Sports equipment matching recommendation system for intelligent fitness bus station

Abstract

The invention relates to the technical field of intelligent fitness bus stations, and particularly discloses a sports equipment matching recommendation system for an intelligent fitness bus station. According to the method, the duration constraint influence factor is calculated according to the predicted waiting duration of the target passenger and the effective exercise duration of each to-be-analyzed sports apparatus, and the time limit of the passenger staying at the bus station is fully considered, so that the recommended apparatus can achieve a certain exercise effect within the time range acceptable by the passenger, and the user experience is improved. The recommendation feasibility and practicability are improved, the final matching recommendation degree is calculated according to the basic matching recommendation degree and the duration constraint influence factor, the recommendation result is further optimized on the basis of comprehensively considering the physiological features, the exercise preference and the time constraint, the finally recommended exercise equipment is made to better meet the actual requirements and conditions of passengers, and the user experience is improved. Sports equipment selection more conforming to the overall condition is provided for passengers, the satisfaction degree of the passengers on recommendation results is improved, and the willingness of the passengers to use the sports equipment of the intelligent fitness bus station is enhanced.

Description

Technical Field

[0001] This invention relates to the field of intelligent fitness bus station technology, specifically to a sports equipment matching and recommendation system for intelligent fitness bus stations. Background Technology

[0002] With the acceleration of urbanization and the increasingly fast pace of life, the effective utilization of fragmented time has become an important issue for improving quality of life. Bus stops, as key nodes in urban transportation networks, handle a large volume of passengers daily, and the diversified utilization of their space resources is gradually attracting attention. At the same time, the concept of national fitness is gaining popularity, and people's demand for exercise and fitness is constantly growing, but they face practical problems such as time constraints and a lack of fitness venues. Against this backdrop, smart fitness bus stops have emerged, organically combining traditional bus stops with fitness facilities, providing citizens with a new scenario for exercising during their commutes.

[0003] Existing systems rely on a limited set of methods to identify the status of fitness equipment. They typically use simple sensors to determine whether the equipment is occupied, but cannot accurately identify equipment malfunctions. This can lead to users using equipment with potential safety hazards, affecting their user experience and even causing safety accidents.

[0004] Traditional fitness equipment recommendations often rely on simple classifications or subjective user choices, lacking a comprehensive consideration of the user's physiological characteristics. For example, they fail to adequately consider key indicators such as age, body mass index (BMI), and body fat percentage, leading to recommended equipment that may be incompatible with the user's physical condition. This not only affects training effectiveness but may also increase the risk of sports injuries. Furthermore, the analysis of user exercise preferences is often superficial, failing to accurately grasp the user's exercise goals (such as fat loss, muscle gain, rehabilitation, etc.) and desired training areas (such as chest, legs, core, etc.), making personalized recommendations difficult.

[0005] In scenarios like bus stops, users' dwell time is often limited by bus schedules. However, existing recommendation systems rarely consider users' time constraints, potentially recommending equipment that requires a longer workout time. This can prevent users from achieving their desired workout results within the limited time, reducing the practicality of the recommendations and user satisfaction.

[0006] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0007] The purpose of this invention is to provide a sports equipment matching and recommendation system for smart fitness bus stations, in order to solve the problems mentioned in the background.

[0008] The objective of this invention can be achieved through the following technical solution: a sports equipment matching and recommendation system for smart fitness bus stations, comprising: The exercise equipment status recognition module is used to obtain the types of exercise equipment in the target smart fitness bus station, obtain various types of exercise equipment in the target smart fitness bus station, identify the current available status of various types of exercise equipment in the target smart fitness bus station, including idle status, occupied status and fault status, and mark the exercise equipment corresponding to each idle status as marked exercise equipment, thereby obtaining the marked exercise equipment of the target smart fitness bus station. The passenger motion information collection module is used to collect motion-related information of the target passenger; The module for determining the exercise equipment to be analyzed is used to determine the various exercise equipment to be analyzed in the target smart fitness bus station based on the exercise contraindications of the target passengers. The passenger physiological characteristic matching module is used to analyze the physiological characteristic matching index between the target passenger and the sports equipment to be analyzed based on the basic physiological characteristic information of the target passenger. The passenger exercise preference analysis module is used to extract the exercise experience tags of target passengers from the repository and analyze the degree of preference of target passengers with each exercise equipment to be analyzed, so as to obtain the evaluation index of the degree of preference of target passengers with each exercise equipment to be analyzed. Among them, the exercise experience tags include first-time exercisers and non-first-time exercisers. The matching recommendation analysis module is used to analyze the basic matching recommendation degree between the target passenger and each piece of sports equipment to be analyzed based on the matching index of the target passenger's physical characteristics and the evaluation index of the preference degree between the target passenger and each piece of sports equipment to be analyzed. The constraint impact analysis module is used to analyze the time constraint impact factors of the target passenger and each piece of sports equipment under analysis based on the time constraints of the target passenger and each piece of sports equipment under analysis. The matching recommendation optimization module is used to analyze the final matching recommendation degree between the target passenger and each sports equipment to be analyzed based on the basic matching recommendation degree and the influence factor of time constraint. The intelligent recommendation terminal is used to recommend corresponding exercise methods and rankings of various matched exercise equipment. The database stores the excluded exercise equipment corresponding to each exercise contraindication, the applicable age range of various types of exercise equipment in the target smart fitness bus station, the body type value corresponding to each body type, the index range of various applicable body requirements for various types of exercise equipment in the target smart fitness bus station, the association mapping table between various types of exercise equipment and exercise targets and desired training parts, and the exercise methods of various types of exercise equipment.

[0009] The beneficial effects of this invention are: This invention analyzes the current availability of various exercise equipment at the target smart fitness bus station, providing a reliable basis for subsequently identifying available equipment for analysis. This avoids recommending unusable or unsafe equipment to passengers. Furthermore, it determines the equipment to be analyzed based on the target passengers' exercise contraindications, fully considering their specific physical conditions and avoiding recommending unsuitable equipment. This ensures the scientific and safe nature of the exercise, and to a certain extent avoids unreasonable matching recommendations while improving the passenger's exercise experience and effectiveness. This invention analyzes the physiological characteristic matching index between target passengers and the exercise equipment under analysis from multiple aspects, including age, body mass index, body fat percentage index, body type, and various physical indicators. It comprehensively considers the compatibility between passengers' physiological characteristics and equipment suitability requirements, providing a scientific physiological basis for equipment recommendations. This helps passengers achieve better results during exercise while reducing the risk of injury due to unsuitable equipment. Furthermore, it accurately grasps passengers' preferences for exercise equipment, whether they are first-time users or not, helping to recommend equipment that matches their interests and habits, thus improving their exercise enthusiasm and satisfaction. Finally, it calculates a basic matching recommendation degree based on the physiological characteristic matching index and preference assessment index between the target passenger and each piece of exercise equipment under analysis. By comprehensively considering two important factors—physiological characteristics and exercise preferences—the basic matching recommendation degree more comprehensively reflects the degree of matching between passengers and equipment, providing a more accurate basis for subsequent recommendations. This invention calculates a duration constraint influencing factor based on the target passenger's expected waiting time and the effective exercise time of each piece of exercise equipment to be analyzed. This fully considers the time constraints passengers face at bus stops, ensuring that the recommended equipment can achieve a certain exercise effect within an acceptable timeframe for passengers, thus improving the feasibility and practicality of the recommendations. The final matching recommendation degree is calculated based on the basic matching recommendation degree and the duration constraint influencing factor. By comprehensively considering physiological characteristics, exercise preferences, and time constraints, the recommendation results are further optimized, making the final recommended exercise equipment more aligned with the actual needs and conditions of passengers. This provides passengers with exercise equipment choices that better suit their overall situation, increasing passenger satisfaction with the recommendation results and enhancing their willingness to use the exercise equipment at smart fitness bus stops. Attached Figure Description

[0010] The invention will now be further described with reference to the accompanying drawings.

[0011] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a logical schematic diagram of the sports equipment status recognition module of the present invention; Figure 3This is a logical diagram of the passenger motion preference analysis module of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Please see Figures 1-3 As shown, this invention is a sports equipment matching and recommendation system for intelligent fitness bus stations, comprising: a sports equipment status recognition module, a passenger exercise information collection module, a sports equipment determination module, a passenger physiological characteristic matching module, a passenger exercise preference analysis module, a matching recommendation analysis module, a constraint influence analysis module, a matching recommendation optimization module, an intelligent recommendation terminal, and a database. The modules are connected as follows: the sports equipment status recognition module and the passenger exercise information collection module are connected to the sports equipment determination module; the passenger exercise information collection module is connected to the passenger physiological characteristic matching module and the passenger exercise preference analysis module; the sports equipment determination module is connected to the passenger physiological characteristic matching module and the passenger exercise preference analysis module; the passenger physiological characteristic matching module and the passenger exercise preference analysis module are connected to the matching recommendation analysis module; the matching recommendation analysis module is connected to the constraint influence analysis module; the constraint influence analysis module is connected to the matching recommendation optimization module; the matching recommendation optimization module is connected to the intelligent recommendation terminal; and the database is connected to the sports equipment determination module, the passenger physiological characteristic matching module, the passenger exercise preference analysis module, and the intelligent recommendation terminal.

[0014] The exercise equipment status recognition module is used to obtain the types of exercise equipment in the target smart fitness bus station, obtain various types of exercise equipment in the target smart fitness bus station, identify the current available status of various types of exercise equipment in the target smart fitness bus station, including idle status, occupied status and fault status, and mark the exercise equipment corresponding to each idle status as marked exercise equipment, thereby obtaining the marked exercise equipment of the target smart fitness bus station. It should be noted that the various types of exercise equipment at the target smart fitness bus station include, but are not limited to: smart reclining exercise bikes, smart bicep curl machines, smart lateral flexion machines, smart chest and back machines, and smart leg press machines.

[0015] Specifically, the process of identifying the current availability of various types of exercise equipment at the target smart fitness bus station is as follows: The contact pressure of each detection point of various types of sports equipment is detected by pressure sensors in various types of sports equipment during the current monitoring period. The contact pressure of each detection point of various types of sports equipment during the current monitoring period is obtained, and the average value is calculated. The average value is used as the contact pressure of various types of sports equipment during the current monitoring period. Vibration sensors in various types of sports equipment are used to detect the vibration amplitude at each detection point during the current monitoring period. The detected vibration amplitude values ​​at each detection point are then obtained, and a vibration amplitude waveform for each type of sports equipment during the current monitoring period is constructed. This waveform is then compared with a preset standard vibration amplitude waveform to obtain the overlap length of the vibration amplitude waveforms for each type of sports equipment during the current monitoring period, denoted as . , i represents the number of each type of sports equipment, i=1,2,....,m; m is the maximum value of each type of sports equipment number.

[0016] The sound intensity of various types of sports equipment is detected at each detection point during the current monitoring period using sound sensors. The detected sound intensity values ​​at each detection point are then obtained, and a sound intensity waveform for each type of sports equipment during the current monitoring period is constructed. This waveform is then compared with a preset standard sound intensity waveform to obtain the overlap length of the sound intensity waveforms for each type of sports equipment during the current monitoring period, denoted as . ; Using formula Analysis yielded operational evaluation indicators for various types of sports equipment during the current monitoring period. e represents the natural constant. These represent the overlap lengths of the preset reference vibration amplitude waveform and the reference sound intensity waveform, respectively, the sizes of which can be customized by those skilled in the art based on actual use.

[0017] If the contact pressure of a certain type of sports equipment is greater than the preset contact pressure threshold and the operation evaluation index is less than the preset operation evaluation index threshold during the current monitoring period, then the current available state of that type of sports equipment is determined to be the occupied state. If the contact pressure of a certain type of sports equipment is less than the preset contact pressure threshold and the operation evaluation index is less than the preset operation evaluation index threshold during the current monitoring period, then the current available state of that type of sports equipment is determined to be idle. If the operational evaluation index of a certain type of sports equipment exceeds the preset operational evaluation index threshold during the current monitoring period, the current usability status of that type of sports equipment is determined to be a fault state. This allows us to determine the current availability of various types of exercise equipment at the target smart fitness bus station.

[0018] The passenger motion information collection module is used to collect motion-related information of the target passenger.

[0019] It should be noted that the target passenger's exercise-related information includes: the target passenger's age, gender, height, weight, exercise goals, desired training areas, and exercise contraindications.

[0020] It should be further explained that the target passenger's age, gender, exercise goals, desired training areas, and exercise contraindications are collected by the user information collection terminal of the target smart fitness bus station (such as a touch screen questionnaire interaction), while the target passenger's height and weight are collected by the smart body measurement area of ​​the target smart fitness bus station.

[0021] The module for determining the exercise equipment to be analyzed is used to determine the various exercise equipment to be analyzed in the target smart fitness bus station based on the exercise contraindications of the target passengers. Specifically, the process of determining the various exercise equipment to be analyzed at the target smart fitness bus station is as follows: Read the target passenger's exercise contraindications. If the target passenger has no exercise contraindications, then record each marked exercise equipment at the target smart fitness bus station as an exercise equipment to be analyzed. If the target passenger has exercise contraindications, the content of the target passenger's exercise contraindications is obtained and matched with the excluded exercise equipment corresponding to each exercise contraindication content stored in the database to obtain the excluded exercise equipment of the target passenger. Based on the excluded exercise equipment of the target passenger, each marked exercise equipment of the target smart fitness bus station is excluded to obtain the excluded exercise equipment, which is used as the exercise equipment to be analyzed. This determines the various exercise equipment to be analyzed at the target intelligent fitness bus station.

[0022] It should be noted that the contraindications for exercise include, but are not limited to: exercise contraindications related to characteristic physiological states: such as pregnancy (including early, mid, and late pregnancy), menstruation, postoperative recovery period (including surgical and orthopedic surgery), and fatigue; exercise contraindications related to diseases: musculoskeletal system diseases (including knee joint injuries and arthritis), cardiovascular system diseases (including coronary heart disease and hypertension), respiratory system diseases (including asthma and chronic obstructive pulmonary disease), and metabolic system diseases (including diabetes and gout).

[0023] In one specific embodiment, the present invention analyzes the current availability of various types of exercise equipment at the target smart fitness bus station, providing a reliable basis for subsequently identifying available exercise equipment to be analyzed. This avoids recommending unusable or unsafe equipment to passengers. Furthermore, it determines the exercise equipment to be analyzed based on the target passengers' exercise contraindications, fully considering the passengers' special physical conditions and avoiding recommending equipment unsuitable for them. This ensures the scientific and safe nature of the exercise, and to a certain extent avoids unreasonable matching recommendations and improves the passengers' exercise experience and effectiveness.

[0024] The passenger physiological characteristic matching module is used to analyze the physiological characteristic matching index between the target passenger and the sports equipment to be analyzed based on the basic physiological characteristic information of the target passenger. Specifically, the process of analyzing the physiological characteristic matching index between the target passenger and the exercise equipment to be analyzed is as follows: Obtain the age of the target passenger and record it as follows: The system retrieves the applicable age ranges for various types of exercise equipment at the target smart fitness bus station from the database. It then filters these ranges to obtain the applicable age ranges for each piece of equipment to be analyzed. The average of the upper and lower limits of the applicable age range for each piece of equipment is extracted as the reference applicable age for that equipment, denoted as [reference age range]. j represents the number of each piece of sports equipment to be analyzed, j=1,2,....,n; n is the maximum value of the number of each piece of sports equipment to be analyzed.

[0025] By analyzing the formula The age matching factors between the target passengers and each piece of sports equipment to be analyzed were obtained. , These represent the upper and lower limits of the applicable age range for each piece of sports equipment to be analyzed; Through BMI algorithm The target passenger's body mass index was calculated. , These represent the target passenger's weight and height, respectively. BMI calculation method Calculate the target passenger's body fat percentage assessment index , This is represented as a gender coefficient. When the target passenger's gender is male, the gender coefficient is 1, and when the target passenger's gender is female, the gender coefficient is 0. The target passenger's body mass index is matched with the preset body mass index range corresponding to each BMI level to obtain the target passenger's BMI level, which includes: high BMI level, normal BMI level and low BMI level. The body fat percentage assessment index of the target passenger is matched with the body fat percentage assessment index corresponding to each preset body fat percentage level to obtain the body fat percentage level of the target passenger. The body fat percentage levels include: high body fat percentage, normal body fat percentage and low body fat percentage. The target passenger's body type is analyzed by combining the target passenger's BMI level and body fat percentage level, and then matched with the corresponding body type values ​​stored in the database to obtain the target passenger's body type value; It should be noted that the analysis of the target passenger's body type combines their BMI and body fat percentage levels. The specific analysis steps are as follows: If the target passenger has low body fat and low BMI, the target passenger's body type is determined to be lean; if the target passenger has low body fat and normal BMI, the target passenger's body type is determined to be lean; if the target passenger has low body fat and high BMI, the target passenger's body type is determined to be muscular. If the target passenger has normal body fat percentage and low BMI, the target passenger's body type is determined to be slender; if the target passenger has normal body fat percentage and normal BMI, the target passenger's body type is determined to be standard; if the target passenger has normal body fat percentage and high BMI, the target passenger's body type is determined to be overweight. If the target passenger has high body fat percentage and low BMI, the target passenger's body type is determined to be weak; if the target passenger has positive high body fat percentage and normal BMI, the target passenger's body type is determined to be hidden obesity; if the target passenger has high body fat percentage and high BMI, the target passenger's body type is determined to be obese.

[0026] Based on the target passenger's height, weight, and body type, various physical indicators are constructed. The applicable physical requirement ranges for different types of exercise equipment at the target smart fitness bus station are retrieved from the database. These ranges are then filtered to obtain the applicable physical requirement ranges for each piece of exercise equipment to be analyzed. The target passenger's physical indicators are compared with the applicable physical requirement ranges for each piece of exercise equipment to be analyzed, and the deviation between the target passenger's and the indicators of each piece of exercise equipment is recorded as follows: ; It should be noted that when a target passenger's physical indicator falls within the range of applicable physical requirements for a particular piece of exercise equipment, the deviation between the target passenger and that indicator is 0. When a target passenger's physical indicator is not within the range of applicable physical requirements for that piece of exercise equipment but is greater than the maximum value of the range, the absolute value of the difference between the target passenger's physical indicator and the maximum value of the range is taken to obtain the deviation between the target passenger and that indicator. When a target passenger's physical indicator is not within the range of applicable physical requirements for that piece of exercise equipment but is less than the minimum value of the range, the absolute value of the difference between the target passenger's physical indicator and the minimum value of the range is taken to obtain the deviation between the target passenger and that indicator.

[0027] By analyzing the formula The matching factors of the target passenger's physical indicators with the various exercise equipment to be analyzed were obtained. ; The age matching factor and physical indicator matching factor of the target passenger and each piece of sports equipment to be analyzed are summed to obtain the physiological characteristic matching index of the target passenger and each piece of sports equipment to be analyzed.

[0028] The passenger exercise preference analysis module is used to extract the exercise experience tags of target passengers from the database and analyze the degree of preference of target passengers with each exercise equipment to be analyzed, so as to obtain the evaluation index of the degree of preference of target passengers with each exercise equipment to be analyzed. Among them, the exercise experience tags include first-time exercisers and non-first-time exercisers. Specifically, the steps for analyzing the preference of target passengers for each piece of sports equipment to be analyzed are as follows: If the target passenger is a first-time exerciser, obtain the target passenger's exercise goals and desired training areas. The exercise goals include, but are not limited to: fat loss, muscle gain, rehabilitation and body shaping. The desired training areas include, but are not limited to: chest, legs, core and whole body. At the same time, extract the association mapping table between various types of exercise equipment and exercise goals and desired training areas stored in the database, and then filter to obtain the association mapping table between each type of exercise equipment to be analyzed and exercise goals and desired training areas. The target passenger's exercise goals and desired training areas are compared and analyzed with the correlation mapping table between each exercise equipment and the exercise goals and desired training areas. If the target passenger's movement target and desired training part match the mapping table of the movement target and desired training part of the exercise equipment to be analyzed, then a movement target matching signal and a desired training part matching signal between the target passenger and the exercise equipment to be analyzed are generated. If the target passenger's motion target and desired training part only have a motion target match in the association mapping table between a certain exercise equipment to be analyzed and the motion target and desired training part, then a motion target matching signal between the target passenger and the exercise equipment to be analyzed is generated. If the target passenger's movement target and desired training part are matched with the correlation mapping table of a certain exercise equipment to be analyzed and its movement target and desired training part, then a matching signal between the target passenger and the desired training part of the exercise equipment to be analyzed is generated. If there is no match between the target passenger's motion target and desired training part and the association mapping table between a certain exercise equipment to be analyzed and its motion target and desired training part, then a motion target mismatch signal and a desired training part mismatch signal between the target passenger and the exercise equipment to be analyzed are generated. When generating a moving target matching signal, the moving target matching degree is assigned as follows: When a moving target mismatch signal is generated, the moving target matching degree is assigned as follows: When generating the desired training region matching signal, the desired training region matching degree is assigned as follows: When a mismatch signal is generated for the desired training region, the desired training region is assigned a matching degree of 1. This allows us to statistically determine the matching degree between the target passenger and the motion target of each piece of exercise equipment to be analyzed. Matching degree with desired training area , , ; According to the formula The preference index between the target passenger and each piece of sports equipment to be analyzed was calculated as follows: , These represent the weighting factors of the preference evaluation index corresponding to the preset matching degree of the exercise target and the matching degree of the expected training part, respectively; It should be noted that the weighting factors of the preference evaluation index corresponding to the preset matching degree of the exercise target and the matching degree of the expected training part can be set to 0.6 and 0.4, respectively.

[0029] Exercise goals are the "underlying logic" of a user's fitness journey (e.g., a user aiming to "lose fat" chooses an elliptical machine, while a user aiming to "gain muscle" chooses a dumbbell rack). This directly determines whether the equipment's functions match the user's goals. If the goals don't match, it's meaningless no matter how suitable the equipment is for the training area. Therefore, the weight factor for the preference assessment index corresponding to the exercise goal matching degree is assigned a value of 0.6. Training areas are a "refined supplement" to the goals (e.g., a user aiming to "lose fat" may focus more on burning fat in the "legs" or may accept "full-body" training). This does not affect the matching of core functions but can improve the accuracy of recommendations. Therefore, the weight factor for the preference assessment index corresponding to the expected training area matching degree is assigned a value of 0.4.

[0030] If the target passenger is not a first-time exerciser, obtain the target passenger's historical exercise records at the target smart fitness bus station, and obtain the number of times the target passenger exercised with each exercise equipment to be analyzed, the duration of each exercise, and the duration of the interval between each two adjacent exercise sessions. Calculate the average exercise duration by averaging the duration of each exercise session, and calculate the average exercise interval by averaging the duration of each two adjacent exercise sessions. Extract the number of movements, average movement duration, and average movement interval of the target passenger and each piece of exercise equipment to be analyzed, and record them as follows: Normalize the result and take its value, then substitute it into the preset Softplus function formula. The preference index between the target passenger and each piece of exercise equipment to be analyzed was calculated. ; This yields the preference index between the target passengers and each piece of sports equipment to be analyzed. , .

[0031] The matching recommendation analysis module is used to analyze the basic matching recommendation degree between the target passenger and each piece of sports equipment to be analyzed based on the matching index and preference evaluation index of the target passenger's physical characteristics and the sports equipment to be analyzed.

[0032] Specifically, the basic matching degree between the target passenger and each exercise device to be analyzed includes: Extract the body characteristic matching index and preference evaluation index between the target passenger and each piece of exercise equipment to be analyzed, and substitute them into the preset hyperbolic tangent function formula. The basic matching recommendation degree between the target passenger and each piece of sports equipment to be analyzed was calculated. , This is represented as the matching index of the target passenger's physical characteristics with each piece of sports equipment to be analyzed.

[0033] In one specific embodiment, this invention analyzes the physiological characteristic matching index between the target passenger and the analyzed exercise equipment in detail from multiple aspects such as age, body mass index, body fat percentage index, body type, and various physical indicators. This comprehensive consideration of the compatibility between the passenger's physiological characteristics and the equipment's suitability requirements provides a scientific physiological basis for equipment recommendations, helping passengers achieve better results during exercise while reducing the risk of physical injury due to unsuitable equipment. Furthermore, it accurately grasps the passenger's preference for exercise equipment, whether for first-time or non-first-time users, helping to recommend equipment that matches their interests and habits, thus improving passenger motivation and satisfaction. Finally, based on the physiological characteristic matching index and preference assessment index between the target passenger and each piece of exercise equipment, a basic matching recommendation degree is calculated. This comprehensive consideration of both physiological characteristics and exercise preferences allows the basic matching recommendation degree to more fully reflect the matching degree between the passenger and the equipment, providing a more accurate basis for subsequent recommendations.

[0034] The constraint impact analysis module is used to analyze the time constraint impact factors of the target passenger and each piece of sports equipment under analysis based on the time constraints of the target passenger and each piece of sports equipment under analysis.

[0035] Specifically, the process of analyzing the duration constraint influencing factors of the target passengers and each piece of sports equipment to be analyzed is as follows: Through the touchscreen query terminals deployed at the smart fitness bus station, based on the target passenger's input of the bus route and current stop, the estimated arrival time of the target passenger's bus route is obtained. The difference between this estimated arrival time and the current time is calculated to obtain the target passenger's estimated waiting time, denoted as . ; Historical exercise records of various types of exercise equipment in the target smart fitness bus station are extracted, and the gender of passengers for each exercise session is obtained. The data for each exercise session with male and female passengers are integrated separately to obtain the number of exercise sessions for each type of exercise equipment with male passengers and the number of exercise sessions for each type of exercise equipment with female passengers. The exercise duration when the calories burned in each exercise session with male passengers and the exercise duration when the calories burned in each exercise session with female passengers reach the preset calorie consumption threshold are extracted. The average exercise duration when the calories burned in each exercise session with male passengers and the exercise duration when the calories burned in each exercise session with female passengers reach the preset calorie consumption threshold are calculated. These average exercise durations are then used as the effective exercise duration for each type of exercise equipment with male passengers and female passengers, respectively. Based on the target passenger's gender, the effective exercise time for male passengers on each type of exercise equipment to be analyzed, and the effective exercise time for female passengers on each type of exercise equipment, the effective exercise time for the target passenger and each type of exercise equipment to be analyzed is obtained and denoted as . ; According to the formula Calculate the duration constraint influence factors for the target passenger and each piece of sports equipment to be analyzed. .

[0036] The matching recommendation optimization module is used to analyze the final matching recommendation degree between the target passenger and each sports equipment to be analyzed, based on the basic matching recommendation degree between the target passenger and each sports equipment to be analyzed and the influence factor of time constraint.

[0037] Specifically, the process of analyzing the final matching recommendation rate between the target passenger and each exercise device to be analyzed is as follows: Extract the basic matching recommendation degree and constraint influencing factors of each exercise device to be analyzed between the target passenger and the target smart fitness bus station, and then use the formula... The final matching recommendation rate between the target passenger and each exercise device to be analyzed at the target smart fitness bus station was calculated. .

[0038] In one specific embodiment, the present invention calculates a duration constraint influencing factor based on the target passenger's expected waiting time and the effective exercise time of each piece of exercise equipment to be analyzed. This fully considers the time limit for passengers staying at the bus station, ensuring that the recommended equipment can achieve a certain exercise effect within an acceptable time range for passengers, thus improving the feasibility and practicality of the recommendations. The final matching recommendation degree is calculated based on the basic matching recommendation degree and the duration constraint influencing factor. By comprehensively considering physiological characteristics, exercise preferences, and time constraints, the recommendation results are further optimized, making the final recommended exercise equipment more in line with the actual needs and conditions of passengers. This provides passengers with exercise equipment choices that better suit their overall situation, improves passenger satisfaction with the recommendation results, and enhances passengers' willingness to use the exercise equipment at the smart fitness bus station.

[0039] The intelligent recommendation terminal is used to recommend corresponding exercise methods and rankings of various matched exercise equipment.

[0040] Specifically, the ranking of the recommended sports equipment is obtained as follows: The final matching recommendation score between the target passenger and various types of exercise equipment at the target smart fitness bus station is compared with a preset matching recommendation score threshold. If the final matching recommendation score between the target passenger and a certain type of exercise equipment at the target smart fitness bus station is greater than or equal to the preset matching recommendation score threshold, then that type of exercise equipment is determined to be a match recommendation. In this way, each matched recommended exercise equipment is obtained, and the matching recommendation scores of each matched recommended exercise equipment are sorted in descending order to obtain the recommendation ranking of each matched recommended exercise equipment.

[0041] The database stores the excluded exercise equipment corresponding to each exercise contraindication, the applicable age range of various types of exercise equipment in the target smart fitness bus station, the body type value corresponding to each body type, the index range of various applicable body requirements for various types of exercise equipment in the target smart fitness bus station, the association mapping table between various types of exercise equipment and exercise targets and desired training parts, and the exercise methods of various types of exercise equipment.

[0042] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A system for matching and recommending exercise equipment for smart fitness bus stops, characterized in that, include: The exercise equipment status recognition module is used to obtain the types of exercise equipment in the target smart fitness bus station, obtain various types of exercise equipment in the target smart fitness bus station, identify the current available status of various types of exercise equipment in the target smart fitness bus station, including idle status, occupied status and fault status, and mark the exercise equipment corresponding to each idle status as marked exercise equipment, thereby obtaining the marked exercise equipment of the target smart fitness bus station. The module for determining the exercise equipment to be analyzed is used to determine the various exercise equipment to be analyzed in the target smart fitness bus station based on the exercise contraindications of the target passengers. The passenger physiological characteristic matching module is used to analyze the physiological characteristic matching index between the target passenger and the sports equipment to be analyzed based on the basic physiological characteristic information of the target passenger. The passenger exercise preference analysis module is used to extract the exercise experience tags of target passengers from the repository and analyze the degree of preference of target passengers with each exercise equipment to be analyzed, so as to obtain the evaluation index of the degree of preference of target passengers with each exercise equipment to be analyzed. Among them, the exercise experience tags include first-time exercisers and non-first-time exercisers. The matching recommendation analysis module is used to analyze the basic matching recommendation degree between the target passenger and each piece of sports equipment to be analyzed based on the matching index of the target passenger's physical characteristics and the evaluation index of the preference degree between the target passenger and each piece of sports equipment to be analyzed. The constraint impact analysis module is used to analyze the time constraint impact factors of the target passenger and each piece of sports equipment under analysis based on the time constraints of the target passenger and each piece of sports equipment under analysis. The matching recommendation optimization module is used to analyze the final matching recommendation degree between the target passenger and each sports equipment to be analyzed, based on the basic matching recommendation degree between the target passenger and each sports equipment to be analyzed and the influence factor of time constraint.

2. The exercise equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, Also includes: The passenger motion information collection module is used to collect motion-related information of the target passenger; The intelligent recommendation terminal is used to recommend corresponding exercise methods and rankings of various matched exercise equipment. The database stores the excluded exercise equipment corresponding to each exercise contraindication, the applicable age range of various types of exercise equipment in the target smart fitness bus station, the body type value corresponding to each body type, the index range of various applicable body requirements for various types of exercise equipment in the target smart fitness bus station, the association mapping table between various types of exercise equipment and exercise targets and desired training parts, and the exercise methods of various types of exercise equipment.

3. The exercise equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, The specific process for identifying the current availability of various types of exercise equipment at the target smart fitness bus station is as follows: The contact pressure of each detection point of various types of sports equipment is detected by pressure sensors in various types of sports equipment during the current monitoring period. The contact pressure of each detection point of various types of sports equipment during the current monitoring period is obtained, and the average value is calculated. The average value is used as the contact pressure of various types of sports equipment during the current monitoring period. Vibration sensors in various types of sports equipment are used to detect the vibration amplitude at each detection point during the current monitoring period. The vibration amplitude values ​​at each detection point during the current monitoring period are obtained, and a vibration amplitude waveform of various types of sports equipment during the current monitoring period is constructed. This waveform is then compared with a preset standard vibration amplitude waveform to obtain the overlap length of the vibration amplitude waveform of various types of sports equipment during the current monitoring period. The sound intensity of various types of sports equipment is detected at each detection point during the current monitoring period by sound sensors in various types of sports equipment. The sound intensity detection values ​​of each detection point during the current monitoring period are obtained, and the sound intensity waveform of various types of sports equipment during the current monitoring period is constructed. The waveform is then compared with the preset standard sound intensity waveform to obtain the overlap length of the sound intensity waveform of various types of sports equipment during the current monitoring period. Based on the overlap length analysis of vibration amplitude waveform and sound intensity waveform, the operational evaluation indicators of various types of sports equipment during the current monitoring period are obtained. If the contact pressure of a certain type of sports equipment is greater than the preset contact pressure threshold and the operation evaluation index is less than the preset operation evaluation index threshold during the current monitoring period, then the current available state of that type of sports equipment is determined to be the occupied state. If the contact pressure of a certain type of sports equipment is less than the preset contact pressure threshold and the operation evaluation index is less than the preset operation evaluation index threshold during the current monitoring period, then the current available state of that type of sports equipment is determined to be idle. If the operational evaluation index of a certain type of sports equipment exceeds the preset operational evaluation index threshold during the current monitoring period, the current usability status of that type of sports equipment is determined to be a fault state. This allows us to determine the current availability of various types of exercise equipment at the target smart fitness bus station.

4. The exercise equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, The process of determining the various exercise equipment to be analyzed at the target smart fitness bus station is as follows: Read the target passenger's exercise contraindications. If the target passenger has no exercise contraindications, then record each marked exercise equipment at the target smart fitness bus station as an exercise equipment to be analyzed. If the target passenger has exercise contraindications, the content of the target passenger's exercise contraindications is obtained and matched with the excluded exercise equipment corresponding to each exercise contraindication content stored in the database to obtain the excluded exercise equipment of the target passenger. Based on the excluded exercise equipment of the target passenger, each marked exercise equipment of the target smart fitness bus station is excluded to obtain the excluded exercise equipment, which is used as the exercise equipment to be analyzed. This determines the various exercise equipment to be analyzed at the target intelligent fitness bus station.

5. The exercise equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, The process of analyzing the physiological characteristic matching index between the target passenger and the sports equipment to be analyzed is as follows: The target passenger's age is obtained, and the applicable age range of various types of exercise equipment at the target smart fitness bus station is retrieved from the database. Then, the applicable age range of each piece of exercise equipment to be analyzed is obtained, and the average of the upper and lower limits of the applicable age range of each piece of exercise equipment to be analyzed is extracted as the reference applicable age of each piece of exercise equipment to be analyzed. The age matching factor between the target passenger and each piece of exercise equipment to be analyzed is obtained through analysis. The body mass index of the target passenger is calculated using the BMI algorithm; The body fat percentage assessment index of the target passenger was calculated using the BMI calculation method; The target passenger's body mass index is matched with the preset body mass index range corresponding to each BMI level to obtain the target passenger's BMI level, which includes: high BMI level, normal BMI level and low BMI level. The body fat percentage assessment index of the target passenger is matched with the body fat percentage assessment index corresponding to each preset body fat percentage level to obtain the body fat percentage level of the target passenger. The body fat percentage levels include: high body fat percentage, normal body fat percentage and low body fat percentage. The target passenger's body type is analyzed by combining the target passenger's BMI level and body fat percentage level, and then matched with the corresponding body type values ​​stored in the database to obtain the target passenger's body type value; Based on the target passenger's height, weight, and body type, various physical indicators of the target passenger are constructed. The applicable physical requirements of various types of sports equipment at the target smart fitness bus station are retrieved from the database. Then, the applicable physical requirements of each piece of sports equipment to be analyzed are selected. The target passenger's various physical indicators are compared with the applicable physical requirements of each piece of sports equipment to be analyzed to obtain the deviation between the target passenger and each piece of sports equipment to be analyzed. The corresponding analysis is then performed to obtain the matching factor of the target passenger's physical indicators with each piece of sports equipment to be analyzed. The age matching factor and physical indicator matching factor of the target passenger and each piece of sports equipment to be analyzed are summed to obtain the physiological characteristic matching index of the target passenger and each piece of sports equipment to be analyzed.

6. The exercise equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, The steps for analyzing the preference of target passengers for each piece of sports equipment to be analyzed are as follows: If the target passenger is a first-time sports user, obtain the target passenger's sports goals and desired training areas. At the same time, extract the association mapping tables between various types of sports equipment and sports goals and desired training areas stored in the database, and then filter to obtain the association mapping tables between each sports equipment to be analyzed and sports goals and desired training areas. The target passenger's movement target and desired training body part are compared and analyzed with the association mapping table between each exercise equipment to be analyzed and the movement target and desired training body part, generating signal types between the target passenger and each exercise equipment to be analyzed. The signal types include: movement target matching signal, desired training body part matching signal, movement target mismatch signal, and desired training body part mismatch signal. When a motion target matching signal is generated, a motion target matching degree is assigned; when a motion target mismatch signal is generated, a motion target matching degree is assigned; when a desired training part matching signal is generated, a desired training part matching degree is assigned; when a desired training part mismatch signal is generated, a desired training part matching degree is assigned. Thus, the motion target matching degree and desired training part matching degree between the target passenger and each motion equipment to be analyzed are statistically obtained. Calculate the preference index between the target passengers and each piece of sports equipment to be analyzed; If the target passenger is not a first-time exerciser, obtain the target passenger's historical exercise records at the target smart fitness bus station, and obtain the number of times the target passenger exercised with each exercise equipment to be analyzed, the duration of each exercise, and the duration of the interval between each two adjacent exercise sessions. Calculate the average exercise duration by averaging the duration of each exercise session, and calculate the average exercise interval by averaging the duration of each two adjacent exercise sessions. Extract the number of times, average duration, and average interval between the target passenger and each piece of exercise equipment to be analyzed, normalize them, and take the values. Substitute these values ​​into the preset Softplus function formula to calculate the preference evaluation index between the target passenger and each piece of exercise equipment to be analyzed. This yields an evaluation index of the target passengers' preference for each piece of exercise equipment to be analyzed.

7. The exercise equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, The basic matching degree between the target passenger and each exercise device to be analyzed includes: Extract the body characteristic matching index and preference evaluation index between the target passenger and each piece of sports equipment to be analyzed, and substitute them into the preset hyperbolic tangent function formula to calculate the basic matching recommendation degree between the target passenger and each piece of sports equipment to be analyzed.

8. The exercise equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, The process of analyzing the influence factors of the duration constraints on the target passengers and each piece of sports equipment to be analyzed is as follows: By using the touch screen query terminal deployed at the smart fitness bus station, the system obtains the expected arrival time of the target passenger's bus route based on the bus route and current stop entered by the target passenger, and calculates the difference between the estimated arrival time and the current time to obtain the expected waiting time of the target passenger. Historical exercise records of various types of exercise equipment in the target smart fitness bus station are extracted, and the gender of passengers for each exercise session is obtained. The data for each exercise session with male and female passengers are integrated separately to obtain the number of exercise sessions for each type of exercise equipment with male passengers and the number of exercise sessions for each type of exercise equipment with female passengers. The exercise duration when the calories burned in each exercise session with male passengers and the exercise duration when the calories burned in each exercise session with female passengers reach the preset calorie consumption threshold are extracted. The average exercise duration when the calories burned in each exercise session with male passengers and the exercise duration when the calories burned in each exercise session with female passengers reach the preset calorie consumption threshold are calculated. These average exercise durations are then used as the effective exercise duration for each type of exercise equipment with male passengers and female passengers, respectively. Based on the gender of the target passenger, the effective exercise time of male passengers and female passengers for each type of exercise equipment to be analyzed, the effective exercise time of the target passenger and each type of exercise equipment to be analyzed is obtained. The duration constraint influence factors of the target passenger and each piece of sports equipment to be analyzed are calculated based on the formula.

9. A sports equipment matching and recommendation system for smart fitness bus stations according to claim 1, characterized in that, The process of analyzing the final matching recommendation rate between the target passenger and each exercise device to be analyzed is as follows: The basic matching recommendation degree and constraint influencing factors of each exercise equipment to be analyzed between the target passenger and the target smart fitness bus station are extracted, and the final matching recommendation degree between the target passenger and each exercise equipment to be analyzed between the target passenger and the target smart fitness bus station is calculated by formula.

10. A sports equipment matching and recommendation system for smart fitness bus stations according to claim 2, characterized in that, The ranking of the recommended sports equipment is obtained as follows: The final matching recommendation score between the target passenger and various types of exercise equipment at the target smart fitness bus station is compared with a preset matching recommendation score threshold. If the final matching recommendation score between the target passenger and a certain type of exercise equipment at the target smart fitness bus station is greater than or equal to the preset matching recommendation score threshold, then that type of exercise equipment is determined to be a match recommendation. In this way, each matched recommended exercise equipment is obtained, and the matching recommendation scores of each matched recommended exercise equipment are sorted in descending order to obtain the recommendation ranking of each matched recommended exercise equipment.

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