daily physical fitness is calculated. Then, based on the user's fatigue level from the previous day, daily training load, fatigue weight, and second training load weight, the user's daily fatigue level is calculated. Finally, based on the ratio of the user's daily physical fitness to their daily fatigue level, and the user profile, the user's training status value and fusion coefficient are determined. The product of the user's sleep impact value and the fusion coefficient is summed with the user's training status value to obtain the user's physical status assessment value. This allows for the display of health recommendations generated based on the user's physical status assessment value. This solution assesses the user's physical status from multiple dimensions to accurately predict the user's physical function, thereby providing targeted and personalized health recommendations. It solves the problem of current methods that cannot accurately provide health recommendations tailored to the user's actual physical condition, providing precise and relevant health advice and significantly improving user satisfaction.

[0082] 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.

[0083] Example 3

[0084] Figure 3 This is a schematic diagram of a health advice generation device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0085] The first data calculation module 310 is used to calculate the user's physical fitness for the day based on the user's training load and potential physical fitness, and to calculate the user's fatigue level for the day based on the user's fatigue level the day before yesterday and the user's training load for the day.

[0086] The second data calculation module 320 is used to determine the user's training status value and fusion coefficient based on the ratio of the user's daily physical fitness to the user's daily fatigue level and the user profile.

[0087] The physical condition assessment value calculation module 330 is used to sum the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical condition assessment value.

[0088] The health advice display module 340 is used to display health advice generated based on the user's physical condition assessment value.

[0089] The technical solution of this invention calculates the user's daily physical fitness based on the user's daily training load and potential physical fitness, and calculates the user's daily fatigue level based on the user's fatigue level from the previous day and the user's daily training load. Then, based on the ratio of the user's daily physical fitness to the user's daily fatigue level, and the user profile, the user's training status value and fusion coefficient are determined. Finally, the product of the user's sleep impact value and the fusion coefficient is summed with the user's training status value to obtain the user's physical status assessment value, which then displays health recommendations generated based on the user's physical status assessment value. This solution assesses the user's physical status from multiple dimensions to accurately predict the user's physical function, thereby providing targeted and personalized health recommendations. It solves the problem of current methods that cannot accurately provide health recommendations tailored to the user's actual physical condition, and can provide health recommendations that are precisely aligned with the user's actual physical condition, greatly improving user satisfaction.

[0090] Optionally, the first data calculation module 310 is used to determine the physical fitness weight, fatigue weight, first training load weight, and second training load weight; calculate the user's physical fitness for the day based on the user's daily training load, the user's potential physical fitness, the physical fitness weight, and the first training load weight; and calculate the user's fatigue for the day based on the user's fatigue level from the day before yesterday, the user's daily training load, the fatigue weight, and the second training load weight.

[0091] Optionally, the second data calculation module 320 is used to determine the user age mapping parameter based on the user profile; divide the ratio of the user's daily physical fitness to the user's daily fatigue level by the user age mapping parameter to obtain the user training state value; and determine the fusion coefficient based on the matching result between the user training state value and the target training state interval.

[0092] Optionally, the second data calculation module 320 includes a fusion coefficient determination unit, used to use a first coefficient as the fusion coefficient when the user training state value successfully matches the target training state interval; and to use a second coefficient as the fusion coefficient when the user training state value fails to match the target training state interval.

[0093] Optionally, the health advice generation device includes a user sleep impact value calculation module, used to obtain the user's sleep duration and determine the sleep state impact function corresponding to the user's sleep duration; and to determine the user's sleep impact value based on the sleep state impact function corresponding to the user's sleep duration.

[0094] Optionally, the health advice generation device includes a boundary clipping module for clipping the user's physical condition assessment value according to the boundary protection rules of the user's physical condition assessment value.

[0095] Optionally, the health advice generation device includes a health advice generation module, used to acquire user exercise advice-related data; and generate health advice based on the user's physical condition assessment value after boundary clipping, the user exercise advice-related data, and the health advice strategy library.

[0096] The health advice generation device provided in this embodiment of the invention can execute the health advice generation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0097] Example 4

[0098] Figure 4 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. The electronic device can represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., glasses, watches, etc.), and other similar 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.

[0099] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as ROM 12, RAM 13, etc., 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 into the RAM 13 from the storage unit 18. The RAM 13 can 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 I / O interface 15 is also connected to the bus 14. The ROM 12 is a read-only memory, the RAM 13 is a random access memory, and the I / O interface 15 is an input / output interface.

[0100] 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.

[0101] 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 the health recommendation generation method.

[0102] In some embodiments, the health recommendation generation 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 installed 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 health recommendation generation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the health recommendation generation method by any other suitable means (e.g., by means of firmware).

[0103] 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.

[0104] 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.

[0105] 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 of the foregoing. 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, RAM, 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 of the foregoing.

[0106] 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).

[0107] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or 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.

[0108] 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 servers, such as high management difficulty and weak business scalability.

[0109] This application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the health advice generation method provided in any embodiment of this application. This program product and the health advice generation methods disclosed in the embodiments of this application belong to the same inventive concept, and therefore will not be described in detail here.

[0110] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0111] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. 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 invention should be included within the scope of protection of this invention.

Claims

1. A method for generating health recommendations, characterized in that, include: Calculate the user's physical fitness for the day based on the user's training load and potential physical fitness, and calculate the user's fatigue level for the day based on the user's fatigue level from the day before yesterday and the user's training load for the day. Based on the ratio of the user's daily physical fitness to the user's daily fatigue level, and the user profile, determine the user's training status value and fusion coefficient; The sum of the product of the user's sleep impact value and the fusion coefficient, and the user's training state value, is used to obtain the user's physical state assessment value. Display health recommendations generated based on the user's physical condition assessment values.

2. The method according to claim 1, characterized in that, Based on the user's daily training load and potential physical fitness, calculate the user's daily physical fitness, and based on the user's fatigue level from the previous day and the user's daily training load, calculate the user's daily fatigue level, including: Determine the weights for physical fitness, fatigue, first training load, and second training load; The user's daily physical fitness is calculated based on the user's daily training load, the user's potential physical fitness, the physical fitness weight, and the first training load weight. The user's fatigue level for the day is calculated based on the user's fatigue level from the day before yesterday, the user's training load for the day, the fatigue level weight, and the second training load weight.

3. The method according to claim 1, characterized in that, Based on the ratio of the user's daily physical fitness to the user's daily fatigue level, and the user profile, the user's training status value and fusion coefficient are determined, including: Based on the user profile, determine the user age mapping parameters; The ratio of the user's daily physical fitness to the user's daily fatigue level is divided by the user's age mapping parameter to obtain the user's training status value. The fusion coefficient is determined based on the matching result between the user's training state value and the target training state interval.

4. The method according to claim 3, characterized in that, The fusion coefficient is determined based on the matching result between the user training state value and the target training state interval, including: When the user training state value successfully matches the target training state interval, the first coefficient is used as the fusion coefficient; When the user's training state value fails to match the target training state interval, the second coefficient is used as the fusion coefficient.

5. The method according to claim 1, characterized in that, Before summing the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical state assessment value, the process also includes: Obtain the user's sleep duration and determine the sleep state influence function corresponding to the user's sleep duration; The sleep impact value of the user is determined based on the sleep state impact function corresponding to the user's sleep duration.

6. The method according to claim 1, characterized in that, After summing the product of the user's sleep impact value and the fusion coefficient with the user's training state value to obtain the user's physical state assessment value, the method further includes: According to the boundary protection rules of the user's physical condition assessment value, the user's physical condition assessment value is subjected to boundary clipping processing.

7. The method according to claim 6, characterized in that, After performing boundary clipping on the user's physical condition assessment values, the process also includes: Obtain user exercise suggestion related data; Based on the user's physical condition assessment value processed by boundary clipping, the user's exercise suggestion association data, and the health suggestion strategy library, health suggestions are generated.

8. An electronic device, characterized in that, The electronic device includes: At least one processor, and a memory communicatively connected to said at least one processor; 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 health advice generation method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the health advice generation method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the health advice generation method according to any one of claims 1-7.

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