Competition alliance platform management system and method

Abstract

The invention provides a match alliance platform management system and method, and the system comprises a match authentication module constructed by a block chain, an AI-driven qualification auditing module, a process management module, a convolutional neural network, a big data analysis module, and an RBAC authority management module. Wherein the match authentication module dynamically analyzes and stores authenticated match rule data through an intelligent contract, and realizes rule version tracing by using an encrypted hash chain; the AI-driven qualification auditing module integrates a natural language processing engine to analyze the selection standard and construct a multi-dimensional evaluation model, and adopts a machine learning algorithm to analyze historical data of athletes to generate a visual qualification report; the process management module supported by cloud computing automatically generates an application process based on a rule engine, adopts a Monte Carlo algorithm to execute multi-constraint drawing, and constructs a directed graph model to dynamically generate a schedule topological structure containing a three-person rotation time sequence.

Description

Technical Field

[0001] This invention belongs to the field of platform management, and specifically relates to a competition league platform management system and method. Background Technology

[0002] Currently, sports event management systems face systemic technical deficiencies when dealing with innovative competition formats, with bottlenecks in their technical architecture and algorithm design:

[0003] Traditional competition certification systems use centralized databases to store rule data. The rule update process lacks a reliable traceability mechanism, and version control relies on manual maintenance, resulting in incomplete historical change records. Automated verification and conflict detection cannot be achieved when rules are executed. Qualification review systems mostly use rule engines and static evaluation models, which are insufficient in parsing unstructured selection criteria, making it difficult to effectively integrate multidimensional and heterogeneous athlete data. Furthermore, the lack of a dynamic weight adjustment mechanism leads to insufficient objectivity in the evaluation results.

[0004] The scheduling module generally uses linear programming algorithms, which cannot establish accurate time-series dependency models when faced with complex competition systems such as multiple player rotations and dynamic scoring. Resource allocation strategies lack real-time optimization capabilities, and sudden event adjustments can easily lead to cascading scheduling errors. Equipment authentication technology remains at the single coding verification stage and has not been deeply integrated with physical feature recognition technology. Anti-counterfeiting detection lacks a multi-modal data cross-validation mechanism, and the digital credentials generated during the authentication process have not formed a closed-loop management with the event process.

[0005] Data analysis platforms mostly adopt offline batch processing architecture, which cannot meet the tactical analysis needs in terms of the frequency of real-time motion data collection and processing latency. The visualization module is limited to two-dimensional chart display and lacks the ability to integrate spatiotemporal dimensions for three-dimensional modeling. The permission management system adopts a static role division mechanism and fails to combine behavioral pattern analysis to achieve dynamic access control. In multi-party collaboration scenarios, there is a lack of secure and efficient data sharing protocols, and the dispute resolution process suffers from information silos.

[0006] Significant technical heterogeneity exists at the system integration level. There is a lack of a unified communication protocol between blockchain, machine learning and IoT components. Cross-module data interaction faces dual obstacles of format conversion and semantic understanding. Distributed transactions lack atomicity guarantee mechanisms. Cloud computing resource scheduling strategies are rigid and cannot dynamically adjust the load of computing nodes according to the progress of the event. Service response has performance bottlenecks in high-concurrency scenarios. The imperfect disaster recovery mechanism leads to reduced system availability.

[0007] These technical deficiencies collectively result in systemic shortcomings in existing systems regarding rule traceability, scientific qualification evaluation, reasonable event scheduling, reliable equipment certification, real-time data analysis, secure access control, and system scalability, thus hindering the evolution of sports event management towards intelligence and collaboration. Summary of the Invention

[0008] This invention proposes a competition alliance platform management system and method. This system solves the technical defects of traditional competition management systems, such as unreliable rule traceability, low efficiency of qualification review, rigid competition schedule, insufficient accuracy of equipment certification, lagging data analysis, static access control, and poor multi-module collaboration. It realizes full-process digital integration and intelligent decision-making.

[0009] The technical solution of the present invention is implemented as follows: a competition alliance platform management system includes a competition authentication module built on blockchain, an AI-driven qualification review module, a process management module, a convolutional neural network, a big data analysis module, and an RBAC permission management module, wherein the competition authentication module dynamically parses and stores the authenticated competition rule data through smart contracts and uses a cryptographic hash chain to achieve rule version traceability;

[0010] The AI-driven qualification review module integrates a natural language processing engine to analyze selection criteria and build a multi-dimensional evaluation model, and uses machine learning algorithms to analyze athletes' historical data to generate a visual qualification report.

[0011] The cloud-supported process management module automatically generates the registration process based on the rule engine, uses the Monte Carlo algorithm to perform multi-constraint lottery, and builds a directed graph model to dynamically generate the competition schedule topology structure containing the rotation sequence of the three players.

[0012] The equipment authentication module implemented by the convolutional neural network identifies the anti-counterfeiting features of the equipment through a mobile terminal scanning device and binds them to the blockchain evidence storage system with timestamps.

[0013] The big data analysis module collects athletes' movement trajectories and hitting parameters in real time, uses cluster analysis to establish a competition evaluation index system and generate a three-dimensional visualized tactical map;

[0014] The RBAC permission management module, based on attribute encryption, assigns dynamic access credentials to multiple roles and records behavioral trajectories in a distributed ledger. It integrates a secure computing protocol to achieve collaborative decision-making in disputes, communicates through a service mesh architecture, and coordinates workflows based on a unified event bus. Blockchain nodes ensure the transaction consistency of key operations, realizing the full-process digital integration of event certification, qualification review, process management, equipment verification, data analysis, and multi-role collaboration.

[0015] Traditional event management systems rely on centralized databases to store rule data, which poses a single point of failure risk and lacks a reliable traceability mechanism for version changes. This system uses blockchain smart contracts to achieve dynamic parsing and storage of rule data, uses encrypted hash chains to build an immutable version traceability system, and uses a finite state machine model to formally verify complex rules such as three-person rotation, thus overcoming the technical challenges of rule execution conflict detection and accurate historical state backtracking.

[0016] To address the issues of low efficiency and high subjectivity caused by manual verification in qualification review, the system integrates a natural language processing engine to parse unstructured selection criteria, constructs an evaluation model that integrates multi-dimensional features, and uses machine learning algorithms to achieve in-depth mining and dynamic weight allocation of athletes' historical data, breaking through the technical bottleneck that traditional static evaluation systems cannot adapt to the dynamic evolution of selection criteria. In the face of the time sequence conflict problem in the scheduling of competitions under complex competition systems, the process management module automatically generates the registration process under multiple constraints based on the rule engine, uses the Monte Carlo algorithm to simulate the probability distribution to optimize the fairness of the lottery, and combines the directed graph topological sorting algorithm to construct a dynamic competition scheduling model, solving the problem of resource competition and time sequence dependency modeling in multi-person rotation scenarios.

[0017] Traditional equipment authentication relies on single-code verification, which is easily counterfeited. This system extracts the microscopic texture features of anti-counterfeiting marks through convolutional neural networks, integrates mobile terminal optical scanning devices to achieve multimodal feature cross-verification, and binds it with a blockchain evidence storage system for timestamps, overcoming the technical obstacles of integrating physical anti-counterfeiting and digital authentication. Addressing the lack of real-time performance in event data analysis, the system uses a streaming computing pipeline to collect motion trajectories and hitting parameters in real time. It constructs a dynamic evaluation index system through cluster analysis and combines it with a 3D visualization engine to achieve three-dimensional modeling of tactical maps, breaking through the data latency and display dimension limitations of traditional offline batch processing. The access control system extends the RBAC model through attribute encryption, dynamically allocates access credentials and records behavioral trajectories to a distributed ledger, and integrates secure computing protocols to achieve multi-party collaborative decision-making in dispute resolution, solving the problems of rigid security strategies and information silos caused by static role division.

[0018] The system adopts a service mesh architecture to achieve efficient communication between multiple modules, coordinates cross-module workflows based on a unified event bus, and ensures transaction consistency for key operations such as lottery drawing and score entry through lightweight blockchain nodes, thus overcoming the core technical challenges of collaborative operation of heterogeneous technology components and atomicity guarantee of distributed transactions.

[0019] As a preferred implementation, in the blockchain-based event authentication module, the smart contract uses a finite state machine model to formally verify the event rules. The encrypted hash chain uses a Merkle Patricia Trie data structure to construct a version control tree. Each rule update operation generates a state snapshot containing a timestamp and digital signature, and distributed rule synchronization is achieved through lightweight nodes. The smart contract is configured with a rule compliance verification protocol based on zero-knowledge proofs, which is used to verify the legality of the three-person rotation sequence in real time during the event execution.

[0020] As a preferred implementation, in the AI-driven qualification review module, the machine learning algorithm uses a deep residual network to extract features from the athlete's historical data and introduces an attention mechanism to dynamically adjust the weight allocation of the selection criteria. The multi-dimensional evaluation model constructs a feature space mapping layer that includes adversarial sample detection. The visualized qualification report generates a decision heatmap through gradient-weighted class activation mapping and writes the review results to the permissioned chain node for cross-institutional verification through an oracle service.

[0021] As a preferred implementation, in the cloud computing-supported process management module, the Monte Carlo algorithm uses Markov chain Monte Carlo sampling to optimize the probability distribution model under multiple constraints. The directed graph model uses a topological sorting algorithm to parse the temporal dependencies of the three-person rotation and introduces a conflict detection mechanism to eliminate resource competition in the schedule arrangement. The schedule topology structure uses a two-layer graph neural network for load balancing optimization and dynamically adjusts the computing resource allocation strategy of the cloud server in real time.

[0022] In a preferred embodiment, the convolutional neural network-implemented equipment authentication module uses a multi-scale feature pyramid network to extract micro-texture features for anti-counterfeiting mark feature recognition, and integrates a channel attention mechanism to improve the recognition accuracy of local details. The mobile terminal scanning device integrates a polarization light sensing module to obtain the optical properties of the equipment surface. The authentication data is written to a hybrid architecture of edge computing nodes and centralized blockchain using a sharded storage strategy.

[0023] A method for managing a competition league platform, the method comprising the following steps:

[0024] The competition rules data are dynamically parsed and stored through blockchain smart contracts, the rule versions are traced and managed using encrypted hash chains, and the three-person rotation sequence rules are formally verified based on a finite state machine model.

[0025] The selection criteria are analyzed using a natural language processing engine, and a multi-dimensional evaluation model is constructed that includes international rankings, competition results and anti-doping status. The athlete's historical data is analyzed through machine learning algorithms to generate a visual qualification assessment report, which is then publicly verified on the alliance chain node.

[0026] The event registration process is automatically generated based on a rule engine. The Monte Carlo algorithm is used to simulate the probability distribution model under multiple constraints for intelligent lottery. The directed graph topology sorting algorithm is combined to analyze the temporal dependency relationship of the three-person rotation, dynamically generate the event scheduling logic and allocate cloud computing resources.

[0027] The optical features of the anti-counterfeiting marks of the equipment are collected by a mobile terminal scanning device, and micro-texture recognition is performed by a convolutional neural network. The authentication data is timestamped with the blockchain evidence storage system and written to the distributed nodes of the hybrid architecture based on the sharding storage strategy.

[0028] The system collects athletes' movement trajectories and hitting parameters in real time, cleans the data through a streaming computing pipeline, constructs competition performance evaluation indicators using cluster analysis, and generates dynamic tactical maps using a 3D visualization engine.

[0029] The attribute-based RBAC model assigns dynamic access credentials to multiple roles, records operational behavior in a distributed ledger, and realizes collaborative decision-making logic for dispute resolution through a multi-party secure computation protocol. Each step communicates and interacts through a service mesh architecture, coordinates cross-module workflows based on a unified event bus, and uses lightweight blockchain nodes to ensure transaction consistency in lottery, results entry, and equipment certification operations, thus completing closed-loop management from event certification and qualification review to event execution.

[0030] After adopting the above technical solutions, the beneficial effects of this invention are as follows: This system achieves a paradigm shift in the competition management system through the integration of multiple technologies: a blockchain-based smart contract architecture constructs an immutable storage mechanism for rule data, combined with a cryptographic hash chain to achieve full lifecycle traceability of version changes, and formal verification technology ensures the logical completeness of complex competition rules; the AI-driven qualification review system uses natural language processing and deep learning models to work together to achieve semantic parsing and multi-dimensional feature space mapping of unstructured selection standards, and improves the environmental adaptability of the evaluation model through a dynamic weight allocation mechanism;

[0031] The process management module integrates a rule engine and a probabilistic graphical model to establish an optimal decision path generation algorithm under multiple constraints. Based on topological sorting and temporal dependency analysis, it constructs a dynamic optimization framework for competition scheduling, significantly enhancing the robustness of resource scheduling under complex competition systems. The equipment authentication system extracts microscopic physical features through convolutional neural networks and combines multimodal sensor data fusion technology to achieve cross-dimensional verification of anti-counterfeiting labels. It constructs a two-way binding mechanism between physical entities and digital credentials, forming a closed-loop trusted system for anti-counterfeiting authentication.

[0032] The real-time data analysis platform employs a streaming computing architecture to achieve high-frequency acquisition and real-time processing of motion parameters. It constructs a tactical decision support model based on spatiotemporal feature clustering algorithms and uses a 3D visualization engine to achieve three-dimensional mapping of multi-dimensional data fields, breaking through the dimensional limitations of traditional analysis tools. The access control system extends the dynamic policy adjustment capabilities of the RBAC model through attribute-based encryption. Combined with behavioral trajectory evidence storage and multi-party secure computation protocols, it constructs a collaborative decision verification mechanism in a decentralized environment, achieving a closed-loop process for access control and dispute resolution. At the system integration level, a service mesh architecture is adopted to achieve adaptive protocol communication between heterogeneous modules. A unified event bus establishes a collaborative triggering mechanism for cross-domain workflows, and combined with the transaction atomicity guarantee technology of lightweight blockchain nodes, it overcomes the challenge of collaborative optimization of data consistency and system scalability in a distributed environment.

[0033] The overall technical solution achieves multi-dimensional technological breakthroughs through the deep integration of core technologies such as blockchain's trusted storage, artificial intelligence's cognitive computing, and distributed system's collaborative scheduling. These breakthroughs include automated verification of rule execution, intelligent decision-making for qualification evaluation, dynamic optimization of competition schedules, accurate identification of equipment certification, real-time processing of data analysis, and secure control of access management. The solution builds an intelligent event management ecosystem with autonomous evolution capabilities, providing a full-stack solution with high reliability, strong scalability, and deep intelligence for the digital transformation of sports events. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0036] 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.

[0037] Example:

[0038] like Figure 1As shown, a competition alliance platform management system includes a competition authentication module built on blockchain, an AI-driven qualification review module, a process management module, a convolutional neural network, a big data analysis module, and an RBAC permission management module. The competition authentication module dynamically parses and stores the authenticated competition rule data through smart contracts and uses a cryptographic hash chain to achieve rule version traceability.

[0039] The AI-driven qualification review module integrates a natural language processing engine to analyze selection criteria and build a multi-dimensional evaluation model, and uses machine learning algorithms to analyze athletes' historical data to generate a visual qualification report.

[0040] The cloud-supported process management module automatically generates the registration process based on the rule engine, uses the Monte Carlo algorithm to perform multi-constraint lottery, and builds a directed graph model to dynamically generate the competition schedule topology structure containing the rotation sequence of the three players.

[0041] The equipment authentication module implemented by the convolutional neural network identifies the anti-counterfeiting features of the equipment through a mobile terminal scanning device and binds them to the blockchain evidence storage system with timestamps.

[0042] The big data analysis module collects athletes' movement trajectories and hitting parameters in real time, uses cluster analysis to establish a competition evaluation index system and generate a three-dimensional visualized tactical map;

[0043] The RBAC permission management module, based on attribute encryption, assigns dynamic access credentials to multiple roles and records behavioral trajectories in a distributed ledger. It integrates a secure computing protocol to achieve collaborative decision-making in disputes, communicates through a service mesh architecture, and coordinates workflows based on a unified event bus. Blockchain nodes ensure the transaction consistency of key operations, realizing the full-process digital integration of event certification, qualification review, process management, equipment verification, data analysis, and multi-role collaboration.

[0044] Introducing robot applications into the table tennis event management system to form a human-machine collaborative closed loop: The referee robot integrates multispectral vision sensors and edge computing units to capture the ball trajectory in real time and transmit the raw data to the big data analysis module through the service mesh architecture. Its built-in convolutional neural network accelerator completes the compliance judgment of the landing point within 300ms. In disputed scenarios, the blockchain evidence storage module automatically triggers the timestamp binding process to generate an immutable electronic ruling.

[0045] The intelligent inspection robot is equipped with a macro optical module and an NFC scanning device. Based on the CNN model of the equipment authentication module, it performs three-dimensional reconstruction of the micro-texture of the racket rubber. It compares the data with the Table Tennis Association's equipment database in real time through a lightweight blockchain node. When non-compliant equipment is detected, it automatically locks the RFID tag and synchronizes the evidence of non-compliance to the RBAC permission management module to freeze the participant's rights.

[0046] The tactical training robot connects directly to the big data analysis module via the OBD interface. Based on the real-time 3D tactical map, it dynamically adjusts the six-degree-of-freedom robotic arm's offensive and defensive strategies. Combined with the multi-dimensional evaluation model of the qualification review module, it generates personalized training plans. The training data is fed back to the AI-driven module to optimize the weight allocation of the machine learning model. The event service robot cluster autonomously plans service paths based on the event schedule topology structure of the process management module. It obtains dynamic permissions through attribute encryption credentials. When providing augmented reality tactical commentary in the audience area, it calls the intelligent decision chain to generate holographic projection content. When the blockchain node detects a change in the event schedule, it automatically triggers the service mesh's rerouting mechanism.

[0047] The maintenance and inspection robot is equipped with multimodal sensors to monitor the status of cloud computing resources. When it detects that the computing load of the process management module exceeds the threshold, it automatically expands container instances through the elastic resource scheduling protocol and uses a two-factor authentication mechanism to complete behavior auditing of the distributed ledger. All robots achieve millisecond-level collaboration through a unified event bus. After critical operations reach consensus through an improved fault-tolerant algorithm, they are written to the blockchain, building an intelligent management ecosystem covering adjudication, equipment supervision, training assistance, audience services, and system maintenance.

[0048] The implementation of this application in the table tennis league scenario is reflected in a collaborative working mechanism that integrates multiple technologies: the event management organization creates a smart contract through the event certification module built on blockchain, which transforms the three-person rotation system rules approved by the Table Tennis Association into executable code logic. Formal verification methods are used to ensure the completeness of core clauses such as the scoring mechanism and rotation sequence. Each rule revision generates a new encrypted hash node and forms a version evolution chain. The referee group and participating clubs obtain authoritative rule interpretations in real time through decentralized application interfaces, eliminating the risk of version confusion in traditional paper rule manuals.

[0049] During the athlete registration phase, the qualification review module activates a natural language processing engine to analyze the selection criteria document released by the Table Tennis Association, constructs a multi-dimensional feature space including technical level points, international competition performance, anti-doping records, etc., uses a deep neural network to extract motion features from historical match video data, and generates a technical ability assessment map by combining it with competitive training. Finally, it outputs a visualized qualification report with a time-series evolution curve and writes it into the permission chain node to achieve full-chain traceability of the review process.

[0050] The process management module triggers dynamic allocation of cloud computing resources based on registration data. It analyzes parameters such as the size of participating teams and venue resource constraints through a rule engine, and uses an improved Markov chain Monte Carlo algorithm to simulate the probability distribution of hundreds of thousands of lottery schemes. It generates the optimal lottery results that satisfy strategies such as regional avoidance and seed avoidance. Then, it uses a directed graph model to construct the competition topology network, and uses a topology sorting algorithm to analyze the time sequence dependency of the three-person rotation. It dynamically generates a three-dimensional competition schedule matrix that includes competition time periods, venue allocation, and referee scheduling, monitors the progress of the competition in real time, and automatically triggers postponement and adjustment strategies.

[0051] During the equipment certification process, referees use mobile terminals with integrated polarization light sensing modules to scan the microscopic texture of the racket rubber. Convolutional neural networks extract sub-pixel-level features of anti-counterfeiting marks through a multi-scale feature pyramid structure and compare them with the registration information in the Table Tennis Association's equipment database. After the certification results reach a distributed consensus through an improved practical Byzantine fault-tolerant algorithm, the timestamp-bound data is written into a hybrid storage architecture of edge computing nodes and central blockchain to form an irrefutable electronic certification file.

[0052] During the match, multimodal sensors deployed in the venue collect parameters such as athlete displacement trajectory, hitting angle, and spin speed in real time. The streaming computing engine performs noise reduction and feature extraction on the raw data, and uses a density clustering algorithm to construct a tactical pattern recognition model. The 3D visualization engine maps the match situation into a dynamic heat map, and the coaching team can access an augmented reality tactical panel in real time, which includes attack line prediction and defensive weak area identification. The access control module assigns dynamic access credentials to athletes, referees, and technical officials based on the RBAC extended model, and uses an attribute-based encryption algorithm to achieve fine-grained data access control. All operations are recorded in a distributed ledger based on Merkle trees. When a ruling dispute occurs, a multi-party secure computation protocol is initiated to achieve privacy-preserving collaborative verification of sensitive data. The service mesh architecture automatically coordinates data interaction between modules, and the event bus system captures event changes in the match status in real time and triggers corresponding workflows. The blockchain light node cluster ensures transaction consistency for key operations such as lottery result announcement and score entry through an atomic broadcast protocol.

[0053] The system comprehensively covers closed-loop management from event preparation, qualification review, event execution to post-event analysis. Through smart contracts, it automatically executes subsequent processes such as prize money allocation and points updates, forming a sustainably evolving technological ecosystem. This implementation process fully demonstrates the advantages of deep integration between blockchain and artificial intelligence: smart contracts ensure rigid constraints on rule execution, machine learning enables intelligent enhancement of complex decision-making, and the distributed architecture guarantees a trustworthy environment for multi-party collaboration, ultimately building a modern event management paradigm characterized by transparent rules, efficient execution, and reliable data.

[0054] In the daily operation of a major table tennis league, the event certification module utilizes blockchain technology to manage the entire lifecycle of the rules. During the season preparation phase, the organizing committee encodes the ITTF-approved three-person rotation system rules into smart contracts, employing a finite state machine model to formally model core clauses such as the scoring mechanism and rotation trigger conditions. For example, when player A scores an attack, the system automatically triggers a state transition, verifying whether the next round meets the three-person rotation conditions (e.g., whether the required number of hits or scoring threshold has been reached), and generates compliance evidence through a zero-knowledge proof protocol, ensuring that the legality verification of the rotation operation does not require the disclosure of specific tactical details. Rule version control uses a Merkle PatriciaTrie (MPT) data structure. Each rule revision (such as adjusting the rotation frequency or scoring weight) is generated by an authorized administrator through digital signatures to create a new node. Historical versions form an immutable evolutionary chain through parent node hashes. Lightweight nodes are deployed on the management terminals of each participating club, synchronizing only the active rule branches of the current season. When the referee team triggers a rule query during a match, the node quickly verifies the rule's validity through Merkle proofs, avoiding data redundancy from full node synchronization. In practical applications, when a dispute arises regarding the rotation order in a match, the arbitration committee can retrieve historical snapshots from the MPT version tree, combine timestamps and digital signatures to trace rule change records, and use zero-knowledge proofs to verify whether state transitions during the match conform to the then-effective rule version. This efficiently resolves disputes arising from differences in rule interpretation. The application of this module increases the transparency of rule enforcement and reduces match interruptions caused by rule disputes during the season.

[0055] In the athlete selection process for the Table Tennis Championships, an AI-powered qualification review module constructs an intelligent evaluation system. The system first uses a natural language processing engine to parse the selection criteria document released by the Table Tennis Association, transforming unstructured text requirements (such as "experience in the top eight of international competitions" and "meeting technical and tactical stability standards") into quantifiable feature dimensions (competition level weights, technical fluctuation coefficients, etc.). A deep residual network (ResNet) performs hierarchical feature extraction on the historical match videos of registered athletes: shallow layers identify basic motion parameters (swing speed, footwork trajectory), while deep layers capture high-order tactical features (efficiency of offensive and defensive transitions, ability to handle key balls). An attention mechanism dynamically adjusts feature weights based on the current selection focus (such as potential assessment in youth championships), for example, increasing the weight of growth curve analysis when selecting young athletes. An adversarial example detection layer constructs a robust feature space using a generative adversarial network (GAN) to effectively identify and filter artificially manipulated match data (such as edited and spliced ​​fake video clips). During the review process, Gradient Weighted Class Activation Mapping (Grad-CAM) technology visualizes the decision-making process of the neural network, generating a graph that displays hotspots in the technical capability assessment (e.g., highlighting weak areas in backhand techniques in red), assisting the human review committee in understanding the evaluation basis of the AI ​​model. The review results are then linked to off-chain evaluation data and on-chain identity information via an oracle service and written to permissioned blockchain nodes. For example, an athlete's doping test results are atomically verified against the anti-doping agency's database via a cross-chain protocol, ensuring data authenticity and real-time performance.

[0056] In the scheduling management of cross-regional table tennis leagues, the cloud computing process management module enables intelligent resource scheduling. After the registration phase, the system constructs a joint probability model based on constraints such as the number of participating teams, venue distribution, and available referee time slots, using Markov Chain Monte Carlo (MCMC) sampling. For example, in a league with 32 teams, the algorithm simulates 100,000 draw schemes, quickly converging to the optimal solution set that satisfies regional avoidance (teams from the same province do not meet in the first round) and seed protection (high-ranked teams are staggered). During the schedule arrangement phase, the directed graph model abstracts each match as a node, and the temporal dependencies caused by the rotation of three players (e.g., team A must participate after team B has completed a certain match) are transformed into directed edges. The topological sorting algorithm generates an acyclic match sequence, and the conflict detection mechanism uses a sliding window algorithm to monitor the time slot occupancy of resources such as venues and referees, automatically avoiding time overlaps. The upper layer of the two-layer graph neural network learns global load characteristics (such as the daily schedule density of each venue), while the lower layer optimizes the task allocation strategy of a single cloud server. When a competition area needs to be temporarily adjusted due to weather reasons, the system recalculates the load distribution in real time and triggers elastic scaling: automatically migrates the affected schedules to container instances in backup venues, and dynamically adjusts CDN node resources to ensure stable live streaming traffic.

[0057] In the equipment inspection process of national-level table tennis competitions, a convolutional neural network (CNN) authentication module achieves high-precision anti-counterfeiting identification. Referees use mobile terminals equipped with polarization light sensing modules to scan the surface of the rubber on athletes' rackets from multiple angles: the polarization light sensor captures the microscopic reflection characteristics under different polarization directions, constructing an equipment fingerprint containing 135-dimensional optical feature vectors. A multi-scale feature pyramid network (FPN) extracts macroscopic contour features (trademark character shape) and microscopic texture details (rubber particle density) of the anti-counterfeiting mark in parallel. A channel attention mechanism dynamically enhances the weight of key areas through the Squeeze-and-Excitation (SE) module; for example, the recognition weight of the ITTF certification mark area is increased by 30%. Authentication data adopts a sharded storage strategy. High-frequency access real-time verification data (such as the current season's equipment whitelist hash) is stored on edge nodes, while low-frequency traceability data (historical authentication records) is written to the central blockchain. When an athlete submits a new racket, the edge nodes quickly complete local verification using an improved Raft consensus algorithm. If an anomaly is detected (such as a similarity to the database below a threshold), cross-node verification on the central chain is triggered. The counterfeit product appears identical to the genuine product under normal lighting, but polarized light feature analysis reveals an abnormal surface refractive index distribution. Multi-scale CNN further detects micron-level deviations in the arrangement of colloidal particles. The system automatically generates an authentication report containing optical feature comparison images, preventing the entry of unauthorized equipment.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A competition league platform management system, characterized in that, It includes a blockchain-based event certification module, an AI-driven qualification review module, a process management module, a convolutional neural network, a big data analysis module, and an RBAC permission management module. The event certification module dynamically parses and stores certified event rule data through smart contracts and uses a cryptographic hash chain to achieve rule version traceability. The AI-driven qualification review module integrates a natural language processing engine to analyze selection criteria and build a multi-dimensional evaluation model, and uses machine learning algorithms to analyze athletes' historical data to generate a visual qualification report. The cloud-supported process management module automatically generates the registration process based on the rule engine, uses the Monte Carlo algorithm to perform multi-constraint lottery, and builds a directed graph model to dynamically generate the competition schedule topology structure containing the rotation sequence of the three players. The equipment authentication module implemented by the convolutional neural network identifies the anti-counterfeiting features of the equipment through a mobile terminal scanning device and binds them to the blockchain evidence storage system with timestamps. The big data analysis module collects athletes' movement trajectories and hitting parameters in real time, uses cluster analysis to establish a competition evaluation index system and generate a three-dimensional visualized tactical map; The RBAC permission management module, based on attribute encryption, assigns dynamic access credentials to multiple roles and records behavioral trajectories in a distributed ledger. It integrates a secure computing protocol to achieve collaborative decision-making in disputes, communicates through a service mesh architecture, and coordinates workflows based on a unified event bus. Blockchain nodes ensure the transaction consistency of key operations, realizing the full-process digital integration of event certification, qualification review, process management, equipment verification, data analysis, and multi-role collaboration.

2. The tournament league platform management system as described in claim 1, characterized in that: In the competition authentication module built on the blockchain, the smart contract uses a finite state machine model to formally verify the competition rules. The encrypted hash chain uses the Merkle Patricia Trie data structure to build a version control tree. Each rule update operation generates a state snapshot containing a timestamp and digital signature, and distributed rule synchronization is achieved through lightweight nodes. The smart contract is configured with a rule compliance verification protocol based on zero-knowledge proof, which is used to verify the legality of the three-person rotation sequence in real time during the competition execution.

3. The tournament league platform management system as described in claim 1, characterized in that: In the AI-driven qualification review module, the machine learning algorithm uses a deep residual network to extract features from athletes' historical data and introduces an attention mechanism to dynamically adjust the weight allocation of selection criteria. The multi-dimensional evaluation model constructs a feature space mapping layer that includes adversarial sample detection. The visualized qualification report generates a decision heatmap through gradient-weighted class activation mapping and writes the review results to permissioned chain nodes for cross-institutional verification through an oracle service.

4. The tournament league platform management system as described in claim 1, characterized in that: In the cloud computing-supported process management module, the Monte Carlo algorithm uses Markov chain Monte Carlo sampling to optimize the probability distribution model under multiple constraints. The directed graph model uses a topology sorting algorithm to parse the temporal dependencies of the three-person rotation and introduces a conflict detection mechanism to eliminate resource competition in the schedule arrangement. The schedule topology structure uses a two-layer graph neural network for load balancing optimization and dynamically adjusts the cloud server's computing resource allocation strategy in real time.

5. The tournament league platform management system as described in claim 1, characterized in that: In the equipment authentication module implemented by the convolutional neural network, the anti-counterfeiting mark feature recognition adopts a multi-scale feature pyramid network to extract micro-texture features and integrates a channel attention mechanism to improve the recognition accuracy of local details. The mobile terminal scanning device integrates a polarization light sensing module to obtain the optical characteristics of the equipment surface. The authentication data adopts a sharded storage strategy to write to a hybrid architecture of edge computing nodes and centralized blockchain.

6. A method for managing a competition league platform, characterized in that: The method includes the following steps: The competition rules data are dynamically parsed and stored through blockchain smart contracts, the rule versions are traced and managed using encrypted hash chains, and the three-person rotation sequence rules are formally verified based on a finite state machine model. The selection criteria are analyzed using a natural language processing engine, and a multi-dimensional evaluation model is constructed that includes international rankings, competition results and anti-doping status. The athlete's historical data is analyzed through machine learning algorithms to generate a visual qualification assessment report, which is then publicly verified on the alliance chain node. The event registration process is automatically generated based on a rule engine. The Monte Carlo algorithm is used to simulate the probability distribution model under multiple constraints for intelligent lottery. The directed graph topology sorting algorithm is combined to analyze the temporal dependency relationship of the three-person rotation, dynamically generate the event scheduling logic and allocate cloud computing resources. The optical features of the anti-counterfeiting marks of the equipment are collected by a mobile terminal scanning device, and micro-texture recognition is performed by a convolutional neural network. The authentication data is timestamped with the blockchain evidence storage system and written to the distributed nodes of the hybrid architecture based on the sharding storage strategy. The system collects athletes' movement trajectories and hitting parameters in real time, cleans the data through a streaming computing pipeline, constructs competition performance evaluation indicators using cluster analysis, and generates dynamic tactical maps using a 3D visualization engine. The attribute-based RBAC model assigns dynamic access credentials to multiple roles, records operational behavior in a distributed ledger, and realizes collaborative decision-making logic for dispute resolution through a multi-party secure computation protocol. Each step communicates and interacts through a service mesh architecture, coordinates cross-module workflows based on a unified event bus, and uses lightweight blockchain nodes to ensure transaction consistency in lottery, results entry, and equipment certification operations, thus completing closed-loop management from event certification and qualification review to event execution.

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