Innovation and Practice of Socialized Development Models for Winter Sports in Higher Education Institutions in Heilongjiang Province Driven by Resource Advantages

This study addresses the demand for socialized sharing of ice and snow sports resources in higher education institutions in Heilongjiang Province, proposing a "dual-core driven" development model.It constructs an intelligent resource integration platform based on the k-nearest neighbor clustering algorithm and optimizes teacher participation mechanisms using an agency incentive model.Through simulation experiments, the method is validated to significantly enhance resource sharing efficiency.The k-nearest neighbor clustering algorithm achieves 99.74% accuracy and 98.98% recall rate (converging after 30 iterations), representing a 6-percentage-point improvement over traditional methods.Under 500 concurrent users, resource sharing speed reaches 583.94 MB/s, with a peak throughput of 760.05 MB/s, marking a 31.7%improvement over cloud computing solutions.The sports resource information integration system constructed in this paper demonstrates strong transmission stability, with a resource loss rate of only 3.6% under high-concurrency scenarios (1,000 users), which is 1.4-2.6 percentage points lower than the comparison method.Case analysis shows that resource integration efficiency reaches 99.17%, with an average efficiency exceeding 98% across the five major resource categories.Empirical evidence indicates that this model effectively addresses the challenges of scattered, low-quality, and stagnant winter sports resources through technological innovation and mechanism coordination, providing a scalable path for the socialized development of winter sports in cold-region universities.