Penalty model for delay of bidding section construction period in South-to-North Water Diversion Eastern Route Project from perspective of programs

According to the multi-project and program management theory, this paper analyzes the program generation principle and establishes a program based on progress goals. On the basis of the present situation of calculation of penalty for delay of the bidding section construction period with the critical path method, we studied the effects of contractor-induced delay of the bidding section construction period in detail, including the effects on the construction period of the bidding section itself, the earliest start times of the next bidding section and other subsequent bidding sections, and the construction period of the program, and then constructed a penalty model for delay of the bidding section construction period from the perspective of programs. Using the penalty model, we conducted a practical analysis of penalty for delay of the construction period of the Baoying station program in the South-to-North Water Diversion Project. The model can help determine the amount of penalty for delay of the construction period in bidding sections scientifically and reasonably,

Image detection and verification of visual navigation route during cotton field management period

In order to meet the actual operation demand of visual navigation during cotton field management period,image detection algorithm of visual navigation route during this period was investigated in this research.Firstly,for the operation images under natural environment,the approach of color component difference,which is applicable for cotton field management,was adopted to extract the target characteristics of different regions inside and outside cotton field.Secondly,the median filtering method was employed to eliminate noise in the images and realize smoothing process of the images.Then,according to the regional vertical cumulative distribution graph of the images,the boundary characteristic of the cotton seedling region was obtained and the central position of the cotton seedling row was determined.Finally,the detection of the candidate points cluster was realized,and the navigation route was extracted by Hough transformation passing the known point.The testing results showed that the algorithms could rapidly and accurately detect the navigation route during cotton field management period.And the average processing time periods for each frame image at the emergence,strong seedling,budding and blooming stages were 41.43 ms,67.83 ms,68.80 ms and 74.05 ms,respectively.The detection has the advantage of high accuracy,strong robustness and fast speed,and is simultaneously less vulnerable to interference from external environment,which satisfies the practical operation requirements of cotton field management machinery.