Intelligent stadium operation analysis method and system based on multi-source data fusion

Abstract

The application provides a kind of intelligent stadium operation analysis method and system based on multi-source data fusion, the method includes: by obtaining venue internal data, operating data and external environment data, and the multi-source data is cleaned and pretreated, establishes distributed storage architecture, based on the data after standardization processing multi-source data fusion analysis, constructs correlation analysis model, generates data analysis result, and then executes venue operation optimization, and real-time monitoring and continuous optimization are carried out to operation optimization result.The application constructs the complete closed loop of data acquisition-processing-application, solves the problems such as inaccurate flow statistics, low resource utilization, lack of data support for operation decision in traditional stadium operation, realizes all-round intelligent management of stadium operation, improves stadium utilization rate, reduces operation cost and improves user satisfaction.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method and system for intelligent sports venue operation analysis based on multi-source data fusion. Background Technology

[0002] Traditional sports venues face challenges in operation and management, including inaccurate visitor statistics, low resource utilization, and a lack of data support for operational decisions. Existing technologies are typically limited to single-dimensional data collection, lacking systematic data fusion and in-depth analysis, making it difficult to form a closed-loop data-driven decision-making system.

[0003] In the current operation and management of sports venues, data collection mainly relies on manual recording or simple electronic devices, making it difficult to achieve comprehensive, real-time, and accurate data acquisition. For example, pedestrian flow statistics often rely on manual counting or simple entrance counters, which cannot reflect the actual usage and dwell time of different areas; venue usage statistics are mainly based on reservation records, making it difficult to distinguish between reservations that have not been fulfilled, actual usage, and idle status; environmental parameter monitoring equipment is scattered and the data is isolated, making it difficult to form an overall environmental management system.

[0004] In data processing and analysis, traditional methods often employ single-dimensional statistical analysis, lacking the ability to correlate data from multiple sources. For example, they cannot effectively correlate weather factors, traffic conditions, and venue occupancy rates; they struggle to identify the complex relationships between visitor flow, venue bookings, and revenue; and they cannot delve into user behavior patterns and preferences. This limitation in analytical capabilities prevents venue managers from obtaining comprehensive and in-depth data insights, hindering the realization of data-driven decision-making.

[0005] In terms of operational decision support, existing technologies mostly remain at the level of basic reports and simple analysis, lacking intelligent decision-making suggestions and automated optimization capabilities. For example, venue pricing strategies often adopt fixed models, making it difficult to dynamically adjust according to changes in demand; resource scheduling mainly relies on experience-based judgment, making it difficult to achieve optimal allocation; and user services lack personalized recommendation mechanisms, failing to meet the differentiated needs of different users.

[0006] In summary, existing technologies face problems such as incomplete data collection, unsystematic data processing, and insufficient data application in the operation and management of sports venues, making it difficult to support data-driven intelligent operation of venues. There is an urgent need for a sports venue operation analysis method and system that can achieve multi-source data fusion analysis and intelligent decision support. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide an intelligent sports venue operation analysis method and system based on multi-source data fusion. By constructing a complete closed loop of data collection-processing-application, it solves the problems of inaccurate traffic statistics, low resource utilization, and lack of data support for operational decisions in traditional sports venue operations, and realizes comprehensive intelligent management of venue operations.

[0008] To address the aforementioned technical problems, this invention provides a method for intelligent sports venue operation analysis based on multi-source data fusion, comprising: acquiring internal venue data, venue operation data, and external environmental data to form a multi-source dataset as input data for standardized processing; performing data cleaning and preprocessing on the multi-source dataset, establishing a distributed storage architecture, and obtaining standardized data; performing multi-source data fusion analysis based on the standardized data, constructing a correlation analysis model, and generating data analysis results as input data for operation optimization; performing venue operation optimization based on the data analysis results to obtain operation optimization results including resource scheduling optimization, pricing strategy formulation, and user service improvement; and monitoring and continuously optimizing the operation optimization results in real time, adjusting the optimization strategy based on actual operation feedback.

[0009] Preferably, the acquisition of venue internal data, venue operation data, and external environment data includes: collecting venue internal physical environment parameters and personnel activity data to obtain real-time venue internal data; collecting venue booking, revenue, and cost expenditure data to obtain venue operation data; and accessing surrounding commercial, transportation, and weather data to obtain external environment data.

[0010] Preferably, the step of cleaning and preprocessing the multi-source dataset includes: performing outlier detection and processing on the multi-source dataset to generate a data quality score; repairing and completing the data based on the data quality score to obtain cleaned data; and converting the cleaned data into a unified format and standard to obtain standardized data.

[0011] Preferably, the step of cleaning and preprocessing the multi-source dataset and establishing a distributed storage architecture includes: receiving the standardized data, constructing a dynamic directed graph structure, setting each venue as an independent data node; establishing path connections for data transmission between the independent data nodes; configuring data transmission priorities based on the weights of the path connections, and obtaining data transmission priority results.

[0012] Preferably, the multi-source data fusion analysis based on the standardized data includes: constructing a weak Byzantine fault tolerance mechanism based on the data transmission priority results to ensure data consistency among nodes and obtain a distributed node management strategy; establishing a distributed edge computing model based on the distributed node management strategy, performing real-time data processing, and obtaining real-time processed data; and applying a federated learning method based on the real-time processed data to perform collaborative analysis while protecting data privacy.

[0013] Preferably, the step of optimizing venue operations based on the data analysis results includes: modeling the venue optimization problem data from the data analysis results as a quadratic unconstrained binary optimization problem to obtain a set of decision options; using the annealing mean field descent method to solve the venue optimization problem data based on the set of decision options to obtain an optimization scheme; and making operational decisions and adjusting resource allocation based on the optimization scheme.

[0014] Preferably, the real-time monitoring and continuous optimization of the operation optimization results includes: receiving the operation optimization results, constructing an association rule mining model based on the LRU enhanced genetic algorithm to obtain an association rule set; evaluating the optimization effect using a two-factor decision verification system based on the association rule set to obtain an optimization effect evaluation result; and dynamically adjusting the optimization strategy and parameter configuration according to the optimization effect evaluation result.

[0015] Preferably, the construction of the association rule mining model based on the LRU enhanced genetic algorithm includes: setting up a rule cache library to record the explored association rules; adjusting the rule priority according to the access frequency and time decay to obtain the adjusted rule priority; and performing rule evolution and optimization based on the adjusted rule priority.

[0016] Preferably, the evaluation of the optimization effect using the two-factor decision verification system includes: dividing the mined set of association rules into decision rules and verification rules; using the decision rules to generate a preliminary optimization scheme; and evaluating the feasibility of the scheme through the verification rules.

[0017] Preferably, the method further includes: constructing a cognitive bias correction mechanism to identify cognitive traps in data analysis; establishing a decision-making effect feedback loop to record the decision implementation effect; and optimizing the correlation analysis model and decision-making strategy based on the decision implementation effect.

[0018] Preferably, establishing a path connection for data transmission between the independent data nodes includes: setting an initial connection weight based on the frequency of data interaction between nodes; dynamically adjusting the connection relationship based on the initial connection weight according to network conditions and business needs; and maintaining data synchronization between nodes through a distributed consensus algorithm.

[0019] Preferably, the method of using annealing mean field descent to solve the venue optimization problem data includes: setting the initial system temperature and cooling rate parameters; maintaining the exploration degrees of freedom of the decision variables during the high-temperature stage; and gradually converging to a definite binary solution as the temperature decreases.

[0020] Preferably, the step of adjusting rule priority based on access frequency and time decay includes: calculating the access frequency and the most recent access time of each rule to obtain the access frequency; updating the activity score of the rule based on the time decay function and the access frequency; and dynamically adjusting the priority of the rule in the cache according to the activity score.

[0021] This invention also provides an intelligent sports venue operation analysis system based on multi-source data fusion, comprising:

[0022] The data acquisition module is used to acquire data from inside the venue, venue operations, and external environment, forming a multi-source dataset as input data for standardized processing.

[0023] The data processing module is used to clean and preprocess the multi-source dataset, establish a distributed storage architecture, and obtain standardized data.

[0024] The data analysis module is used to perform multi-source data fusion analysis based on the standardized data, construct a correlation analysis model, and generate data analysis results as input data for operational optimization.

[0025] The decision support module is used to perform venue operation optimization based on the data analysis results, and obtain operation optimization results including resource scheduling optimization, pricing strategy formulation and user service improvement;

[0026] The monitoring module is optimized to monitor and continuously improve the operational optimization results in real time, and to adjust the optimization strategy based on actual operational feedback.

[0027] The beneficial effects of this invention include:

[0028] 1. This invention realizes the systematization and intelligentization of sports venue operation and management by constructing a complete multi-source data acquisition-processing-application closed loop, which improves the venue utilization rate by 15-30% and optimizes resource allocation efficiency.

[0029] 2. This invention adopts an innovative data storage and analysis architecture, including distributed storage with a dynamic directed graph structure and a weak Byzantine fault tolerance mechanism, which significantly improves the reliability and performance of the system in complex environments.

[0030] 3. This invention introduces advanced optimization algorithms, such as the annealing mean field descent method and the LRU enhanced genetic algorithm, which provide efficient solution strategies for venue operation decisions and reduce operating costs by 10-20%.

[0031] 4. The two-factor decision verification system and cognitive bias correction mechanism designed in this invention significantly improve the reliability and robustness of decision-making, provide scientific data support for venue operation decisions, and reduce investment risks.

[0032] 5. This invention, through systematic data management and application, constructs data assets for sports venues, improves user satisfaction and retention rates, and provides continuous value for the long-term development of venues. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of the intelligent sports venue operation analysis method based on multi-source data fusion according to the present invention;

[0035] Figure 2 This is an architecture diagram of the data acquisition system of the present invention;

[0036] Figure 3 This is an architecture diagram of the data processing system of the present invention;

[0037] Figure 4 This is a dynamic diagram of the path connection and a schematic diagram of the structure of the self-stabilizing weak Byzantine distributed nodes of the present invention.

[0038] Figure 5 This is a flowchart of the quadratic unconstrained binary optimization based on the annealing mean field descent of the present invention.

[0039] Figure 6 This is a schematic diagram of the structure of the intelligent sports venue operation analysis system based on multi-source data fusion according to the present invention. Detailed Implementation

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0041] like Figure 1 As shown, the intelligent sports venue operation analysis method based on multi-source data fusion provided by this invention includes the following steps:

[0042] Step S1: Acquire internal venue data, venue operation data, and external environment data to form a multi-source dataset as input data for standardization processing;

[0043] Step S2: Perform data cleaning and preprocessing on the multi-source dataset, establish a distributed storage architecture, and obtain standardized data.

[0044] In this embodiment, outlier detection and processing are performed on the collected multi-source datasets to generate a data quality score. This process employs a combination strategy of various anomaly detection algorithms, including statistical methods (such as Z-score and box plots) to identify numerical anomalies, time series model-based methods (such as ARIMA and exponential smoothing) to detect time series anomalies, and density-based methods (such as DBSCAN) to discover outliers in the multidimensional feature space. For example, in pedestrian traffic data, the system can identify zero-value anomalies caused by sensor malfunctions or abnormal peaks caused by special activities; in environmental data, it can detect gradual anomalies caused by temperature and humidity sensor drift. For each data point, a comprehensive quality score is calculated based on dimensions such as completeness, consistency, timeliness, and reasonableness, and the score results provide a reference for subsequent processing.

[0045] Next, the data is repaired and completed based on the data quality score, resulting in cleaned, high-quality data. For data with low scores, the system will adopt appropriate repair strategies based on the specific circumstances. For missing values, the system will select an appropriate imputation method based on the data type and missing pattern. For example, for short-term missing values ​​in time series data, interpolation or time series prediction models will be used for imputation; for random missing values ​​in structured data, collaborative imputation based on similar records or machine learning model prediction may be used. For outliers, the system will decide whether to replace, correct, retain, or mark them based on the degree of anomaly and confidence level. For example, when the system detects that the pedestrian flow sensor data in a certain area suddenly all return to zero, it will automatically estimate reasonable values ​​through data from adjacent areas and historical patterns to ensure data continuity and availability.

[0046] Then, the cleaned data is converted into a unified format and standard to obtain standardized data. This step resolves inconsistencies in time standards, spatial reference systems, units of measurement, and coding standards among multi-source data. For example, timestamps from different sources are uniformly converted to UTC standard time, taking time zone factors into account; coordinates from different spatial positioning systems are unified to the same reference system; and temperature data is standardized to degrees Celsius. Furthermore, the system performs structured transformation on the data, ensuring that all data conforms to predefined data models and field specifications, facilitating subsequent storage and retrieval. This standardization process lays the foundation for the fusion analysis of multi-source data, enabling data from different systems and devices to be compared and correlated within the same framework.

[0047] After data cleaning and standardization, the standardized data is received, a dynamic directed graph structure is constructed, and each venue is set as an independent data node, establishing a distributed storage architecture. This architecture is particularly suitable for multi-venue, cross-regional sports venue groups. Each venue, as an independent data node, maintains autonomous data management while establishing connections with other nodes through dynamic paths, forming an organic data network. This distributed structure greatly improves the system's scalability and fault tolerance, while ensuring efficient data access.

[0048] In this dynamic directed graph structure, path connections for data transmission are established between independent data nodes. These connections are not statically fixed but dynamically adjusted according to business needs and network conditions. The system first sets initial connection weights based on the frequency of data interaction between nodes; for example, higher-weight connections are established between venue nodes that are geographically close or have high business relevance. Subsequently, the system dynamically adjusts these connection relationships based on actual network conditions, changes in data interaction frequency, and the evolution of business needs, ensuring that data transmission always follows the optimal path. Furthermore, the system maintains data synchronization between nodes through distributed consensus algorithms (such as Paxos or Raft), ensuring data consistency and reliability in a distributed environment.

[0049] Finally, data transmission priorities are configured based on the weights of path connections to obtain the data transmission priority results. This mechanism ensures that critical business data can be transmitted and processed preferentially when network resources are limited or the load is high. For example, when a venue detects a sudden security incident, the relevant alarm data will be given the highest transmission priority to ensure timely delivery to relevant nodes and the central management system; while routine statistical data and historical archived information may be given a lower priority and transmitted during network off-peak hours. This intelligent data transmission strategy greatly improves the system's operating efficiency and reliability in complex network environments.

[0050] Through this series of data cleaning, preprocessing, and distributed storage architecture construction, the system provides a high-quality, structured data foundation and an efficient computing environment for subsequent multi-source data fusion analysis, laying a solid foundation for realizing intelligent sports venue operation analysis.

[0051] Step S3: Based on the standardized data, perform multi-source data fusion analysis, construct a correlation analysis model, and generate data analysis results as input data for operational optimization;

[0052] In this embodiment, based on the data transmission priority results obtained in the previous step, a weak Byzantine fault tolerance mechanism is constructed to ensure data consistency among nodes, resulting in a distributed node management strategy. The weak Byzantine fault tolerance mechanism is an advanced technology adapted to the imperfectly trusted environment of distributed systems, allowing the system to operate normally even when some nodes fail or data is inconsistent. Under this mechanism, each venue node not only stores its own data but also saves redundant data fragments from highly correlated venues, forming a "soft backup" network for data. The system provides quality and stability based on historical data, assigns a reputation score to each node, and dynamically adjusts its weight and decision-making influence in the network accordingly. When abnormal or inconsistent data is detected in a node, the system automatically initiates a consensus algorithm to determine the true state through data verification by a majority of nodes and isolates the problematic node. For example, when abnormal fluctuations occur in the visitor flow data of a venue, the system cross-validates with historical patterns and data from similar venues to avoid single-point anomalies affecting the overall analysis results. This self-stabilizing design enables the system to maintain data reliability and analytical continuity when facing network fluctuations, equipment failures, or even malicious attacks.

[0053] Next, based on a distributed node management strategy, a distributed edge computing model is established to perform real-time data processing and obtain the processed data. This distributed edge computing model distributes computing power to each venue node, enabling each node to have local analysis capabilities and independently handle data analysis tasks with high real-time requirements. This "computing close to the data source" architecture significantly reduces data transmission volume and response latency, improving the system's real-time performance and efficiency. For example, each venue node can process tasks such as crowd monitoring and environmental parameter anomaly detection in real time, only reporting the processing results and anomalies to the central system, rather than transmitting the raw data stream. Simultaneously, the edge nodes employ "data digest transmission" technology, transmitting only the locally processed data features and analysis results to other nodes or the central analysis unit. This technology reduces the amount of transmitted data by more than 90% while maintaining analysis quality, significantly reducing network bandwidth requirements and storage pressure. In practical applications, when a sports center hosts a large-scale event, the on-site edge nodes can process crowd density monitoring and safety risk assessment in real time, only sending alarms to the central system when congestion risks or safety hazards are detected, achieving efficient monitoring without unnecessary interference.

[0054] Then, based on the real-time processed data, federated learning is applied to conduct collaborative analysis while protecting data privacy. Federated learning is an innovative distributed machine learning paradigm that allows multiple data holders to jointly train models without sharing the original data. In the sports venue scenario, each venue node does not need to upload sensitive user behavior data or business operation data; they only need to train the model locally and then share the model parameters to collaboratively build a more comprehensive and accurate analytical model. For example, in user behavior analysis, different venues can train user preference models locally based on their respective member activity data, and then exchange model parameters through a secure aggregation protocol to jointly build a cross-venue user profile system without disclosing any personally identifiable information. This mechanism is particularly suitable for complex scenarios with multiple owners and multiple venues, effectively balancing the value of data sharing with the need for privacy protection, and also complying with increasingly stringent data protection regulations.

[0055] In terms of model building, the system integrates various advanced technologies such as spatiotemporal data analysis, deep learning, and knowledge graphs to construct a multi-layered correlation analysis model. The bottom layer is a basic statistical model that handles descriptive analysis, such as pedestrian flow distribution and venue utilization. The middle layer is a predictive model that, based on time series analysis and machine learning algorithms, predicts future trends in passenger flow and booking demand. The top layer is an explanatory model that utilizes causal inference and knowledge graph technology to explore the causal relationships and business logic behind the data. For example, the system can not only identify the surface correlation of "rainy days → increased demand for indoor venues," but also reveal the detailed mechanisms behind this phenomenon through multi-factor analysis, such as the different relationships between the degree of impact of rain and rainfall amount, duration, and weekend / weekday conditions, providing in-depth insights for accurate decision-making.

[0056] Finally, the analysis results are visualized and semantically interpreted, transforming complex data analysis into intuitive and easy-to-understand business insights. The system not only generates traditional data dashboards and trend charts but also automatically generates natural language analysis reports explaining key findings and recommendations. For example, "Badminton court usage is low on Wednesday evenings (average only 65%), a significant drop compared to Tuesdays and Thursdays (both exceeding 85%). It is recommended to implement specific promotional activities or member offers for Wednesday evenings." This multi-dimensional presentation combining visualization and textual explanation greatly improves the usability of the analysis results, enabling managers without technical backgrounds to easily understand and apply data insights.

[0057] Through this series of multi-source data fusion analysis and correlation model construction, scattered data points are transformed into systematic business knowledge, providing comprehensive, accurate and in-depth data support for venue operation optimization, and realizing the key transformation from "data-driven" to "intelligent decision-making".

[0058] Step S4: Based on the data analysis results, venue operation optimization is performed to obtain operation optimization results including resource scheduling optimization, pricing strategy formulation, and user service improvement;

[0059] In this embodiment, venue operation optimization is performed based on data analysis results. Advanced mathematical optimization techniques and decision support mechanisms are used to generate and implement a comprehensive operation optimization plan, including resource scheduling optimization, pricing strategy formulation, and user service improvement. This step is a crucial link in transforming data analysis into actual business value, directly impacting the venue's operational efficiency and economic benefits.

[0060] First, the venue optimization problem derived from the data analysis results is modeled as a quadratic unconstrained binary optimization (QUBO) problem, resulting in a set of decision options. QUBO is a powerful mathematical modeling framework capable of uniformly expressing various complex combinatorial optimization problems, particularly suitable for multi-objective, multi-constraint decision-making scenarios in sports venue operations. In this model, the system encodes each possible decision option (such as whether to open a venue during a specific time period, whether to implement promotions for a specific user group, whether to increase staff during a certain time period, etc.) as binary variables, describing the expected effects and interactions of these decisions through a quadratic objective function. For example, in the resource scheduling optimization problem, the objective function comprehensively considers multiple factors such as revenue maximization, cost minimization, user satisfaction, and resource utilization, assigning different weights to them according to business priorities to form a unified optimization objective. Simultaneously, the system also encodes various business constraints (such as staff working time limits, equipment maintenance requirements, and space capacity limitations) into the model, ensuring that the generated solution is both optimized and feasible. This QUBO-based mathematical modeling method enables the system to handle complex decision-making problems that are difficult to solve using traditional methods, such as coordinated scheduling of multiple venues and time periods, and personalized pricing strategy design.

[0061] Next, based on the decision option set, the Annealed Mean Field Descent (SAMF) method is used to solve the venue optimization problem data, yielding an optimized solution. SAMF is an innovative algorithm for efficiently solving QUBO problems, particularly suitable for large-scale combinatorial optimization scenarios. Compared to traditional exact solution methods, SAMF can quickly find a high-quality solution close to the global optimum with limited computational resources, and its performance advantage becomes more pronounced as the problem size increases. The algorithm first sets the initial system temperature and cooling rate parameters and enters the simulated annealing process. During the high-temperature stage, the system maintains the exploration freedom of the decision variables, allowing each binary decision variable to take a "soft value" (i.e., a probability value between 0 and 1, representing the tendency to choose that decision), rather than directly limiting it to 0 or 1. This "soft decision" mechanism allows the algorithm to explore the solution space more broadly, avoiding premature entrapment in local optima. As the temperature gradually decreases during the simulated annealing process, the values ​​of the decision variables gradually tend towards a definite 0 or 1, eventually converging to a high-quality binary solution, i.e., the specific optimization scheme. For example, in optimizing venue staff scheduling, the system may ultimately determine: reduce the number of front desk staff on Monday to Thursday mornings, increase venue assistants on weekends, and adjust the working hours of cleaning staff to avoid peak hours, thereby minimizing labor costs while ensuring service quality.

[0062] In practical operation, several innovative improvements were made to the annealing mean-field descent method. First, an adaptive temperature adjustment mechanism was introduced, dynamically adjusting the cooling rate based on the improvement of the objective function during optimization. When the search stalls, the temperature is automatically increased to help escape local optima, and cooling is accelerated during rapid convergence to improve efficiency. Second, a hierarchical solution strategy was adopted, decomposing large, complex problems into multiple sub-problems. The framework scheme is first determined at a coarse-grained level, and then specific parameters are optimized at a fine-grained level, significantly improving solution efficiency. Third, the system also incorporates a constraint softening mechanism, transforming strict hard constraints into soft constraints with penalty terms, increasing the continuity of the solution space and improving the algorithm's robustness and adaptability. These innovations enable the system to efficiently generate optimization schemes that meet both business requirements and are feasible for implementation in the complex and ever-changing operating environment of sports venues.

[0063] Then, based on the optimized plan, specific operational decisions are made, and resource allocation is adjusted. This process is not simply the execution of the plan, but rather the transformation of mathematical optimization results into actionable measures by combining the actual business environment and operational experience. The system generates detailed implementation plans, including specific resource scheduling arrangements (such as staff shift schedules and equipment usage schedules), pricing strategy implementation plans (such as pricing tables for different time periods and venues, and membership discount strategies), and user service improvement measures (such as personalized recommendations and service process optimizations). Simultaneously, the system provides key performance indicators (KPI) forecasts for the plan's implementation, such as expected revenue growth, cost savings, and changes in customer traffic, providing managers with decision-making references. For example, for badminton court operational optimization, the system will not only provide a decision to "reduce prices by 30% on weekday mornings," but also a complete implementation plan, including a specific price list, promotional copy suggestions, implementation schedule, and expected effect evaluation (such as an expected 20% increase in utilization and a 10% increase in total revenue).

[0064] In terms of dynamic response, an online adjustment mechanism has been developed, enabling rapid correction and optimization of plans based on real-time feedback and environmental changes. When external conditions change (such as sudden weather changes, large-scale events in the vicinity, or public transportation disruptions), the system can automatically adjust its established operational plans based on preset event response rules and real-time data. For example, when a sudden rainstorm is detected, the system will automatically prioritize the supply of indoor venues, adjust the booking policy for outdoor venues, and simultaneously push venue replacement suggestions and special offers to affected users, minimizing the impact of adverse factors and seizing potential business opportunities. This dynamic optimization capability gives venue operations a high degree of adaptability to complex and ever-changing environments.

[0065] Finally, a tiered authorization and intelligent execution mechanism for operational decisions was designed. Based on the scope and importance of the decision, the system categorizes optimization suggestions into three levels: automatic execution, manual confirmation, and strategic discussion. Routine adjustments with limited impact (such as minor short-term price fluctuations) can be executed directly by the system; decisions with moderate impact (such as resource reallocation during critical periods) require manager confirmation before execution; and major strategic adjustments (such as long-term pricing strategy changes or significant resource investment decisions) must be submitted to the management team for discussion. This tiered mechanism ensures system efficiency while preserving the strategic judgment and experience of human managers, achieving intelligent decision-making through human-machine collaboration.

[0066] Through this series of venue operation optimization measures, the data analysis results will be transformed into practical operational initiatives, achieving optimal resource allocation, precise pricing strategies, and continuous improvement of user services, thereby comprehensively enhancing the operational efficiency and economic value of sports venues.

[0067] Step S5: Monitor and continuously optimize the operational optimization results in real time, and adjust the optimization strategy based on actual operational feedback.

[0068] In this embodiment, the system receives operational optimization results and constructs an association rule mining model based on an LRU-enhanced genetic algorithm to obtain an association rule set. This model is an innovative upgrade to traditional association rule mining and is particularly suitable for the complex and ever-changing data environment in sports venue operations. The system encodes the relationships between various operational measures (such as price adjustments, resource allocation changes, and service process improvements) and business indicators (such as booking rates, visitor traffic, revenue, and user satisfaction) as potential association rules, and continuously optimizes the rule set through the evolutionary mechanism of the genetic algorithm. Unlike traditional Apriori or FP-Growth algorithms, the LRU-enhanced genetic algorithm does not pursue exhaustive enumeration of all possible association rules, but rather focuses on business value and uses evolutionary computation to find the most practical rule patterns. The system sets up a rule cache library to record explored association rules and their evaluation results. This cache library is dynamically managed using an LRU (Least Recently Used) strategy. When a new rule is generated and evaluated, the system will prioritize checking the cache library to avoid duplicate calculations; at the same time, the cache library will automatically adjust the retention priority of rules based on the access frequency and time decay of the rules. For example, when a rule (such as "rainy weekends → increased demand for indoor basketball courts → reduced pricing elasticity") is frequently verified as effective, the system will increase its priority in the cache; while rules that have not been verified for a long time or have poor effects will be de-prioritized or removed from the active cache. This mechanism ensures that algorithmic resources are concentrated on the most promising search space, significantly improving computational efficiency and rule quality.

[0069] In terms of rule priority adjustment, a sophisticated mechanism combining time decay and access frequency is employed. The system first calculates the access frequency and most recent access time for each rule, forming an initial access frequency index. Then, based on the designed time decay function (usually using an exponential decay model), and combined with the access frequency, an activity score is calculated for each rule. This score comprehensively reflects the rule's historical value and recent activity; a higher activity score indicates that the rule is more likely to have high reference value in the current business environment. Finally, the system dynamically adjusts the priority of rules in the cache based on the activity score, ensuring that the most valuable rules always maintain high accessibility. For example, a rule describing the effectiveness of a specific promotional activity may be frequently accessed and verified as effective during the activity, obtaining a high activity score; however, after the activity ends, although the rule's historical value remains, its activity will gradually decrease over time, and its priority will be lowered accordingly, until it is reactivated when a similar activity is conducted again. This dynamic balancing mechanism allows the system to retain valuable historical experience while focusing on the most relevant current business models, achieving efficient utilization of computing resources.

[0070] Next, based on the association rule set, a two-factor decision verification system is applied to evaluate the optimization effect, yielding the optimization effect evaluation results. The two-factor decision verification system is an innovative decision reliability assurance mechanism that significantly improves the accuracy and security of optimization decisions through two independent but mutually verifying decision paths. The system first divides the mined association rule set into two main categories: decision rules and verification rules. Decision rules focus on the direct relationship between actions and results, used to generate specific optimization suggestions; while verification rules focus on the decision environment and boundary conditions, used to evaluate the effectiveness and potential risks of the decision. The system uses decision rules to generate preliminary optimization solutions, such as "It is recommended to implement member-only discounts in the indoor fitness area on rainy afternoons to increase facility utilization during off-peak hours." Then, the feasibility of the solution is evaluated through verification rules, checking whether the solution meets known business constraints and risk thresholds, such as fitness area capacity limitations, member sensitivity to price changes, and ease of implementation. Only optimization solutions that pass both the decision rules and verification rules are recognized as effective by the system and recommended for continued implementation or further optimization. This two-factor validation mechanism effectively reduces the decision-making risks that may arise from a single data model, and improves the reliability and robustness of the optimization scheme.

[0071] In practical operation, the two-factor decision verification system has demonstrated significant value. For example, in the intelligent pricing system of a comprehensive sports center, traditional single-factor decision rules might only suggest a significant price increase during specific periods based on historical demand data; while the two-factor system, through verification rules, assesses the impact of such adjustments on long-term customer loyalty and compares prices with those of competing venues in the surrounding area, ultimately providing a more balanced and sustainable pricing strategy. As another example, in optimizing equipment maintenance plans, the system not only considers cost-minimizing maintenance schedules but also uses verification rules to ensure that maintenance activities are not overly concentrated, leading to service interruptions or conflicts with important events. This multi-dimensional decision verification mechanism enables sports venues to pursue short-term operational efficiency while also considering long-term business sustainability and user experience, achieving truly intelligent operation and management.

[0072] Then, based on the evaluation results of the optimization effect, the optimization strategy and parameter configuration are dynamically adjusted to form a closed-loop optimization system. This process not only adjusts specific operational measures but also optimizes the underlying algorithm model and parameter settings, achieving continuous improvement in system performance. For example, when it is found that a certain price elasticity model is inaccurate in predicting specific site types, the system will automatically adjust the model's parameters or select a more suitable model structure; when it is identified that a certain type of optimization measure frequently encounters operational obstacles during implementation, the system will modify the relevant constraints and generate a more feasible solution. This self-adjustment mechanism enables the system to continuously learn and adapt to complex and ever-changing operating environments, continuously improving optimization effectiveness.

[0073] Furthermore, a cognitive bias correction mechanism has been established to identify and correct various cognitive traps that may arise during data analysis. The system automatically identifies "comfort" conclusions that are highly consistent with existing cognition and applies stricter verification standards to them. Simultaneously, for counterintuitive but data-supported findings, the system provides more detailed evidence chains and explanations to help decision-makers overcome cognitive limitations. For example, data may show that some venues considered to be operating during "prime time" actually have lower-than-average profit margins. The system will analyze cost structures and usage patterns in detail to help managers reassess traditional cognition and avoid decision-making biases based on erroneous assumptions.

[0074] Finally, a complete decision-making effect feedback loop was established, recording the implementation process and effect data of each optimization measure, forming a rich decision-making-effect knowledge base. This knowledge base is not only used to evaluate the effectiveness of current decisions, but also provides experience references for decisions in similar future scenarios. The system automatically analyzes the applicability of various optimization strategies in different scenarios based on the implementation effects of decisions, identifies best practices and potential risks, and uses this knowledge to optimize correlation analysis models and decision-making strategies. For example, through long-term accumulated effect data, the system may discover the differences in the effectiveness of a certain type of promotional activity in different seasons and among different user groups, thereby designing and targeting such activities more accurately in future decisions to maximize return on investment.

[0075] Through this series of real-time monitoring and continuous optimization mechanisms, a complete closed loop has been formed, encompassing data collection, analysis and processing, solution generation, effect evaluation, and strategy adjustment. This enables truly intelligent and adaptive optimization of sports venue operations. This data-driven continuous optimization strategy ensures that sports venue operations remain highly efficient, responding promptly to market changes, accurately meeting user needs, and ultimately achieving the operational goals of maximizing resource utilization and sustained revenue growth.

[0076] In step S1, acquiring venue internal data, venue operation data, and external environment data includes: collecting venue internal physical environment parameters and personnel activity data to obtain real-time venue internal data; collecting venue booking, revenue, and cost expenditure data to obtain venue operation data; and accessing surrounding commercial, transportation, and weather data to obtain external environment data.

[0077] like Figure 2 As shown, the data acquisition system includes three core modules: internal venue data acquisition, operational data acquisition, and external data access, which together construct a comprehensive data infrastructure for sports venues.

[0078] The venue's internal data acquisition module is primarily responsible for collecting real-time data on the venue's physical environment and user activities. This module uses a people flow monitoring system to capture changes and distribution of visitors within the venue, accurately calculating personnel density, movement trajectories, and dwell time in each functional area, providing a basis for space utilization optimization and personnel flow management. The utilization rate statistics system focuses on recording actual venue usage, distinguishing between reserved but not yet booked, actually used, and idle states, enabling refined management of venue resources. The environmental parameter acquisition system continuously monitors environmental factors within the venue, such as temperature, humidity, noise, and air quality. These parameters not only affect user experience but are also closely related to energy consumption and equipment maintenance. For example, in a swimming pool setting, this module can monitor water temperature, air humidity, and chlorine concentration in real time, ensuring a comfortable and safe environment while providing data support for intelligent energy management.

[0079] The pedestrian flow monitoring module implementation scheme includes: deploying an infrared sensor array and a high-definition camera network to cover the main areas of the venue; employing deep learning algorithms to achieve pedestrian counting, trajectory tracking, and regional heat map analysis; designing a Wi-Fi probe array to capture mobile device signals and determining pedestrian locations through signal strength triangulation; and constructing a spatiotemporal variation model of pedestrian flow to identify venue usage patterns. The system can automatically generate a venue heat map, displaying the distribution of pedestrian density at different times, and can predict short-term pedestrian flow trends.

[0080] The implementation scheme for the environmental parameter acquisition module includes: constructing an Internet of Things (IoT) sensor network to monitor environmental parameters such as temperature, humidity, noise, and air quality; using wireless transmission technology to achieve real-time reporting of sensor data; and establishing a correlation model between environmental parameters and user experience.

[0081] The operational data acquisition module focuses on acquiring data related to venue operations and business. The booking statistics system automatically records booking information for different time slots and venue types, including booking channels, advance booking times, and cancellation rates. This data directly reflects market demand and user preferences. The revenue data acquisition system integrates diverse revenue sources such as venue rentals, membership fees, training courses, and merchandise to build a comprehensive financial data view. The cost expenditure analysis system tracks operating costs such as labor, energy, maintenance, and taxes, identifying cost savings through cost structure analysis. Taking a basketball court as an example, this module can identify weekday evenings and weekend mornings as the most popular times, adjusting pricing strategies and service configurations accordingly, while also identifying cost optimization opportunities during off-peak periods.

[0082] The external data access module links venue data with external environmental factors. The surrounding commercial data access system integrates customer flow and activity information from nearby shopping malls, restaurants, and entertainment facilities, analyzing synergistic effects and competitive relationships. The transportation data access system acquires public transportation, road conditions, and parking usage data, assessing the impact of transportation convenience on customer flow. The weather and climate data access system incorporates meteorological data to analyze the differentiated impact of weather factors on the utilization rates of indoor and outdoor venues. For example, in a multi-purpose sports center scenario, this module can identify patterns where rainy weather causes demand to shift from outdoor to indoor spaces and predict the impact of seasonal changes on different projects, providing forward-looking guidance for resource allocation.

[0083] The data access and integration solution includes: developing standardized API interfaces to access external data such as weather, traffic, and surrounding businesses; designing a data format conversion engine to convert heterogeneous data into the system's standard format; and implementing a data source reliability assessment mechanism to ensure the quality of the accessed data.

[0084] In the implementation of the pedestrian flow monitoring module, a high-definition network camera with a resolution of at least 1080p and a frame rate of at least 25fps, and a field of view of at least 100°, is used to ensure coverage of main passages and functional areas. The infrared sensor is a bidirectional counting type, installed at a height of 3 meters, with an accuracy of over 95%. The Wi-Fi probe has a sampling interval of 30 seconds, a detection radius of 25 meters, and achieves a positional accuracy of ±3 meters through a three-point positioning algorithm. The image processing algorithm uses the lightweight deep learning model MobileNet-SSD, enabling real-time processing on edge devices (processing latency <200ms). In the environmental parameter acquisition system, the temperature and humidity sensor samples every 5 minutes, the carbon dioxide concentration detector every 2 minutes, and the noise detector every 1 minute. All sensor data is transmitted to the local gateway via the MQTT protocol and then uploaded to the central server via an encrypted channel, with data transmission latency controlled within 3 seconds. Each sensor adopts a low-power design, supporting wired power supply and backup battery power supply modes. In battery mode, it can operate continuously for more than 72 hours, ensuring system reliability.

[0085] In step S2, the data cleaning and preprocessing of the multi-source dataset includes: performing outlier detection and processing on the multi-source dataset to generate a data quality score; repairing and completing the data based on the data quality score to obtain cleaned data; and converting the cleaned data into a unified format and standard to obtain standardized data.

[0086] like Figure 3 As shown, the data processing system comprises three key modules: data cleaning and preprocessing, data storage and management, and data correlation analysis. Together, they are responsible for transforming raw data into reliable, orderly, and insightful information assets.

[0087] The data cleaning and preprocessing module focuses on improving the quality and consistency of raw data. The data format unification and conversion system standardizes multi-source heterogeneous data into a unified format, resolving issues such as inconsistent timestamps, inconsistent units, and different coding standards, laying the foundation for subsequent data fusion. The anomaly detection and processing system automatically identifies and processes outliers, missing values, and duplicate values ​​in the data, ensuring data quality. The data completion and repair system uses statistical and machine learning methods to reasonably estimate and repair incomplete data. In real-world scenarios, when a pedestrian flow sensor in a certain area temporarily malfunctions, this module can automatically estimate the missing pedestrian flow data based on historical patterns and data from adjacent areas, ensuring that analysis is not interrupted due to localized data loss. This module effectively solves the unavoidable quality problems in multi-source data acquisition, providing a reliable data foundation for decision-making.

[0088] The data cleaning and anomaly handling solution includes: designing a combination of anomaly detection algorithms based on statistics and machine learning to identify outliers in the data; developing an intelligent data completion engine to automatically repair missing data based on historical patterns; and constructing a data quality scoring system to quantify data reliability.

[0089] The process of cleaning and preprocessing the multi-source dataset and establishing a distributed storage architecture further includes: receiving the standardized data, constructing a dynamic directed graph structure, setting each venue as an independent data node; establishing path connections for data transmission between the independent data nodes; configuring data transmission priority based on the weights of the path connections, and obtaining data transmission priority results.

[0090] The step of establishing a path connection for data transmission between the independent data nodes includes: setting an initial connection weight based on the frequency of data interaction between nodes; dynamically adjusting the connection relationship based on the initial connection weight according to network conditions and business needs; and maintaining data synchronization between nodes through a distributed consensus algorithm.

[0091] like Figure 4As shown, the dynamic graph with path connections and the self-stabilizing weak Byzantine distributed node technology represent an innovative extension of the traditional centralized architecture. At the data storage architecture level, this technology abandons the traditional centralized data warehouse model, instead employing a dynamic directed graph structure to organize data nodes distributed across various venues. Each venue acts as an independent data node, maintaining data autonomy while remaining connected to other nodes through dynamic paths. In this structure, data propagates between nodes in the form of event streams, with path weighting algorithms determining data transmission priorities and path selection. For example, when a venue detects abnormal fluctuations in passenger flow, the relevant data is first transmitted to geographically proximate or business-related venue nodes, enabling these venues to proactively address potential passenger flow changes. Furthermore, the dynamic graph structure adjusts the connections between nodes in real time based on data interaction frequency, network conditions, and business needs, ensuring optimal data transmission efficiency even when network conditions change.

[0092] The data storage and management module is responsible for organizing and maintaining data assets. The distributed storage architecture system features efficient data structures and storage strategies, supporting rapid access to massive amounts of data, such as sharding storage by time and venue area to optimize query performance. The data security and privacy protection system implements data encryption, anonymization, and access control to ensure user privacy and the security of business secrets. The historical data archiving and management system is responsible for data lifecycle management, maintaining high availability of recent high-value data while compressing and archiving historical data to balance storage costs and query efficiency. In large-scale integrated sports center scenarios, this module can simultaneously process tens of millions of sensor data points and transaction records generated daily, ensuring real-time query performance while preserving and mining long-term data value, and strictly protecting the security of members' personal information and consumption data.

[0093] The distributed storage architecture implementation scheme includes: adopting a hybrid architecture of time-series databases and relational databases, optimizing the storage structure for different types of data; implementing a data cold and hot separation storage strategy to balance query performance and storage costs; and designing a data security hierarchical protection mechanism to ensure the security of user privacy and commercially sensitive data.

[0094] The data correlation analysis module transforms scattered data points into systematic knowledge. The multi-source data fusion algorithm system correlates and integrates data from different sources and of different types, discovering relationships between cross-domain data. The spatiotemporal data correlation analysis system focuses on mining patterns and regularities in data across time and space dimensions, such as the dynamic changes in venue usage heatmaps over time. The causal relationship inference system explores potential causal connections from correlated data, providing deeper evidence for decision-making. In practical applications, this module can discover the combined impact mechanism of weather changes, traffic conditions, and surrounding activities on venue visitor flow, and even quantify the influence weight of different factors, such as finding a pattern where for every 5-degree Celsius increase in temperature, outdoor tennis court booking rates decrease by 12%, while demand for indoor air-conditioned venues increases by 8%.

[0095] The implementation scheme of multi-source data fusion algorithm includes: developing a spatiotemporal data correlation analysis engine to mine the correlation between data in time and space dimensions; constructing a Bayesian network model to infer the causal relationship between data; and implementing multi-dimensional feature engineering to extract the implicit correlation between data.

[0096] In step S3, the multi-source data fusion analysis based on the standardized data includes: constructing a weak Byzantine fault tolerance mechanism based on the data transmission priority results to ensure data consistency among nodes and obtain a distributed node management strategy; establishing a distributed edge computing model based on the distributed node management strategy, performing real-time data processing, and obtaining real-time processed data; and applying a federated learning method based on the real-time processed data to perform collaborative analysis while protecting data privacy.

[0097] Regarding the node autonomy mechanism, a weak Byzantine fault-tolerant design is adopted, allowing the system to continue operating normally even when some nodes fail or data is inconsistent. Each venue node, in addition to storing its own data, also retains redundant data fragments from highly correlated venues, ensuring the reliability of critical data through a distributed consensus algorithm. The system introduces a reputation scoring mechanism, dynamically adjusting the weight of each node in the overall network based on the accuracy, timeliness, and completeness of the data it provides. This self-stabilizing design enables the system to automatically isolate problematic nodes and reconstruct data transmission paths in the face of network fluctuations, equipment failures, or even malicious attacks, ensuring the resilience and reliability of the overall system.

[0098] At the data analytics level, a hybrid architecture combining distributed edge computing and centralized analysis is implemented. Each venue node possesses local analytics capabilities, enabling it to independently handle real-time data analysis tasks such as crowd monitoring and environmental parameter anomaly detection. Simultaneously, through "data summary transmission" technology, each node only transmits locally processed data features and analysis results to neighboring nodes or the central analysis unit, significantly reducing data transmission volume. The central analysis unit focuses on comprehensive cross-venue analysis and in-depth data mining tasks, such as regional passenger flow migration patterns and cross-user analysis of different types of venues. Furthermore, the system employs "transfer learning" technology, allowing different venue nodes to share the training results of the analysis model while retaining their respective business characteristics for adaptation.

[0099] In terms of data privacy protection, a "regional federated learning" mechanism has been introduced. Each venue node participates in distributed model training while preserving the locality of the original data. For example, in user behavior analysis, different venues do not need to share original user data; they only need to exchange model parameters to collaboratively build a more comprehensive user profile system. This mechanism is particularly suitable for complex scenarios with multiple owners and multiple venues, effectively balancing the value of data sharing with the need for privacy protection.

[0100] During operation, the adaptive load balancing algorithm dynamically adjusts task allocation and data routing strategies based on the computing resources, network bandwidth, and task urgency of each node. During peak periods, the system prioritizes the real-time processing and transmission of core business data and intelligently degrades or delays non-critical tasks. When a node is detected to be overloaded, related tasks are automatically migrated to nearby low-load nodes, achieving optimal global resource utilization.

[0101] In this embodiment, the federated learning method is implemented using a parameter server architecture, with each venue node acting as a participant and the central analysis unit acting as a coordinator. The training process includes the following steps: First, the coordinator initializes the global model parameters and distributes them to each participant; then, each participant trains a local model using local data, employing the Adam optimizer with a learning rate of 0.001 and a batch size of 64; next, participants only upload model gradients, not the original data, and the coordinator uses a secure aggregation protocol (i.e., isomorphic encryption) to merge gradients and update the global model; finally, the updated global model is distributed to each participant. To ensure model performance, a minimum participation rate of 80% is set, meaning at least 80% of participants must submit valid gradients before a global update is performed. The model architecture uses a multilayer perceptron suitable for the characteristics of venue data, containing three hidden layers, each with 128, 64, and 32 neurons respectively. The activation function used is ReLU, and the output layer selects an appropriate activation function based on the task type (e.g., Sigmoid for binary classification problems).

[0102] In step S4, the execution of venue operation optimization based on the data analysis results includes: modeling the venue optimization problem data from the data analysis results as a quadratic unconstrained binary optimization problem to obtain a set of decision options; using the annealing mean field descent method to solve the venue optimization problem data based on the set of decision options to obtain an optimization scheme; and making operational decisions and adjusting resource allocation based on the optimization scheme.

[0103] The method of using annealing mean field descent to solve the venue optimization problem data includes: setting the initial system temperature and cooling rate parameters; maintaining the exploration degrees of freedom of the decision variables during the high-temperature stage; and gradually converging to a definite binary solution as the temperature decreases.

[0104] The data application system consists of three core modules: business decision support, financial service application, and user service optimization. It is a key link in transforming data analysis results into actual business value.

[0105] like Figure 5 As shown, annealing mean-field descent (AMD) is an advanced mathematical optimization method for quadratic unconstrained binary optimization (SAMF-QUBO), particularly suitable for complex optimization problems in sports venues. At the mathematical model level, the SAMF-QUBO scheme unifies various optimization problems in sports venues into quadratic unconstrained binary optimization problems. This model uses binary variables to represent decision options (such as whether to open a specific venue during a certain time period, or whether to implement promotions for a specific user group), and describes the decision objective and constraints through a quadratic objective function. For example, in resource scheduling problems involving multiple venues and time periods, the objective function can comprehensively consider multiple factors such as revenue maximization, user satisfaction, energy consumption, and operation and maintenance costs. Traditional exact solution methods face combinatorial explosion when the number of variables increases, while this scheme provides an efficient approximate solution strategy.

[0106] At the core algorithm level, a mean-field approximation is first introduced to simplify the complex interactions between binary variables into interactions between each variable and the mean field. Initially, the system assigns a probability value (rather than a simple 0 / 1 binary value) to each decision variable, representing the probability strength of that decision being chosen. Through iterative updates, the probability value of each variable is gradually adjusted based on its interaction with other parts of the system. This "soft decision-making" approach allows the algorithm to explore a wider solution space during the search process, avoiding getting trapped in local optima.

[0107] Meanwhile, this scheme incorporates a simulated annealing temperature control mechanism, maintaining a relatively high "system temperature" in the early stages of optimization to allow decision variables sufficient degrees of freedom for exploration. As iterations proceed, the system temperature gradually decreases, and the probability values ​​of the decision variables gradually converge to a definite 0 or 1, ultimately converging to a high-quality binary solution. An adaptive strategy is employed for temperature reduction, dynamically adjusting the cooling rate based on the degree of improvement in the objective function, achieving a balance between exploration and utilization.

[0108] The operational decision support module directly supports the venue management's operational decisions. The venue operation optimization system provides suggestions for improving venue operations based on data analysis results, including optimal venue maintenance schedules, staffing plans, and equipment upgrade plans. The pricing strategy recommendation system combines market demand and cost analysis to design differentiated pricing strategies for different time periods and membership levels to maximize revenue. The venue expansion assessment system analyzes the market potential and revenue forecasts of potential new venue locations to support investment decisions. In practice, this module might identify that badminton courts are saturated with demand on weekday evenings but idle in the mornings, and then recommend reducing morning prices by 30% and launching a "morning practice discount package," while also suggesting increasing the number of venue assistants during weekend peak hours to optimize the user experience. These precise, data-driven suggestions significantly improve operational efficiency and venue revenue.

[0109] The venue operation optimization solution includes: building a predictive model based on historical data to predict the future utilization rate of the venue; developing an intelligent scheduling algorithm to optimize venue equipment maintenance time and staffing; and designing a dynamic pricing engine to automatically adjust pricing strategies for different time periods based on demand forecasts.

[0110] The financial services application module extends the value of venue data to the financial sector. The risk assessment model system constructs a quantitative assessment system for venue operational risks, predicting cash flow and identifying operational risk points. The loan quota calculation system provides loan decision support to financial institutions based on the venue's stable income and historical operating data. The insurance payout ratio calculation system analyzes the venue's security incident history and usage to optimize insurance plans and rates. In a new sports center project, this module can build a financial forecasting model based on existing venue operational data, providing detailed data support to banks to obtain more favorable loan terms; simultaneously, through quantitative analysis of security risks, it can negotiate customized insurance plans with insurance companies, reducing insurance costs while improving the level of protection.

[0111] The risk assessment model implementation plan includes: building a venue operation risk quantification model to assess the venue's operating status in real time; developing a cash flow forecasting engine to support financial decision-making; and designing a security incident analysis system to optimize insurance pricing and claims processes.

[0112] The user service optimization module focuses on enhancing member experience and customer value. The personalized recommendation system provides customized recommendations for venues, courses, and services based on user history and preferences. The user behavior analysis system delves into user patterns and habits when using venues, identifying characteristics of high-value user groups. The service quality assessment system collects and analyzes user feedback to evaluate service quality and guide improvement. In a real-world application scenario, the system might identify a user as a basketball enthusiast who plays regularly every Friday evening and occasionally attends fitness classes. The system will automatically send a venue booking reminder on Thursdays and intelligently recommend gym deals after the user books a basketball court, while also providing personalized membership upgrade options based on their usage frequency. This data-driven personalized service significantly improves user satisfaction and loyalty.

[0113] The user behavior analysis implementation plan includes: establishing a user profile system to identify user characteristics such as exercise preferences and consumption habits; developing a collaborative filtering-based recommendation algorithm to provide users with personalized venue and service recommendations; and designing a user lifecycle management model to improve user retention and conversion rates.

[0114] In practical applications, the SAMF-QUBO technology features a specially designed problem decomposition and hierarchical solution mechanism. For ultra-large-scale optimization problems, such as quarterly resource planning across multiple venues, the system first decomposes the problem into multiple sub-problems, solves them separately, and then integrates them. The decomposition strategy is based on the coupling strength between variables, organizing strongly coupled variables into the same sub-problem. Furthermore, the algorithm employs a multi-scale solution strategy, first obtaining a rough framework of the global solution at a coarse-grained level, and then gradually refining it to a fine-grained level, significantly improving computational efficiency.

[0115] To address the frequent dynamic changes in sports venue environments, this solution also incorporates an online dynamic adjustment mechanism. When external conditions change (such as sudden weather changes or temporary large-scale events), the system does not need to resolve the entire problem. Instead, it makes local adjustments based on the current state and new constraints, achieving real-time decision-making response. For example, when rain is detected causing a decrease in demand for outdoor venues, the system can quickly reallocate resources and adjust the opening hours and pricing strategies for indoor venues.

[0116] Regarding parameter adaptation, this solution possesses learning capabilities, automatically adjusting algorithm parameters based on historical optimization results. The system records algorithm performance and convergence characteristics under different scenarios, constructing a mapping relationship between parameters and problem characteristics, and automatically selecting the optimal algorithm configuration for new problems. This self-learning mechanism enables the algorithm to gradually adapt to the specific business characteristics of a particular venue, improving solution efficiency and solution quality.

[0117] In this embodiment, in the specific implementation of the annealing mean field descent method, the initial system temperature is set to 8.0, the cooling rate parameter is 0.92, and the iteration termination condition is that the temperature is below 0.01 or the objective function changes by less than 0.0001 for 10 consecutive iterations. During the high-temperature stage (temperature > 5.0), the decision variable is allowed to take values ​​in the range [0.1, 0.9], representing the probability of choosing that decision. As the temperature decreases (1.0 < temperature < 5.0), the range of the decision variable gradually narrows to [0.05, 0.95]. When the temperature further decreases (temperature < 1.0), the decision variable rapidly approaches the binary solution, eventually converging to a definite 0 or 1 value. For large-scale optimization problems, the system adopts a problem decomposition strategy, grouping variables with a coupling degree higher than 0.7 into the same subproblem. The size of a single subproblem is controlled within 100-200 variables, and the solutions to each subproblem are integrated through coordinating variables.

[0118] In step S5, the real-time monitoring and continuous optimization of the operation optimization results includes: receiving the operation optimization results, constructing an association rule mining model based on the LRU enhanced genetic algorithm to obtain an association rule set; applying a two-factor decision verification system to evaluate the optimization effect based on the association rule set to obtain an optimization effect evaluation result; and dynamically adjusting the optimization strategy and parameter configuration according to the optimization effect evaluation result.

[0119] A rule mining model based on the LRU enhanced genetic algorithm is constructed, including: setting up a rule cache library to record the explored association rules; adjusting the rule priority according to the access frequency and time decay to obtain the adjusted rule priority; and performing rule evolution and optimization based on the adjusted rule priority.

[0120] The optimization effect is evaluated by applying a two-factor decision verification system, including: dividing the mined set of association rules into decision rules and verification rules; using the decision rules to generate a preliminary optimization scheme; and evaluating the feasibility of the scheme through the verification rules.

[0121] The priority of rules is adjusted based on access frequency and time decay, including: calculating the access frequency and the most recent access time of each rule to obtain the access frequency; updating the activity score of the rule based on the time decay function and the access frequency; and dynamically adjusting the priority of the rule in the cache based on the activity score.

[0122] Specifically, the system first receives operational optimization results generated by the decision support module, including resource scheduling plans, pricing strategies, and user service improvement measures. After these optimization results are implemented in actual operations, they generate a series of business data and user feedback data. This implementation effect data is continuously collected and compared with the expected goals to evaluate the actual effectiveness of the optimization plan. Based on this implementation data, an association rule mining model based on the LRU enhanced genetic algorithm is constructed. This model can automatically discover complex relationships between optimization results and actual business indicators, such as the relationship between specific pricing strategies and user booking behavior, and the relationship between resource scheduling plans and equipment utilization, thereby forming a complete set of association rules.

[0123] When constructing the LRU-enhanced genetic algorithm, a dynamically optimized rule cache is established. This cache not only stores discovered high-value association rules but also dynamically adjusts their priorities based on usage frequency and timeliness. For example, if an association rule (such as "weekday lunchtime → price discount → increased tennis court bookings") is repeatedly confirmed as effective in recent validations, its priority is increased; conversely, rules that have not been validated for a long time or whose validation results are poor are degraded or removed from the active cache. This mechanism ensures that computational resources are focused on discovering and validating the most commercially valuable rules, significantly improving the system's computational efficiency and rule quality.

[0124] After obtaining the set of association rules, an innovative two-factor decision verification mechanism is applied to comprehensively evaluate the optimization effect. This mechanism divides association rules into two categories: decision rules and verification rules. Decision rules are used to directly generate optimization suggestions, such as "reducing venue prices on weekday mornings can improve venue utilization." Verification rules, on the other hand, serve as an independent verification mechanism to assess the reliability and potential risks of these optimization suggestions, such as "price adjustments exceeding 15% may lead to changes in user perception." Only optimization schemes that pass both the decision rules and verification rules are recognized as effective by the system and recommended for continued implementation. This two-factor verification mechanism effectively reduces the decision-making risks that may arise from a single data model and improves the reliability and robustness of optimization schemes.

[0125] Finally, based on the optimization effect evaluation results, the parameter configuration of existing optimization strategies is automatically adjusted to form a closed-loop optimization. For example, if a pricing strategy is found to be highly effective under specific weather conditions but less effective under other conditions, the triggering conditions and parameter settings of the strategy will be automatically adjusted to make it more accurately applied to the most suitable scenario. Simultaneously, based on long-term accumulated performance data, the parameters of the underlying algorithm model will be continuously optimized, such as adjusting the crossover and mutation probabilities in the genetic algorithm and updating the time decay coefficient of the LRU cache, making the entire optimization system increasingly intelligent and accurate with data accumulation. Through this continuous self-learning and adjustment mechanism, it can adapt to the ever-changing market environment and user needs, always maintaining a leading edge in optimization performance.

[0126] This data-driven, continuous optimization strategy ensures that sports venues operate at a consistently high efficiency, responding promptly to market changes and accurately meeting user needs, ultimately achieving the operational goals of maximizing resource utilization and sustained revenue growth. In practice, sports venues adopting this method typically observe significant operational improvements within 3-6 months, including a 15-30% increase in venue utilization, over 20% improvement in user satisfaction, and a 10-20% reduction in operating costs.

[0127] Furthermore, the two-factor decision verification system is specifically designed to address the reliability and risk control issues that may arise when traditional data analysis is applied to practical decision-making. This system draws inspiration from the two-factor authentication concept in financial security and innovatively applies it to the data-driven decision-making process. Through two independent yet mutually verifying decision paths, it significantly improves the accuracy and reliability of intelligent sports venue operation decisions.

[0128] In the core architecture of the two-factor decision validation system, the mined set of association rules is first divided into two main categories: decision rules and validation rules. Decision rules primarily focus on the direct relationship between actions and results, such as "increasing badminton court prices by 20% on weekend evenings can increase total revenue by 15%." These rules typically have clear action orientations, directly guiding specific operational decisions. Validation rules, on the other hand, focus on the decision-making environment and boundary conditions, such as "increasing badminton court prices by more than 30% will lead to a decrease in booking rates of more than 10%" or "price sensitivity is significantly reduced on rainy days." These rules are not directly used to generate decisions but serve as standards for decision effectiveness and risk assessment. This rule separation mechanism ensures that the system can comprehensively consider decisions from different perspectives when evaluating them.

[0129] In practice, the two-factor decision validation system employs a three-step workflow. First, preliminary optimization plans are generated based on the decision rule set, such as "It is recommended to implement a tiered pricing strategy for tennis courts from Tuesday to Thursday evenings, increasing prices by 15% during peak hours, maintaining the original price during shoulder-peak hours, and decreasing prices by 20% during off-peak hours." These preliminary plans represent the optimal course of action based on historical data analysis. Second, the system automatically submits these preliminary plans to the validation rules for evaluation, checking whether the plans violate known constraints or risk thresholds, such as assessing whether the price adjustment is within acceptable limits, whether seasonal factors have been considered, and whether it aligns with the operational strategies of other areas of the venue. Finally, the system retains only plans that pass both the decision rule generation and validation rule verification, assigning these plans credibility scores to help managers identify the most reliable optimization options.

[0130] Another key feature of the two-factor decision validation system is its adaptive learning capability. It continuously tracks and records the effectiveness of each validated decision after actual implementation, using this feedback to constantly adjust and optimize the weights and credibility of the decision and validation rules. For example, if a decision rule repeatedly generates solutions that perform well in practice, the system will increase the weight of that rule; conversely, if a validation rule frequently and incorrectly rejects solutions that have proven effective, the system will decrease the strictness of that rule or adjust its applicability. Through this continuous learning mechanism, the two-factor decision validation system becomes increasingly accurate and reliable as operational data accumulates.

[0131] In terms of risk control, the two-factor decision verification system is specifically designed with an anomaly handling mechanism. When the system detects special or unprecedented operating environments (such as sudden large-scale events, extreme weather, or equipment failures), it automatically increases the stringency of the verification rules and requires more supporting evidence to pass the decision verification. This "safety-first" design philosophy ensures that a more conservative and secure decision-making strategy is adopted when uncertainty increases, effectively preventing the risk of over-optimization that may arise from data analysis.

[0132] In practical applications, two-factor decision verification systems have demonstrated significant value in multiple scenarios. For example, in the intelligent pricing system of a comprehensive sports center, traditional single-factor decision rules might only suggest a significant price increase during specific periods based on historical demand data; however, a two-factor system uses verification rules to assess the impact of such adjustments on long-term customer loyalty and compare prices with those of competing venues in the surrounding area, ultimately providing a more balanced and sustainable pricing strategy. As another example, in optimizing equipment maintenance plans, it not only considers cost-minimizing maintenance schedules but also uses verification rules to ensure that maintenance activities are not overly concentrated, leading to service interruptions or conflicts with important events. This multi-dimensional decision verification mechanism enables sports venues to pursue short-term operational efficiency while also considering long-term business sustainability and user experience, achieving truly intelligent operation and management.

[0133] Through the two-factor decision verification system, this invention provides a scientific, rigorous, practical and reliable intelligent auxiliary tool for sports venue operation decision-making. It significantly reduces the possible biases and risks in data-driven decision-making, improves the success rate of implementing optimization measures, and lays a solid foundation for venues to achieve stable and efficient intelligent operation.

[0134] The multi-source data mining correlation optimization scheme based on LRU enhanced genetic algorithm and two-factor decision system is an innovative extension of the original sports venue data analysis and mining methods. This scheme integrates the Least Recently Used (LRU) caching strategy, the improved genetic algorithm, and the two-factor validation decision mechanism to construct an efficient, adaptive data correlation mining framework with cognitive correction capabilities. It is particularly suitable for discovering and utilizing implicit relationships in multi-source heterogeneous data from sports venues, providing in-depth data support for refined operations.

[0135] In the specific implementation of the LRU boosted genetic algorithm, the initial population size is set to 150 individuals, the crossover probability is 0.85, and the initial mutation probability is 0.2, which decreases linearly to 0.05 with the number of iterations. The rule cache capacity is set to the most recent 1000 rules, and the time decay function adopts the exponential decay form f(t) = e^(-t / t). -λt Where λ = 0.05, and t is the time interval (in days) since the last access. The activity score is calculated as Score = access frequency × (1 - time decay value). When the activity score is below 0.2, the corresponding rule will be downgraded in priority; when it is below 0.05, it will be removed from the activity cache. During the rule evolution process, a tournament selection strategy is adopted, randomly selecting 5 individuals from the population each time, and selecting the one with the highest fitness to enter the next generation; an elite retention strategy retains the best 10% of individuals in each generation to directly enter the next generation.

[0136] At the association rule mining level, this solution innovatively introduces a genetic algorithm framework enhanced with an LRU (Least Recently Used) caching strategy, solving the efficiency and accuracy problems of traditional association rule algorithms when dealing with high-dimensional sparse data. Unlike traditional Apriori or FP-Growth algorithms, this solution does not aim to exhaustively enumerate all possible association rules, but rather focuses on optimization objectives, using evolutionary computation to find the most business-valuable association patterns. The system encodes potential association rules as "chromosomes," continuously optimizing the rule set by simulating biological evolution, while introducing an LRU mechanism to manage population memory, ensuring that algorithm resources are concentrated on the most active and valuable rule exploration paths.

[0137] The core of the LRU (Least Recently Used) enhancement mechanism lies in maintaining an adaptive rule cache that records recently explored rules and their evaluation results. When the algorithm generates new candidate rules, the system first checks the cache to avoid redundant calculations. Simultaneously, the cache automatically adjusts the retention priority of rules based on access frequency and time decay, ensuring that computational resources are focused on the search space with potential. This mechanism is particularly suitable for association rule mining in sports venue environments, such as analyzing multi-step association chains like "rainy weather → increased indoor badminton court bookings → increased umbrella sales in the venue's retail area," which can be efficiently discovered even in sparse data conditions.

[0138] In terms of genetic algorithm design, this scheme adopts a multi-objective optimization framework, considering four dimensions simultaneously: support, confidence, lift, and business value of the rules. The algorithm not only focuses on statistically strong associations but also emphasizes business practicality, comprehensively evaluating rules through a custom fitness function. For example, while the rule "weekend → high foot traffic" may have high support, its business value is limited; whereas "specific member groups' venue preferences at specific times" may have lower support, but it has significant value for personalized services and revenue optimization. This algorithm balances these considerations, prioritizing the discovery of association rules with high commercial value.

[0139] Meanwhile, the scheme employs adaptive crossover and mutation operators to dynamically adjust genetic operation parameters based on evolutionary stage and population diversity. In the initial exploration phase, the system maintains a high mutation rate to broadly search the solution space; as superior rules emerge, the algorithm gradually reduces the mutation rate, concentrating resources on refined searches of advantageous regions. Furthermore, the algorithm introduces rule structure complexity constraints to avoid generating overly complex rules that are difficult to interpret and apply, ensuring the interpretability and practicality of the mining results.

[0140] At the decision-making application level, this solution innovatively proposes a two-factor decision verification system, addressing the risks that may arise from directly applying traditional data mining results to decision-making. The system categorizes the mined association rules into two types: direct decision rules and auxiliary verification rules. Direct decision rules are used to generate preliminary decision suggestions, while auxiliary verification rules serve as an independent verification mechanism to assess the rationality and potential risks of the preliminary decisions. Only decisions that pass verification by both types of rules are recommended for execution by the system, significantly improving the reliability of decision-making.

[0141] For example, when the system suggests raising badminton court prices on Wednesday evenings based on the association rule "Wednesday evenings → young white-collar users → high demand for badminton courts," the validation rule will check the historical response pattern of "price increase → change in bookings," as well as whether current special factors (such as weather, surrounding events, etc.) might affect the applicability of this rule. Only when the validation rule confirms that this pricing adjustment will not lead to a significant decrease in bookings will the system ultimately recommend this decision.

[0142] The scheme also includes constructing a cognitive bias correction mechanism to identify cognitive traps in data analysis; establishing a decision-making effect feedback loop to record the implementation effect of decisions; and optimizing the correlation analysis model and decision-making strategy based on the implementation effect of decisions.

[0143] Furthermore, this solution incorporates a cognitive bias correction mechanism to identify and correct cognitive traps such as confirmation bias and survivorship bias that may occur in data analysis. The system automatically identifies "comfort" conclusions that are highly consistent with existing cognition and applies stricter verification standards to them. Simultaneously, for counterintuitive but data-supported findings, the system provides more detailed chains of evidence and explanations to help decision-makers overcome cognitive limitations. For example, data might show that some venues considered to operate during "prime time" actually have lower-than-average profit margins; the system will analyze cost structures and usage patterns in detail to help managers reassess traditional perceptions.

[0144] In practical operation, this solution also incorporates a continuous learning mechanism, constantly adjusting and optimizing the association rule model based on feedback from the actual effects of decision implementation. The system records each decision made based on association rules and its actual effects, constructing a decision-effect knowledge base. This feedback loop enables the system to automatically identify which types of association rules are more valuable in practical applications, thus prioritizing the exploration of these directions in subsequent mining processes, achieving continuous evolution and self-improvement of the model.

[0145] In addition, such as Figure 6 As shown, this invention implements an intelligent sports venue operation analysis system based on multi-source data fusion, comprising:

[0146] Data acquisition module 101 is used to acquire data from inside the venue, venue operation data and external environment data, forming a multi-source dataset as input data for standardized processing;

[0147] Data processing module 102 is used to clean and preprocess the multi-source dataset, establish a distributed storage architecture, and obtain standardized data.

[0148] The data analysis module 103 is used to perform multi-source data fusion analysis based on the standardized data, construct a correlation analysis model, and generate data analysis results as input data for operation optimization.

[0149] The decision support module 104 is used to perform venue operation optimization based on the data analysis results, and obtain operation optimization results including resource scheduling optimization, pricing strategy formulation and user service improvement;

[0150] The optimization monitoring module 105 is used to monitor and continuously optimize the operational optimization results in real time, and adjust the optimization strategy based on actual operational feedback.

[0151] The implementation process of data analysis and mining methods in sports venues can be divided into five stages: data collection deployment and implementation, data cleaning and preprocessing implementation, data storage and management implementation, data analysis and mining implementation, and decision support and application implementation.

[0152] In the data acquisition, deployment, and implementation phase, a comprehensive site survey is conducted first, resulting in a detailed site plan with functional areas clearly marked. Based on this plan, a sensor network is planned, determining the quantity and installation locations of various sensors and developing a detailed deployment scheme. This step requires comprehensive consideration of factors such as coverage, signal strength, and installation costs to ensure the comprehensiveness and accuracy of data acquisition. Next, hardware installation and debugging are carried out, including installing high-definition cameras at the site entrance, main passageways, and functional areas to ensure no blind spots; deploying Wi-Fi probes within the site with a coverage rate exceeding 95%; installing infrared sensors at key locations to complement the cameras; and deploying environmental sensor arrays for temperature, humidity, noise, and light. Finally, operational data access channels and external data access channels are established.

[0153] In the data cleaning and preprocessing phase, the first step is data format standardization. This involves converting the collected multi-source, heterogeneous data into a unified format, including unifying timestamp formats, spatial coordinate systems, and data units. Next, data quality checks and processing are performed, conducting a comprehensive quality assessment of the collected data, including checks for completeness, consistency, accuracy, and timeliness. Then, advanced data preprocessing is implemented, including noise reduction, smoothing, normalization, and feature extraction on the raw data. Finally, a data quality monitoring system is established.

[0154] In the data storage and management implementation phase, the first step is to construct a tiered storage architecture, designing multi-layered storage structures based on data type and access frequency. Next, data security mechanisms are implemented, including designing and implementing data encryption schemes and establishing a role-based access control system. Then, a data service middleware layer is built, developing unified data access interfaces and services to shield the underlying storage details. Finally, a data lifecycle management system is established, defining data retention strategies and clarifying retention periods and archiving rules for various data types.

[0155] In the data analysis and mining implementation phase, the first step is to conduct basic statistical analysis, performing descriptive statistical analysis on various types of data, including venue usage time distribution, visitor flow fluctuation characteristics, booking patterns, and consumption behavior. Next, advanced data mining is implemented, building time-series forecasting models based on historical data to predict future visitor flow and venue utilization; cluster analysis is applied to identify user group characteristics and usage patterns; association rules are used to analyze service combinations and consumption chains; and regression analysis is used to identify key factors affecting revenue and satisfaction. Then, multi-source data fusion analysis is conducted, linking internal and external venue data to explore the impact of environmental factors on venue operations. Finally, a mechanism for validating and optimizing the analysis results is established.

[0156] In the decision support and application implementation phase, the first step is to establish an operational decision support system, develop intuitive data visualization dashboards to display key operational indicators and early warning information in real time, and build a venue operation optimization recommendation engine to provide optimization suggestions for different scenarios, such as personnel scheduling optimization, energy use optimization, and maintenance plan optimization. Next, marketing and user service applications are implemented, building a personalized recommendation system based on user profiles and behavioral analysis to recommend suitable venues, time slots, and supporting services to users; a dynamic pricing system is developed to adjust pricing strategies for different time slots and venues in real time based on demand forecasts. Then, risk management and financial applications are developed, building a venue operation risk early warning system to monitor and warn of operational, safety, and reputational risks; and financial forecasting and planning tools to support budget preparation and investment decisions. Finally, system iteration and continuous optimization are implemented, establishing an application effect tracking mechanism to evaluate the business value and user satisfaction of each application system; collecting user feedback and new requirements to plan system function iterations; identifying data gaps and quality issues to optimize data collection strategies; and continuously introducing new technologies and methods to improve the system's intelligence level.

[0157] The following specific implementation scenario examples further illustrate the implementation principle of the present invention:

[0158] Intelligent Crowd Management: After implementing this system in a comprehensive sports center, the system collects real-time crowd flow data through cameras (resolution no less than 1080p, frame rate 25fps) and Wi-Fi probe arrays (95% coverage) distributed throughout various functional areas. It uses a YOLO v5-based target detection algorithm to identify and count people, achieving an accuracy rate of over 95%. When the crowd density in the basketball area approaches the safe threshold (1.5 people per square meter), the system automatically pushes alternative court information to users via the venue's app. Simultaneously, based on historical data predictions showing a 23% increase in crowd flow in that area within the next 30 minutes, the system automatically dispatches two staff members to assist in crowd control and management. Through this function, the venue has improved safety management efficiency, reduced the frequency of crowd congestion, optimized staff allocation, and increased human resource utilization by 18%.

[0159] Dynamic Pricing Optimization: The system analyzed nearly six months of historical booking data for a badminton hall, combining it with weather data and surrounding event information. It found that on rainy weekends, the utilization rate of indoor badminton courts was 35% higher than on sunny weekends, while the advance booking time decreased by 40% (mostly last-minute decisions to play indoor sports). Based on this finding, the system recommended implementing a dynamic pricing strategy for indoor badminton courts on weekends with a predicted precipitation probability greater than 70%: a 20% discount for advance bookings (>48 hours), a 10% discount for bookings 24-48 hours in advance, and the original price for last-minute bookings (<12 hours). After implementing this strategy, the overall venue utilization rate increased by 27%, total revenue increased by 15%, booking times were more evenly distributed, and the workload of the system and front desk was reduced.

[0160] Personalized User Service: By analyzing Mr. Zhang's booking and usage data over the past three months, the system identified that he regularly plays badminton every Thursday evening from 7:00 PM to 9:00 PM, prefers court number 2, and has a 67% probability of purchasing sports drinks after each workout. Based on this user profile, the system automatically sends Mr. Zhang a badminton court booking reminder every Wednesday, prioritizing his preferred court number 2, and automatically sending him a sports drink coupon after he confirms the booking. Furthermore, the system detected that Mr. Zhang viewed but did not book the fitness area on Tuesdays for three consecutive weeks, and intelligently recommended a fitness + badminton combo package for Tuesdays, ultimately successfully increasing user activity and purchase frequency. At this venue, similar personalized services increased member activity by 31% and repurchase rate by 23%.

[0161] Through the above implementation process, the present invention can realize comprehensive data-driven operation management in sports venues, significantly improve venue operation efficiency and user experience, and create sustainable business value.

[0162] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for intelligent sports venue operation analysis based on multi-source data fusion, characterized in that, include: Acquire data from within the venue, venue operations, and the external environment to form a multi-source dataset, which serves as input data for standardized processing. The multi-source dataset is cleaned and preprocessed, and a distributed storage architecture is established to obtain standardized data. Based on the standardized data, multi-source data fusion analysis is performed to construct a correlation analysis model and generate data analysis results as input data for operational optimization. Based on the data analysis results, venue operation optimization is performed to obtain operational optimization results including resource scheduling optimization, pricing strategy formulation, and user service improvement; The operational optimization results are monitored and continuously optimized in real time, and optimization strategies are adjusted based on actual operational feedback. Specifically, the execution of venue operational optimization based on the data analysis results includes: modeling the venue optimization problem data from the data analysis results as a quadratic unconstrained binary optimization problem, obtaining a set of decision options, introducing a mean field approximation, simplifying the interaction between binary decision variables in the quadratic unconstrained binary optimization problem to an interaction between each decision variable and the mean field, assigning each decision variable a probability value between 0 and 1 to characterize the probability strength of the decision variable being selected; based on the decision option set, using the annealing mean field descent method to solve the venue optimization problem data to obtain an optimization scheme, including: setting initial temperature and cooling rate parameters, maintaining the exploration degrees of freedom of the decision variables and iteratively updating the probability values ​​of the decision variables, and gradually converging the probability values ​​to a definite binary solution as the temperature decreases; formulating operational decisions based on the optimization scheme and adjusting resource allocation; specifically, the real-time monitoring and continuous optimization of the operational optimization results includes... The process involves: receiving the operational optimization results; constructing an association rule mining model based on an LRU-enhanced genetic algorithm to obtain an association rule set; encoding potential association rules as chromosomes for the genetic algorithm; optimizing the association rule set using the support, confidence, and lift of association rules as optimization objectives through the evolutionary mechanism of the LRU-enhanced genetic algorithm; and utilizing a rule cache library, checking the rule cache library before generating candidate rules to avoid redundant calculations; evaluating the optimization effect based on the association rule set using a two-factor decision verification system to obtain optimization effect evaluation results. The association rule set is divided into decision rules and verification rules. The decision rules characterize the direct relationship between actions and results and generate preliminary optimization schemes. The verification rules characterize the decision environment and boundary conditions and evaluate the feasibility and potential risks of the preliminary optimization schemes. Only when the preliminary optimization scheme passes the tests of both the decision rules and the verification rules is it confirmed as a valid optimization effect evaluation result. The optimization strategy and parameter configuration are dynamically adjusted based on the optimization effect evaluation results.

2. The method according to claim 1, characterized in that, The acquisition of venue internal data, venue operation data, and external environment data includes: collecting venue internal physical environment parameters and personnel activity data to obtain real-time venue internal data; collecting venue booking, revenue, and cost expenditure data to obtain venue operation data; and accessing surrounding commercial, transportation, and weather data to obtain external environment data.

3. The method according to claim 1, characterized in that, The step of cleaning and preprocessing the multi-source dataset includes: performing outlier detection and processing on the multi-source dataset to generate a data quality score; repairing and completing the data based on the data quality score to obtain cleaned data; and converting the cleaned data into a unified format and standard to obtain standardized data.

4. The method according to claim 3, characterized in that, The step of cleaning and preprocessing the multi-source dataset and establishing a distributed storage architecture includes: receiving the standardized data, constructing a dynamic directed graph structure, setting each venue as an independent data node; establishing path connections for data transmission between the independent data nodes; configuring data transmission priorities based on the weights of the path connections, and obtaining data transmission priority results.

5. The method according to claim 4, characterized in that, The multi-source data fusion analysis based on the standardized data includes: constructing a weak Byzantine fault tolerance mechanism based on the data transmission priority results to ensure data consistency among nodes and obtain a distributed node management strategy; establishing a distributed edge computing model based on the distributed node management strategy, performing real-time data processing, and obtaining real-time processed data; and applying a federated learning method based on the real-time processed data to perform collaborative analysis while protecting data privacy.

6. The method according to claim 1, characterized in that, The construction of the association rule mining model based on the LRU enhanced genetic algorithm further includes: setting up a rule cache library to record the explored association rules; adjusting the rule priority according to the access frequency and time decay to obtain the adjusted rule priority; and performing rule evolution and optimization based on the adjusted rule priority.

7. The method according to claim 1, characterized in that, Also includes: Construct a cognitive bias correction mechanism to identify cognitive traps in data analysis; establish a decision-making effect feedback loop to record the effects of decision implementation. The correlation analysis model and decision-making strategy are optimized based on the implementation effect of the aforementioned decisions.

8. The method according to claim 4, characterized in that, The step of establishing a path connection for data transmission between the independent data nodes includes: setting an initial connection weight based on the frequency of data interaction between nodes; dynamically adjusting the connection relationship based on the initial connection weight according to network conditions and business needs; and maintaining data synchronization between nodes through a distributed consensus algorithm.

9. The method according to claim 6, characterized in that, The step of adjusting rule priority based on access frequency and time decay includes: calculating the access frequency and the most recent access time of each rule to obtain the access frequency; updating the activity score of the rule based on the time decay function and the access frequency; and dynamically adjusting the priority of the rule in the cache according to the activity score.

10. A smart sports venue operation analysis system based on multi-source data fusion, characterized in that, include: The data acquisition module is used to acquire data from inside the venue, venue operations, and external environment, forming a multi-source dataset as input data for standardized processing. The data processing module is used to clean and preprocess the multi-source dataset, establish a distributed storage architecture, and obtain standardized data. The data analysis module is used to perform multi-source data fusion analysis based on the standardized data, construct a correlation analysis model, and generate data analysis results as input data for operational optimization. The decision support module is used to perform venue operation optimization based on the data analysis results, and obtain operation optimization results including resource scheduling optimization, pricing strategy formulation and user service improvement; The decision support module is specifically used to: model the venue optimization problem data of the data analysis results as a quadratic unconstrained binary optimization problem, obtain a set of decision options, introduce mean field approximation, simplify the interaction between binary decision variables in the quadratic unconstrained binary optimization problem to the interaction between each decision variable and the mean field, and assign a probability value between 0 and 1 to each decision variable to characterize the probability strength of the decision variable being selected. Based on the aforementioned decision option set, the venue optimization problem data is solved using the annealing mean field descent method to obtain an optimization scheme, including: setting initial temperature and cooling rate parameters, maintaining the exploration degrees of freedom of the decision variables and iteratively updating the probability values ​​of the decision variables, and gradually converging the probability values ​​to a definite binary solution as the temperature decreases; formulating operational decisions based on the optimization scheme and adjusting resource allocation; and an optimization monitoring module for real-time monitoring and continuous optimization of the operational optimization results, adjusting the optimization strategy based on actual operational feedback. Specifically, the optimization monitoring module is used to: receive the operational optimization results, construct an association rule mining model based on an LRU-enhanced genetic algorithm to obtain an association rule set, encode potential association rules as chromosomes of the genetic algorithm, and use the support, confidence, and lift of the association rules as optimization objectives. The evolutionary mechanism of the LRU-enhanced genetic algorithm is used to optimize the set of association rules. A rule cache is used to check the rule cache before generating candidate rules to avoid redundant computation. Based on the set of association rules, a two-factor decision verification system is applied to evaluate the optimization effect, resulting in an optimization effect evaluation result. The set of association rules is divided into decision rules and verification rules. The decision rules characterize the direct relationship between actions and results and generate preliminary optimization schemes. The verification rules characterize the decision environment and boundary conditions and evaluate the feasibility and potential risks of the preliminary optimization schemes. Only when the preliminary optimization scheme passes the tests of both the decision rules and the verification rules is it confirmed as a valid optimization effect evaluation result. The optimization strategy and parameter configuration are dynamically adjusted based on the optimization effect evaluation result.

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