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

Abstract

The invention provides an intelligent stadium operation analysis method and system based on multi-source data fusion, and the method comprises the steps: obtaining internal data, operation data and external environment data of a stadium, carrying out the cleaning and preprocessing of the multi-source data, building a distributed storage architecture, and carrying out the cleaning and preprocessing of the multi-source data; and performing multi-source data fusion analysis based on the standardized data, constructing a correlation analysis model, generating a data analysis result, executing venue operation optimization, and performing real-time monitoring and continuous optimization on the operation optimization result. According to the system, a complete closed loop of data acquisition-processing-application is constructed, the problems of inaccurate people flow statistics, low resource utilization rate, lack of data support in operation decision and the like in traditional stadium operation are solved, omnibearing intelligent management of stadium operation is realized, the stadium utilization rate is improved, the operation cost is reduced, and the user satisfaction is improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to an intelligent sports stadium operation analysis method and system based on multi-source data fusion. Background Art

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

[0003] In existing sports venue operations and management, data collection primarily relies on manual record-keeping or simple electronic devices, making it difficult to achieve comprehensive, real-time, and accurate data acquisition. For example, crowd flow statistics often rely on manual counting or simple entrance counters, which fail to reflect the actual usage and length of stay in different areas. Venue usage statistics are primarily based on reservation records, making it difficult to distinguish between no-shows, actual use, and idle status. Environmental parameter monitoring equipment is dispersed and data is isolated, making it difficult to form a holistic environmental management system.

[0004] Traditional data processing and analysis methods often rely on single-dimensional statistical analysis and lack the ability to correlate data from multiple sources. For example, they are unable to effectively correlate weather and traffic conditions with venue utilization; they struggle to identify the complex relationships between visitor flow, venue reservations, and revenue; and they are unable to deeply explore user behavior patterns and preferences. These analytical limitations prevent venue managers from gaining comprehensive and in-depth data insights, hindering the implementation of data-driven decision-making.

[0005] In terms of operational decision support, existing technologies are mostly limited to basic reporting and simple analysis, lacking intelligent decision-making recommendations and automated optimization capabilities. For example, venue pricing strategies often use fixed models, making it difficult to dynamically adjust to changing demand; resource scheduling relies primarily on empirical judgment, making it difficult to achieve optimal configuration; and user services lack personalized recommendation mechanisms, failing to meet the differentiated needs of different users.

[0006] To sum up, the existing technology faces the problems of incomplete data collection, unsystematic data processing, and shallow data application in the operation and management of sports venues. It is difficult to support the venues to achieve data-driven intelligent operation. There is an urgent need for a sports venue operation analysis method and system that can realize multi-source data fusion analysis and intelligent decision support. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide an intelligent sports stadium 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 crowd statistics, low resource utilization, and lack of data support for operational decisions in traditional sports stadium operations, and realizes all-round intelligent management of venue operations.

[0008] To solve the above technical problems, the present invention provides an intelligent sports stadium operation analysis method based on multi-source data fusion, comprising: acquiring internal stadium data, stadium operation data and external environment data to form a multi-source data set as input data for standardized processing; performing data cleaning and preprocessing on the multi-source data set, 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; executing venue operation optimization based on the data analysis results, and obtaining operation optimization results including resource scheduling optimization, price strategy formulation and user service improvement; performing real-time monitoring and continuous optimization on the operation optimization results, and 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 physical environment parameters and personnel activity data inside the venue to obtain real-time data inside the venue; collecting venue reservation, income and cost expenditure data to obtain venue operation data; accessing surrounding business, traffic and weather data to obtain external environment data.

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

[0011] Preferably, the data cleaning and preprocessing of the multi-source data set and the establishment of a distributed storage architecture include: receiving the standardized data, constructing a dynamic directed graph structure, and setting each venue as an independent data node; establishing a path connection for data transmission between the independent data nodes; configuring the data transmission priority based on the weight of the path connection to obtain a data transmission priority result.

[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 result to ensure data consistency between 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; based on the real-time processed data, applying a federated learning method to perform collaborative analysis while protecting data privacy.

[0013] Preferably, performing venue operation optimization based on the data analysis results includes: modeling the venue optimization problem data of the data analysis results as a quadratic unconstrained binary optimization problem to obtain a decision option set; based on the decision option set, using the annealing mean field descent method to solve the venue optimization problem data to obtain an optimization plan; formulating operation decisions based on the optimization plan and adjusting resource allocation.

[0014] Preferably, the real-time monitoring and continuous optimization of the operation optimization results include: receiving the operation optimization results, constructing an association rule mining model based on the LRU enhanced genetic algorithm, and obtaining an association rule set; based on the association rule set, applying a two-factor decision verification system to evaluate the optimization effect and 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 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 application of the dual-factor decision verification system to evaluate the optimization effect includes: dividing the mined association rule set into decision rules and verification rules; generating a preliminary optimization plan using the decision rules; and evaluating the feasibility of the plan using 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 association 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 according to network conditions and business needs based on the initial connection weight; and maintaining data synchronization between nodes through a distributed consistency algorithm.

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

[0020] Preferably, the adjusting rule priority according to 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] The present invention also provides an intelligent sports stadium operation analysis system based on multi-source data fusion, comprising:

[0022] The data acquisition module is used to obtain the venue's internal data, venue operation data, and external environmental data to form a multi-source data set as input data for standardization processing;

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

[0024] A data analysis module is used to perform multi-source data fusion analysis based on the standardized data, build a correlation analysis model, and generate data analysis results as input data for operation optimization;

[0025] A 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 optimization monitoring module is used to monitor and continuously optimize the operation optimization results in real time, and adjust the optimization strategy based on actual operation feedback.

[0027] The beneficial effects of the present invention include:

[0028] 1. The present invention realizes the systematic and intelligent operation and management of sports venues by building a complete multi-source data collection-processing-application closed loop, increases the utilization rate of venues by 15-30%, and optimizes the efficiency of resource allocation.

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

[0030] 3. The present 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 dual-factor decision verification system and cognitive bias correction mechanism designed in this invention greatly improve the reliability and robustness of decision-making, provide scientific data support for venue management decisions, and reduce investment risks.

[0032] 5. Through systematic data management and application, the present invention builds the data assets of sports venues, improves user satisfaction and retention rate, and provides continuous value for the long-term development of the venues. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 This is a flow chart of the intelligent sports stadium operation analysis method based on multi-source data fusion of the present invention;

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

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

[0037] Figure 4 A schematic diagram of the structure of the dynamic graph of path connections and the self-stable weak Byzantine decentralized nodes of the present invention;

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

[0039] Figure 6 This is a structural diagram of the intelligent sports stadium operation analysis system based on multi-source data fusion of the present invention. DETAILED DESCRIPTION

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

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

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

[0043] Step S2, performing data cleaning and preprocessing on the multi-source data set, establishing a distributed storage architecture, and obtaining standardized data;

[0044] In this embodiment, outlier detection and processing are performed on the collected multi-source data sets to generate a data quality score. This process adopts a combination strategy of multiple anomaly detection algorithms, including statistical methods (such as Z-score, box plot method) to identify numerical anomalies, time series anomalies based on time series models (such as ARIMA, exponential smoothing), and density-based methods (such as DBSCAN) to find outliers in multidimensional feature space. For example, in pedestrian flow data, the system can identify zero-value anomalies caused by sensor failure or abnormal peaks caused by special activities; in environmental data, it can detect gradual anomalies caused by temperature and humidity sensor drift. For each piece of data, a comprehensive quality score will be calculated based on its integrity, consistency, timeliness and rationality, and the score results will provide a reference for subsequent processing.

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

[0046] The cleaned data is then converted to a unified format and standard to obtain standardized data. This step resolves inconsistencies in multi-source data in terms of time standards, spatial reference systems, units of measurement, and encoding specifications. 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 into the same reference system; and temperature data is uniformly expressed in degrees Celsius. In addition, the system also performs structured conversion on the data to ensure that all data conforms to predefined data models and field specifications, facilitating subsequent storage and querying. 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 associated within the same framework.

[0047] After data cleaning and standardization, the standardized data is received and a dynamic directed graph structure is constructed. Each venue is set up as an independent data node, establishing a distributed storage architecture. This architecture is particularly suitable for multi-venue, cross-regional sports stadium group applications. Each venue acts as an independent data node, maintaining independent data management rights while connecting to 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 are dynamically adjusted according to business needs and network conditions. The system first sets the initial connection weight based on the frequency of data interaction between nodes. For example, higher-weight connections will be established between venue nodes with similar locations or high business relevance. Subsequently, the system will dynamically adjust these connection relationships based on the actual network conditions, changes in data interaction frequency, and the evolution of business needs in actual operation to ensure that data transmission always follows the optimal path. In addition, the system maintains data synchronization between nodes through distributed consensus algorithms (such as Paxos or Raft) to ensure data consistency and reliability in a distributed environment.

[0049] Finally, data transmission priorities are configured based on the weights of the path connections, resulting in a data transmission priority result. This mechanism ensures that critical business data is transmitted and processed first even when network resources are limited or the network load is high. For example, when a sudden security incident is detected at a venue, the relevant alarm data is given the highest transmission priority to ensure timely delivery to the relevant nodes and central management system. Daily statistical data and historical archived information may be given a lower priority and transmitted during network idle periods. This intelligent data transmission strategy significantly improves the system's operational 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, performing multi-source data fusion analysis based on the standardized data, building a correlation analysis model, and generating data analysis results as input data for operation 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 between nodes and obtain a distributed node management strategy. Weak Byzantine fault tolerance is an advanced technology that adapts to the incompletely trusted environments 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 for highly correlated venues, forming a "soft backup" network of data. The system assigns a reputation score to each node based on the quality and stability of historical data, and dynamically adjusts its weight and decision-making influence in the network accordingly. When a node's data is detected to be abnormal or inconsistent, the system automatically initiates a consensus algorithm, verifies the true state through data verification from a majority of nodes, and isolates the problematic node. For example, if a venue's passenger flow data experiences unusual fluctuations, the system will cross-validate the data by referencing historical patterns and data from nearby venues to prevent single-point anomalies from affecting the overall analysis results. This self-stabilizing design ensures data reliability and analysis continuity in the face of network fluctuations, device failures, and 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 generate real-time processed data. This distributed edge computing model decentralizes computing power to individual venue nodes, enabling each node to possess local analysis capabilities and independently handle real-time data analysis tasks. 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 handle tasks such as crowd flow monitoring and environmental parameter anomaly detection in real time, reporting only processing results and anomaly events to the central system rather than transmitting the raw data stream. Furthermore, edge nodes utilize "data summary transmission" technology to transmit only locally processed data features and analysis results to other nodes or the central analysis unit. This technology reduces data transmission by over 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, on-site edge nodes can perform real-time crowd density monitoring and safety risk assessments, sending alerts to the central system only when congestion risks or safety hazards are detected, achieving efficient, "non-intrusive" monitoring.

[0054] Then, based on the real-time processed data, federated learning methods are 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 stadium scenario, each venue node does not need to upload sensitive user behavior data or business operation data. Instead, they only need to train the model locally and then share the model parameters to collaboratively build a more comprehensive and accurate analysis 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 profiling system without disclosing any personally identifiable information. This mechanism is particularly suitable for complex scenarios with multiple owners and venues. It effectively balances the value of data sharing with the need for privacy protection, and also complies with increasingly stringent data protection regulations.

[0055] In terms of model construction, we integrate 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 foot traffic distribution and venue utilization. The middle layer is a predictive model, which uses time series analysis and machine learning algorithms to predict future trends such as passenger flow and booking demand. The top layer is an explanatory model, which uses causal inference and knowledge graph technology to explore the causal relationships and business logic behind the data. For example, the system not only identifies the superficial correlation of "rainy days → increased demand for indoor venues," but also reveals the detailed mechanisms behind this phenomenon through multi-factor analysis, such as the relationship between the impact of rainy days and rainfall amount, duration, and weekend / weekday variations, providing deep insights for precise 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 produces natural language analysis reports explaining key findings and recommendations. For example, "Badminton court usage is low on Wednesday evenings (averaging only 65%), significantly lower than on Tuesdays and Thursdays (both exceeding 85%). It is recommended to implement specific promotions or membership discounts for Wednesday evenings." This multi-dimensional presentation, combining visualization and textual explanations, greatly improves the usability of analysis results, making it easy for even non-technical managers to 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, performing venue operation optimization based on the data analysis results, and obtaining operation optimization results including resource scheduling optimization, price strategy formulation and user service improvement;

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

[0060] First, the venue optimization problem resulting from the data analysis is modeled as a quadratic unconstrained binary optimization (QUBO) problem, generating a set of decision options. QUBO is a powerful mathematical modeling framework that can unify various complex combinatorial optimization problems and is particularly well-suited 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 specific user groups, or whether to increase staffing during a certain time period) as a binary variable. A quadratic objective function describes the expected effects and interactions of these decisions. For example, in a 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 each of these factors based on business priorities to form a unified optimization goal. Furthermore, the system incorporates various business constraints (such as staff work hour restrictions, equipment maintenance requirements, and space capacity limitations) into the model to ensure that the resulting solutions are both optimal and feasible. This QUBO-based mathematical modeling approach enables the system to address complex decision-making problems that are difficult to solve with traditional methods, such as the coordinated scheduling of multiple venues and time periods and the design of personalized pricing strategies.

[0061] Next, based on the set of decision options, the annealing mean field descent method is used to solve the venue optimization problem data and obtain an optimized solution. The annealing mean field descent (SAMF) method is an innovative algorithm for efficiently solving QUBO problems, particularly suitable for large-scale combinatorial optimization scenarios. Compared with traditional exact solution methods, SAMF can quickly find high-quality solutions close to the global optimum even with limited computing resources. Its performance advantage becomes more pronounced as the problem scale increases. The algorithm first sets the system's initial temperature and cooling rate parameters and enters a simulated annealing process. During the high-temperature phase, the system maintains the freedom to explore the decision variables, allowing each binary decision variable to take a "soft value" (i.e., a probability value between 0 and 1, indicating the propensity for choosing that decision) rather than being directly constrained to 0 or 1. This "soft decision" mechanism enables the algorithm to explore the solution space more broadly, avoiding premature regression into local optima. As the temperature gradually decreases during the simulated annealing process, the values of the decision variables gradually approach a fixed value of 0 or 1, ultimately converging to a high-quality binary solution, i.e., the specific optimization solution. For example, in the optimization of venue staff scheduling, the system may ultimately determine: reduce front desk staffing from Monday to Thursday mornings, increase venue assistant staff on weekends, and adjust the working hours of cleaning staff to avoid peak passenger flow periods, thereby minimizing labor costs while ensuring service quality.

[0062] In actual operation, a number of innovative improvements have been made to the annealing mean field descent method. First, an adaptive temperature regulation mechanism is introduced to dynamically adjust the cooling rate according to the degree of improvement of the objective function during the optimization process. When the search stagnates, the temperature is automatically increased to promote the escape from the local optimum. In the rapid convergence stage, the cooling is accelerated to improve efficiency. Secondly, a hierarchical solution strategy is adopted to decompose large and complex problems into multiple sub-problems. The framework solution is first determined at the coarse-grained level, and then the specific parameters are optimized at the fine-grained level, which significantly improves the solution efficiency. Thirdly, the system also designs a constraint softening mechanism to convert strict hard constraints into soft constraints with penalty terms, increase the continuity of the solution space, and improve the robustness and adaptability of the algorithm. These innovations enable the system to efficiently generate optimization solutions that both meet business needs and have implementation feasibility in the complex and changing sports venue operation environment.

[0063] Specific operational decisions and resource allocation adjustments are then made based on the optimization plan. This process goes beyond simple plan execution; rather, it integrates the actual business environment and operational experience to translate the mathematical optimization results into concrete, actionable measures. The system generates a detailed implementation plan, including specific resource scheduling arrangements (such as staff schedules and equipment usage schedules), pricing strategy implementation plans (such as pricing tables for different venues at different times of day and member discount policies), and user service improvement measures (such as personalized content recommendations and service process optimization points). The system also provides key performance indicator (KPI) forecasts for the implementation plan, such as expected revenue growth, cost savings, and customer flow changes, to provide managers with decision-making guidance. For example, for a badminton court operation optimization, the system not only proposes a 30% price reduction on weekday mornings but also provides a complete implementation plan, including a specific price list, recommended promotional copy, implementation schedule, and an evaluation of expected results (such as a 20% increase in usage and a 10% increase in total revenue).

[0064] In terms of dynamic response, an online adjustment mechanism has been developed that can quickly modify optimization plans based on real-time feedback and environmental changes. When external conditions change (such as sudden weather changes, large-scale events in the surrounding area, public transportation interruptions, etc.), the system can automatically adjust the established operation plan based on preset event response rules and real-time data. For example, when a sudden rainstorm is detected, the system will automatically increase the supply priority of indoor venues, adjust the reservation policy of outdoor venues, and push venue change suggestions and special offers to affected users, minimizing the impact of adverse factors and seizing potential business opportunities. This dynamic optimization capability enables venue operations to be highly adaptable to complex and changing environments.

[0065] Finally, a hierarchical 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 small, short-term price fluctuations) can be executed directly by the system; medium-impact decisions (such as resource reallocation during critical periods) require manager confirmation before execution; and major strategic adjustments (such as long-term pricing strategy changes and major resource investment decisions) must be submitted to the management team for discussion. This hierarchical mechanism ensures the efficiency of system operation while retaining the strategic judgment and experience of human managers, enabling intelligent decision-making through human-machine collaboration.

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

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

[0068] In this embodiment, operational optimization results are received and an association rule mining model based on an LRU-enhanced genetic algorithm is constructed to generate an association rule set. This model represents an innovative upgrade to traditional association rule mining and is particularly well-suited for the complex and volatile data environment of sports stadium 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, passenger flow, revenue, and user satisfaction) as potential association rules. This rule set is continuously optimized through the evolutionary mechanism of the genetic algorithm. Unlike traditional Apriori or FP-Growth algorithms, the LRU-enhanced genetic algorithm does not exhaustively enumerate all possible association rules. Instead, it is guided by business value and uses evolutionary computation to identify the most practical rule patterns. The system maintains a rule cache to record explored association rules and their evaluation results. This cache is dynamically managed using an LRU (least recently used) strategy. When new rules are generated and evaluated, the system prioritizes the cache to avoid duplicate calculations. Furthermore, the cache automatically adjusts the retention priority of rules based on their access frequency and time decay. For example, when a rule (such as "Rainy weekends → increased demand for indoor basketball courts → decreased pricing elasticity") is frequently verified to be effective, the system will increase its priority in the cache. Rules that have not been verified for a long time or have performed poorly will be downgraded or removed from the active cache. This mechanism ensures that algorithm resources are focused on the most promising search space, significantly improving computational efficiency and rule quality.

[0069] To adjust rule priorities, a sophisticated mechanism combining time decay and access frequency is employed. The system first calculates the access frequency and most recent access time of each rule to form an initial access frequency metric. Then, based on a designed time decay function (typically an exponential decay model), combined with the access frequency, a rule activity score is calculated. This score comprehensively reflects the rule's historical value and recent activity. A higher rule activity score indicates a higher likelihood of reference value in the current business context. Finally, the system dynamically adjusts the priority of rules in the cache based on the activity score, ensuring that the most valuable rules remain highly accessible. For example, a rule describing the effectiveness of a specific promotional campaign may be frequently accessed and validated during the campaign, resulting in a high activity score. However, after the campaign ends, while the rule's historical value remains, its activity gradually decreases over time, causing its priority to be lowered until it is reactivated when a similar campaign is launched again. This dynamic balancing mechanism enables the system to retain valuable historical experience while focusing on the most relevant business models at the moment, 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 results, resulting in an optimization evaluation result. This innovative decision-making reliability assurance mechanism significantly improves the accuracy and security of optimization decisions through two independent yet mutually verified decision paths. The system first divides the mined association rule set into two categories: decision rules and verification rules. Decision rules focus on the direct relationship between actions and outcomes, generating specific optimization recommendations. Verification rules, on the other hand, focus on the decision-making context and boundary conditions, assessing the effectiveness and potential risks of decisions. The system uses decision rules to generate preliminary optimization solutions, such as "Provide exclusive member discounts for indoor fitness areas on rainy afternoons to increase facility utilization during off-peak hours." Verification rules are then used to assess the feasibility of these solutions, checking whether they meet known business constraints and risk thresholds, such as fitness area capacity limits, member sensitivity to price fluctuations, and operational ease of implementation. Only optimization solutions that pass both the decision and verification rules are deemed valid and recommended for continued implementation or further optimization. This two-factor verification mechanism effectively reduces the decision-making risks that may be brought about by a single data model and improves the reliability and robustness of the optimization solution.

[0071] In actual operation, the dual-factor decision verification system has demonstrated significant value. For example, in the intelligent pricing system of a comprehensive sports center, the traditional single decision rule may recommend a significant price increase during a specific period based solely on historical demand data; the dual-factor system will use verification rules to evaluate the impact of such adjustments on long-term customer loyalty, as well as the comparison with the prices of surrounding competing venues, ultimately providing a more balanced and sustainable pricing strategy. For example, in the optimization of equipment maintenance plans, the system not only considers the maintenance schedule that minimizes costs, but also uses verification rules to ensure that maintenance activities are not overly concentrated, resulting in service interruptions or conflicts with important events. This multi-dimensional decision verification mechanism enables sports venues to pursue short-term operational efficiency while taking into account long-term business sustainability and user experience, achieving truly intelligent operation and management.

[0072] Then, based on the optimization effect evaluation results, 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 to achieve continuous improvement in system performance. For example, if a price elasticity model is found to be inaccurate for a specific site type, the system will automatically adjust the model's parameters or select a more suitable model structure. If a certain type of optimization measure is identified as frequently encountering operational obstacles during implementation, the system will modify the relevant constraints to generate a more feasible solution. This self-adjustment mechanism enables the system to continuously learn and adapt to complex and changing operational environments, continuously improving optimization results.

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

[0074] Finally, a complete decision-effectiveness feedback loop was established, recording the implementation process and performance data of each optimization measure, forming a rich decision-effectiveness knowledge base. This knowledge base is not only used to evaluate the effectiveness of current decisions but also provides empirical reference for future decisions in similar scenarios. Based on the implementation results of decisions, the system automatically analyzes the applicability of various optimization strategies in different scenarios, identifies best practices and potential risks, and uses this knowledge to optimize correlation analysis models and decision-making strategies. For example, through long-term accumulation of performance data, the system may discover that the effectiveness of a certain promotional campaign varies across different seasons and user groups, allowing for more precise design and targeting of such campaigns in future decisions, maximizing return on investment.

[0075] Through this series of real-time monitoring and continuous optimization mechanisms, a complete closed loop has been formed from data collection, analysis and processing, solution generation, effect evaluation, and strategy adjustment, achieving truly intelligent and adaptive optimization of stadium operations. This data-driven continuous optimization strategy ensures that stadium operations are always efficient, able to respond to market changes in a timely manner, accurately meet user needs, and ultimately achieve the operational goals of maximizing resource utilization and continuously increasing revenue.

[0076] In step S1, the acquisition of venue internal data, venue operation data and external environment data includes: collecting physical environment parameters and personnel activity data inside the venue to obtain real-time data inside the venue; collecting venue reservation, income and cost expenditure data to obtain venue operation data; accessing surrounding business, traffic and weather data to obtain external environment data.

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

[0078] The venue's internal data collection module is primarily responsible for collecting real-time data on the venue's physical environment and user activities. This module uses a traffic monitoring system to capture changes in and distribution of passenger flow within the venue. It can accurately calculate the density, movement trajectory, and dwell time of people in each functional area, providing a basis for optimizing space utilization and diverting personnel. The utilization statistics system focuses on recording the actual use of the venue, distinguishing between unbooked guests, actual use, and idle status, enabling refined management of venue resources. The environmental parameter collection system continuously monitors environmental factors such as temperature, humidity, noise, and air quality within the venue. These parameters are not only related to the user experience but are also closely related to energy consumption and equipment maintenance. For example, in a swimming pool scenario, this module can monitor water temperature, air humidity, and chlorine concentration in real time to ensure a comfortable and safe environment while providing data support for intelligent energy management.

[0079] The implementation of the crowd monitoring module includes: deploying an infrared sensor array and a network of high-definition cameras to cover key areas of the venue; employing deep learning algorithms for people counting, trajectory tracking, and regional heat analysis; designing an array of Wi-Fi probes to capture mobile device signals and triangulate individual locations based on signal strength; and constructing a temporal and spatial variation model for crowd flow to identify patterns of venue usage. The system automatically generates a heat map of the venue, displaying crowd density distribution at different times and predicting future short-term crowd flow trends.

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

[0081] The operational data collection module focuses on acquiring data on venue operations and business aspects. The booking rate statistics system automatically records bookings for various time periods and venue types, including booking channels, lead times, cancellation rates, and other indicators. These data directly reflect market demand and user preferences. The revenue data collection system integrates multiple revenue sources such as venue rentals, membership fees, training courses, and peripheral merchandise to construct a comprehensive financial data view. The cost expenditure analysis system tracks operating costs such as labor, energy, maintenance, and taxes, and identifies savings through cost structure analysis. Taking the basketball arena as an example, this module can identify weekday evenings and weekend mornings as the most popular time periods, adjust pricing strategies and service configurations accordingly, and identify cost optimization opportunities during non-peak hours.

[0082] The external data access module links venue data with external environmental factors. The surrounding commercial data access system integrates passenger flow and activity information of surrounding shopping malls, catering and entertainment facilities to analyze synergy and competitive relationships. The traffic data access system obtains public transportation, road conditions and parking lot usage to evaluate the impact of traffic convenience on passenger flow. The weather and climate data access system introduces meteorological data to analyze the differentiated impact of weather factors on the utilization rate of indoor and outdoor venues. For example, in a comprehensive sports center scenario, this module can identify the pattern of rainy weather causing demand for outdoor venues to shift to indoor venues, and predict the impact of seasonal changes on different projects, providing forward-looking guidance for resource allocation.

[0083] The data access 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 a system standard format; and implementing a data source reliability assessment mechanism to ensure the quality of accessed data.

[0084] The pedestrian flow monitoring module utilizes a high-definition network camera with a resolution of at least 1080p and a frame rate of at least 25fps, ensuring coverage of key corridors and functional areas. The infrared sensor is a bidirectional counting type, installed at a height of 3 meters and boasts an accuracy exceeding 95%. The Wi-Fi probes have a 30-second acquisition interval and a detection radius of 25 meters, achieving a position accuracy of ±3 meters using a three-point positioning algorithm. The image processing algorithm utilizes the lightweight deep learning model MobileNet-SSD, enabling real-time processing on edge devices (processing latency <200ms). In the environmental parameter collection system, the temperature and humidity sensors sample every 5 minutes, the carbon dioxide concentration meter every 2 minutes, and the noise meter every 1 minute. All sensor data is transmitted to a local gateway via the MQTT protocol and then uploaded to the central server via an encrypted channel, with data transmission latency under 3 seconds. Each sensor features a low-power design and supports both wired and battery backup modes. In battery mode, it can operate continuously for over 72 hours, ensuring system reliability.

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

[0086] like Figure 3 As shown, the data processing system includes three key modules: data cleaning and preprocessing, data storage and management, and data correlation analysis, which are jointly 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 unified data format 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 abnormal data detection and processing system automatically identifies and processes abnormal values, missing values, and duplicate values in the data to ensure data quality. The data completion and repair system uses statistical and machine learning methods to reasonably estimate and repair incomplete data. In actual scenarios, when a pedestrian flow sensor in a certain area temporarily fails, this module can automatically infer the missing pedestrian flow data based on historical patterns and data from adjacent areas, ensuring that the analysis is not interrupted due to local data loss. This module effectively solves the inevitable quality issues in multi-source data collection and provides a reliable data basis for decision-making.

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

[0089] When the multi-source data set is cleaned and preprocessed and a distributed storage architecture is established, the method further includes: receiving the standardized data, constructing a dynamic directed graph structure, and setting each venue as an independent data node; establishing a path connection for data transmission between the independent data nodes; and configuring the data transmission priority based on the weight of the path connection to obtain a data transmission priority result.

[0090] The establishing of 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 according to network conditions and business needs based on the initial connection weight; and maintaining data synchronization between nodes through a distributed consistency algorithm.

[0091] like Figure 4As shown, the path-connected dynamic graph and self-stable weakly Byzantine distributed node technology are innovative extensions of the traditional centralized architecture. At the data storage architecture level, this technology abandons the traditional centralized data warehouse model and instead adopts a dynamic directed graph structure to organize data nodes distributed across venues. Each venue acts as an independent data node, maintaining data autonomy while maintaining connections with other nodes through dynamic paths. In this structure, data propagates between nodes in the form of event streams, with a path-weighted algorithm determining data transmission priority and path selection. For example, when a venue detects an unusual passenger flow fluctuation, the relevant data is first transmitted to nodes in geographically close or highly business-related venues, enabling these venues to proactively respond to potential passenger flow changes. Furthermore, the dynamic graph structure adjusts node connections in real time based on data exchange frequency, network conditions, and business needs, ensuring optimal data transmission efficiency despite changing network conditions.

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

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

[0094] The data association analysis module transforms scattered data points into systematic knowledge. The multi-source data fusion algorithm system associates and integrates data from different sources and types to discover relationships between cross-domain data. The spatiotemporal data association analysis system focuses on exploring patterns and regularities in data across time and space, such as the dynamic changes in venue usage heat maps over time. The causal inference system explores potential causal relationships from correlation data, providing a deeper basis for decision-making. In practical applications, this module can identify the combined impact of weather changes, traffic conditions, and surrounding activities on venue traffic, and even quantify the influence weight of different factors. For example, it discovered that for every 5-degree temperature increase, the booking rate for outdoor tennis courts decreases by 12%, while the demand for indoor air-conditioned venues increases by 8%.

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

[0096] In step S3, the multi-source data fusion analysis is performed based on the standardized data, including: constructing a weak Byzantine fault tolerance mechanism based on the data transmission priority result to ensure data consistency between 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; based on the real-time processed data, applying a federated learning method to perform collaborative analysis while protecting data privacy.

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

[0098] At the data analysis level, a hybrid architecture combining distributed edge computing and centralized analysis is implemented. Each venue node possesses local analysis capabilities, enabling it to independently handle real-time data analysis tasks such as crowd flow monitoring and environmental parameter anomaly detection. Furthermore, 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 mining tasks, such as regional passenger flow migration patterns and cross-user analysis across different venue types. Furthermore, the system utilizes "transfer learning" technology, enabling nodes across different venues to share the training results of analytical models while retaining their respective business characteristics.

[0099] To protect data privacy, a "regional federated learning" mechanism has been introduced. Each venue node participates in distributed model training while preserving the locality of original data. For example, in user behavior analysis, different venues can collaborate to build a more comprehensive user profiling system by simply exchanging model parameters, rather than sharing raw user data. This mechanism is particularly suitable for complex scenarios with multiple owners and venues, effectively balancing the value of data sharing with the need for privacy protection.

[0100] During operation, an adaptive load balancing algorithm dynamically adjusts task allocation and data routing strategies based on each node's computing resources, network bandwidth, and task urgency. During peak hours, the system prioritizes the real-time processing and transmission of core business data, intelligently downgrading or delaying non-critical tasks. If a node is detected to be overloaded, related tasks are automatically migrated to a nearby, less-loaded node, 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 a 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 upload only model gradients, not raw data. The coordinator uses a secure aggregation protocol (such as homomorphic encryption) to merge the 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 that at least 80% of participants must submit valid gradients before a global update can be performed. The model architecture uses a multi-layer perceptron (MLP) tailored to the characteristics of the venue data. It consists of three hidden layers, each with 128, 64, and 32 neurons, respectively. Reluctant Unit (ReLU) is used as the activation function, and the output layer uses an appropriate activation function based on the task type (e.g., Sigmoid for binary classification problems).

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

[0103] The annealing mean field descent method is used to solve the venue optimization problem data, including: setting the system initial temperature and cooling rate parameters; maintaining the exploration freedom of the decision variables in 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 the key link in converting data analysis results into actual business value.

[0105] like Figure 5 As shown in the figure, the annealing mean field descent for quadratic unconstrained binary optimization (SAMF-QUBO) is an advanced mathematical optimization method that is particularly suitable for complex optimization problems in sports venues. At the mathematical model level, the SAMF-QUBO scheme uniformly models various optimization problems in sports venues as 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 period of time, whether to implement promotions for specific user groups, etc.), and describes the decision objectives and constraints through quadratic objective functions. For example, in the resource scheduling problem of multiple venues and multiple 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 algorithmic level, the mean field approximation is first introduced, simplifying the complex interactions between binary variables into interactions between each variable and the mean field. Initially, the system assigns a probabilistic value to each decision variable (rather than a simple 0 / 1 binary value), indicating the probability of that decision being chosen. Through iterative updates, the probability of each variable is gradually adjusted based on its interactions with other parts of the system. This "soft decision" approach allows the algorithm to explore a wider solution space during the search process, avoiding being trapped in local optima.

[0107] This solution also incorporates a simulated annealing temperature control mechanism, maintaining a high "system temperature" during the initial optimization phase, allowing the decision variables sufficient freedom for exploration. As iterations proceed, the system temperature gradually decreases, and the probability values of the decision variables gradually approach a fixed 0 or 1, ultimately converging to a high-quality binary solution. The temperature reduction strategy employs an adaptive strategy, dynamically adjusting the cooling rate based on the improvement in the objective function to achieve a balance between exploration and exploitation.

[0108] The operational decision support module directly supports venue management's operational decisions. The venue operations optimization system, based on data analysis, provides operational improvement recommendations, including optimal venue maintenance schedules, staff scheduling, 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 discover that badminton courts are in high demand during weekday evenings and idle in the mornings. It then recommends a 30% reduction in morning prices and the introduction of "early morning practice discount packages." It also suggests increasing the number of venue assistants during peak weekend hours to optimize the user experience. These precise, data-driven recommendations significantly improve operational efficiency and venue revenue.

[0109] The venue operation optimization implementation plan includes: building a prediction model based on historical data to predict the future utilization rate of the venue; developing an intelligent scheduling algorithm to optimize the venue equipment maintenance time and staffing; and designing a dynamic pricing engine to automatically adjust the price strategy 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 operating risks, predicts cash flow, and identifies operational risk points. The loan limit accounting system provides loan decision support to financial institutions based on the venue's stable income and historical operating data. The insurance claims ratio calculation system analyzes the venue's history of security incidents and usage to optimize insurance plans and rates. In a new sports center project, this module can build a financial forecasting model based on the operating data of existing venues, providing detailed data support to banks and obtaining more favorable loan terms. At the same time, through quantitative analysis of safety risks, customized insurance plans can be negotiated with insurance companies to reduce insurance costs while improving the level of protection.

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

[0112] The user service optimization module focuses on improving member experience and customer value. The personalized recommendation system provides customized recommendations for venues, courses, and services based on user historical behavior and preferences. The user behavior analysis system deeply explores the patterns and habits of users in using venues and identifies the characteristics of high-value user groups. The service quality evaluation system collects and analyzes user feedback, evaluates service quality, and guides improvement directions. In actual application scenarios, the system may identify a user as a basketball enthusiast who plays basketball regularly every Friday night and occasionally participates in fitness classes. The system will automatically push venue reservation reminders on Thursdays and intelligently recommend gym discount packages after the user reserves the basketball court. At the same time, it provides personalized membership upgrade plans based on the user's frequency of use. This data-driven personalized service significantly improves user satisfaction and loyalty.

[0113] The user behavior analysis implementation plan includes: establishing a user portrait system to identify user sports preferences, consumption habits and other characteristics; developing a recommendation algorithm based on collaborative filtering 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, SAMF-QUBO technology features a specially designed problem decomposition and hierarchical solution mechanism. For extremely large-scale optimization problems, such as seasonal resource planning across multiple venues, the system first decomposes the problem into multiple subproblems, solves them separately, and then integrates them. This decomposition strategy is based on the coupling strength between variables, grouping strongly coupled variables into the same subproblem. Furthermore, the algorithm employs a multi-scale solution strategy, first obtaining a rough outline of the global solution at a coarse-grained level and then gradually refining it to a finer level, significantly improving computational efficiency.

[0115] To address the dynamic changes often found in sports venue environments, this solution incorporates an online dynamic adjustment mechanism. When external conditions change (such as sudden weather changes or large-scale temporary events), the system doesn't need to re-solve the entire problem. Instead, it makes local adjustments based on the current state and new constraints, enabling real-time decision-making. For example, if it detects a drop in demand for outdoor venues due to rain, the system can quickly reallocate resources and adjust the opening hours and pricing strategies for indoor venues.

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

[0117] In this embodiment, in the specific implementation of the annealing mean field descent method, the initial temperature of the system is set to 8.0, the cooling rate parameter is 0.92, and the iteration termination condition is that the temperature is lower than 0.01 or the objective function changes by less than 0.0001 for 10 consecutive iterations. In the high temperature stage (temperature>5.0), the decision variable is allowed to take values in the range of [0.1, 0.9], indicating the probability of choosing this decision; as the temperature decreases (1.0<temperature<5.0), the value range of the decision variable gradually tightens to [0.05, 0.95]; when the temperature is further reduced (temperature<1.0), the decision variable quickly tends to a binary solution and eventually converges to a certain value of 0 or 1. For large-scale optimization problems, the system adopts a problem decomposition strategy, classifying variables with a coupling degree between variables higher than 0.7 into the same subproblem. The scale of a single subproblem is controlled within 100-200 variables, and each subproblem is integrated through coordination variables after solution.

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

[0119] The invention relates to a method for constructing an association rule mining model based on an LRU enhanced genetic algorithm, comprising: setting a rule cache library to record explored association rules; adjusting rule priorities according to access frequency and time decay to obtain adjusted rule priorities; and executing rule evolution and optimization based on the adjusted rule priorities.

[0120] The dual-factor decision verification system is applied to evaluate the optimization effect, including: dividing the mined association rule set into decision rules and verification rules; generating a preliminary optimization plan using the decision rules; and evaluating the feasibility of the plan through the verification rules.

[0121] Adjusting rule priorities based on access frequency and time decay includes: calculating the access frequency and 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. When 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 correlations 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 building 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 priority based on their frequency of use and timeliness. For example, if an association rule (such as "weekday lunch → price discount → increased tennis court reservations") is found to be valid multiple times in recent verification, its priority is increased. Rules that have not been verified for a long time or have poor verification results are lowered in priority or removed from the active cache. This mechanism ensures that computing resources are focused on discovering and verifying the most commercially valuable rules, significantly improving the system's operational efficiency and rule quality.

[0124] After obtaining the set of association rules, an innovative two-factor decision-making verification mechanism is applied to comprehensively evaluate the optimization results. This mechanism divides association rules into two categories: decision rules and verification rules. Decision rules are used to directly generate optimization recommendations, such as "Lowering venue prices on weekday mornings can improve venue utilization." Verification rules, on the other hand, serve as an independent verification mechanism, assessing the reliability and potential risks of these optimization recommendations, such as "A price adjustment of more than 15% may lead to changes in user perception." Only optimization solutions that pass both decision and verification rules are recognized as valid and recommended for continued implementation. This two-factor verification mechanism effectively reduces the decision-making risks associated with a single data model and improves the reliability and robustness of optimization solutions.

[0125] Finally, based on the optimization effect evaluation results, the parameter configuration of the existing optimization strategy is automatically adjusted to form a closed-loop optimization. For example, when a pricing strategy is found to be effective under specific weather conditions but generally ineffective under other conditions, the trigger conditions and parameter settings of the strategy will be automatically adjusted to enable it to be more accurately applied to the most suitable scenario. At the same time, based on the long-term accumulated effect data, the parameters of the underlying algorithm model will be continuously optimized, such as adjusting the crossover and mutation probabilities in the genetic algorithm, updating the time decay coefficient of the LRU cache, etc., so that the entire optimization system becomes increasingly intelligent and accurate as data accumulates. Through this continuous self-learning and adjustment mechanism, it can adapt to the ever-changing market environment and user needs, and always maintain the leading optimization effect.

[0126] This data-driven, continuous optimization strategy ensures that stadium operations remain highly efficient, enabling timely responses to market changes and precise fulfillment of user needs, ultimately achieving the operational goals of maximizing resource utilization and continuously increasing revenue. In practice, stadiums employing this approach typically see significant operational improvements within three to six months, including a 15-30% increase in venue utilization, a 20%+ increase in user satisfaction, and a 10-20% reduction in operating costs.

[0127] Furthermore, a two-factor decision verification system is designed to address the reliability and risk control issues that traditional data analysis can face when applied to real-world decision-making. This system draws on the concept of two-factor authentication in financial security and innovatively applies it to data-driven decision-making. By using two independent yet mutually verified decision paths, it significantly improves the accuracy and reliability of smart stadium operational decisions.

[0128] In the core architecture of the two-factor decision verification system, the mined association rule set is first divided into two categories: decision rules and verification rules. Decision rules primarily focus on the direct relationship between action and outcome. For example, "Raising badminton court prices by 20% on weekend evenings can increase total revenue by 15%." These rules typically have clear action-oriented guidelines and directly guide specific operational decisions. Verification rules, on the other hand, focus on the decision context and boundary conditions. For example, "Raising badminton court prices by more than 30% will result in a more than 10% drop in bookings" or "Price sensitivity is significantly reduced on rainy days." These rules are not used directly to generate decisions, but rather serve as criteria for decision effectiveness and risk assessment. This rule separation ensures that the system comprehensively considers different perspectives when evaluating decisions.

[0129] In practice, the two-factor decision verification system employs a three-step workflow. First, it generates preliminary optimization scenarios based on a set of decision rules. For example, "It is recommended to implement a gradient pricing strategy for tennis courts from Tuesday to Thursday evenings, increasing prices by 15% during peak hours, maintaining the same price during shoulder hours, and reducing prices by 20% during off-peak hours." These preliminary scenarios represent the optimal course of action based on historical data analysis. Second, the system automatically submits these preliminary scenarios to the verification rules for evaluation, checking whether they violate known constraints or risk thresholds. For example, the system assesses whether the price adjustment is within an acceptable range, whether seasonal factors are accounted for, and whether it is consistent with the operational strategies of other areas of the venue. Finally, the system retains only those scenarios that pass both the decision rule generation and verification rule tests and assigns them a credibility score, helping managers identify the most reliable optimization options.

[0130] Another key feature of the two-factor decision verification system is its adaptive learning capability. It continuously tracks and records the performance of each verified decision solution after implementation, using this feedback to continuously adjust and optimize the weighting and credibility of decision and verification rules. For example, if a decision rule repeatedly generates solutions that perform well in actual implementation, the system will increase the weight of that rule. Conversely, if a verification rule frequently incorrectly rejects proven solutions, the system will reduce the stringency of the verification rule or adjust its applicable conditions. Through this continuous learning mechanism, the two-factor decision verification system can become increasingly accurate and reliable as operational data accumulates.

[0131] In terms of risk control, the dual-factor decision verification system has a specially designed mechanism for handling exceptions. When the system detects unusual or unprecedented operating conditions (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 more conservative and safe decision strategies are adopted when uncertainty increases, effectively preventing the risk of over-optimization caused by data analysis.

[0132] In actual applications, the two-factor decision verification system has demonstrated significant value in multiple scenarios. For example, in the intelligent pricing system of a comprehensive sports center, the traditional single decision rule may recommend a significant price increase during a specific period based solely on historical demand data; the two-factor system will use verification rules to evaluate the impact of such adjustments on long-term customer loyalty, as well as the comparison with the prices of surrounding competing venues, ultimately providing a more balanced and sustainable pricing strategy. For example, in the optimization of equipment maintenance plans, not only is the cost-minimizing maintenance schedule considered, but verification rules are also used to ensure that maintenance activities are not overly concentrated, resulting in service interruptions or conflicts with important events. This multi-dimensional decision verification mechanism enables sports venues to pursue short-term operational efficiency while taking into account long-term business sustainability and user experience, achieving truly intelligent operation and management.

[0133] Through the dual-factor decision verification system, the present invention provides a scientific, rigorous, practical and reliable intelligent auxiliary tool for sports venue operation decision-making, significantly reducing the deviations and risks that may occur in data-driven decision-making, improving the success rate of implementing optimization measures, and laying a solid foundation for the venue to achieve stable and efficient intelligent operation.

[0134] A multi-source data mining association optimization scheme based on an LRU-enhanced genetic algorithm and a two-factor decision-making system is an innovative extension of the original sports stadium data analysis and mining method. This scheme combines a least recently used (LRU) caching strategy, an improved genetic algorithm, and a two-factor verification decision-making mechanism to construct an efficient, adaptive, and cognitively corrective data association mining framework. It is particularly suitable for discovering and leveraging implicit relationships in multi-source heterogeneous sports stadium data, providing in-depth data support for refined operations.

[0135] In the specific implementation of the LRU enhanced genetic algorithm, the initial population size is set to 150 individuals, the crossover probability is 0.85, the initial mutation probability is 0.2 and 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 -λt , where λ = 0.05 and t is the time interval (in days) since the last visit. The activity score is calculated as Score = access frequency × (1 - time decay value). When the activity score falls below 0.2, the corresponding rule is downgraded; when it falls below 0.05, it is removed from the active cache. During rule evolution, a tournament selection strategy is used, randomly selecting five individuals from the population each time and selecting the ones with the highest fitness to advance to the next generation. An elite retention strategy retains the top 10% of individuals in each generation and advances them directly to the next generation.

[0136] At the association rule mining level, this solution first innovatively introduces a genetic algorithm framework enhanced by the LRU (Least Recently Used) caching strategy, addressing the efficiency and accuracy issues of traditional association rule algorithms when faced with high-dimensional sparse data. Unlike traditional Apriori or FP-Growth algorithms, this solution does not pursue the exhaustive enumeration of all possible association rules. Instead, it is guided by optimization goals and uses evolutionary computation to find the most business-valuable association patterns. The system encodes potential association rules as "chromosomes" and continuously optimizes the rule set by simulating the biological evolution process. At the same time, it introduces 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 enhancement mechanism lies in maintaining an adaptive rule cache that records recently explored rules and their evaluation results. When the algorithm generates a new candidate rule, the system first checks the cache to avoid duplicate computation. Simultaneously, the cache automatically adjusts the retention priority of rules based on access frequency and time decay, ensuring that computing resources are focused on promising search spaces. This mechanism is particularly well-suited for association rule mining in sports stadium environments. For example, it can efficiently discover multi-step association chains such as "rainy weather → increased bookings for indoor badminton courts → increased umbrella sales in the stadium's retail area," even in sparse data.

[0138] In terms of genetic algorithm design, this solution employs a multi-objective optimization framework, simultaneously considering four dimensions: rule support, confidence, lift, and business value. The algorithm not only focuses on statistically strong associations but also prioritizes business practicality, comprehensively evaluating rules using a custom fitness function. For example, while the rule "weekends → high traffic" may have high support, its business value is limited. Meanwhile, the rule "venue preferences of specific member groups during specific times," while having lower support, has significant value for personalized service and revenue optimization. This algorithm balances these considerations, prioritizing association rules with high commercial value.

[0139] The solution also incorporates adaptive crossover and mutation operators, dynamically adjusting genetic operation parameters based on the evolutionary stage and population diversity. During the initial exploration phase, the system maintains a high mutation rate to broadly search the solution space. As high-quality rules emerge, the algorithm gradually reduces the mutation rate, concentrating resources on a refined search in advantageous areas. Furthermore, the algorithm incorporates constraints on rule structure complexity 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 associated with directly applying traditional data mining results to decision-making. The system categorizes mined association rules into two categories: direct decision rules and auxiliary verification rules. Direct decision rules are used to generate preliminary decision recommendations, while auxiliary verification rules serve as an independent verification mechanism to assess the rationality and potential risks of preliminary decisions. Only decisions that pass both types of rule verification are recommended for execution, significantly improving decision reliability.

[0141] For example, when the system recommends raising the price of badminton courts on Wednesday nights based on the association rule "Wednesday nights → young white-collar users → high demand for badminton courts," the validation rule will examine the historical response pattern of "price increase → change in bookings" and whether current special factors (such as weather and nearby events) may affect the applicability of this rule. Only when the validation rule confirms that the pricing adjustment will not lead to a significant decrease in bookings will the system finally recommend the decision.

[0142] The solution also includes building a cognitive bias correction mechanism to identify cognitive traps in data analysis; establishing a decision-making effect feedback loop to record the effectiveness of decision implementation; and optimizing association analysis models and decision-making strategies based on the decision implementation effects.

[0143] Furthermore, the solution incorporates a cognitive bias correction mechanism to identify and address cognitive pitfalls such as confirmation bias and survivorship bias that may arise in data analysis. The system automatically identifies "comfortable" conclusions that are highly consistent with existing cognition and applies stricter verification standards to them. Furthermore, for counterintuitive but well-supported findings, the system provides a more detailed chain of evidence and explanations, helping decision makers overcome cognitive limitations. For example, data may reveal that certain venues considered "prime time" actually have lower-than-average profit margins. The system then analyzes cost structures and usage patterns in detail, helping managers reassess conventional wisdom.

[0144] In actual operation, this solution also incorporates a continuous learning mechanism to continuously adjust and optimize 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, building a decision-effect knowledge base. This feedback loop enables the system to automatically identify which types of association rules are most valuable in real-world applications, prioritizing these areas in subsequent mining processes, achieving continuous evolution and self-improvement of the model.

[0145] In addition, if Figure 6 As shown, the present invention implements an intelligent sports stadium operation analysis system based on multi-source data fusion, including:

[0146] Data collection module 101, used to obtain venue internal data, venue operation data and external environment data, and form a multi-source data set as input data for standardization processing;

[0147] The data processing module 102 is used to clean and pre-process the multi-source data set, 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, build a correlation analysis model, and generate data analysis results as input data for operation optimization;

[0149] Decision support module 104, configured to perform venue operation optimization based on the data analysis results, and obtain operation optimization results including resource scheduling optimization, price strategy formulation, and user service improvement;

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

[0151] In the implementation process of sports venue data analysis and mining methods, the entire implementation process 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, decision support and application implementation.

[0152] During the data collection, deployment, and implementation phase, a comprehensive venue environmental survey is first conducted, followed by a detailed floor plan of the venue, with each functional area marked. Based on the floor plan, sensor network planning is carried out, determining the number and installation locations of various sensor devices and developing a detailed deployment plan. This step requires comprehensive consideration of factors such as coverage, signal strength, and installation cost to ensure comprehensive and accurate data collection. Next, hardware equipment installation and commissioning will be carried out. This includes installing high-definition cameras at the venue entrance, main corridors, and various functional areas to ensure a comprehensive field of view. Wi-Fi probes will be deployed throughout the venue, with coverage exceeding 95%. Infrared sensors will be installed in key locations to complement the cameras. An array of environmental sensors for temperature, humidity, noise, and light will also be deployed. Finally, access channels for operational and external data will be established.

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

[0154] During the data storage and management implementation phase, we first build a tiered storage architecture, designing a multi-layer storage structure based on data type and access frequency. Next, we implement data security mechanisms, designing and implementing data encryption solutions and establishing a role-based access control system. Next, we build a data service middleware layer, developing unified data access interfaces and services to shield the underlying storage details. Finally, we establish a data lifecycle management system, formulate a data retention policy, and clearly define the retention periods and archiving rules for various types of data.

[0155] During the data analysis and mining implementation phase, basic statistical analysis begins with descriptive statistics on various data types, including venue usage time distribution, visitor flow fluctuations, booking patterns, and consumer behavior. Advanced data mining is then implemented to construct a time series forecasting model 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 mine and analyze service combinations and consumption links. Regression analysis is used to identify key factors influencing revenue and satisfaction. Multi-source data fusion analysis is then conducted, correlating 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] During the decision support and application implementation phase, we first established an operational decision support system and developed an intuitive data visualization dashboard to display key operational indicators and early warning information in real time. We also built a venue operations optimization recommendation engine to provide optimization suggestions for different scenarios, such as staff scheduling, energy usage, and maintenance schedule optimization. Next, we implemented marketing and user service applications. Based on user profiles and behavioral analysis, we built a personalized recommendation system to recommend suitable venues, time periods, and supporting services. We also developed a dynamic pricing system to adjust pricing strategies for different time periods and venues in real time based on demand forecasts. Next, we developed risk management and financial applications, including a venue operations risk early warning system to monitor and warn of operational, safety, and reputational risks. We also developed financial forecasting and planning tools to support budgeting and investment decisions. Finally, we implemented system iteration and continuous optimization, establishing an application performance tracking mechanism to evaluate the business value and user satisfaction of each application system. We also collected user feedback and new requirements to plan system functionality iterations. We identified data gaps and quality issues and optimized data collection strategies. We also continuously introduced new technologies and methods to enhance the system's intelligence.

[0157] The following is an example of a specific implementation scenario to further illustrate the implementation principle of the present invention:

[0158] Intelligent crowd flow management: After the system of the present invention was implemented in a comprehensive sports center, the system collected crowd flow data in real time through cameras (resolution not less than 1080p, frame rate 25fps) and Wi-Fi probe arrays (coverage 95%) distributed in various functional areas. The target detection algorithm based on YOLO v5 was used to identify and count people, with an accuracy rate of over 95%. When it was detected that the crowd density in the basketball area was close to the safety threshold (1.5 people per square meter), the system automatically pushed alternative venue information to the user through the venue APP. At the same time, based on historical data predictions showing that the crowd flow in this area will continue to increase by 23% in the next 30 minutes, the system automatically deployed 2 staff members to the area to assist in guidance and management. Through this function, the venue has improved the efficiency of safety management, reduced the frequency of congestion, optimized staff allocation, and increased human resource utilization by 18%.

[0159] Dynamic pricing optimization: The system analyzed six months of historical booking data for a badminton hall. Combining weather data with information about nearby events, it found that indoor badminton court utilization was 35% higher on rainy weekends than on sunny days, while lead time for bookings was 40% lower (mostly due to last-minute decisions for indoor sports). Based on this finding, the system recommended implementing a dynamic pricing strategy for indoor badminton courts on weekends with a predicted probability of precipitation greater than 70%: a 20% discount for advance bookings (>48 hours in advance), a 10% discount for bookings 24-48 hours in advance, and the original price for last-minute bookings (<12 hours in advance). After implementing this strategy, overall venue utilization increased by 27%, total revenue increased by 15%, and booking times became more evenly distributed, alleviating pressure on the system and front desk staff.

[0160] Personalized User Services: By analyzing Mr. Zhang's booking and usage data over the past three months, we identified that he regularly plays badminton every Thursday evening from 7:00 PM to 9:00 PM, preferring Court 2. We also discovered that he purchases sports drinks 67% of the time after each workout. Based on this user profile, the system automatically sends Mr. Zhang a badminton court reservation reminder every Wednesday, prioritizing his preferred Court 2. After confirming his reservation, the system also automatically sends him a sports drink coupon. Furthermore, the system detected that Mr. Zhang had viewed but not reserved the fitness area on Tuesdays for three consecutive weeks. Based on this, the system intelligently recommended a fitness and badminton combo package for Tuesdays, ultimately successfully increasing user activity and consumption frequency. At this venue, similar personalized services have increased member activity by 31% and repurchase rates 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 does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the present profession can make some changes or modifications to equivalent embodiments of the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. A smart sports stadium operation analysis method based on multi-source data fusion, characterized in that: include: Acquire venue internal data, venue operation data, and external environment data to form a multi-source data set as input data for standardization processing; Performing data cleaning and preprocessing on the multi-source data sets, establishing a distributed storage architecture, and obtaining standardized data; Perform multi-source data fusion analysis based on the standardized data, build a correlation analysis model, and generate data analysis results as input data for operation optimization; Optimize venue operations based on the data analysis results to obtain operational optimization results including resource scheduling optimization, pricing strategy formulation, and user service improvement; Monitor and continuously optimize the operational optimization results in real time, and adjust the optimization strategy based on actual operational feedback.

2. The method according to claim 1, characterized in that The acquisition of venue internal data, venue operation data and external environment data includes: Collect physical environment parameters and personnel activity data inside the venue to obtain real-time data inside the venue; Collect venue booking, revenue and cost expenditure data to obtain venue operation data; Access surrounding business, traffic and weather data to obtain external environment data.

3. The method according to claim 1, characterized in that The data cleaning and preprocessing of the multi-source data set 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; The cleaned data is converted 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 data set and establishing a distributed storage architecture includes: Receive the standardized data, construct a dynamic directed graph structure, and set each venue as an independent data node; Establishing a path connection for data transmission between the independent data nodes; The data transmission priority is configured based on the weight of the path connection to obtain a data transmission priority result.

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

6. The method according to claim 1, characterized in that The performing of venue operation optimization based on the data analysis results includes: Modeling the venue optimization problem data of the data analysis results as a quadratic unconstrained binary optimization problem to obtain a decision option set; Based on the decision option set, the annealing mean field descent method is used to solve the venue optimization problem data to obtain an optimization solution; Make operational decisions and adjust resource allocation based on the optimization plan.

7. The method according to claim 1, characterized in that The real-time monitoring and continuous optimization of the operation optimization results include: Receiving the operation optimization result, constructing an association rule mining model based on an LRU enhanced genetic algorithm, and obtaining an association rule set; Based on the association rule set, a two-factor decision verification system is applied to evaluate the optimization effect to obtain an optimization effect evaluation result; Dynamically adjust the optimization strategy and parameter configuration according to the optimization effect evaluation results.

8. The method according to claim 7, characterized in that The construction of an association rule mining model based on an LRU enhanced genetic algorithm includes: Set up a rule cache to record the explored association rules; Adjust the rule priority according to the access frequency and time decay to obtain the adjusted rule priority; Rule evolution and optimization are performed based on the adjusted rule priorities.

9. The method according to claim 7, characterized in that The application of the dual-factor decision verification system to evaluate the optimization effect includes: Dividing the mined association rule set into decision rules and verification rules; Generate preliminary optimization solutions using decision rules; Evaluate the feasibility of the solution through verification rules.

10. The method according to claim 1, characterized in that Also includes: Build a cognitive bias correction mechanism to identify cognitive traps in data analysis; Establish a decision-making feedback loop and record the effectiveness of decision implementation; Optimize the association analysis model and decision-making strategy based on the decision implementation effect.

11. The method according to claim 4, characterized in that The establishing a path connection for data transmission between the independent data nodes includes: Set the initial connection weight based on the frequency of data interaction between nodes; Based on the initial connection weights, dynamically adjusting the connection relationship according to network conditions and business needs; Data synchronization between nodes is maintained through a distributed consistency algorithm.

12. The method according to claim 6, characterized in that The method of using the annealing mean field descent method to solve the venue optimization problem data includes: Set the system initial temperature and cooling rate parameters; Maintaining the exploration freedom of decision variables during the high temperature phase; As the temperature decreases, it gradually converges to a definite binary solution.

13. The method according to claim 8, characterized in that The step of adjusting the rule priority according to the access frequency and time decay includes: Calculate the access frequency and the most recent access time of each rule to obtain the access frequency; Based on the time decay function and the access frequency, updating the activity score of the rule; Dynamically adjust the priority of rules in the cache based on the activity score.

14. An intelligent sports stadium operation analysis system based on multi-source data fusion, characterized in that: include: The data acquisition module is used to obtain the venue's internal data, venue operation data, and external environmental data to form a multi-source data set as input data for standardization processing; A data processing module is used to clean and preprocess the multi-source data sets, establish a distributed storage architecture, and obtain standardized data; A data analysis module is used to perform multi-source data fusion analysis based on the standardized data, build a correlation analysis model, and generate data analysis results as input data for operation optimization; A 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 optimization monitoring module is used to monitor and continuously optimize the operation optimization results in real time, and adjust the optimization strategy based on actual operation feedback.

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