Method and device for determining rope skipping recommendation plan based on large model

Abstract

The invention relates to a method and device for determining a rope skipping recommendation plan based on a large model, and belongs to the technical field of training guidance. Determining a class exercise intensity score and an after-class exercise intensity score of the user; determining an after-class exercise quality score, a training effect score, a training stability score and a physique score; determining a physique similarity score, an exercise intensity similarity score and an exercise attitude similarity score; determining plan recommendation degree scores of the training plan of the user i in the current semester and the training plan of the user r in the current semester; according to the plan recommendation degree score, determining a training plan recommended by the semester; constructing a fine tuning data set, loading the large model, and finely tuning the large model to enable the large model to learn how to generate a rope skipping recommendation plan; and outputting a rope skipping recommendation plan through the large model. According to the method, the accurate rope skipping recommendation plan can be generated by fully utilizing the historical use data, and dynamic and personalized rope skipping guidance is provided for the user.

Description

Technical Field

[0001] The present invention belongs to the field of training guidance, and in particular relates to a method and a device for determining a rope skipping recommendation plan based on a large model. Background Art

[0002] At present, rope skipping is a highly efficient aerobic exercise that is deeply loved by the majority of users. However, the existing rope skipping training methods mainly rely on the user's self-planning, lack of scientific guidance and personalized training programs, resulting in unsatisfactory training results. Although the existing intelligent rope skipping equipment can record basic exercise data, it cannot generate a truly personalized training plan based on individual physical fitness differences and exercise goals. The degree of intelligence is limited. How to make full use of the user's historical training data, physical fitness and exercise goals, generate accurate training plans, provide dynamic and personalized rope skipping guidance, effectively make up for the deficiencies in existing technologies, and help users achieve scientific and efficient fitness goals is a problem that needs to be solved. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the purpose of the invention is to provide a method and device for determining a skipping rope recommendation plan based on a large model, which can make full use of historical usage data to generate an accurate skipping rope recommendation plan, provide users with dynamic and personalized skipping rope guidance, effectively make up for the deficiencies in the prior art, and help users achieve scientific and efficient fitness goals.

[0004] In a first aspect of the present invention, a method for determining a recommended rope skipping plan based on a large model is proposed, comprising:

[0005] Obtain the usage data of the smart skipping rope for one semester;

[0006] Determine the user's classroom practice intensity score and after-class practice intensity score based on the usage data of the smart skipping rope for one semester;

[0007] The after-class practice quality score is determined based on the user's classroom practice intensity score, after-class practice intensity score and the one-semester usage data of the smart skipping rope;

[0008] Determine the user's training effect score, training stability score and physical fitness score based on the usage data of the smart skipping rope for one semester;

[0009] Determine a physical fitness similarity score based on the physical fitness scores of user i and user r, determine a sports intensity similarity score based on the classroom exercise intensity scores of user i and user r, and determine a sports attitude similarity score based on the after-class exercise quality scores of user i and user r;

[0010] Determine the plan recommendation scores of the training plans of user i and user r for this semester according to the physical fitness similarity score, the exercise intensity similarity score, the exercise attitude similarity score and the training effect score of user i;

[0011] Determine the recommended training plan for this semester based on the plan recommendation score;

[0012] Build a fine-tuning dataset for rope skipping plan recommendation, load the large model, and use the fine-tuning dataset to fine-tune the large model so that the large model can learn how to generate a rope skipping recommendation plan;

[0013] Output the skipping rope recommendation plan through the big model.

[0014] Furthermore, in the above method for determining a skipping rope recommendation plan based on a large model, the user's classroom exercise intensity score and after-class exercise intensity score are determined based on the usage data of the smart skipping rope for one semester by the following formula:

[0015]

[0016]

[0017]

[0018] in, represents the training mode of each session in the usage data of the smart skipping rope for one semester, i represents the user's serial number, , represents the number of users collected, j represents the sequence number of each training of user i, , represents the number of training times for user i, represents the classroom practice intensity score of user i, Represents the after-class exercise intensity score of user i.

[0019] Furthermore, in the above method for determining a skipping rope recommendation plan based on a large model, the after-class exercise quality score is determined according to the user's classroom exercise intensity score, after-class exercise intensity score and the usage data of the smart skipping rope for one semester by the following formula:

[0020]

[0021] in, It represents the score corresponding to the training results in the usage data of the smart skipping rope for one semester. represents the training mode of each session in the usage data of the smart skipping rope for one semester, i represents the user's serial number, , represents the number of users, j represents the sequence number of each training of user i, , represents the number of training times for user i, represents the classroom practice intensity score of user i, represents the after-class exercise intensity score of user i, Represents the quality score of after-class exercises of user i.

[0022] Furthermore, in the above method for determining a skipping rope recommendation plan based on a large model, the training effect score, training stability score and physical fitness score of the user are determined according to the usage data of the smart skipping rope for one semester by the following formula:

[0023]

[0024]

[0025]

[0026]

[0027] in, Indicates the training results of the smart skipping rope in one semester of usage data , It represents the training process record of the usage data of the smart skipping rope for one semester. Indicates the timestamp of completing the k-th rope skipping. , Indicates the first judgment threshold set, Indicates the set second judgment threshold, Indicates the set third judgment threshold, represents the fourth judgment threshold value set, Indicates the fifth judgment threshold value set, Indicates the sixth judgment threshold value set, represents the training effect score of user i, represents the training stability score of user i, represents the stable sub-score of each training, Indicates the user's height. represents the weight of user i, represents the age of user i, i represents the serial number of user i, , represents the number of users collected, j represents the sequence number of each training of user i, , represents the number of training times for user i, Represents the physical fitness score of user i.

[0028] Furthermore, in the above-mentioned method for determining a skipping rope recommendation plan based on a large model, the physical fitness similarity score is determined according to the physical fitness scores of user i and user r, the exercise intensity similarity score is determined according to the classroom exercise intensity scores of user i and user r, and the exercise attitude similarity score is determined according to the after-class exercise quality scores of user i and user r by the following formula:

[0029]

[0030]

[0031]

[0032] in, represents the physical similarity score, represents the similarity score of exercise intensity, represents the similarity score of sports attitude, Indicates the seventh judgment threshold value set, Indicates the eighth judgment threshold value set, Indicates the ninth judgment threshold set. Indicates the set tenth judgment threshold, Indicates the set eleventh judgment threshold, Indicates the set twelfth judgment threshold, represents the classroom practice intensity score of user i, represents the classroom practice intensity score of user r, represents the physical fitness score of user i, represents the physical fitness score of user r, represents the quality score of after-class exercises of user i, Represents the quality score of after-class exercises of user r.

[0033] Furthermore, in the above-mentioned method for determining a recommended skipping plan based on a large model, the plan recommendation scores of the training plan of user i this semester and the training plan of user r this semester are determined according to the physical fitness similarity score, the exercise intensity similarity score, the exercise attitude similarity score and the training effect score of user i by the following formula:

[0034]

[0035] in, represents the physical similarity score, represents the similarity score of exercise intensity, represents the similarity score of sports attitude, represents the training effect score of user i, The plan recommendation scores of user i's training plan for this semester and user r's training plan for this semester.

[0036] Furthermore, in the above method for determining a recommended skipping plan based on a large model, determining a recommended training plan for this semester according to the plan recommendation degree score includes:

[0037] Select the recommended score of the plan The training plan for this semester corresponding to the highest user r Recommended training plan for this semester as a user ;

[0038] The fine-tuning dataset for constructing the skipping rope plan recommendation is constructed based on each user's classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score, age, training plan for this semester, and recommended training plan for this semester.

[0039] Furthermore, in the above method for determining a rope skipping recommendation plan based on a large model, outputting the rope skipping recommendation plan through the large model includes:

[0040] The classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score and age are taken as inputs of the big model, and the big model outputs a skipping rope recommendation plan.

[0041] A second aspect of the present invention further provides an electronic device, comprising: a processor and a memory;

[0042] The processor calls the program or instruction stored in the memory to execute any of the above methods for determining a recommended rope skipping plan based on a large model.

[0043] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute any of the above methods for determining a recommended rope skipping plan based on a large model.

[0044] The beneficial effects of the present invention are as follows: the present invention obtains the usage data of the smart skipping rope for one semester; determines the user's classroom exercise intensity score and after-class exercise intensity score according to the usage data of the smart skipping rope for one semester; determines the after-class exercise quality score according to the user's classroom exercise intensity score, after-class exercise intensity score and the usage data of the smart skipping rope for one semester; determines the user's training effect score, training stability score and physical fitness score according to the usage data of the smart skipping rope for one semester; determines the physical fitness similarity score according to the physical fitness score of user i and user r, determines the exercise intensity similarity score according to the classroom exercise intensity score of user i and user r, and determines the exercise attitude similarity score according to the after-class exercise quality score of user i and user r; determines the plan recommendation degree score of the training plan of user i this semester and the training plan of user r this semester according to the physical fitness similarity score, exercise intensity similarity score, exercise attitude similarity score and the training effect score of user i; determines the recommended training plan for this semester according to the plan recommendation degree score; constructs a fine-tuning data set for skipping plan recommendation, loads a large model, and uses the fine-tuning data set to fine-tune the large model so that the large model learns how to generate a skipping rope recommendation plan; and outputs the skipping rope recommendation plan through the large model. The present invention can make full use of historical usage data to generate an accurate rope skipping recommendation plan, provide users with dynamic and personalized rope skipping guidance, effectively make up for the deficiencies in the prior art, and help users achieve scientific and efficient fitness goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0046] Figure 1 A diagram of a method for determining a recommended rope skipping plan based on a large model provided by an embodiment of the present invention;

[0047] Figure 2 A schematic block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0049] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0050] In the description of the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be internal communication between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0052] The present invention proposes a method, device, electronic device and storage medium for determining a rope skipping recommendation plan based on a large model, which can fully utilize historical usage data to generate an accurate rope skipping recommendation plan, provide users with dynamic and personalized rope skipping guidance, effectively make up for the deficiencies in the prior art, and help users achieve scientific and efficient fitness goals.

[0053] Method Embodiment

[0054] Figure 1 A diagram of a method for determining a recommended rope skipping plan based on a large model provided in an embodiment of the present invention.

[0055] In the first aspect of the present invention, a method for determining a recommended skipping plan based on a large model is proposed, combining Figure 1 ,include:

[0056] S1: Obtain the usage data of the smart skipping rope for one semester.

[0057] Specifically, in the embodiment of the present invention, the usage data of the smart skipping rope for one semester includes: the height of the user ,weight ,age , each training mode , training results , the score corresponding to the training results , training process records , training plan for this semester , where i is the user's serial number, , is the number of users collected, j is the sequence number of each training of user i, , is the number of training times for user i,

[0058]

[0059] is the timestamp of completing the kth rope skipping, .

[0060] S2: Determine the user's classroom practice intensity score and after-class practice intensity score based on the usage data of the smart skipping rope for one semester.

[0061] Specifically, in an embodiment of the present invention, a method for determining a user's classroom exercise intensity score and after-class exercise intensity score based on the usage data of the smart skipping rope for one semester is described in detail below.

[0062] S3: Determine the after-class practice quality score based on the user's classroom practice intensity score, after-class practice intensity score and the usage data of the smart skipping rope for one semester.

[0063] Specifically, in an embodiment of the present invention, a method for determining an after-class exercise quality score based on a user's classroom exercise intensity score, after-class exercise intensity score, and smart rope skipping usage data for one semester is described in detail below.

[0064] S4: Determine the user's training effect score, training stability score and physical fitness score based on the usage data of the smart skipping rope for one semester.

[0065] Specifically, in an embodiment of the present invention, a method for determining a user's training effect score, training stability score, and physical fitness score based on the usage data of a smart skipping rope for one semester is described in detail below.

[0066] S5: Determine the physical fitness similarity score based on the physical fitness scores of user i and user r, determine the exercise intensity similarity score based on the classroom exercise intensity scores of user i and user r, and determine the exercise attitude similarity score based on the after-class exercise quality scores of user i and user r.

[0067] Specifically, in an embodiment of the present invention, a method for determining a physical fitness similarity score based on the physical fitness scores of user i and user r, determining an exercise intensity similarity score based on the classroom exercise intensity scores of user i and user r, and determining an exercise attitude similarity score based on the after-class exercise quality scores of user i and user r is described in detail below.

[0068] S6: Determine the plan recommendation scores of user i's training plan for this semester and user r's training plan for this semester based on the physical fitness similarity score, exercise intensity similarity score, exercise attitude similarity score and user i's training effect score.

[0069] Specifically, in an embodiment of the present invention, a method for determining the plan recommendation degree score of the training plan of user i this semester and the training plan of user r this semester based on the physical fitness similarity score, the exercise intensity similarity score, the exercise attitude similarity score and the training effect score of user i is described in detail below.

[0070] S7: Determine the recommended training plan for this semester based on the plan recommendation level score.

[0071] Specifically, in an embodiment of the present invention, a method for determining a recommended training plan for this semester based on a plan recommendation degree score is described in detail below.

[0072] S8: Construct a fine-tuning dataset for skipping rope plan recommendation, load the large model, and use the fine-tuning dataset to fine-tune the large model so that the large model can learn how to generate a skipping rope recommendation plan.

[0073] Specifically, in an embodiment of the present invention, a fine-tuning data set for constructing a recommended skipping rope plan is constructed based on each user's classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score, age, training plan for this semester, and recommended training plan for this semester. Loading the big model may be loading the InternVL2 big model, and using the fine-tuning data set to fine-tune the loaded InternVL2 big model allows the big model to learn how to generate a recommended skipping rope plan based on the classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score, and age.

[0074] S9: Output the rope skipping recommendation plan through the big model.

[0075] Specifically, in an embodiment of the present invention, the classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score and age are input into the fine-tuned InternVL2 large model to output a skipping rope recommendation plan.

[0076] Furthermore, in the above method for determining a skipping rope recommendation plan based on a large model, the user's classroom exercise intensity score and after-class exercise intensity score are determined based on the usage data of the smart skipping rope for one semester by the following formula:

[0077]

[0078]

[0079]

[0080] in, represents the training mode of each session in the usage data of the smart skipping rope for one semester, i represents the user's serial number, , represents the number of users collected, j represents the sequence number of each training of user i, , represents the number of training times for user i, represents the classroom practice intensity score of user i, Represents the after-class exercise intensity score of user i.

[0081] Furthermore, in the above method for determining a skipping rope recommendation plan based on a large model, the after-class exercise quality score is determined according to the user's classroom exercise intensity score, after-class exercise intensity score and the usage data of the smart skipping rope for one semester by the following formula:

[0082]

[0083] in, It represents the score corresponding to the training results in the usage data of the smart skipping rope for one semester. represents the training mode of each session in the usage data of the smart skipping rope for one semester, i represents the user's serial number, , represents the number of users, j represents the sequence number of each training of user i, , represents the number of training times for user i, represents the classroom practice intensity score of user i, represents the after-class exercise intensity score of user i, Represents the quality score of after-class exercises of user i.

[0084] Furthermore, in the above method for determining a skipping rope recommendation plan based on a large model, the training effect score, training stability score and physical fitness score of the user are determined according to the usage data of the smart skipping rope for one semester by the following formula:

[0085]

[0086]

[0087]

[0088]

[0089] in, Indicates the training results of the smart skipping rope in one semester of usage data , It represents the training process record of the usage data of the smart skipping rope for one semester. Indicates the timestamp of completing the k-th rope skipping. , Indicates the first judgment threshold set, Indicates the set second judgment threshold, Indicates the set third judgment threshold, represents the fourth judgment threshold value set, Indicates the fifth judgment threshold value set, Indicates the sixth judgment threshold value set, represents the training effect score of user i, represents the training stability score of user i, represents the stable sub-score of each training, Indicates the user's height. represents the weight of user i, represents the age of user i, i represents the serial number of user i, , represents the number of users collected, j represents the sequence number of each training of user i, , represents the number of training times for user i, Represents the physical fitness score of user i.

[0090] Specifically, in the embodiment of the present invention, the first judgment threshold is set , set the second judgment threshold , set the third judgment threshold , set the fourth judgment threshold , set the fifth judgment threshold , set the sixth judgment threshold The size can be flexibly set according to actual conditions.

[0091] Furthermore, in the above-mentioned method for determining a skipping rope recommendation plan based on a large model, the physical fitness similarity score is determined according to the physical fitness scores of user i and user r, the exercise intensity similarity score is determined according to the classroom exercise intensity scores of user i and user r, and the exercise attitude similarity score is determined according to the after-class exercise quality scores of user i and user r by the following formula:

[0092]

[0093]

[0094]

[0095] in, represents the physical similarity score, represents the similarity score of exercise intensity, represents the similarity score of sports attitude, Indicates the seventh judgment threshold value set, Indicates the eighth judgment threshold value set, Indicates the ninth judgment threshold set. Indicates the set tenth judgment threshold, Indicates the set eleventh judgment threshold, Indicates the set twelfth judgment threshold, represents the classroom practice intensity score of user i, represents the classroom practice intensity score of user r, represents the physical fitness score of user i, represents the physical fitness score of user r, represents the quality score of after-class exercises of user i, Represents the quality score of after-class exercises of user r.

[0096] Specifically, in the embodiment of the present invention, the seventh judgment threshold is set , set the eighth judgment threshold , set the ninth judgment threshold , set the tenth judgment threshold , set the eleventh judgment threshold , set the twelfth judgment threshold The size can be flexibly set according to actual conditions.

[0097] Furthermore, in the above-mentioned method for determining a recommended skipping plan based on a large model, the plan recommendation scores of the training plan of user i this semester and the training plan of user r this semester are determined according to the physical fitness similarity score, the exercise intensity similarity score, the exercise attitude similarity score and the training effect score of user i by the following formula:

[0098]

[0099] in, represents the physical similarity score, represents the similarity score of exercise intensity, represents the similarity score of sports attitude, represents the training effect score of user i, The plan recommendation scores of user i's training plan for this semester and user r's training plan for this semester.

[0100] Furthermore, in the above method for determining a recommended skipping plan based on a large model, determining a recommended training plan for this semester according to the plan recommendation degree score includes:

[0101] Select the recommended score of the plan The training plan for this semester corresponding to the highest user r Recommended training plan for this semester as a user ;

[0102] The fine-tuning dataset for constructing the recommended skipping plan is based on each user's classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score, age, and training plan for this semester. , Recommended training plan for this semester Constructed.

[0103] Furthermore, in the above method for determining a rope skipping recommendation plan based on a large model, outputting the rope skipping recommendation plan through the large model includes:

[0104] The classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score and age are taken as inputs of the big model, and the big model outputs a skipping rope recommendation plan.

[0105] Specifically, in the embodiments of the present invention, it is possible to fully utilize the user's classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score and age to generate an accurate rope skipping recommendation plan, provide users with dynamic and personalized rope skipping guidance, effectively make up for the deficiencies in the prior art, and help users achieve scientific and efficient fitness goals.

[0106] A second aspect of the present invention further provides an electronic device, comprising: a processor and a memory;

[0107] The processor calls the program or instruction stored in the memory to execute any of the above methods for determining a recommended rope skipping plan based on a large model.

[0108] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute any of the above methods for determining a recommended rope skipping plan based on a large model.

[0109] Figure 2 It is a schematic block diagram of an electronic device provided by an embodiment of the present invention.

[0110] like Figure 2As shown, the electronic device includes: at least one processor 201, at least one memory 202 and at least one communication interface 203. The various components in the electronic device are coupled together through a bus system 204. The communication interface 203 is used to transmit information between external devices. It can be understood that the bus system 204 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 204 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 2 Various buses are labeled as bus system 204 .

[0111] It can be understood that the memory 202 in this embodiment can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0112] In some implementations, the memory 202 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.

[0113] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. A program that implements any method of the method for determining a recommended skipping plan based on a large model provided in an embodiment of the present invention can be included in the application.

[0114] In an embodiment of the present invention, the processor 201 calls the program or instructions stored in the memory 202, specifically, the program or instructions stored in the application, and the processor 201 is used to execute the steps of each embodiment of a method for determining a recommended rope skipping plan based on a large model provided in an embodiment of the present invention.

[0115] Obtain the usage data of the smart skipping rope for one semester;

[0116] Determine the user's classroom practice intensity score and after-class practice intensity score based on the usage data of the smart skipping rope for one semester;

[0117] The after-class practice quality score is determined based on the user's classroom practice intensity score, after-class practice intensity score and the one-semester usage data of the smart skipping rope;

[0118] Determine the user's training effect score, training stability score and physical fitness score based on the usage data of the smart skipping rope for one semester;

[0119] Determine a physical fitness similarity score based on the physical fitness scores of user i and user r, determine a sports intensity similarity score based on the classroom exercise intensity scores of user i and user r, and determine a sports attitude similarity score based on the after-class exercise quality scores of user i and user r;

[0120] Determine the plan recommendation scores of the training plans of user i and user r for this semester according to the physical fitness similarity score, the exercise intensity similarity score, the exercise attitude similarity score and the training effect score of user i;

[0121] Determine the recommended training plan for this semester based on the plan recommendation score;

[0122] Build a fine-tuning dataset for rope skipping plan recommendation, load the large model, and use the fine-tuning dataset to fine-tune the large model so that the large model can learn how to generate a rope skipping recommendation plan;

[0123] Output the skipping rope recommendation plan through the big model.

[0124] Any of the methods for determining a recommended plan for rope skipping based on a large model provided in an embodiment of the present invention can be applied to the processor 201, or implemented by the processor 201. The processor 201 can be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 201 or an instruction in the form of software. The above-mentioned processor 201 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0125] The steps of any method in the method for determining a recommended plan for rope skipping based on a large model provided by an embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software units in the decoding processor. The software unit can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 202, and the processor 201 reads the information in the memory 202 and completes the steps of the method in combination with its hardware.

[0126] Those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0127] Those skilled in the art will appreciate that the description of each embodiment has its own emphasis, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0128] Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0129] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for determining a rope skipping recommendation plan based on a large model, characterized in that: include: Obtain the usage data of the smart skipping rope for one semester; Determine the user's classroom practice intensity score and after-class practice intensity score based on the usage data of the smart skipping rope for one semester; The after-class practice quality score is determined based on the user's classroom practice intensity score, after-class practice intensity score and the one-semester usage data of the smart skipping rope; Determine the user's training effect score, training stability score and physical fitness score based on the usage data of the smart skipping rope for one semester; Determine a physical fitness similarity score based on the physical fitness scores of user i and user r, determine a sports intensity similarity score based on the classroom exercise intensity scores of user i and user r, and determine a sports attitude similarity score based on the after-class exercise quality scores of user i and user r; Determine the plan recommendation scores of the training plans of user i and user r for this semester according to the physical fitness similarity score, the exercise intensity similarity score, the exercise attitude similarity score and the training effect score of user i; Determine the recommended training plan for this semester based on the plan recommendation score; Build a fine-tuning dataset for rope skipping plan recommendation, load the large model, and use the fine-tuning dataset to fine-tune the large model so that the large model can learn how to generate a rope skipping recommendation plan; Output the skipping rope recommendation plan through the big model.

2. A method for determining a rope skipping recommendation plan based on a large model according to claim 1, characterized in that: The method of determining the user's classroom exercise intensity score and after-class exercise intensity score based on the usage data of the smart skipping rope for one semester is determined by the following formula: ； ； ； in, represents the training mode of each session in the usage data of the smart skipping rope for one semester, i represents the user's serial number, , represents the number of users collected, j represents the sequence number of each training of user i, , represents the number of training times for user i, represents the classroom practice intensity score of user i, Represents the after-class exercise intensity score of user i.

3. The method for determining a rope skipping recommendation plan based on a large model according to claim 1, characterized in that: The after-class exercise quality score is determined based on the user's classroom exercise intensity score, after-class exercise intensity score and the usage data of the smart skipping rope for one semester by the following formula: ； in, It represents the score corresponding to the training results in the usage data of the smart skipping rope for one semester. represents the training mode of each session in the usage data of the smart skipping rope for one semester, i represents the user's serial number, , represents the number of users, j represents the sequence number of each training of user i, , represents the number of training times for user i, represents the classroom practice intensity score of user i, represents the after-class exercise intensity score of user i, Represents the quality score of after-class exercises of user i.

4. The method for determining a rope skipping recommendation plan based on a large model according to claim 1, characterized in that: The training effect score, training stability score and physical fitness score of the user are determined based on the usage data of the smart skipping rope for one semester by the following formula: ； ； ； ； in, Indicates the training results of the smart skipping rope in one semester of usage data , It represents the training process record of the usage data of the smart skipping rope for one semester. Indicates the timestamp of completing the k-th rope skipping. , Indicates the first judgment threshold set, Indicates the set second judgment threshold, Indicates the set third judgment threshold, represents the fourth judgment threshold value set, Indicates the fifth judgment threshold value set, Indicates the sixth judgment threshold value set, represents the training effect score of user i, represents the training stability score of user i, represents the stable sub-score of each training, Indicates the user's height. represents the weight of user i, represents the age of user i, i represents the serial number of user i, , represents the number of users collected, j represents the sequence number of each training of user i, , represents the number of training times for user i, Represents the physical fitness score of user i.

5. The method for determining a rope skipping recommendation plan based on a large model according to claim 1, characterized in that: The determination of the physical fitness similarity score according to the physical fitness scores of user i and user r, the determination of the exercise intensity similarity score according to the classroom exercise intensity scores of user i and user r, and the determination of the exercise attitude similarity score according to the after-class exercise quality scores of user i and user r are determined by the following formula: ； ； ； in, represents the physical similarity score, represents the similarity score of exercise intensity, represents the similarity score of sports attitude, Indicates the seventh judgment threshold value set, Indicates the eighth judgment threshold value set, Indicates the ninth judgment threshold set, Indicates the set tenth judgment threshold, Indicates the set eleventh judgment threshold, Indicates the set twelfth judgment threshold, represents the classroom practice intensity score of user i, represents the classroom practice intensity score of user r, represents the physical fitness score of user i, represents the physical fitness score of user r, represents the quality score of after-class exercises of user i, Represents the quality score of after-class exercises of user r.

6. The method for determining a rope skipping recommendation plan based on a large model according to claim 1, characterized in that: The plan recommendation scores of the training plans of user i and user r for this semester are determined based on the physical fitness similarity score, the exercise intensity similarity score, the exercise attitude similarity score and the training effect score of user i by the following formula: ； in, represents the physical similarity score, represents the similarity score of exercise intensity, represents the similarity score of sports attitude, represents the training effect score of user i, The plan recommendation scores of user i's training plan for this semester and user r's training plan for this semester.

7. The method for determining a rope skipping recommendation plan based on a large model according to claim 1, characterized in that: The training plan recommended for this semester is determined based on the plan recommendation score, including: Select the recommended score of the plan The training plan for this semester corresponding to the highest user r Recommended training plan for this semester as a user ; The fine-tuning dataset for constructing the skipping rope plan recommendation is constructed based on each user's classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score, age, training plan for this semester, and recommended training plan for this semester.

8. The method for determining a rope skipping recommendation plan based on a large model according to claim 1, characterized in that: Outputting the rope skipping recommendation plan through the large model includes: The classroom exercise intensity score, after-class exercise intensity score, after-class exercise quality score, training effect score, training stability score, physical fitness score and age are taken as inputs of the big model, and the big model outputs a skipping rope recommendation plan.

9. An electronic device, characterized in that: include: Processor and memory; The processor is used to execute the method for determining a recommended rope skipping plan based on a large model as described in any one of claims 1 to 7 by calling the program or instruction stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute a method for determining a recommended rope skipping plan based on a large model as described in any one of claims 1 to 7.

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