Note that Go computers have exponentially gotten stronger through the monte-carlo method. The most advanced Go computers simply "test" various moves by playing billions of games to completion, and then uses that to test the "probability of winning" with a particular move.

In short: use Bayesian logic against the following question: "Calculate the probability of winning given that I've moved a stone at X location". Use monte-carlo to attempt to estimate hard numbers.

I'm not familiar with your terminology... my school of Go uses the term "Tesuji" (Japanese) to describe what you seem to call "Dingshi" (sounds like a Chinese name to me...). In any case, a computer with strong Tesuji (or maybe Dingshi in your terminology), combined with a monte-carlo method to look for "long-term strategic" moves is what has gotten Go Computers to where they are today.

Humans are superior at parallel processing. Go players activate the brain region of vision, and literally think by seeing the board state. A lot of Go study is seeing patterns and shapes... 4-point bend is life, or Ko in the corner, Crane Nest, Tiger Mouth, the Ladder... etc. etc.

Go has probably been so hard for computers to "solve" not because Go is "harder" than Chess (it is... but I don't think that's the primary reason), but instead because humans brains are innately wired to be better at Go than at Chess. The vision-area of the human's brain is very large, and "hacking" the vision center of the brain to make it think about Go is very effective.