Research on Risk Dependency Structures and Resource Allocation Optimization in New Energy Technology Collaboration within Enterprise Distributed Innovation

As new energy technology systems grow increasingly complex, corporate innovation activities in photovoltaics, energy storage, wind power, and energy management exhibit distinct distributed characteristics. Cross-enterprise collaboration and cross-regional innovation have become pivotal drivers for industrial upgrading. However, the lengthy new energy industrial chain, numerous participants, and high technical coupling lead to significant knowledge stickiness in knowledge transfer processes, thereby impairing collaborative innovation efficiency.To address this issue, this paper constructs a probabilistic resource allocation optimization model for distributed innovation networks in new energy enterprises. First, Least Squares Support Vector Machine (LSSVM) is employed to predict the success rate of collaborative innovation tasks. Model parameters are jointly optimized using grid search and Particle Swarm Optimization (PSO) under RBF, Polynomial, and Sigmoid kernel functions to adapt to different types of technological coupling and knowledge stickiness structures.Subsequently, a Gaussian Copula is introduced to characterize the risk dependency structure among technological maturity, external market volatility, and R&D collaboration complexity between innovation nodes. This enables the calculation of conditional value at risk (CoVaR) to quantify collaborative innovation risks under high uncertainty in new energy technologies. During resource allocation, a cost-loss model based on Bayesian decision theory is established, and the simulated annealing algorithm is employed to solve for the optimal combination of R&D and collaborative resources.Simulation results demonstrate that under conditions of rapid new energy technology iteration and significant knowledge stickiness, this method reduces resource waste by 17%–23% compared to traditional empirical allocation approaches while enhancing cross-enterprise innovation collaboration efficiency. This study provides a quantifiable and interpretable decision-making method for distributed innovation management in the new energy industry, holding significant implications for building an efficient industrial collaborative innovation ecosystem.