With the development of high-throughput sequencing technology， massive genome sequence data provide a data basis to understand the structure of genome. As an essential part of genomics research， splice site identification plays a vital role in gene discovery and determination of gene structure， and is of great importance for understanding the expression of gene traits. To address the problem that existing models cannot extract high-dimensional features of DNA （DeoxyriboNucleic Acid） sequences sufficiently， a splice site prediction model consisted of BERT （Bidirectional Encoder Representations from Transformers） and parallel Convolutional Neural Network （CNN） was constructed， namely BERT-splice. Firstly， the DNA language model was trained by BERT pre-training method to extract the contextual dynamic association features of DNA sequences and map DNA sequence features with a high-dimensional matrix. Then， the DNA language model was used to map the human reference genome sequence hg19 data into a high-dimensional matrix， and the result was adopted as input of parallel CNN classifier for retraining. Finally， a splice site prediction model was constructed on the basis of the above. Experimental results show that the prediction accuracy of BERT-splice model is 96.55% on the donor set of DNA splice sites and 95.80% on the acceptor set， which improved by 1.55% and 1.72% respectively， compared to that of the BERT and Recurrent Convolutional Neural Network （RCNN） constructed prediction model BERT-RCNN. Meanwhile， the average False Positive Rate （FPR） of donor/acceptor splice sites tested on five complete human gene sequences is 4.74%. The above verifies that the effectiveness of BERT-splice model for gene splice site prediction.

To solve the problem of stockout， increasing inventory level and the fluctuation of order quantity caused by stochastic disturbance， an optimization model of inventory system under stochastic disturbance based on Active Disturbance Rejection Control （ADRC） was proposed. Firstly， according to the operational management logic behind the purchase-sale-storage product and information flows， the transfer function of the inventory system was obtained and transformed to a second-order state space standard form by the Laplace transform. Secondly， an optimization model of inventory system under stochastic disturbance based on ADRC including the tracking differentiator， the extended state observer and the nonlinear state error feedback control law was designed to control and compensate the adverse effects on the inventory system caused by stochastic disturbance under the premise of ensuing system stability. Finally， simulations were carried out by using data collected from the industry to verify the effectiveness of the optimization model on optimization of the inventory system under stochastic disturbance. Simulation results show that compared to the inventory feedback control model without ADRC， the optimization model of inventory system under stochastic disturbance based on ADRC has the residual inventory reduced by 40%， the average order quantity reduced by 47.4%， the order fluctuation decreased by 39.3%， and the stockout of enterprise inventory system caused by stochastic disturbance greatly improved. It can be seen that the optimization model of inventory system under stochastic disturbance based on ADRC can guide enterprises to make a reasonable ordering decision， decrease the inventory level， improve the stability of inventory system dynamically， and provide the scientific theoretical reference and countermeasures for the actual operations of enterprises.