Wireless fitness activity scheduling system based on DTP position

Abstract

The invention discloses a DTP position-based wireless fitness activity scheduling system, which is characterized in that a matched activity area is generated for a user through historical fitness data of the user, the number of idle equipment in the matched activity area is acquired, and DTP position information of other users in the matched activity area and the historical fitness data are combined, so that the user experience is improved. Generating a selection coefficient for each matched activity area, sorting all the matched activity areas according to the selection coefficients, generating an area list, judging whether the matched activity areas in the area list support user activities or not based on the selection coefficients, and if all the matched activity areas in the area list do not support the user activities, judging whether the matched activity areas in the area list support the user activities or not. If yes, generating a recommendation strategy for the user according to the historical fitness data of the user. According to the scheduling system, real-time intelligent activity scheduling based on users can be realized through combination of a wireless technology and a DTP position, utilization of fitness space resources is optimized, and the training effect and experience of participants are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of violation monitoring, and particularly to a wireless fitness activity scheduling system based on DTP location. Background Art

[0002] With the popularization of intelligent fitness equipment and mobile health management, modern fitness venues are gradually developing towards digitalization and intelligence. In order to improve the management efficiency and user experience of fitness venues, the traditional fitness activity scheduling method can no longer meet the flexible and efficient requirements.

[0003] Based on this, the present invention proposes a wireless fitness activity scheduling system based on DTP location. By combining wireless technology with DTP location, it can achieve intelligent activity scheduling based on the real-time situation of users, optimize the utilization of fitness space resources, and improve the experience and training effect of participants. Summary of the Invention

[0004] The purpose of the present invention is to provide a wireless fitness activity scheduling system based on DTP location to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A wireless fitness activity scheduling system based on DTP location, including an activity area matching module, an analysis module, and an information push module:

[0006] Activity area matching module: When a new user accesses the scheduling system, generate a matching activity area for the user based on the user's historical fitness data;

[0007] Analysis module: First, obtain the number of idle equipment in the matching activity area, and then combine the DTP location information and historical fitness data of other users in the matching activity area to generate a selection coefficient for each matching activity area;

[0008] Information push module: After sorting all the matching activity areas according to the selection coefficient, generate a list of areas, and based on the selection coefficient, judge whether the matching activity areas in the list of areas support user activities. If it is judged that all the matching activity areas in the list of areas do not support user activities, then generate a recommendation strategy for the user based on the user's historical fitness data, and send the recommendation strategy, the list of areas, and the judgment result to the user's fitness bracelet.

[0009] In a preferred embodiment, if there is no idle equipment in the matching activity area, the analysis module deletes the matching activity area from the user's activity area set;

[0010] The analysis module obtains the number of users who have not exercised in the matching activity areas with idle equipment based on the user's DTP location information, and obtains the cumulative exercise index and equipment usage frequency index of the users who have not exercised in the matching activity areas;

[0011] Calculate the number of users who have not exercised, the cumulative exercise index, and the equipment usage frequency index in the activity area comprehensively to generate a selection coefficient for the matching activity area.

[0012] In a preferred embodiment, calculate the number of users who have not exercised, the cumulative exercise index, and the equipment usage frequency index in the activity area comprehensively to generate a selection coefficient for the matching activity area. The expression is:

[0013] In the formula, xzf s is the selection coefficient, kxz is the normalized value of the number of idle devices, yhs is the number of users who have not exercised, dlz is the cumulative exercise index, qcs is the equipment usage frequency index, β and γ are the proportionality coefficients of the cumulative exercise index and the equipment usage frequency index respectively, and both β and γ are greater than 0.

[0014] In a preferred embodiment, the information push module sorts all the matching activity areas according to the selection coefficient from small to large to generate a region list, compares the selection coefficients of all the matching activity areas in the region list with a preset selection threshold. The selection threshold is used to determine whether the matching activity areas in the region list support user activities. If the selection coefficient of the matching activity area is less than or equal to the selection threshold, it is determined that the matching activity area in the region list does not support user activities. If the selection coefficient of the matching activity area is greater than the selection threshold, it is determined that the matching activity area in the region list supports user activities.

[0015] In a preferred embodiment, if it is determined that all the matching activity areas in the region list do not support user activities, the information push module generates a recommendation strategy for the user based on the user's historical exercise preferences. If it is determined that there are two or more matching activity areas in the region list, then after sorting the two or more matching activity areas according to the selection coefficient from small to large, send them to the user's fitness bracelet.

[0016] In a preferred embodiment, the calculation expression of the cumulative exercise index is: In the formula, dlz is the cumulative exercise index, T is the length of the total time period, t represents the time point, and t ∈ [0, T], w(t) represents the weight of the time point t, and D(t) represents the user exercise duration at the time point t;

[0017] The calculation logic of the equipment usage frequency index is: obtain the number of equipment in the matching activity area, and after obtaining the frequency of each piece of equipment used by users who have not exercised in the matching activity area, calculate and obtain the average usage frequency and the usage frequency standard deviation. The expression is:

[0018] In the formula, S qis the standard deviation of usage frequency, n is the number of equipment in the activity area, and S i is the frequency of the i-th piece of equipment in the activity area being used by non-exercising users, and S avg is the average usage frequency. The equipment usage frequency index is calculated based on the average usage frequency and the standard deviation of usage frequency. The expression is: In the formula, qcs is the equipment usage frequency index.

[0019] In a preferred embodiment, after the user receives the fitness bracelet at the front desk, the bracelet automatically accesses the scheduling system, identifies the user through the unique identifier of the bracelet, and extracts the user's historical fitness data from the database according to the bracelet ID, including the total usage duration of fitness equipment, fitness items, and fitness frequency in the activity area where the user is located;

[0020] Clean the user's historical fitness data, remove outliers or invalid data, classify the data according to the dimensions of fitness items, duration, and frequency, and form tagged data of the user's fitness behavior.

[0021] In a preferred embodiment, after the activity area matching module obtains the total usage duration of fitness equipment in the activity area, it compares the total usage duration of fitness equipment in the activity area with a preset duration threshold. If the total usage duration of fitness equipment in the activity area is greater than or equal to the duration threshold, mark this activity area as a matching activity area. If the total usage duration of fitness equipment in the activity area is less than the duration threshold, do not mark this activity area, and establish an acquisition area set for all the user's matching activity areas.

[0022] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0023] The present invention generates a matching activity area for the user through the user's historical fitness data, obtains the number of idle equipment in the matching activity area, and then combines the DTP position information and historical fitness data of other users in the matching activity area to generate a selection coefficient for each matching activity area. After sorting all the matching activity areas according to the selection coefficient, a region list is generated, and it is determined whether the matching activity areas in the region list support the user's activities based on the selection coefficient. If it is determined that all the matching activity areas in the region list do not support the user's activities, a recommendation strategy is generated for the user based on the user's historical fitness data. Through the combination of wireless technology and DTP position, this scheduling system can achieve intelligent activity scheduling based on the user in real time, optimize the utilization of fitness space resources, and improve the experience and training effect of participants. Description of the Drawings

[0024] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is the method flow chart of the present invention. Specific embodiments

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, rather than all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0027] Embodiment 1: Please refer to Figure 1 As shown, the wireless fitness activity scheduling system based on DTP location in this embodiment includes an activity area matching module, an analysis module, and an information push module:

[0028] Activity area matching module: When a new user accesses the scheduling system (usually after the user receives the corresponding fitness bracelet at the front desk of the fitness venue, the fitness bracelet with the user's historical fitness data automatically accesses the scheduling system), a matching activity area (the activity area may be one or more) is generated for the user based on the user's historical fitness data, and the matching activity area is sent to the analysis module and the information push module;

[0029] Analysis module: First, obtain the number of idle equipment in the matching activity area, and then combine the DTP location information and historical fitness data of other users in the matching activity area to generate a selection coefficient for each matching activity area, and the selection coefficient is sent to the information push module;

[0030] Information push module: After sorting all the matching activity areas according to the selection coefficient, generate a list of areas, and based on the selection coefficient, judge whether the matching activity areas in the list of areas support the user's activities. If it is judged that all the matching activity areas in the list of areas do not support the user's activities, a recommendation strategy is generated for the user based on the user's historical fitness data, and the recommendation strategy, the list of areas, and the judgment result are sent to the user's fitness bracelet.

[0031] This application generates a matching activity area for the user based on the user's historical fitness data, obtains the number of idle equipment in the matching activity area, and then combines the DTP location information and historical fitness data of other users in the matching activity area to generate a selection coefficient for each matching activity area. After sorting all the matching activity areas according to the selection coefficient, a region list is generated, and it is judged whether the matching activity areas in the region list support the user's activities based on the selection coefficient. If it is judged that all the matching activity areas in the region list do not support the user's activities, a recommendation strategy is generated for the user based on the user's historical fitness data. Through the combination of wireless technology and DTP location, this scheduling system can achieve intelligent activity scheduling based on the user in real time, optimize the utilization of fitness space resources, and improve the experience and training effect of participants.

[0032] Scenario: A new user enters a fitness venue and connects to the wireless fitness activity scheduling system.

[0033] The user arrives at the fitness venue and receives a fitness bracelet at the front desk. The historical fitness data of the user (such as past exercise types, frequencies, intensities, etc.) is stored in this bracelet. The user automatically connects to the scheduling system through the bracelet, and the system reads the user's historical fitness data and starts matching suitable activity areas for the user.

[0034] The activity area matching module generates one or more matching activity areas suitable for the user based on the user's historical fitness data. For example, if the user usually does aerobic exercise, the system may match the user to enter the aerobic area or the treadmill area; if the user prefers strength training, it may recommend entering the weightlifting area or the equipment training area. The generated matching activity areas are transmitted to the analysis module and the information push module.

[0035] The analysis module first checks whether the equipment in the matching activity area is idle. For example, there may be 10 treadmills in the treadmill area, 4 of which are in use and 6 are idle. The analysis module records the number of idle equipment. The system also analyzes the usage situation of this area according to the DTP location data of other fitness users. For example, whether there are other users using the equipment, and whether there are other users staying in the same area but not starting to exercise. Combining the number of idle equipment, the equipment utilization situation in this area, and the behaviors of other users, the analysis module generates a selection coefficient for each matching activity area. The selection coefficient reflects the activity fitness of this area, and the higher the coefficient, the more suitable this area is for the current user to exercise.

[0036] The information push module sorts all the matching activity areas according to the selection coefficients passed by the analysis module. For example, if the selection coefficient of a certain area is 0.8 and that of another area is 0.6, the area with the higher selection coefficient will be recommended first. The system determines whether there are sufficient idle devices in the matching activity areas in the list and whether they meet the user's exercise needs. If the selection coefficient of an area is low, it means that the devices in that area are in relatively high demand or that area is not suitable for the current user, and the system will mark that area as "not supported". If all the matching activity areas are determined to be unsuitable for the user to exercise, the information push module will generate a recommendation strategy based on the user's historical fitness data. For example, the system may suggest that the user try different types of exercises, such as switching from aerobic exercise to strength training, or recommend some low-intensity exercise activities.

[0037] Finally, the information push module sends the sorted list of areas, the support status of each area, and the recommendation strategy to the user's fitness bracelet. The bracelet reminds the user which activity area they can go to for exercise based on this information, or provides a recommended exercise plan if all areas are not suitable.

[0038] Example:

[0039] Suppose user A is a fitness enthusiast who prefers aerobic exercise. Past fitness data shows that he usually does treadmill exercises or rides a spinning bike in the gym. The system matches the treadmill area and the spinning bike area as possible activity areas based on user A's historical data.

[0040] Check if there are idle devices in the treadmill area. It is found that 3 treadmills are in use and 2 are idle in the treadmill area. There are no idle bikes in the spinning bike area. Combining the DTP location information of other users, the analysis module calculates that the selection coefficient of the treadmill area is 0.8 (because there are some idle devices), and the selection coefficient of the spinning bike area is 0.3 (because all the devices are occupied).

[0041] The treadmill area with the higher selection coefficient is ranked first in the recommendation list. If user A doesn't want to wait, the system may push some alternative activities, such as light strength training or yoga classes, as a recommendation strategy. User A finally gets the recommendation through the bracelet and chooses to go to the treadmill area for exercise.

[0042] Summary: This wireless fitness activity scheduling system dynamically generates matching areas, calculates selection coefficients, and optimizes recommendations by combining the user's historical fitness data, the idle device situation in the activity areas, and the behavior data of other users. This can not only improve the utilization efficiency of the equipment in the fitness venue, but also provide personalized activity recommendations for each user and optimize the user's exercise experience.

[0043] The specific working process of the scheduling system is as follows:

[0044] When a new user accesses the scheduling system (usually after the user receives the corresponding fitness bracelet at the front desk of the fitness venue, the fitness bracelet with the user's historical fitness data automatically accesses the scheduling system), based on the user's historical fitness data, matching activity areas are generated for the user (the activity areas may be one or more). First, the number of idle equipment in the matching activity areas is obtained, and then, combined with the DTP location information and historical fitness data of other users in the matching activity areas, a selection coefficient is generated for each matching activity area. After sorting all the matching activity areas according to the selection coefficient, a region list is generated, and it is determined whether the matching activity areas in the region list support the user's activities based on the selection coefficient. If it is determined that none of the matching activity areas in the region list support the user's activities, a recommendation strategy is generated for the user based on the user's historical fitness data, and the recommendation strategy, the region list, and the judgment result are sent to the user's fitness bracelet.

[0045] Example 2:

[0046] When a new user accesses the scheduling system (usually after the user receives the corresponding fitness bracelet at the front desk of the fitness venue, the fitness bracelet with the user's historical fitness data automatically accesses the scheduling system), the activity area matching module generates matching activity areas for the user based on the user's historical fitness data (the activity areas may be one or more);

[0047] After the user receives the fitness bracelet at the front desk, the bracelet automatically accesses the scheduling system. The system identifies the user through the unique identifier (such as ID) of the bracelet, and the system extracts the user's historical fitness data from the database according to the bracelet ID, including but not limited to: the total usage duration of fitness equipment in the activity area, fitness items (such as running, strength training, yoga, etc.), and fitness frequency;

[0048] Clean the user's historical fitness data, remove outliers or invalid data (such as incomplete fitness records), classify the data according to dimensions such as fitness items, durations, and frequencies to form labeled data of the user's fitness behavior, and identify the user's fitness preferences by analyzing the user's fitness history. For example, whether the user prefers aerobic exercise, strength training, or whether they often use specific equipment.

[0049] After obtaining the total usage duration of fitness equipment in the activity area, compare the total usage duration of fitness equipment in the activity area with a preset duration threshold. If the total usage duration of fitness equipment in the activity area is greater than or equal to the duration threshold, mark the activity area as a matching activity area. If the total usage duration of fitness equipment in the activity area is less than the duration threshold, do not mark the activity area, and establish an acquisition area set for all the user's matching activity areas.

[0050] The analysis module first obtains the number of idle equipment in the matching activity area, and then generates a selection coefficient for each matching activity area by combining the DTP location information and historical fitness data of other users in the matching activity area.

[0051] The equipment in the fitness venue is usually equipped with sensors or Internet of Things (IoT) devices that can monitor the usage status of the equipment in real time (such as whether it is occupied, usage duration, etc.). The system obtains real-time data from the equipment status monitoring system through an API or by directly accessing the database. The system establishes a mapping relationship between the activity area and the equipment according to the layout of the fitness venue. For example:

[0052] Strength training area: barbell, dumbbell, strength training machine, etc.

[0053] Aerobic area: treadmill, elliptical machine, spinning bike, etc.

[0054] According to the user's historical fitness data, filter out the types of equipment that the user may be interested in. For example, if the user often uses a treadmill, then focus on the status of the treadmill in the aerobic area.

[0055] Real-time status query: The system obtains the real-time status (idle, occupied, faulty, etc.) of all equipment in the current matching area from the equipment status monitoring system.

[0056] Count the number of idle equipment in the matching area. For example:

[0057] Strength training area: 3 idle barbells, 5 pairs of idle dumbbells, 2 idle strength training machines.

[0058] Aerobic area: 4 idle treadmills, 2 idle elliptical machines.

[0059] If some equipment is in a faulty state, the system should exclude it from the list of idle equipment.

[0060] The system continuously monitors changes in the equipment status to ensure the real-time nature of the number of idle equipment. When a user starts using or finishes using a piece of equipment, the system immediately updates the status of that equipment and recalculates the number of idle equipment.

[0061] The system outputs the number of idle equipment in the matching area as key information. If necessary, the system can also provide specific information about the idle equipment, such as equipment type, location, usage instructions, etc.

[0062] If the failure rate of the equipment in a certain area is relatively high, the system can temporarily remove that area from the recommended list and notify the management for repair. The system records the user's behavior of using the equipment for optimizing future recommendation strategies. The system analyzes the usage of equipment in each area to help the fitness venue optimize the equipment configuration and layout.

[0063] If there are no idle equipment in the matching activity area, the analysis module deletes the matching activity area from the user's activity area set. The analysis module obtains the number of unexercised users in the matching activity area with idle equipment based on the user's DTP location information. Since among the matching activity areas, unexercised users may just pass by the matching activity area, it is necessary to obtain the cumulative exercise index and equipment usage frequency index of the unexercised users in the matching activity area. Then, the number of unexercised users, the cumulative exercise index, and the equipment usage frequency index in the activity area are comprehensively calculated to generate a selection coefficient for the matching activity area. The expression is:

[0064] In the formula, xzf s is the selection coefficient, kxz is the normalized value of the number of idle equipment, yhs is the number of unexercised users, dlz is the cumulative exercise index, qcs is the equipment usage frequency index, β and γ are the proportionality coefficients of the cumulative exercise index and the equipment usage frequency index respectively, and both β and γ are greater than 0. The smaller the selection coefficient of the matching activity area, the more it should be recommended to the user preferentially.

[0065] The calculation expression of the cumulative exercise index is: In the formula, dlz is the cumulative exercise index, T is the length of the total time period, t represents the time point, and t ∈ [0, T]. w(t) represents the weight of the time point t, and D(t) represents the user's exercise duration at the time point t. The larger the cumulative exercise index, the greater the probability that the unexercised users in the matching activity area choose to exercise in this matching activity area, and the lower the recommendation priority of this matching activity area.

[0066] The calculation logic of the equipment usage frequency index is: obtain the number of equipment in the matching activity area, and after obtaining the frequency of each piece of equipment used by unexercised users in the matching activity area, calculate the average usage frequency and the usage frequency standard deviation. The expression is: In the formula, S q is the usage frequency standard deviation, n is the number of equipment in the activity area, S i is the frequency of the i-th piece of equipment used by unexercised users in the activity area, S avg is the average usage frequency. The equipment usage frequency index is calculated based on the average usage frequency and the usage frequency standard deviation. The expression is: In the formula, qcs is the equipment usage frequency index. The larger the equipment usage frequency index, the greater the overall frequency of all equipment used by unexercised users in the matching activity area, and the lower the recommendation priority of this matching activity area.

[0067] An unexercised user refers to those who stay in a certain activity area but do not participate in exercise. The behaviors of these users may include passing by the area, staying in the area without engaging in high-intensity activities, or users who will choose to exercise in the area.

[0068] The analysis module obtains the number of unexercised users in the activity area where there are idle devices matching based on the user's DTP location information. Define DTP location information: DTP location information represents the relationship between the user and the device or activity area. In this scenario, the user's DTP location information can include the user's current location coordinates, activity area, status of nearby devices, etc.

[0069] The system collects each user's DTP location information through sensors, location tracking technology, or user behavior data. Specifically, the system will obtain in real time whether the user stays in a specific activity area and whether the area contains devices that are in use or idle.

[0070] The activity area refers to the areas in the fitness venue that are divided into different activity types, such as the aerobic area, strength training area, yoga area, etc. The system determines which areas are suitable for the user according to the user's needs (such as fitness goals, intensity preferences). The system will match the user's current location with these activity areas and select the areas that meet the user's needs. In each activity area, the system needs to obtain the usage status of the devices in the area. For example, whether there are idle devices (such as treadmills, weightlifting equipment, etc.). This information is usually obtained through device networking, sensor detection, etc.

[0071] An unexercised user refers to a user who stays in a specific activity area but has not started using the device or exercising. The system can identify these users by tracking the user's activities (such as whether to use the device, whether to participate in exercise, etc.). Through the user's activity data (such as the duration of stay in the activity area, whether to use the device, whether to participate in other light activities, etc.), the system can identify users in the "unexercised" state. Unexercised users usually stay in the area but do not immediately start exercising.

[0072] The system needs to calculate the number of idle devices in each activity area. For each device, the system can confirm whether the device is idle through the device networking status, sensor feedback, etc. Based on the confirmed idle devices, the system will count the number of unexercised users in the activity area according to the DTP location information. Specifically, the system calculates the number of users who stay in the area but have not started exercising by tracking the location and status of unexercised users in each activity area.

[0073] The system can further analyze the behavior patterns of inactive users. For example, some users may stay for a long time but do not choose to exercise. The system can record this behavior and analyze it to provide more appropriate recommendations later (such as recommending low-intensity activities, reminding users to start exercising, etc.). By counting the distribution of inactive users in each matching activity area, the system evaluates which areas have a high proportion of idle devices and inactive users, and which areas may need to adjust the device configuration or activity arrangement.

[0074] Based on the statistical data of idle devices and inactive users, the system can dynamically adjust the recommendations for users. For example, if there are many idle devices and few inactive users in a certain area, the system may recommend that newly entered users choose that area, and vice versa. The system can push appropriate exercise plans or reminders according to the number of idle devices and inactive users in the matching area. For example, if there are idle devices but many inactive users in a certain area, the system may remind users to start exercising or push an exercise plan suitable for beginners.

[0075] After sorting all the matching activity areas according to the selection coefficient, the information push module generates a list of areas, and based on the selection coefficient, determines whether the matching activity areas in the list of areas support user activities. If it is determined that all the matching activity areas in the list of areas do not support user activities, a recommendation strategy is generated for the user based on the user's historical fitness data, and the recommendation strategy, the list of areas, and the determination result are sent to the user's fitness bracelet;

[0076] The information push module sorts all the matching activity areas in ascending order according to the selection coefficient to generate a list of areas, compares the selection coefficients of all the matching activity areas in the list of areas with a preset selection threshold. The selection threshold is used to determine whether the matching activity areas in the list of areas support user activities. If the selection coefficient of the matching activity area is less than or equal to the selection threshold, it is determined that the matching activity area in the list of areas does not support user activities. If the selection coefficient of the matching activity area is greater than the selection threshold, it is determined that the matching activity area in the list of areas supports user activities;

[0077] If it is determined that all the matching activity areas in the list of areas do not support user activities, it indicates that there is no suitable activity area for the user to use. The information push module generates a recommendation strategy for the user based on the user's historical exercise preferences. If it is determined that there are two or more matching activity areas in the list of areas, then the two or more matching activity areas are sorted in ascending order according to the selection coefficient and sent to the user's fitness bracelet;

[0078] The recommendation strategy includes:

[0079] Even if certain areas are not in the user's historical preferences, if the device usage rate in these areas is low or the user density is small, the system can recommend that the user try these areas. Recommend that the user go to other floors or other types of activity areas (such as recommending from the strength training area to the aerobic area). Based on the user's historical exercise time preferences, recommend that the user exercise during off-peak hours outside the current time period. Provide the estimated end time of the current peak hour to help the user plan their exercise time.

[0080] Based on the user's historical exercise data, recommend alternative exercise programs similar to the preferred programs to the user. For example: If the user prefers the treadmill, outdoor running or spinning can be recommended. If the user prefers strength training, free weight training or functional training can be recommended. Recommend that the user try new exercise programs to enrich the exercise experience. Based on the usage situation of the current area and equipment, estimate the user's waiting time for the equipment and provide it to the user for reference. If the waiting time is short, it is recommended that the user wait for a while; if the waiting time is long, recommend that the user choose other areas or programs.

[0081] Based on the user's historical exercise data and goals, generate a personalized exercise plan to help the user maximize the exercise effect under limited conditions. Recommend exercise programs that do not require equipment to the user, such as bodyweight training, yoga or stretching.

[0082] Based on historical data and real-time information, predict the load situation in each area for a period of time in the future. Recommend that the user come to exercise during low-load periods.

[0083] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data and performing software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0084] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0085] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A wireless fitness activity scheduling system based on DTP location, characterized by: Including activity area matching module, analysis module, and information push module: Activity area matching module: When a new user accesses the scheduling system, a matching activity area is generated for the user based on the user's historical fitness data; Analysis module: first obtain the number of free equipment in the matching activity area, then combine the DTP location information and historical fitness data of other users in the matching activity area to generate a selection coefficient for each matching activity area; Information push module: After sorting all matching activity areas according to the selection coefficient, an area list is generated, and based on the selection coefficient, it is determined whether the matching activity areas in the area list support user activities. If it is determined that all matching activity areas in the area list do not support user activities, a recommendation strategy is generated for the user based on the user's historical fitness data, and the recommendation strategy, area list and judgment results are sent to the user's fitness bracelet.

2. The DTP location-based wireless fitness activity scheduling system according to claim 1, characterized in that: If there is no idle equipment in the matching activity area, the analysis module deletes the matching activity area from the user's activity area set; The analysis module obtains the number of non-exercising users in the matching activity area with idle equipment based on the user DTP location information, and obtains the cumulative exercise index and equipment use frequency index of the non-exercising users in the matching activity area; The number of non-exercising users in the activity area, the cumulative exercise index, and the equipment use frequency index are calculated comprehensively to generate a selection coefficient for matching the activity area.

3. The DTP location-based wireless fitness activity scheduling system according to claim 2, characterized in that: The number of non-exercising users in the activity area, the cumulative exercise index and the equipment use frequency index are calculated comprehensively to generate a selection coefficient for matching the activity area. The expression is: In the formula, xzf s is the selection coefficient, kxz is the normalized value of the number of idle devices, yhs is the number of users who have not exercised, dlz is the cumulative exercise index, qcs is the equipment use frequency index, β and γ are the proportional coefficients of the cumulative exercise index and the equipment use frequency index respectively, and β and γ are both greater than 0.

4. The DTP location-based wireless fitness activity scheduling system according to claim 3, characterized in that: The information push module sorts all matching activity areas from small to large according to the selection coefficient, generates an area list, compares the selection coefficients of all matching activity areas in the area list with a preset selection threshold, the selection threshold is used to determine whether the matching activity areas in the area list support user activities, if the selection coefficient of the matching activity area is less than or equal to the selection threshold, it is determined that the matching activity areas in the area list do not support user activities, if the selection coefficient of the matching activity area is greater than the selection threshold, it is determined that the matching activity areas in the area list support user activities.

5. The DTP location-based wireless fitness activity scheduling system according to claim 4, characterized in that: If all matching activity areas in the judgment area list do not support user activities, the information push module generates a recommendation strategy for the user based on the user's historical exercise preferences. If there are two or more matching activity areas in the judgment area list, the two or more matching activity areas are sorted from small to large according to the selection coefficient and sent to the user's fitness bracelet.

6. The DTP location-based wireless fitness activity scheduling system according to claim 3, characterized in that: The calculation expression of the cumulative exercise index is: Where dlz is the cumulative exercise index, T is the length of the total time period, t represents the time point, and t∈[0,T], w(t) represents the weight of time point t, and D(t) represents the user's exercise time at time point t; The calculation logic of the equipment usage frequency index is: after obtaining the number of equipment in the matching activity area and the frequency of historical use of each equipment by non-exercising users in the matching activity area, the average usage frequency and the standard deviation of the usage frequency are calculated. The expression is: In the formula, S q is the standard deviation of the frequency of use, n is the number of equipment in the activity area, S i is the frequency of the i-th equipment being used by non-exercising users in the activity area, S avg is the average frequency of use. The equipment use frequency index is calculated based on the average frequency of use and the standard deviation of the frequency of use. The expression is: Where qcs is the equipment usage frequency index.

7. The DTP location-based wireless fitness activity scheduling system according to claim 1, characterized in that: After the user receives the fitness bracelet at the front desk, the activity area matching module automatically connects the bracelet to the scheduling system, identifies the user through the unique identifier of the bracelet, and extracts the user's historical fitness data from the database according to the bracelet ID, including the total time of fitness equipment use, fitness items and fitness frequency in the activity area; Clean the user's historical fitness data, remove outliers or invalid data, classify the data according to fitness items, duration, and frequency dimensions, and form labeled data of the user's fitness behavior.

8. The DTP location-based wireless fitness activity scheduling system according to claim 2, characterized in that: After the activity area matching module obtains the total usage time of the fitness equipment in the activity area, the total usage time of the fitness equipment is compared with a preset time threshold. If the total usage time of the fitness equipment in the activity area is greater than or equal to the time threshold, the activity area is marked as a matching activity area. If the total usage time of the fitness equipment in the activity area is less than the time threshold, the activity area is not marked, and an acquisition area set is established for all matching activity areas of the user.

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