AI grouping method and device based on historical rope skipping assessment data and medium

Abstract

The invention relates to an AI grouping method and device based on historical rope skipping assessment data and a medium, and belongs to the technical field of grouping. Respectively determining an excellent judgment score, a good judgment score, a pass judgment score and a dispass judgment score according to the key indexes; respectively determining an excellent score, a good score, a qualified score and a disqualified score of the big arm according to the key indexes; according to the key indexes, the excellent score, the good score, the qualified score and the disqualified score of the jump height are determined respectively; respectively determining an excellent score, a good score, a qualified score and a disqualified score of the action specification; determining a big arm influence degree score, determining a jump height influence degree score, and determining an action specification influence degree score; according to the three influence degree scores, determining a comprehensive ability score of each assessed person; and determining AI groups according to the comprehensive ability scores and outputting the AI groups. The teaching pertinence and the overall training effect are improved through grouping.

Description

Technical Field

[0001] This invention belongs to the field of AI grouping technology, and in particular relates to an AI grouping method, device and medium based on historical rope skipping assessment data. Background Technology

[0002] Traditional physical education grouping is often based on simple performance rankings or teacher subjective judgment, lacking scientific basis and personalized consideration. Existing technologies fail to fully utilize multi-dimensional athletic performance data for accurate grouping, resulting in poor teaching effectiveness. Therefore, it is necessary to design a method for intelligently grouping students by analyzing multi-dimensional indicators in historical rope skipping assessment data. By employing artificial intelligence algorithms, scientific and reasonable student grouping can be achieved, improving the relevance of teaching and overall training effectiveness. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of the invention is to provide an AI grouping method, device and medium based on historical rope skipping assessment data. By analyzing the multi-dimensional indicators in the historical rope skipping assessment data and using artificial intelligence algorithms, the invention achieves scientific and reasonable student grouping, thereby improving the pertinence of teaching and the overall training effect.

[0004] A first aspect of the present invention proposes an AI grouping method based on historical rope skipping assessment data, comprising:

[0005] Obtain historical jump rope assessment data, including the number of jumps, jump rope score, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps.

[0006] Based on the number of jump rope attempts, scores are determined as Excellent, Good, Pass, and Fail.

[0007] The scores for excellent, good, passable, and failable upper arms are determined based on the excellent judgment score, good judgment score, passable judgment score, and failable judgment score, as well as the average upper arm angle.

[0008] The scores for excellent, good, passing, and failing jump heights are determined based on the excellent judgment score, good judgment score, passing judgment score, failing judgment score, and average jump height, respectively.

[0009] The scores for excellent, good, passing, and failing movements are determined based on the following criteria: excellent judgment score, good judgment score, passing judgment score, failing judgment score, number of broken jumps, and number of tiptoe jumps.

[0010] The score for the degree of influence of the upper arm is determined based on the scores for excellent upper arm, good upper arm, passing upper arm, and failing upper arm. The score for the degree of influence of the jump height is determined based on the scores for excellent jump height, good jump height, passing jump height, and failing jump height. The score for the degree of influence of the movement standard is determined based on the scores for excellent movement standard, good movement standard, passing movement standard, and failing movement standard.

[0011] The comprehensive ability score of each examinee is determined based on the scores of the influence of upper arm, jump height, and movement standard, the score of excellent judgment, rope skipping performance, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps.

[0012] AI groups are determined and output based on comprehensive ability scores.

[0013] Furthermore, in the aforementioned AI grouping method based on historical rope skipping assessment data, the formulas for determining the excellent, good, pass, and fail scores according to the number of rope skips assessed are as follows:

[0014]

[0015]

[0016] Among them, g ij Let represent the number of jump rope tests, i represent the test number, N represent the number of tests, j represent the student ID of the test taker, n1 represent the number of test takers, and g1 represent the number of test takers. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij This indicates the score for failing the test.

[0017] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the formulas for determining the excellent, good, passing, and failing scores for the upper arm based on the excellent, good, passing, and failing scores and the average upper arm angle are as follows:

[0018]

[0019] Where, θ ij The value represents the average angle of the upper arm, i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the average angle of the upper arm. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4ij The score for failing is indicated by g. 11 Indicates a high score for the upper arm, g 12 Indicates a good score for the upper arm, g 13 This indicates the passing score for the upper arm and g. 14 This indicates a failing grade for the upper arm.

[0020] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the formulas for determining the excellent jump height score, good jump height score, passing jump height score, and failing jump height score based on the excellent judgment score, good judgment score, passing judgment score, failing judgment score, and average jump height are as follows:

[0021]

[0022] Among them, h ij The values ​​represent the average jump height, i represents the jump rope assessment sequence number, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the average jump height. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij The score for failing is indicated by g. 21 G indicates a high score for a jump. 22 A good score is given for a jump height, g 23 The score for a passing jump is indicated by the height of the jump. 24 A score is given for a jump that is not high enough.

[0023] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the formulas for determining the excellent, good, passing, and failing scores based on the excellent, good, passing, and failing judgment scores, the number of broken jumps, and the number of tiptoe jumps are as follows:

[0024]

[0025]

[0026] Where i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the number of assessors. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij This indicates the score for failing the test, gd ij Indicates the number of jump breaks, gzij g represents the number of treadmill jumps. 31 A score of g indicates excellent performance and proper technique. 32 A score of g indicates good technique and form. 33 This indicates a passing grade for proper execution of the movement, g. 34 This indicates a failing grade for incorrect execution of the action.

[0027] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the influence score of the upper arm is determined according to the scores for excellent, good, passing, and failing upper arm; the influence score of the jump height is determined according to the scores for excellent, good, passing, and failing jump height; and the formula for determining the influence score of the movement standardization is as follows:

[0028]

[0029] Among them, g 11 Indicates a high score for the upper arm, g 12 Indicates a good score for the upper arm, g 13 Indicates the passing score for the upper arm, g 14 Indicates a failing grade for the upper arm, g 21 G indicates a high score for a jump. 22 A good score is given for a jump height, g 23 The score for a passing jump is indicated by the height of the jump. 24 A score is given for a failed jump in height, g 31 A score of g indicates excellent performance and proper technique. 32 A score of g indicates good performance and proper technique. 33 This indicates a passing grade for proper execution of the movement, g. 34 The scores indicate the degree of failure in proper technique, with g1 indicating the degree of influence of the upper arm, g2 indicating the degree of influence of jump height, and g3 indicating the degree of influence of proper technique.

[0030] Furthermore, in the aforementioned AI grouping method based on historical rope skipping assessment data, the formula for determining each examinee's comprehensive ability score based on the following criteria: upper arm influence score, jump height influence score, movement standard influence score, excellent judgment score, rope skipping performance, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps:

[0031]

[0032] Where g1 represents the score for the influence of the upper arm, g2 represents the score for the influence of jump height, g3 represents the score for the influence of movement standardization, i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, and G... j G1 represents the overall ability score of the j-th examinee. ij The score represents the excellent judgment score, ts1 represents the set first judgment threshold, ts2 represents the set second judgment threshold, and h represents the excellent judgment score. ij Average jump height, θ ij Average upper arm angle, s ij jump rope score, gd ij Indicates the number of jumps, gz ij This indicates the number of times you perform a stepping jump.

[0033] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, determining and outputting AI groups according to comprehensive ability scores includes:

[0034] The K-means clustering method was used to cluster the set of students' comprehensive ability scores to obtain the clustering results;

[0035] Use the elbow rule to determine the optimal number of clusters k;

[0036] Based on the clustering results, the number of examinees is divided into k groups with the optimal number of clusters.

[0037] Output the k groups as the AI ​​grouping results;

[0038] In this case, the assessors for each group of content are the same as the assessors in the corresponding cluster of the clustering results.

[0039] A second aspect of the present invention also provides an electronic device comprising: a processor and a memory;

[0040] The processor executes an AI grouping method based on historical jump rope assessment data by calling programs or instructions stored in memory.

[0041] A third aspect of the invention also proposes a computer-readable storage medium that stores a program or instructions that cause a computer to execute an AI grouping method based on historical jump rope assessment data as described above.

[0042] The beneficial effects of this invention are as follows: This invention obtains historical rope skipping assessment data including the number of rope skips, rope skipping scores, average upper arm angle, average jump height, number of broken jumps, and number of footwork jumps; based on the number of rope skips, it determines excellent, good, pass, and fail scores; based on the excellent, good, pass, and fail scores and the average upper arm angle, it determines excellent, good, pass, and fail scores for upper arm; based on the excellent, good, pass, and fail scores and the average upper arm angle, it determines excellent, good, pass, and fail scores for jump height; based on the excellent, good, pass, and fail scores and the average jump height, it determines excellent, good, pass, and fail scores for jump height; based on the excellent, good, pass, and fail scores, the number of broken jumps and footwork jumps, it determines... The number of jumps determines scores for excellent, good, passing, and failing movements. The impact of upper arm movement is assessed based on these scores. Similarly, the impact of jump height is determined by these scores, as well as the overall movement standardization score. Finally, each examinee's comprehensive ability score is calculated based on their overall ability score, including upper arm impact, jump height impact, movement standardization impact, excellent judgment score, jump rope performance, average upper arm angle, average jump height, number of missed jumps, and number of treadle jumps. AI grouping is then determined and output based on these comprehensive ability scores. This invention analyzes multi-dimensional indicators from historical jump rope assessment data and utilizes artificial intelligence algorithms to achieve scientific and reasonable student grouping, improving the relevance of teaching and overall training effectiveness. Attached Figure Description

[0043] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0044] Figure 1 An AI grouping method based on historical rope skipping assessment data is provided in this embodiment of the invention. Figure 1 ;

[0045] Figure 2 An AI grouping method based on historical rope skipping assessment data is provided in this embodiment of the invention. Figure 2 ;

[0046] Figure 3This is a schematic block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0047] 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 with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely 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 those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0048] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0049] In the description of this invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0051] This invention proposes an AI-based grouping method, device, and medium based on historical rope skipping assessment data. By analyzing multi-dimensional indicators in historical rope skipping assessment data and applying artificial intelligence algorithms, it achieves scientific and reasonable student grouping, thereby improving the relevance of teaching and the overall training effect.

[0052] Method Implementation Examples

[0053] Figure 1 An AI grouping method based on historical rope skipping assessment data is provided in this embodiment of the invention. Figure 1 .

[0054] In a first aspect, the present invention proposes an AI grouping method based on historical rope skipping assessment data, combined with Figure 1It includes eight steps from S1 to S8:

[0055] S1: Obtain the number of jumps, jump scores, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps from historical jump rope assessment data.

[0056] Specifically, in this embodiment of the invention, the number of rope skipping tests g in the historical rope skipping test data is obtained. ij Jump rope score s ij >0, average included angle of upper arm θ ij Average jump height h ij Number of jumps (gd) ij Number of tiptoe jumps (gz) ij Where i is the sequence number of the rope skipping assessment, N is the number of assessments, j is the student ID, n1 is the number of students, the number of rope skipping assessments is in minutes, the number of rope skipping scores is in jumps, the average angle of the upper arm is in degrees, the average jump height is in centimeters, the number of broken jumps is in times, and the number of step jumps is in times.

[0057] S2: Determine the excellent, good, pass, and fail scores based on the number of jump rope attempts.

[0058] Specifically, in this embodiment of the invention, the methods for determining the excellent, good, pass, and fail scores based on the number of jump rope attempts are described in detail below.

[0059] S3: Determine the excellent score, good score, passable score, and failable score of the upper arm based on the excellent judgment score, good judgment score, passable score, and failable score of the upper arm, respectively, and the average angle of the upper arm.

[0060] Specifically, in this embodiment of the invention, an excellent upper arm score is determined based on the excellent judgment score and the average upper arm angle; a good upper arm score is determined based on the good judgment score and the average upper arm angle; a passing upper arm score is determined based on the passing judgment score and the average upper arm angle; and a failing upper arm score is determined based on the failing judgment score and the average upper arm angle. The method for determining the excellent upper arm score, the good upper arm score, the passing upper arm score, and the failing upper arm score is described in detail below.

[0061] S4: Determine the excellent score, good score, pass score, and fail score for jump height based on the excellent judgment score, good judgment score, pass score, and fail score, and the average jump height, respectively.

[0062] Specifically, in this embodiment of the invention, an excellent score for jump height is determined based on an excellent judgment score and an average jump height; a good score for jump height is determined based on a good judgment score and an average jump height; a passing score for jump height is determined based on a passing judgment score and an average jump height; and a failing score for jump height is determined based on a failing judgment score and an average jump height. The method for determining the excellent score, good score, passing score, and failing score for jump height is described in detail below.

[0063] S5: Determine the scores for excellent, good, passing, and failing movements based on the scores for excellent, good, passing, and failing judgments, as well as the number of broken jumps and the number of tiptoe jumps.

[0064] Specifically, in this embodiment of the invention, an excellent score for movement standardization is determined based on the excellent judgment score, the number of broken jumps, and the number of treadmill jumps; a good score for movement standardization is determined based on the good judgment score, the number of broken jumps, and the number of treadmill jumps; a passing score for movement standardization is determined based on the passing judgment score, the number of broken jumps, and the number of treadmill jumps; and a failing score for movement standardization is determined based on the good judgment score, the number of broken jumps, and the number of treadmill jumps. The method for determining the excellent score, good score, passing score, and failing score for movement standardization is described in detail below.

[0065] S6: The upper arm influence score is determined based on the upper arm excellent score, upper arm good score, upper arm pass score, and upper arm failure score; the jump height influence score is determined based on the jump height excellent score, jump height good score, jump height pass score, and jump height failure score; and the movement standard influence score is determined based on the movement standard excellent score, movement standard good score, movement standard pass score, and movement standard failure score.

[0066] Specifically, in this embodiment of the invention, the method for determining the degree of influence of the upper arm based on the scores of excellent upper arm, good upper arm, passing upper arm, and failing upper arm is described in detail below. The method for determining the degree of influence of the jump height based on the scores of excellent jump height, good jump height, passing jump height, and failing jump height is described in detail below.

[0067] S7: The comprehensive ability score of each examinee is determined based on the scores of the influence of upper arm, jump height, and movement standard, the score of excellent judgment, rope skipping performance, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps.

[0068] Specifically, in this embodiment of the invention, the method for determining the comprehensive ability score of each examinee based on the score of the degree of influence of the upper arm, the score of the degree of influence of the jump height, the score of the degree of influence of the action standard, the excellent judgment score, the rope skipping performance, the average angle of the upper arm, the average jump height, the number of broken jumps, and the number of step jumps is described in detail below.

[0069] S8: Determine AI groups based on comprehensive ability scores and output them.

[0070] Specifically, in this embodiment of the invention, the method for determining AI groups and outputting based on comprehensive ability scores is described in detail below.

[0071] Furthermore, in the aforementioned AI grouping method based on historical rope skipping assessment data, the formulas for determining the excellent, good, pass, and fail scores according to the number of rope skips assessed are as follows:

[0072]

[0073] Among them, g ij Let represent the number of jump rope tests, i represent the test number, N represent the number of tests, j represent the student ID of the test taker, n1 represent the number of test takers, and g1 represent the number of test takers. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij This indicates the score for failing the test.

[0074] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the formulas for determining the excellent, good, passing, and failing scores for the upper arm based on the excellent, good, passing, and failing scores and the average upper arm angle are as follows:

[0075]

[0076]

[0077] Where, θ ij The value represents the average angle of the upper arm, i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the average angle of the upper arm. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij The score for failing is indicated by g. 11 Indicates a high score for the upper arm, g 12 Indicates a good score for the upper arm, g13 This indicates the passing score for the upper arm and g. 14 This indicates a failing grade for the upper arm.

[0078] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the formulas for determining the excellent jump height score, good jump height score, passing jump height score, and failing jump height score based on the excellent judgment score, good judgment score, passing judgment score, failing judgment score, and average jump height are as follows:

[0079]

[0080] Among them, h ij The values ​​represent the average jump height, i represents the jump rope assessment sequence number, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the average jump height. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij The score for failing is indicated by g. 21 G indicates a high score for a jump. 22 A good score is given for a jump height, g 23 The score for a passing jump is indicated by the height of the jump. 24 A score is given for a jump that is not high enough.

[0081] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the formulas for determining the excellent, good, passing, and failing scores based on the excellent, good, passing, and failing judgment scores, the number of broken jumps, and the number of tiptoe jumps are as follows:

[0082]

[0083] Where i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the number of assessors. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij This indicates the score for failing the test, gd ij Indicates the number of jump breaks, gz ij g represents the number of treadmill jumps. 31 A score of g indicates excellent performance and proper technique. 32 A score of g indicates good performance and proper technique. 33 This indicates a passing grade for proper execution of the movement, g. 34This indicates a failing grade for incorrect execution of the action.

[0084] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, the influence score of the upper arm is determined according to the scores for excellent, good, passing, and failing upper arm; the influence score of the jump height is determined according to the scores for excellent, good, passing, and failing jump height; and the formula for determining the influence score of the movement standardization is as follows:

[0085]

[0086] Among them, g 11 Indicates a high score for the upper arm, g 12 Indicates a good score for the upper arm, g 13 Indicates the passing score for the upper arm, g 14 G indicates a failing grade for the upper arm. 21 G indicates a high score for a jump. 22 A good score is given for a jump height, g 23 The score for a passing jump is indicated by the height of the jump. 24 A score is given for a failed jump in height, g 31 A score of g indicates excellent performance and proper technique. 32 A score of g indicates good technique and form. 33 This indicates a passing grade for proper execution of the movement, g. 34 The scores indicate the degree of failure in proper technique, with g1 indicating the degree of influence of the upper arm, g2 indicating the degree of influence of jump height, and g3 indicating the degree of influence of proper technique.

[0087] Furthermore, in the aforementioned AI grouping method based on historical rope skipping assessment data, the formula for determining each examinee's comprehensive ability score based on the following criteria: upper arm influence score, jump height influence score, movement standard influence score, excellent judgment score, rope skipping performance, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps:

[0088]

[0089] Where g1 represents the score for the influence of the upper arm, g2 represents the score for the influence of jump height, g3 represents the score for the influence of movement standardization, i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, and G... j G1 represents the overall ability score of the j-th examinee. ij The score represents the excellent judgment score, ts1 represents the set first judgment threshold, ts2 represents the set second judgment threshold, and h represents the excellent judgment score.ij Average jump height, θ ij Average angle of upper arm, s ij jump rope score, gd ij Indicates the number of jumps, gz ij This indicates the number of times you perform a stepping jump.

[0090] Figure 2 An AI grouping method based on historical rope skipping assessment data is provided in this embodiment of the invention. Figure 2 .

[0091] Furthermore, in the aforementioned AI grouping method based on historical jump rope assessment data, AI groups are determined and output according to comprehensive ability scores, combined with... Figure 2 It includes four steps, S21 to S24:

[0092] S21: Use the K-means clustering method to cluster the set of students' comprehensive ability scores to obtain the clustering results;

[0093] S22: Use the elbow rule to determine the optimal number of clusters k;

[0094] S23: Based on the clustering results, divide the number of examinees into k groups with the optimal number of clusters;

[0095] S24: Output the k groups as the result of AI grouping;

[0096] In this case, the assessors for each group of content are the same as the assessors in the corresponding cluster of the clustering results.

[0097] Specifically, in this embodiment of the invention, the K-means clustering method is used to cluster the set of students' comprehensive ability scores {G}. j Clustering is performed, and the elbow rule is used to determine the optimal number of clusters k. Based on the clustering results, students are divided into k groups, with each group containing the same students as the students in the corresponding cluster. The generated k groups are then output as the AI ​​grouping results. By employing artificial intelligence algorithms, scientific and reasonable student grouping is achieved, improving the relevance of teaching and the overall training effect.

[0098] A second aspect of the present invention also provides an electronic device comprising: a processor and a memory;

[0099] The processor executes an AI grouping method based on historical jump rope assessment data by calling programs or instructions stored in memory.

[0100] A third aspect of the invention also proposes a computer-readable storage medium that stores a program or instructions that cause a computer to execute an AI grouping method based on historical jump rope assessment data as described above.

[0101] Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention.

[0102] like Figure 3 As shown, the electronic device includes at least one processor 301, at least one memory 302, and at least one communication interface 303. The various components of the electronic device are coupled together via a bus system 304. The communication interface 303 is used for information transmission with external devices. It is understood that the bus system 304 is used to implement communication between these components. In addition to a data bus, the bus system 304 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 3 The general designated all buses as Bus System 304.

[0103] It is understood that the memory 302 in this embodiment may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0104] In some implementations, memory 302 stores elements such as executable units or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0105] The operating system, comprising various system programs such as the framework layer, core library layer, and driver layer, is used to implement various basic business functions and handle hardware-based tasks. The application programs, including media players and browsers, are used to implement various application functions. A program implementing any method in the AI ​​grouping method based on historical rope skipping assessment data provided in this embodiment of the invention can be included in the application programs.

[0106] In this embodiment of the invention, the processor 301 executes the steps of various embodiments of the AI ​​grouping method based on historical jump rope assessment data provided by the present invention by calling the program or instructions stored in the memory 302, specifically, the program or instructions stored in the application program.

[0107] Obtain historical jump rope assessment data, including the number of jumps, jump rope score, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps.

[0108] Based on the number of jump rope attempts, scores are determined as Excellent, Good, Pass, and Fail.

[0109] The scores for excellent, good, passable, and failable upper arms are determined based on the excellent, good, passable, and failable judgment scores and the average upper arm angle, respectively.

[0110] The scores for excellent, good, passing, and failing jump heights are determined based on the excellent judgment score, good judgment score, passing judgment score, failing judgment score, and average jump height, respectively.

[0111] The scores for excellent, good, passing, and failing movements are determined based on the following criteria: excellent judgment score, good judgment score, passing judgment score, failing judgment score, number of missed jumps, and number of tiptoe jumps.

[0112] The score for the degree of influence of the upper arm is determined based on the scores for excellent upper arm, good upper arm, passing upper arm, and failing upper arm. The score for the degree of influence of the jump height is determined based on the scores for excellent jump height, good jump height, passing jump height, and failing jump height. The score for the degree of influence of the movement standard is determined based on the scores for excellent movement standard, good movement standard, passing movement standard, and failing movement standard.

[0113] The comprehensive ability score of each examinee is determined based on the scores of the influence of upper arm, jump height, and movement standard, the score of excellent judgment, rope skipping performance, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps.

[0114] AI groups are determined and output based on comprehensive ability scores.

[0115] Any method in the AI ​​grouping method based on historical rope skipping assessment data provided in this embodiment of the invention can be applied to, or implemented by, processor 301. Processor 301 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed through integrated logic circuits in the hardware of processor 301 or through software instructions. Processor 301 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 gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor.

[0116] The steps of any method in the AI ​​grouping method based on historical rope skipping assessment data provided in this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software units in the decoding processor. The software units can reside in mature storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 302, and processor 301 reads the information in memory 302 and combines it with hardware to complete the steps of the method.

[0117] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0118] Those skilled in the art will understand that the descriptions of the various embodiments have different focuses, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0119] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention. All such modifications and variations fall within the scope defined by the appended claims. The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0120] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An AI grouping method based on historical jump rope assessment data, characterized in that, include: Obtain historical jump rope assessment data, including the number of jumps, jump rope score, average upper arm angle, average jump height, number of broken jumps, and number of tiptoe jumps. Based on the number of jump rope attempts, scores are determined as follows: Excellent, Good, Pass, and Fail. The scores for excellent, good, passing, and failing upper arms are determined based on the excellent, good, passing, and failing upper arm scores, respectively, and the average upper arm angle. The excellent jump height score, good jump height score, passable jump height score, and failable jump height score are determined based on the excellent jump height score, the good jump height score, the passable jump height score, the failable jump height score, and the average jump height, respectively. The following scores are used to determine the excellent, good, passing, and failing scores for movement standardization: excellent, good, passing, and failing. The upper arm influence score is determined based on the upper arm excellent score, upper arm good score, upper arm pass score, and upper arm failure score; the jump height influence score is determined based on the jump height excellent score, jump height good score, jump height pass score, and jump height failure score; and the movement standardization influence score is determined based on the movement standardization excellent score, movement standardization good score, movement standardization pass score, and movement standardization failure score. The comprehensive ability score of each examinee is determined based on the following scores: the influence of upper arm, the influence of jump height, the influence of movement standard, the excellent judgment score, the rope skipping performance, the average upper arm angle, the average jump height, the number of broken jumps, and the number of tiptoe jumps. AI groups are determined and output based on the comprehensive ability score.

2. The AI ​​grouping method based on historical rope skipping assessment data according to claim 1, characterized in that, The formulas for determining the excellent, good, pass, and fail scores based on the number of jump rope attempts are as follows: Among them, g ij Let represent the number of jump rope tests, i represent the test number, N represent the number of tests, j represent the student ID of the test taker, n1 represent the number of test takers, and g1 represent the number of test takers. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij This indicates the score for failing the test.

3. The AI ​​grouping method based on historical rope skipping assessment data according to claim 1, characterized in that, The formulas for determining the excellent, good, passing, and failing scores of the upper arm based on the excellent, good, passing, and failing scores and the average upper arm angle are as follows: Where, θ ij The value represents the average angle of the upper arm, i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the average angle of the upper arm. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij The score for failing is indicated by g. 11 Indicates a high score for the upper arm, g 12 Indicates a good score for the upper arm, g 13 This indicates the passing score for the upper arm and g. 14 This indicates a failing grade for the upper arm.

4. The AI ​​grouping method based on historical rope skipping assessment data according to claim 1, characterized in that, The formulas for determining the excellent jump height score, good jump height score, passable jump height score, and failable jump height score based on the excellent judgment score, good jump height score, passable jump height score, and failable jump height score, respectively, and the average jump height, are as follows: Among them, h ij The values ​​represent the average jump height, i represents the jump rope assessment sequence number, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the average jump height. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij The score for failing is indicated by g. 21 G indicates a high score for a jump. 22 A good score is given for a jump height, g 23 The score for a passing jump is indicated by the height of the jump. 24 A score is given for a jump that is not high enough.

5. The AI ​​grouping method based on historical rope skipping assessment data according to claim 1, characterized in that, The formulas for determining the excellent, good, pass, and fail scores for movement standardization based on the excellent, good, pass, and fail scores, the number of missed jumps, and the number of tiptoe jumps are as follows: Where i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, n1 represents the number of assessors, and g1 represents the number of assessors. ij Indicates an excellent judgment score, g2 ij Indicates a good judgment score, g3 ij This indicates the passing score, g4 ij This indicates the score for failing the test, gd ij Indicates the number of jump breaks, gz ij Indicates the number of treadmill jumps, g 31 A score of g indicates excellent performance and proper technique. 32 A score of g indicates good performance and proper technique. 33 This indicates a passing grade for proper execution of the movement, g. 34 This indicates a failing grade for incorrect execution of the action.

6. The AI ​​grouping method based on historical rope skipping assessment data according to claim 1, characterized in that, The influence score of the upper arm is determined based on the scores for excellent, good, passing, and failing upper arm. The influence score of the jump height is determined based on the scores for excellent, good, passing, and failing jump height. The formula for determining the influence score of the movement standardization is as follows: Among them, g 11 Indicates a high score for the upper arm, g 12 Indicates a good score for the upper arm, g 13 Indicates the passing score for the upper arm, g 14 G indicates a failing grade for the upper arm. 21 G indicates a high score for a jump. 22 A good score is given for a jump height, g 23 The score for a passing jump is indicated by the height of the jump. 24 A score is given for a failed jump in height, g 31 A score of g indicates excellent performance and proper technique. 32 A score of g indicates good performance and proper technique. 33 This indicates a passing grade for proper execution of the movement, g. 34 The scores indicate the degree of failure in proper technique, with g1 indicating the degree of influence of the upper arm, g2 indicating the degree of influence of jump height, and g3 indicating the degree of influence of proper technique.

7. The AI ​​grouping method based on historical rope skipping assessment data according to claim 1, characterized in that, The formula for determining each examinee's comprehensive ability score based on the following factors: upper arm influence score, jump height influence score, movement standard influence score, excellent judgment score, rope skipping performance, average upper arm angle, average jump height, number of missed jumps, and number of tiptoe jumps. Where g1 represents the score for the influence of the upper arm, g2 represents the score for the influence of jump height, g3 represents the score for the influence of movement standardization, i represents the sequence number of the jump rope assessment, N represents the number of assessments, j represents the student ID of the assessor, and G... j G1 represents the overall ability score of the j-th examinee. ij The score represents the excellent judgment score, ts1 represents the set first judgment threshold, ts2 represents the set second judgment threshold, and h represents the excellent judgment score. ij Average jump height, θ ij Average angle of upper arm, s ij jump rope score, gd ij Indicates the number of jumps, gz ij This indicates the number of times you perform a stepping jump.

8. The AI ​​grouping method based on historical rope skipping assessment data according to claim 1, characterized in that, AI groups are determined and output based on the comprehensive ability score, including: The K-means clustering method was used to cluster the set of students' comprehensive ability scores to obtain the clustering results; Use the elbow rule to determine the optimal number of clusters k; Based on the clustering results, the number of examinees is divided into k groups with the optimal number of clusters. Output the k groups as the AI ​​grouping results; In this case, the assessors for each group of content are the same as the assessors in the corresponding cluster of the clustering results.

9. An electronic device, characterized in that, include: Processor and memory; The processor executes an AI grouping method based on historical rope skipping assessment data as described in any one of claims 1 to 8 by calling the program or instructions stored in the memory.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that cause a computer to execute an AI grouping method based on historical jump rope assessment data as described in any one of claims 1 to 8.

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