Intelligent footpath interaction large screen data management system

Abstract

The invention relates to the technical field of smart footpath interaction, and discloses a smart footpath interaction large screen data management system, and the system process comprises the steps: carrying out the recognition processing of motion data monitored by a smart footpath through an interaction large screen system network, obtaining the motion states and motion levels of different users, carrying out the clustering analysis of the users, and obtaining the motion states and motion levels of the users; and obtaining a motion mode category of the user, and performing motion trail recommendation based on user preference based on a footpath motion network. According to the method, the motion unbalance degree and the motion intensity of the user are obtained through calculation by using the smart footpath, clustering processing is carried out on the user by enabling the motion state difference between the users of different motion mode categories to reach the maximum cut-off distance based on local density information selection, and the users of the same motion mode category have similar motion habits; and calculating a motion trail recommendation result by combining preference characteristic values of different types of users for different footpath areas, and presenting a motion grade and the motion trail recommendation result by utilizing an interactive large screen.

Description

Technical Field

[0001] The present invention relates to the field of smart trail interaction, and in particular to a smart trail interactive large-screen data management system. Background Art

[0002] With the improvement of people's living standards and the enhancement of health awareness, hiking has gradually become a popular leisure activity. As the infrastructure of hiking, trails can not only allow people to get close to nature and breathe fresh air, but also exercise and release stress. However, in today's society, people have a fast pace of life and high work pressure. Many people lack regular exercise habits, which leads to health problems. Therefore, by scientifically and rationally planning trail routes, people can better carry out outdoor sports and improve their quality of life. In view of this, the present invention proposes a smart trail interactive large-screen data management method, which uses wearable devices and interactive large-screen technology to analyze the individual's movement status, including indicators such as gait, heart rate, and exercise intensity, to understand the user's exercise needs and ability level, and can recommend suitable trail routes for them in a targeted manner. Summary of the invention

[0003] In view of this, the present invention provides a smart trail interactive large-screen data management system, the purpose of which is to: 1) use the smart trail to monitor the user's gait, heart rate, pace and motion trajectory data, and calculate the user's motion imbalance and motion intensity as the user's motion state, and perform cluster analysis on the users currently on the smart trail in combination with the user's motion state. In the cluster analysis process, based on the local density information, a cutoff distance is selected so that the motion state difference between users of different motion mode categories reaches the maximum, and the user's motion mode category is obtained, wherein users with the same motion mode category have similar exercise habits, and the calculation and classification of the user's motion state are realized, and the same motion trajectory recommendation results are provided to users of the same category, so as to facilitate sports communication among users of the same category; 2) combine users of different categories with each other to form a cluster analysis of the users. The user's preference characteristic values ​​for different trail areas are used to construct a fitness function that characterizes the smoothness of the motion trajectory recommendation results. The motion trajectory recommendation results are iteratively processed, where the fitness function tends to select a more preferred trail area as the recommendation result, and the distance between adjacent motion trajectory points in the motion trajectory recommendation results will not be too long. During the iterative process, multiple iterative methods are adopted for mixed iteration to effectively ensure the diversity of motion trajectory recommendation results, with better exploration performance and avoid missing the optimal solution. The interactive large-screen system network transmits the motion trajectory recommendation results and the user's exercise level to the interactive large screen. The interactive large screen displays the exercise levels of different users and the motion trajectory recommendation results, and sends the motion trajectory recommendation results and the exercise level to the user's wearable device to realize the data management of the smart trail interactive large screen.

[0004] To achieve the above purpose, the present invention provides a smart trail interactive large-screen data management system operation process, including the following steps:

[0005] S1: Connect the wearable device to the interactive large-screen system network, and use the wearable device to transmit the user's motion data on the smart trail to the interactive large-screen system, where the motion data includes gait, heart rate, pace, and motion trajectory data;

[0006] S2: The interactive large screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users;

[0007] S3: Perform cluster analysis on users currently on the smart trail in combination with their motion states to obtain the user's motion pattern category, where users with the same motion pattern category have similar motion habits. Motion pattern clustering that integrates motion state analysis is the main implementation method for user cluster analysis.

[0008] S4: Construct a trail motion network and perform preference analysis on the motion trajectories of users in the same motion mode category. The improved attention mechanism is the main implementation method for constructing the trail motion network.

[0009] S5: Recommending exercise trajectories based on user preferences based on the trail motion network, and obtaining exercise trajectory recommendation results for user groups corresponding to different exercise mode categories;

[0010] S6: The motion track recommendation result and the user's motion level are transmitted to the interactive large screen, and the interactive large screen presents the user with the motion level and the motion track recommendation result.

[0011] As a further improvement method of the present invention:

[0012] Optionally, in step S1, the wearable device is connected to the interactive large-screen system network, and the wearable device is used to transmit the user's motion data on the smart trail to the interactive large-screen system, including:

[0013] Connect the wearable device to the interactive large-screen system network. The user wears the wearable device and exercises on the smart trail. The smart trail monitors the user's exercise data and uses the wearable device to transmit the user's exercise data on the smart trail to the interactive large-screen system. The exercise data includes gait, heart rate, pace and exercise trajectory data. The user's exercise data on the smart trail is in the form of:

[0014]

[0015] in:

[0016] s n represents the movement data of the nth user on the smart trail, and N represents the number of users on the smart trail;

[0017] The gait, heart rate, pace and movement trajectory data of the nth user on the smart trail are shown in sequence;

[0018] It represents the average pressure exerted on the ground by the left foot of the nth user as sensed by the smart trail. It represents the average pressure exerted on the ground by the right foot of the nth user as sensed by the smart trail;

[0019] represents the heart rate skewness, average heart rate and heart rate standard deviation of the nth user respectively;

[0020] It represents the pace skewness, average pace and pace standard deviation of the nth user respectively;

[0021] Represents motion trajectory data num n The motion trajectory coordinate points, num n Represents motion trajectory data In the embodiment of the present invention, the smart trail monitors the user's gait, heart rate, pace and motion trajectory every Δ time, and updates the user's motion data.

[0022] Optionally, in step S2, the interactive large screen system network identifies and processes the received motion data, including:

[0023] The interactive large screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users. The motion data identification and processing process is as follows:

[0024] S21: Calculate the motion imbalance of the user, where the motion imbalance calculation formula of the nth user is:

[0025]

[0026] in:

[0027] ban n represents the motion imbalance of the nth user;

[0028] S22: Calculate the exercise intensity of the user's exercise, where the exercise intensity calculation formula for the nth user is:

[0029] st n =(st n (1),st n (2)

[0030]

[0031] in:

[0032] st n represents the exercise intensity of the nth user, st n (1) represents the exercise heart rate intensity of the nth user, st n (2) Indicates the exercise pace intensity of the nth user; the interactive large screen divides the user into 7 exercise levels according to the user's exercise intensity. If the user's exercise intensity reaches the preset intensity threshold of the exercise level, the corresponding exercise level is assigned to the user. The higher the exercise intensity, the higher the exercise level.

[0033] time n Indicates the current exercise time of the nth user on the smart trail;

[0034] exp(·) represents an exponential function with a natural constant as the base;

[0035] S23: The user's motion imbalance and motion intensity are used as the user's motion state, where the motion state of the nth user is R n =[ban n ,st n ].

[0036] Optionally, in step S3, cluster analysis is performed on users currently on the smart trail in combination with their motion states to obtain the user's motion mode categories, including:

[0037] Based on the user's motion status, cluster analysis is performed on the N users currently on the smart trail to obtain the user's motion mode category. The cluster analysis process is as follows:

[0038] S31: Calculate the motion pattern similarity between any two users among the N users, where the motion pattern similarity calculation formula between the nth user and the ith user is:

[0039]

[0040] in:

[0041] p(n,i) represents the similarity of motion patterns between the nth user and the ith user, i∈[1,N];

[0042] R i represents the motion status of the i-th user;

[0043] ||·|| represents the L1 norm;

[0044] The similarity of motion patterns between different users is used as the distance between users, and the K nearest neighbors of each user are marked;

[0045] S32: Calculate the cutoff distance in the clustering process based on local density information:

[0046]

[0047] in:

[0048] Represents the local density information of N users. The larger the local density information, the smaller the difference in local distance density between different users, and the less obvious the distribution difference.

[0049] Indicates that dist c As the cutoff distance, the local distance density of the nth user;

[0050] Indicates that dit c As the cutoff distance, the local distance density ratio of the nth user;

[0051] represents the cutoff distance calculated based on local density information;

[0052] S33: Calculate the weighted local distance density of each user according to the calculated cutoff distance, where the weighted local distance density of the nth user is:

[0053]

[0054] in:

[0055] ρ n represents the weighted local distance density of the nth user;

[0056] Sim(j,n) represents the number of overlapping users between the K nearest neighbors of the nth user and the K nearest neighbors of the jth user;

[0057] represents the influence weight of the j-th user on the local distance density of the n-th user;

[0058] S34: Calculate the cluster center weight of each user, where the cluster center weight of the nth user is:

[0059]

[0060] in:

[0061] weight nrepresents the cluster center weight of the nth user;

[0062] δ n Indicates the closest distance to the nth user in the set of users whose weighted local distance density is higher than that of the nth user; if the weighted local distance density of the nth user is the maximum, then δ n is the maximum distance between the nth user and other users;

[0063] S35: Select the users with the highest weights among G cluster centers as cluster centers, where G represents the number of motion mode categories, calculate the distances from non-cluster center users to the cluster centers, merge the non-cluster center users into the cluster clusters with the nearest cluster centers as users of the same motion mode category, and the cluster analysis results are:

[0064]

[0065] in:

[0066] L g represents the set of users of the g-th sports mode category, Indicates the count in the g-th motion pattern category g users, count g Represents the number of users in the g-th sports mode category.

[0067] Optionally, constructing the trail movement network in step S4 includes:

[0068] Construct a trail motion network, where the trail motion network divides the smart trail into Q trail areas:

[0069] (area1,area2,...,area q ,...,area Q )

[0070] in:

[0071] area q represents the qth trail area obtained by division;

[0072] Based on the trail motion network, the motion trajectory preference of users in the same motion mode category is analyzed.

[0073] Optionally, the performing preference analysis on the movement trajectories of users of the same movement mode category based on the trail movement network includes:

[0074] Based on the trail motion network, the preference analysis of the motion trajectories of users in the same motion mode category is performed, and the preference analysis process of users in the g-th motion mode category is as follows:

[0075] S41: Extract the motion trajectory data of each user in the g-th motion mode category, and map the motion trajectory data to Q trail areas in the trail motion network to obtain the user's motion area vector, where the user The motion area vector is:

[0076]

[0077] in:

[0078] Indicates user The motion area vector, Indicates user The motion trajectory in the qth trail area represents the characteristics, This means that the user The motion trajectory data does not contain any motion trajectory coordinate points located in the qth trail area. This means that the user The motion trajectory data of has motion trajectory coordinate points located in the qth trail area;

[0079] represents the rth user in the gth motion mode category, r∈[1,count g ];

[0080] S42: Generate the attention preference weight of each user in the g-th motion pattern category, where user The attention preference weight is:

[0081]

[0082] in:

[0083] Indicates user The attention preference weight of

[0084] S43: Calculate and obtain the user preference of the g-th sports mode category user:

[0085]

[0086] in:

[0087] F g represents the user preference of the g-th sports mode category user, F g (1),F g (2),...,F g (q),...,F g (Q) represents the preference feature values ​​of users of the g-th sports mode category for the 1-Q-th trail areas respectively.

[0088] Optionally, in step S5, using the trail motion network to recommend a motion trajectory based on user preferences includes:

[0089] The trail motion network is used to recommend motion trajectories based on user preferences, and the motion trajectory recommendation results for users of different motion mode categories are obtained. The motion trajectory recommendation process for users of the g-th motion mode category is as follows:

[0090] S51: Setting the length of the motion trajectory recommendation result to M;

[0091] S52: Initialize and generate U motion track recommendation results for the g-th motion mode category user, wherein the u-th motion track recommendation result generated by initialization is:

[0092]

[0093] in:

[0094] represents the recommended result of the u-th motion trajectory generated by initialization;

[0095] Indicates the motion trajectory recommendation result There are M motion trajectory points in , Indicates the motion trajectory recommendation result The mth motion trajectory point in , where each motion trajectory point corresponds to a trail area,

[0096] S53: Set the current iteration number of each motion trajectory recommendation result to t, and the maximum iteration number to Max, then the t-th iteration result of the generated u-th motion trajectory recommendation result is

[0097] S54: construct a fitness function with the motion trajectory recommendation result as input, input the motion trajectory recommendation result into the fitness function, and obtain the fitness function value of the motion trajectory recommendation result, wherein the motion trajectory recommendation result The fitness function value is:

[0098]

[0099] in:

[0100] Indicates the motion trajectory recommendation result The fitness function value of

[0101] Indicates the motion trajectory recommendation result The preference characteristic value of the trail area corresponding to the mth motion trajectory point in ;

[0102] DIS represents the distance threshold. Represents the motion trajectory point The Euclidean distance between the centers of the corresponding trail areas; in this embodiment of the present invention, Represents the current average position coordinates of all users in the g-th motion mode category and The Euclidean distance between

[0103] S55: The U motion trajectory recommendation results obtained in the t-th iteration are sequentially used as the input of the fitness function, and the motion trajectory recommendation result with the largest fitness function value is selected as the optimal motion trajectory recommendation result obtained in the t-th iteration

[0104] S56: Iterate each motion trajectory recommendation result, wherein the motion trajectory recommendation result The iterative process is:

[0105] If t is an even number, the following iterative formula is used for iteration:

[0106]

[0107] in:

[0108] rand(0,1) represents a random number between 0 and 1;

[0109] median t Represents the median of the fitness function values ​​of the U motion trajectory recommendation results obtained in the t-th iteration;

[0110] Indicates that any one of the two obtained in the tth iteration is Different motion trajectory recommendation results;

[0111] If t is an odd number, the following iterative formula is used for iteration:

[0112]

[0113] S57: Let t=t+1, return to step S55, until the maximum number of iterations is reached, and use the U motion trajectory recommendation results at this time as the input of the fitness function in sequence, and select the motion trajectory recommendation result with the trajectory coordinate points in the range of [1,Q] and the largest fitness function value as the motion trajectory recommendation result for the g-th motion mode category user.

[0114] Optionally, the step S6 transmits the motion track recommendation result and the motion level to the interactive large screen, and the interactive large screen presents the motion level and the motion track recommendation result to the user, including:

[0115] The interactive large-screen system network transmits the motion trajectory recommendation results and the user's exercise level to the interactive large screen. The interactive large screen displays the exercise levels and motion trajectory recommendation results of different users, and sends the motion trajectory recommendation results and exercise level to the user's wearable device.

[0116] In order to solve the above problems, the present invention provides a smart trail interactive large-screen data management system, characterized in that the system comprises:

[0117] The motion data collection and processing module is used to transmit the user's motion data on the smart trail to the interactive large-screen system using wearable devices. The interactive large-screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users;

[0118] The user clustering module is used to perform cluster analysis on users currently on the smart trail based on their motion status, obtain the user's motion mode category, build a trail motion network, and perform preference analysis on the motion trajectories of users in the same motion mode category;

[0119] The motion trajectory recommendation device is used to recommend motion trajectories based on user preferences based on the trail motion network, obtain motion trajectory recommendation results for user groups corresponding to different motion mode categories, and transmit the motion trajectory recommendation results and the user's motion level to the interactive large screen. The interactive large screen presents the user's motion level and motion trajectory recommendation results.

[0120] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0121] A memory storing at least one instruction;

[0122] Communication interface, enabling electronic equipment to communicate; and

[0123] The processor executes the instructions stored in the memory to implement the operation process of the smart trail interactive large-screen data management system described above.

[0124] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned smart trail interactive large-screen data management system.

[0125] Compared with the prior art, the present invention proposes a smart trail interactive large-screen data management system, which has the following advantages:

[0126] First, this scheme proposes a user clustering method that integrates the user's motion status. Combined with the user's motion status, cluster analysis is performed on the N users currently on the smart trail to obtain the user's motion mode category. The clustering analysis process is: calculate the motion mode similarity between any two users among the N users, and the motion mode similarity calculation formula between the nth user and the i-th user is:

[0127]

[0128] Where: p(n,i) represents the similarity of the motion patterns between the nth user and the ith user, i∈[1,N]; R i represents the motion state of the i-th user; ||·|| represents the L1 norm; the motion pattern similarity between different users is used as the distance between users, and the K nearest neighbors of each user are marked; the cutoff distance in the clustering process is calculated based on the local density information:

[0129]

[0130] in: Represents the local density information of N users; Indicates that dist c As the cutoff distance, the local distance density of the nth user; Indicates that dist c As the cutoff distance, the local distance density ratio of the nth user; represents the cutoff distance calculated based on the local density information; according to the calculated cutoff distance, the weighted local distance density of each user is calculated, where the weighted local distance density of the nth user is:

[0131]

[0132] Where: n represents the weighted local distance density of the nth user; Sim(j,n) represents the number of overlapping users between the K nearest neighbors of the nth user and the K nearest neighbors of the jth user; Represents the weight of the influence of the jth user on the local distance density of the nth user; calculates the cluster center weight of each user, and takes the G users with the highest cluster center weights as the cluster center, where G represents the number of motion mode categories; calculates the distance from the non-cluster center user to the cluster center, and merges the non-cluster center user into the cluster cluster with the nearest cluster center. This solution uses the smart trail to monitor the user's gait, heart rate, pace, and motion trajectory data, and calculates the user's motion imbalance and motion intensity as the user's motion state. Combined with the user's motion state, cluster analysis is performed on the users currently on the smart trail. In the cluster analysis process, based on the local density information, the cutoff distance that maximizes the difference in motion state between users of different motion mode categories is selected to obtain the user's motion mode category, where users with the same motion mode category have similar motion habits, and the calculation and classification of the user's motion state are realized, providing the same motion trajectory recommendation results for users of the same category, facilitating sports communication among users of the same category.

[0133] At the same time, this scheme proposes a motion trajectory recommendation method that integrates user preference characteristics, and uses the trail motion network to recommend motion trajectories based on user preferences to obtain motion trajectory recommendation results for users of different motion mode categories. The motion trajectory recommendation process for users of the g-th motion mode category is as follows: set the length of the motion trajectory recommendation result to M; initialize and generate U motion trajectory recommendation results for users of the g-th motion mode category, where the u-th motion trajectory recommendation result generated by initialization is:

[0134]

[0135] in: represents the recommended result of the u-th motion trajectory generated by initialization;

[0136] Indicates the motion trajectory recommendation result There are M motion trajectory points in , Indicates the motion trajectory recommendation result The mth motion trajectory point in , where each motion trajectory point corresponds to a trail area, Set the current iteration number of each motion trajectory recommendation result to t and the maximum iteration number to Max, then the t-th iteration result of the u-th motion trajectory recommendation result is Construct a fitness function with motion trajectory recommendation results as input, input the motion trajectory recommendation results into the fitness function, and obtain the fitness function value of the motion trajectory recommendation results, where the motion trajectory recommendation results The fitness function value is:

[0137]

[0138] in:

[0139] Indicates the motion trajectory recommendation result The fitness function value of

[0140] Indicates the motion trajectory recommendation result The preferred characteristic value of the trail area corresponding to the mth motion trajectory point in ; DIS represents the distance threshold, Represents the motion trajectory point The Euclidean distance between the centers of the corresponding trail areas; the U motion trajectory recommendation results obtained in the t-th iteration are sequentially used as the input of the fitness function, and the motion trajectory recommendation result with the largest fitness function value is selected as the optimal motion trajectory recommendation result obtained in the t-th iteration Iterate each motion trajectory recommendation result. This solution combines the preference feature values ​​of different categories of users for different trail areas, constructs a fitness function that characterizes the smoothness of the motion trajectory recommendation results, and iterates the motion trajectory recommendation results. The fitness function tends to select more preferred trail areas as recommendation results, and ensures that the distance between adjacent motion trajectory points in the motion trajectory recommendation results is not too long. During the iteration process, multiple iteration methods are used for mixed iteration to effectively ensure the diversity of motion trajectory recommendation results, have better exploration performance, and avoid missing the optimal solution. The interactive large screen system network transmits the motion trajectory recommendation results and the user's exercise level to the interactive large screen. The interactive large screen displays the exercise levels of different users and the motion trajectory recommendation results, and sends the motion trajectory recommendation results and exercise levels to the user's wearable device to realize the data management of the smart trail interactive large screen. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] Figure 1 A schematic diagram of the operation flow of a smart trail interactive large-screen data management system provided by one embodiment of the present invention;

[0142] Figure 2 A functional module diagram of a smart trail interactive large-screen data management system provided by an embodiment of the present invention;

[0143] Figure 2 Middle: 100 smart trail interactive large screen data management system, 101 motion data acquisition and processing module, 102 user clustering module, 103 motion trajectory recommendation device;

[0144] Figure 3 A schematic diagram of the structure of an electronic device for implementing a smart trail interactive large-screen data management system provided by one embodiment of the present invention.

[0145] Figure 3 In: 1 electronic device, 10 processor, 11 memory, 12 program, 13 communication interface;

[0146] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0147] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0148] The embodiment of the present application provides a smart trail interactive large-screen data management system. The execution subject of the smart trail interactive large-screen data management system includes but is not limited to at least one of the electronic devices such as the server and the terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the smart trail interactive large-screen data management system can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0149] Embodiment 1:

[0150] S1: Connect the wearable device to the interactive large-screen system network, and use the wearable device to transmit the user's motion data on the smart trail to the interactive large-screen system, where the motion data includes gait, heart rate, pace, and motion trajectory data.

[0151] In step S1, the wearable device is connected to the interactive large-screen system network, and the wearable device is used to transmit the user's motion data on the smart trail to the interactive large-screen system, including:

[0152] Connect the wearable device to the interactive large-screen system network. The user wears the wearable device and exercises on the smart trail. The smart trail monitors the user's exercise data and uses the wearable device to transmit the user's exercise data on the smart trail to the interactive large-screen system. The exercise data includes gait, heart rate, pace and exercise trajectory data. The user's exercise data on the smart trail is in the form of:

[0153]

[0154] in:

[0155] s n represents the movement data of the nth user on the smart trail, and N represents the number of users on the smart trail;

[0156] The gait, heart rate, pace and movement trajectory data of the nth user on the smart trail are shown in sequence; It represents the average pressure exerted on the ground by the left foot of the nth user as sensed by the smart trail. It represents the average pressure exerted on the ground by the right foot of the nth user as sensed by the smart trail;

[0157] represents the heart rate skewness, average heart rate and heart rate standard deviation of the nth user respectively;

[0158] It represents the pace skewness, average pace and pace standard deviation of the nth user respectively;

[0159] Represents motion trajectory data num n The motion trajectory coordinate points, num n Represents motion trajectory data The number of coordinate points of the motion trajectory in .

[0160] S2: The interactive large screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users.

[0161] In step S2, the interactive large screen system network identifies and processes the received motion data, including:

[0162] The interactive large screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users. The motion data identification and processing process is as follows:

[0163] S21: Calculate the motion imbalance of the user, where the motion imbalance calculation formula of the nth user is:

[0164]

[0165] in:

[0166] ban n represents the motion imbalance of the nth user;

[0167] S22: Calculate the exercise intensity of the user's exercise, where the exercise intensity calculation formula for the nth user is:

[0168] st n =(st n (1),st n (2)

[0169]

[0170] in:

[0171] st nrepresents the exercise intensity of the nth user, st n (1) represents the exercise heart rate intensity of the nth user, st n (2) Indicates the exercise pace intensity of the nth user; the interactive large screen divides the user into 7 exercise levels according to the user's exercise intensity. If the user's exercise intensity reaches the preset intensity threshold of the exercise level, the corresponding exercise level is assigned to the user. The higher the exercise intensity, the higher the exercise level.

[0172] time n Indicates the current exercise time of the nth user on the smart trail;

[0173] exp(·) represents an exponential function with a natural constant as the base;

[0174] S23: The user's motion imbalance and motion intensity are used as the user's motion state, where the motion state of the nth user is R n =[ban n ,st n ].

[0175] S3: Perform cluster analysis on users currently on the smart trail in combination with their exercise status to obtain the user's exercise mode category, where users with the same exercise mode category have similar exercise habits.

[0176] In step S3, cluster analysis is performed on users currently on the smart trail in combination with their motion states to obtain the user's motion mode categories, including:

[0177] Based on the user's motion status, cluster analysis is performed on the N users currently on the smart trail to obtain the user's motion mode category. The cluster analysis process is as follows:

[0178] S31: Calculate the motion pattern similarity between any two users among the N users, where the motion pattern similarity calculation formula between the nth user and the ith user is:

[0179]

[0180] in:

[0181] p(n,i) represents the similarity of motion patterns between the nth user and the ith user, i∈[1,N];

[0182] R i represents the motion status of the i-th user;

[0183] ||·|| represents the L1 norm;

[0184] The similarity of motion patterns between different users is used as the distance between users, and the K nearest neighbors of each user are marked;

[0185] S32: Calculate the cutoff distance in the clustering process based on local density information:

[0186]

[0187]

[0188] in:

[0189] Represents the local density information of N users. The larger the local density information, the smaller the difference in local distance density between different users, and the less obvious the distribution difference.

[0190] Indicates that dist c As the cutoff distance, the local distance density of the nth user;

[0191] Indicates that dist c As the cutoff distance, the local distance density ratio of the nth user;

[0192] represents the cutoff distance calculated based on local density information;

[0193] S33: Calculate the weighted local distance density of each user according to the calculated cutoff distance, where the weighted local distance density of the nth user is:

[0194]

[0195] in:

[0196] ρ n represents the weighted local distance density of the nth user;

[0197] Sim(j,n) represents the number of overlapping users between the K nearest neighbors of the nth user and the K nearest neighbors of the jth user;

[0198] represents the influence weight of the j-th user on the local distance density of the n-th user;

[0199] S34: Calculate the cluster center weight of each user, where the cluster center weight of the nth user is:

[0200]

[0201] in:

[0202] weight n represents the cluster center weight of the nth user;

[0203] δ n Indicates the closest distance to the nth user in the set of users whose weighted local distance density is higher than that of the nth user; if the weighted local distance density of the nth user is the maximum, then δ n is the maximum distance between the nth user and other users;

[0204] S35: Select the users with the highest weights among G cluster centers as cluster centers, where G represents the number of motion mode categories, calculate the distances from non-cluster center users to the cluster centers, merge the non-cluster center users into the cluster clusters with the nearest cluster centers as users of the same motion mode category, and the cluster analysis results are:

[0205]

[0206] in:

[0207] L g represents the set of users of the g-th sports mode category, Indicates the count in the g-th motion pattern category g users, count g Indicates the number of users in the g-th sports mode category.

[0208] S4: Construct a trail motion network and perform preference analysis on the motion trajectories of users in the same motion mode category.

[0209] The step S4 constructs a trail movement network, including:

[0210] Construct a trail motion network, where the trail motion network divides the smart trail into Q trail areas:

[0211] (area1,area2,...,area q ,...,area Q )

[0212] in:

[0213] area q represents the qth trail area obtained by division;

[0214] Based on the trail motion network, the motion trajectory preference of users in the same motion mode category is analyzed.

[0215] The preference analysis of the movement trajectories of users in the same movement mode category based on the trail movement network includes:

[0216] Based on the trail motion network, the preference analysis of the motion trajectories of users in the same motion mode category is performed, and the preference analysis process of users in the g-th motion mode category is as follows:

[0217] S41: Extract the motion trajectory data of each user in the g-th motion mode category, and map the motion trajectory data to Q trail areas in the trail motion network to obtain the user's motion area vector, where the user The motion area vector is:

[0218]

[0219] in:

[0220] Indicates user The motion area vector, Indicates user The motion trajectory in the qth trail area represents the characteristics, This means that the user The motion trajectory data does not contain any motion trajectory coordinate points located in the qth trail area. This means that the user The motion trajectory data of has motion trajectory coordinate points located in the qth trail area;

[0221] represents the rth user in the gth motion mode category, r∈[1,count g ];

[0222] S42: Generate the attention preference weight of each user in the g-th motion pattern category, where user The attention preference weight is:

[0223]

[0224] in:

[0225] Indicates user The attention preference weight of

[0226] S43: Calculate and obtain the user preference of the g-th sports mode category user:

[0227]

[0228] in:

[0229] F g represents the user preference of the g-th sports mode category user, F g (1),F g (2),...,Fg (q),...,F g (Q) represents the preference feature values ​​of users of the g-th sports mode category for the 1-Q-th trail areas respectively.

[0230] S5: Recommend motion trajectories based on user preferences based on the trail motion network, and obtain motion trajectory recommendation results for user groups corresponding to different motion mode categories.

[0231] In step S5, the trail motion network is used to recommend motion trajectories based on user preferences, including:

[0232] The trail motion network is used to recommend motion trajectories based on user preferences, and the motion trajectory recommendation results for users of different motion mode categories are obtained. The motion trajectory recommendation process for users of the g-th motion mode category is as follows:

[0233] S51: Setting the length of the motion trajectory recommendation result to M;

[0234] S52: Initialize and generate U motion track recommendation results for the g-th motion mode category user, wherein the u-th motion track recommendation result generated by initialization is:

[0235]

[0236] in:

[0237] represents the recommended result of the u-th motion trajectory generated by initialization;

[0238] Indicates the motion trajectory recommendation result There are M motion trajectory points in , Indicates the motion trajectory recommendation result The mth motion trajectory point in , where each motion trajectory point corresponds to a trail area,

[0239] S53: Set the current iteration number of each motion trajectory recommendation result to t, and the maximum iteration number to Max, then the t-th iteration result of the generated u-th motion trajectory recommendation result is

[0240] S54: construct a fitness function with the motion trajectory recommendation result as input, input the motion trajectory recommendation result into the fitness function, and obtain the fitness function value of the motion trajectory recommendation result, wherein the motion trajectory recommendation result The fitness function value is:

[0241]

[0242] in:

[0243] Indicates the motion trajectory recommendation result The fitness function value of

[0244] Indicates the motion trajectory recommendation result The preference characteristic value of the trail area corresponding to the mth motion trajectory point in ;

[0245] DIS represents the distance threshold. Represents the motion trajectory point The Euclidean distance between the centers of the corresponding trail areas;

[0246] S55: The U motion trajectory recommendation results obtained in the t-th iteration are sequentially used as the input of the fitness function, and the motion trajectory recommendation result with the largest fitness function value is selected as the optimal motion trajectory recommendation result obtained in the t-th iteration

[0247] S56: Iterate each motion trajectory recommendation result, wherein the motion trajectory recommendation result The iterative process is:

[0248] If t is an even number, the following iterative formula is used for iteration:

[0249]

[0250] in:

[0251] rand(0,1) represents a random number between 0 and 1;

[0252] median t Represents the median of the fitness function values ​​of the U motion trajectory recommendation results obtained in the t-th iteration;

[0253] Indicates that any one of the two obtained in the tth iteration is Different motion trajectory recommendation results;

[0254] If t is an odd number, the following iterative formula is used for iteration:

[0255]

[0256] S57: Let t=t+1, return to step S55, until the maximum number of iterations is reached, and use the U motion trajectory recommendation results at this time as the input of the fitness function in sequence, and select the motion trajectory recommendation result with the trajectory coordinate points in the range of [1,Q] and the largest fitness function value as the motion trajectory recommendation result for the g-th motion mode category user.

[0257] S6: The motion track recommendation result and the user's motion level are transmitted to the interactive large screen, and the interactive large screen presents the user with the motion level and the motion track recommendation result.

[0258] The step S6 transmits the motion track recommendation result and the motion level to the interactive large screen, and the interactive large screen presents the motion level and the motion track recommendation result to the user, including:

[0259] The interactive large-screen system network transmits the motion trajectory recommendation results and the user's exercise level to the interactive large screen. The interactive large screen displays the exercise levels and motion trajectory recommendation results of different users, and sends the motion trajectory recommendation results and exercise level to the user's wearable device.

[0260] Embodiment 2:

[0261] like Figure 2 As shown, it is a functional module diagram of a smart trail interactive large-screen data management system provided by an embodiment of the present invention, which can implement the smart trail interactive large-screen data management system in Example 1.

[0262] The smart trail interactive large-screen data management system 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the smart trail interactive large-screen data management system can include a motion data acquisition and processing module 101, a user clustering module 102, and a motion trajectory recommendation device 103. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0263] The motion data collection and processing module 101 is used to transmit the motion data of the user on the smart trail to the interactive large-screen system using the wearable device. The interactive large-screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users;

[0264] The user clustering module 102 is used to perform cluster analysis on users currently on the smart trail in combination with their motion states, obtain the user's motion mode category, construct a trail motion network, and perform preference analysis on the motion trajectories of users with the same motion mode category;

[0265] The motion trajectory recommendation device 103 is used to recommend motion trajectories based on user preferences based on the trail motion network, obtain motion trajectory recommendation results for user groups corresponding to different motion mode categories, and transmit the motion trajectory recommendation results and the user's motion level to the interactive large screen, which presents the user's motion level and motion trajectory recommendation results.

[0266] In detail, the modules in the smart trail interactive large screen data management system 100 described in the embodiment of the present invention are used in the same manner as described above. Figure 1 The smart trail interactive large-screen data management system described in the text has the same technical means and can produce the same technical effects, so I will not go into details here.

[0267] Embodiment 3:

[0268] like Figure 3 The figure is a schematic diagram of the structure of an electronic device for implementing a smart trail interactive large-screen data management system provided by one embodiment of the present invention.

[0269] The electronic device 1 may include a processor 10 , a memory 11 , a communication interface 13 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0270] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may be used not only to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also to temporarily store data that has been output or is to be output.

[0271] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (such as the program 12 for realizing the data management of the smart trail interactive large screen), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0272] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection and communication between internal components of the electronic device.

[0273] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0274] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0275] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0276] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0277] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0278] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0279] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0280] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart trail interactive large-screen data management system, characterized in that: The system process includes: S1: Connect the wearable device to the interactive large-screen system network, and use the wearable device to transmit the user's motion data on the smart trail to the interactive large-screen system, where the motion data includes gait, heart rate, pace, and motion trajectory data; S2: The interactive large screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users; S3: performing cluster analysis on users currently on the smart trail based on their exercise status to obtain the user's exercise mode category, where users with the same exercise mode category have similar exercise habits; S4: Construct a trail motion network and perform preference analysis on the motion trajectories of users in the same motion mode category; S5: Recommending exercise trajectories based on user preferences based on the trail motion network, and obtaining exercise trajectory recommendation results for user groups corresponding to different exercise mode categories; S6: The motion track recommendation result and the user's motion level are transmitted to the interactive large screen, and the interactive large screen presents the user with the motion level and the motion track recommendation result.

2. The operation process of a smart trail interactive large-screen data management system as claimed in claim 1 is characterized in that: In step S1, the wearable device is connected to the interactive large-screen system network, and the wearable device is used to transmit the user's motion data on the smart trail to the interactive large-screen system, including: Connect the wearable device to the interactive large-screen system network. The user wears the wearable device and exercises on the smart trail. The smart trail monitors the user's exercise data and uses the wearable device to transmit the user's exercise data on the smart trail to the interactive large-screen system. The exercise data includes gait, heart rate, pace and exercise trajectory data. The user's exercise data on the smart trail is in the form of: in: s n represents the movement data of the nth user on the smart trail, and N represents the number of users on the smart trail; The gait, heart rate, pace and movement trajectory data of the nth user on the smart trail are shown in sequence; It represents the average pressure exerted on the ground by the left foot of the nth user as sensed by the smart trail. It represents the average pressure exerted on the ground by the right foot of the nth user as sensed by the smart trail; represents the heart rate skewness, average heart rate and heart rate standard deviation of the nth user respectively; It represents the pace skewness, average pace and pace standard deviation of the nth user respectively; Represents motion trajectory data num n The motion trajectory coordinate points, num n Represents motion trajectory data The number of coordinate points of the motion trajectory in .

3. The operation process of a smart trail interactive large-screen data management system as described in claim 2 is characterized in that: In step S2, the interactive large screen system network identifies and processes the received motion data, including: The interactive large screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users. The motion data identification and processing process is as follows: S21: Calculate the motion imbalance of the user, where the motion imbalance calculation formula of the nth user is: in: ban n represents the motion imbalance of the nth user; S22: Calculate the exercise intensity of the user's exercise, where the exercise intensity calculation formula for the nth user is: st n =(st n (1),st n (2)) in: st n represents the exercise intensity of the nth user, st n (1) represents the exercise heart rate intensity of the nth user, st n (2) Indicates the exercise pace intensity of the nth user; the interactive large screen divides the user into 7 exercise levels according to the user's exercise intensity. If the user's exercise intensity reaches the preset intensity threshold of the exercise level, the corresponding exercise level is assigned to the user. The higher the exercise intensity, the higher the exercise level. time n Indicates the current exercise time of the nth user on the smart trail; exp(·) represents an exponential function with a natural constant as the base; S23: The user's motion imbalance and motion intensity are used as the user's motion state, where the motion state of the nth user is R n =[ban n ,st n ].

4. The operation process of a smart trail interactive large-screen data management system as described in claim 3 is characterized in that: In step S3, cluster analysis is performed on users currently on the smart trail in combination with their motion states to obtain the user's motion mode categories, including: Based on the user's motion status, cluster analysis is performed on the N users currently on the smart trail to obtain the user's motion mode category. The cluster analysis process is as follows: S31: Calculate the motion pattern similarity between any two users among the N users, where the motion pattern similarity calculation formula between the nth user and the ith user is: in: p(n,i) represents the similarity of motion patterns between the nth user and the ith user, i∈[1,N]; R i represents the motion status of the i-th user; ||·|| represents the L1 norm; The similarity of motion patterns between different users is used as the distance between users, and the K nearest neighbors of each user are marked; S32: Calculate the cutoff distance in the clustering process based on local density information: in: Represents the local density information of N users. The larger the local density information, the smaller the difference in local distance density between different users, and the less obvious the distribution difference. Indicates that dist c As the cutoff distance, the local distance density of the nth user; Indicates that dist c As the cutoff distance, the local distance density ratio of the nth user; represents the cutoff distance calculated based on local density information; S33: Calculate the weighted local distance density of each user according to the calculated cutoff distance, where the weighted local distance density of the nth user is: in: ρ n represents the weighted local distance density of the nth user; Sim(j,n) represents the number of overlapping users between the K nearest neighbors of the nth user and the K nearest neighbors of the jth user; represents the influence weight of the j-th user on the local distance density of the n-th user; S34: Calculate the cluster center weight of each user, where the cluster center weight of the nth user is: in: weight n represents the cluster center weight of the nth user; δ n Indicates the closest distance to the nth user in the set of users whose weighted local distance density is higher than that of the nth user; if the weighted local distance density of the nth user is the maximum, then δ n is the maximum distance between the nth user and other users; S35: Select the users with the highest weights among G cluster centers as cluster centers, where G represents the number of motion mode categories, calculate the distances from non-cluster center users to the cluster centers, merge the non-cluster center users into the cluster clusters with the nearest cluster centers as users of the same motion mode category, and the cluster analysis results are: in: L g represents the set of users of the g-th sports mode category, Indicates the count in the g-th motion pattern category g users, count g Indicates the number of users in the g-th sports mode category.

5. The operation process of a smart trail interactive large-screen data management system according to claim 1 is characterized in that: The step S4 constructs a trail movement network, including: Construct a trail motion network, where the trail motion network divides the smart trail into Q trail areas: (area1,area2,...,area q ,...,area Q ) in: area q represents the qth trail area obtained by division; Based on the trail motion network, the motion trajectory preference of users in the same motion mode category is analyzed.

6. The operation process of a smart trail interactive large-screen data management system as claimed in claim 5 is characterized in that: The preference analysis of the movement trajectories of users in the same movement mode category based on the trail movement network includes: Based on the trail motion network, the preference analysis of the motion trajectories of users in the same motion mode category is performed, and the preference analysis process of users in the g-th motion mode category is as follows: S41: Extract the motion trajectory data of each user in the g-th motion mode category, and map the motion trajectory data to Q trail areas in the trail motion network to obtain the user's motion area vector, where the user The motion area vector is: in: Indicates user The motion area vector, Indicates user The motion trajectory in the qth trail area represents the characteristics, This means that the user The motion trajectory data does not contain any motion trajectory coordinate points located in the qth trail area. This means that the user The motion trajectory data of has motion trajectory coordinate points located in the qth trail area; represents the rth user in the gth motion mode category, r∈[1,count g ]; S42: Generate the attention preference weight of each user in the g-th motion pattern category, where user The attention preference weight is: in: Indicates user The attention preference weight of S43: Calculate and obtain the user preference of the g-th sports mode category user: in: F g represents the user preference of the g-th sports mode category user, F g (1),F g (2),...,F g (q),...,F g (Q) represents the preference feature values ​​of users of the g-th sports mode category for the 1-Q-th trail areas respectively.

7. The operation process of a smart trail interactive large-screen data management system as claimed in claim 6 is characterized in that: In step S5, the trail motion network is used to recommend motion trajectories based on user preferences, including: The trail motion network is used to recommend motion trajectories based on user preferences, and the motion trajectory recommendation results for users of different motion mode categories are obtained. The motion trajectory recommendation process for users of the g-th motion mode category is as follows: S51: Setting the length of the motion trajectory recommendation result to M; S52: Initialize and generate U motion track recommendation results for the g-th motion mode category user, wherein the u-th motion track recommendation result generated by initialization is: in: represents the recommended result of the u-th motion trajectory generated by initialization; Indicates the motion trajectory recommendation result There are M motion trajectory points in , Indicates the motion trajectory recommendation result The mth motion trajectory point in , where each motion trajectory point corresponds to a trail area, S53: Set the current iteration number of each motion trajectory recommendation result to t, and the maximum iteration number to Max, then the t-th iteration result of the generated u-th motion trajectory recommendation result is S54: construct a fitness function with the motion trajectory recommendation result as input, input the motion trajectory recommendation result into the fitness function, and obtain the fitness function value of the motion trajectory recommendation result, wherein the motion trajectory recommendation result The fitness function value is: in: Indicates the motion trajectory recommendation result The fitness function value of Indicates the motion trajectory recommendation result The preference characteristic value of the trail area corresponding to the mth motion trajectory point in ; DIS represents the distance threshold. Represents the motion trajectory point The Euclidean distance between the centers of the corresponding trail areas; S55: The U motion trajectory recommendation results obtained in the t-th iteration are sequentially used as the input of the fitness function, and the motion trajectory recommendation result with the largest fitness function value is selected as the optimal motion trajectory recommendation result obtained in the t-th iteration S56: Iterate each motion trajectory recommendation result; S57: Let t=t+1, return to step S55, until the maximum number of iterations is reached, and use the U motion trajectory recommendation results at this time as the input of the fitness function in sequence, and select the motion trajectory recommendation result with the trajectory coordinate points in the range of [1,Q] and the largest fitness function value as the motion trajectory recommendation result for the g-th motion mode category user.

8. The operation process of a smart trail interactive large-screen data management system according to claim 1 is characterized in that: The step S6 transmits the motion track recommendation result and the motion level to the interactive large screen, and the interactive large screen presents the motion level and the motion track recommendation result to the user, including: The interactive large-screen system network transmits the motion trajectory recommendation results and the user's exercise level to the interactive large screen. The interactive large screen displays the exercise levels and motion trajectory recommendation results of different users, and sends the motion trajectory recommendation results and exercise level to the user's wearable device.

9. A smart trail interactive large-screen data management system, characterized in that: The system comprises: The motion data collection and processing module is used to transmit the user's motion data on the smart trail to the interactive large-screen system using wearable devices. The interactive large-screen system network identifies and processes the received motion data to obtain the motion status and motion level of different users; The user clustering module is used to perform cluster analysis on users currently on the smart trail based on their motion status, obtain the user's motion mode category, build a trail motion network, and perform preference analysis on the motion trajectories of users in the same motion mode category; A motion trajectory recommendation device is used to recommend motion trajectories based on user preferences based on a trail motion network, obtain motion trajectory recommendation results for user groups corresponding to different motion mode categories, transmit the motion trajectory recommendation results and the user's motion level to an interactive large screen, and the interactive large screen presents the user's motion level and motion trajectory recommendation results to realize an operation process of a smart trail interactive large screen data management system as described in any one of claims 1-8.

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