Fitness room resource sharing system based on mobile internet and implementation method thereof

Abstract

The invention relates to a gymnasium resource sharing system based on a mobile internet and an implementation method thereof, and aims to solve the problems that a traditional gymnasium is low in resource allocation efficiency, difficult in user decision making, lack of transaction trust, homogenized in service and the like. According to the invention, a four-in-one framework of intelligent matching, block chain transaction, two-way evaluation and personalized recommendation is constructed; an intelligent matching module is in real-time butt joint with user demands and dynamic resources of a gymnasium through a multi-factor decision model, and supports cross-regional payment permission transfer according to times; the block chain transaction module integrates digital currency payment and an intelligent contract, and realizes fund supervision and automatic settlement in combination with verification of the Internet of Things; the two-way credit evaluation module establishes a user and gymnasium mutual evaluation mechanism, and dynamic credit sub-drive service quality optimization; and the personalized recommendation module predicts a fitness interruption risk based on the LSTM neural network, and generates a customized scheme to activate a long tail demand.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile Internet applications, and particularly to a gym resource sharing system based on the mobile Internet and its implementation method. Background Art

[0002] There are significant pain points in the traditional gym operation model:

[0003] Inefficient resource allocation: The distribution of equipment, group classes, and coach resources is uneven, with overcrowding during peak hours and idle time during non-peak hours coexisting, and the industry average equipment utilization rate is less than 50%;

[0004] Difficult user decision-making: Lack of a scientific matching mechanism, and users rely on word-of-mouth or random selection, resulting in a deviation rate of up to 40% between the service experience and expectations;

[0005] Lack of transaction trust: The prepayment model is prone to financial disputes, the industry complaint rate increases by 15% annually, and the rights and interests of cross-store consumption are difficult to guarantee;

[0006] Severe service homogenization: The standardized courses are out of touch with personalized needs, the user fitness interruption rate exceeds 60%, and the repurchase rate is only 25%.

[0007] Existing technologies mostly focus on optimizing single scenarios. For example, group buying platforms solve the problem of price transparency, but do not address the core contradictions such as dynamic resource scheduling, trust mechanism reconstruction, and service personalization. The present invention constructs a four-in-one resource sharing system by integrating technologies such as big data, blockchain, and AI, and systematically solves the deep pain points of the industry. Summary of the Invention

[0008] In order to solve the problems of the existing technology, the present invention provides a gym resource sharing system based on the mobile Internet and its implementation method.

[0009] In order to solve the above technical problems, the present invention is realized through the following technical solutions: A gym resource sharing system based on the mobile Internet, including:

[0010] Intelligent matching module: Using big data analysis and intelligent algorithms, it collects and processes in real time the data of the gym's address, equipment type, group class arrangement, coach expertise, and the user's historical fitness behavior and preference information, and generates a precise matching plan for users and gyms through a multi-factor decision-making model;

[0011] Blockchain-based transaction module: Integrating a digital currency payment interface, using smart contracts to achieve on-chain supervision and automatic transfer of transaction funds, and combining Internet of Things devices to complete user entry verification and transaction confirmation;

[0012] Two-way Credit Evaluation Module: Build a mutual evaluation mechanism between users and gyms, generate dynamic credit scores based on evaluation data and transaction records, and link the credit scores with platform rights and interests;

[0013] Personalized Recommendation Module: Analyze users' fitness cycles, physical data changes, and social needs through machine learning, and predict and push customized fitness plans.

[0014] In this application, by building an innovative architecture of "intelligent matching - blockchain transaction - two-way evaluation - personalized recommendation", the industry pain points of idle gym resources and inefficient user consumption decisions are systematically solved. Its core functions are reflected in: achieving precise docking of supply and demand through big data analysis, increasing the utilization rate of gym equipment / group classes / coaches and other resources by more than 30%; reconstructing the transaction trust mechanism with the help of blockchain smart contracts, reducing the dispute rate and shortening the settlement cycle; establishing a two-way credit evaluation system to form a service closed-loop, promoting the standardized upgrade of gym services; predicting user behavior through machine learning, activating long-tail demands and increasing the repurchase rate. This solution not only reconstructs the traditional gym operation model, but also provides a technical paradigm for the deep application of the sharing economy in the fitness field, which is expected to effectively improve the industry operation efficiency, increase user satisfaction, and effectively promote the digital transformation of the national fitness industry.

[0015] In a specific implementation manner of the first aspect, the intelligent matching module further includes:

[0016] Data Acquisition Unit: Real-time obtain users' geographical locations, fitness goals, and gym dynamic load data;

[0017] Algorithm Unit: Adopt a decision tree algorithm combined with a collaborative filtering mechanism to generate a multi-dimensional sorted list including distance, equipment matching degree, group class type, and coach evaluation;

[0018] Dynamic Scheduling Unit: Support the pay-per-use permission transfer function for users across regional gyms.

[0019] In a specific implementation manner of the first aspect, the blockchain-based transaction module includes:

[0020] Payment Unit: Support the central bank digital currency (DCEP) and third-party payment interfaces, and use zero-knowledge proof technology to protect user privacy;

[0021] Smart Contract Unit: Set the rules for phased transfer of funds. The funds paid by users enter the supervision account, and the settlement of the gym is completed according to the actual consumption;

[0022] Internet of Things Verification Unit: Read the user's dynamic QR code through the gym entrance gate or NFC device to trigger the on-chain transaction confirmation.

[0023] In a specific implementation manner of the first aspect, the two-way credit evaluation module includes:

[0024] User evaluation unit: providing a 5-level scoring system for environmental hygiene, equipment maintenance, coach professionalism, and course quality;

[0025] Gym feedback unit: recording the user appointment fulfillment rate and the compliance rate of equipment usage specifications;

[0026] Credit score calculation unit: generating a credit score using a weighted algorithm. Users with a credit score higher than the threshold enjoy discount benefits, and high-quality gyms receive platform traffic tilt.

[0027] In a specific implementation manner of the first aspect, the personalized recommendation module includes:

[0028] Data analysis unit: integrating health data from wearable devices, fitness frequency, and social interaction records;

[0029] Model training unit: using an LSTM neural network to predict the user's fitness interruption risk and generate an incentive plan;

[0030] Recommendation unit: dynamically pushing new coaches, courses, or cross-store promotions based on the user profile and the gym resource pool.

[0031] In the second aspect, a computer device for implementing a gym resource sharing system based on mobile Internet according to any one of claims 1-5 includes a memory, a processor, and a computer program stored on the memory and executable on the processor. It is characterized in that when the processor executes the program, the following steps are implemented:

[0032] Receiving a user's fitness demand request and calling the intelligent matching module to generate a list of candidate gyms;

[0033] Completing a blockchain-based transaction according to the payment method selected by the user and the gym smart contract rules;

[0034] After the fitness service is completed, triggering a two-way credit evaluation process;

[0035] Regularly running a personalized recommendation algorithm to update the user's fitness plan.

[0036] In the third aspect, a non-transitory computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the functions of a gym resource sharing system based on mobile Internet.

[0037] The beneficial effects of the present invention are:

[0038] 1. Through the multi-factor decision-making model of the intelligent matching module, combining user behavior data with gym dynamic load data, precise docking of users and gym resources is achieved. The dynamic scheduling unit supports cross-regional pay-per-use permission transfer, effectively revitalizing idle equipment, group classes, and coach resources, and is expected to increase equipment utilization rate by more than 30%. The intelligent contract automatic transfer mechanism of the blockchain-based transaction module shortens the settlement cycle to real-time completion, reducing the capital occupation cost of the gym;

[0039] 2. Blockchain technology is adopted to achieve fund supervision and automatic settlement, protect user privacy through zero-knowledge proof, and ensure transaction authenticity in combination with Internet of Things verification devices. The two-way credit evaluation module establishes a mutual evaluation system between users and gyms, with dynamic credit scores linked to platform rights and interests, forming a closed-loop service quality. Users with credit scores higher than the threshold can enjoy discounts, and high-quality gyms receive traffic tilt, effectively reducing the dispute rate and promoting the standardization upgrade of the industry service;

[0040] 3. The personalized recommendation module predicts the risk of users' fitness interruption through the LSTM neural network, generates customized solutions in combination with wearable device data and social needs, activates long-tail demand, and increases the repurchase rate. The collaborative filtering algorithm of the intelligent matching module combines user portraits to dynamically push cross-store discounts and new coach courses, enhancing user stickiness. The overall system promotes the digital transformation of the national fitness industry, and it is expected that user satisfaction will increase by more than 25%. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic diagram of the system architecture of the present invention.

[0042] Figure 2 is a schematic diagram of the intelligent matching process of the present invention.

[0043] Figure 3 is a schematic diagram of the blockchain transaction of the present invention.

[0044] Figure 4 is a schematic diagram of the principle of the credit evaluation algorithm of the present invention.

[0045] Figure 5 is a schematic diagram of the architecture of the personalized recommendation engine of the present invention.

[0046] Figure 6 is a schematic diagram of the Internet of Things verification time sequence of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] As Figures 1 to 4 shown, a gym resource sharing system based on the mobile Internet and its implementation method.

[0049] Implementation details of the intelligent matching module

[0050] Data collection layer: Collect fitness goals (fat loss / muscle gain) and historical course preference (yoga / HIIT) data through user-side APP logging; access gym Internet of Things sensors to obtain real-time equipment usage rate and group class reservation saturation; call third-party map APIs to obtain user LBS data and gym geographical fence information.

[0051] Algorithm implementation: Adopt an improved XGBoost model to fuse collaborative filtering algorithms. Feature engineering includes:

[0052] User features: Fitness frequency, historical evaluation, social tags

[0053] Gym features: Equipment density (number of equipment per unit area), coach certification level, course popularity index

[0054] Environmental features: Transportation convenience, density of surrounding competitors

[0055] Dynamic scheduling mechanism: When a user selects a gym across regions, the system generates a temporary permission NFT, and records the usage limit through chain metadata to achieve atomic splitting of cross-store rights and interests.

[0056] 2. Implementation details of the blockchain-based transaction module

[0057] Smart contract design: Deploy on the FISCO BCOS consortium chain using Solidity language, and set up fund supervision rules:

[0058] After the user pays, the funds enter a multi-signature account

[0059] After the gym service is completed, it is necessary to upload an Internet of Things verification certificate (user entry timestamp + device usage log hash).

[0060] The funds are automatically transferred according to the ratio of "basic service fee + equipment usage fee + course share"

[0061] Privacy protection solution: Use zk-SNARK technology to generate payment vouchers. Sensitive information such as transaction amount and user ID is encrypted off-chain, and only the validity of the transaction is verified.

[0062] Internet of Things verification process:

[0063] The user APP generates a dynamic QR code (including order ID + timestamp).

[0064] After the gym turnstile is scanned, a verification request is sent to the node.

[0065] If the on-chain verification passes, an entry Token is returned.

[0066] 3. Implementation details of the two-way credit evaluation module

[0067] Scoring dimension weights:

[0068] User evaluation: environmental hygiene (20%), equipment maintenance (15%), coach professionalism (35%), course quality (30%).

[0069] Gym feedback: performance rate (40%), equipment usage specification (30%), social interaction contribution (30%).

[0070] Credit score algorithm: adopt the time decay model:

[0071] CreditScore = Σ(evaluation score × time decay factor^Δt) / total number of valid evaluations.

[0072] Where Δt is the time interval between the evaluation time and the current time (unit: month), and the decay factor is set to 0.95.

[0073] 4. Implementation details of the personalized recommendation module

[0074] Hybrid recommendation engine:

[0075] Content filtering: recommend course difficulty based on the user's fitness stage (beginner / advanced).

[0076] Collaborative filtering: find the high-frequency selections of similar user groups.

[0077] Deep learning: use the LSTM network to predict the probability of fitness interruption in the next 30 days and trigger incentive push.

[0078] Dynamic resource pool construction:

[0079] Real-time crawl of the gym course schedule and coach schedule.

[0080] Combine the user's social graph (gyms frequently visited by friends) to generate a candidate set.

[0081] Sort and push according to the predicted conversion rate.

[0082] 5. System architecture and interaction process

[0083] Technology stack:

[0084] Backend: Spring Cloud microservice architecture, MySQL+Redis database cluster.

[0085] Blockchain: FISCO BCOS node network, WeBASE management platform.

[0086] Front-end: React Native cross-platform APP, integrated with Amap SDK.

[0087] Typical interaction process:

[0088] The user initiates a request for "muscle gain + within 3 kilometers" → intelligent matching returns the top 3 gyms.

[0089] Select a store to complete DCEP payment → generate the transaction hash of the regulatory account.

[0090] After the admission verification, trigger the smart contract → automatically push the service evaluation after completion.

[0091] Update the recommendation model according to the behavior data of this time → push customized courses the next day.

[0092] This implementation method completely presents the realizable path of the functions described in the claims by quantifying key parameters, clarifying technology selection, and refining the interaction process.

[0093] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A gym resource sharing system based on the mobile Internet, characterized in that, Including: Intelligent matching module: Utilize big data analysis and intelligent algorithms to collect and process in real time the address, equipment type, group class schedule, coach expertise data of gyms, as well as the user's historical fitness behaviors and preference information, and generate an accurate matching plan between the user and the gym through a multi-factor decision-making model; Blockchain-based transaction module: Integrate a digital currency payment interface, use smart contracts to achieve on-chain supervision and automatic transfer of transaction funds, and combine with Internet of Things devices to complete user entry verification and transaction confirmation; Two-way credit evaluation module: Build a mutual evaluation mechanism between users and gyms, generate dynamic credit scores based on evaluation data and transaction records, and link the credit scores with platform rights and interests; Personalized recommendation module: Analyze the user's fitness cycle, physical data changes, and social needs through machine learning, and predict and push customized fitness plans.

2. The gym resource sharing system based on the mobile Internet and its implementation method according to claim 1, characterized in that: The intelligent matching module further includes: Data collection unit: Obtain the user's geographical location, fitness goals, and gym dynamic load data in real time; Algorithm unit: Use the decision tree algorithm combined with the collaborative filtering mechanism to generate a multi-dimensional sorted list including distance, equipment matching degree, group class type, and coach evaluation; Dynamic scheduling unit: Support the pay-per-use permission transfer function for users across regional gyms.

3. A gym resource sharing system based on mobile Internet and its implementation method according to claim 1, characterized in that: The blockchain-based transaction module includes: Payment unit: Support the central bank digital currency (DCEP) and third-party payment interfaces, and use zero-knowledge proof technology to protect user privacy; Smart contract unit: Set the rules for phased transfer of funds. The user's paid funds enter the supervision account, and the gym settlement is completed according to the actual consumption; Internet of Things verification unit: Read the user's dynamic QR code through the gym entrance gate or NFC device to trigger on-chain transaction confirmation.

4. A gym resource sharing system based on the mobile Internet according to claim 1, characterized in that: The two-way credit evaluation module includes: User evaluation unit: Provide a 5-level scoring system for environmental hygiene, equipment maintenance, coach professionalism, and course quality; Gym feedback unit: Record the user's appointment fulfillment rate and the compliance rate of equipment usage specifications; Credit score calculation unit: Generate credit scores using a weighted algorithm. Users with credit scores higher than the threshold enjoy discount rights and interests, and high-quality gyms receive platform traffic tilt.

5. The gym resource sharing system based on the mobile Internet according to claim 1, wherein: The personalized recommendation module includes: Data analysis unit: Integrate wearable device health data, fitness frequency, and social interaction records; Model training unit: Use the LSTM neural network to predict the user's fitness interruption risk and generate an incentive plan; Recommendation unit: Based on the user portrait and the gym resource pool, dynamically push new coaches, courses, or cross-store promotions.

6. A computer device for implementing a gym resource sharing system based on the mobile Internet according to any one of claims 1-5, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the following steps are implemented: Receive the user's fitness demand request, and call the intelligent matching module to generate a list of candidate gyms; Complete the blockchain-based transaction according to the user's selected payment method and the gym smart contract rules; After the fitness service is completed, trigger the two-way credit evaluation process; Regularly run the personalized recommendation algorithm to update the user's fitness plan.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it realizes the functions of the system described in claims 1-5.

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