import numpy as npfrom scipyoptimize import minimizegongyi_houdu = 005419093 000184155 -086020508gongyi_kongxilv = 750643042e+01 351974149e-01 260499105e-02 -154168336e-03 -175355368

The code imports the necessary libraries: numpy for numerical computations and scipy.optimize for optimization.
The code defines three lists: "gongyi_houdu", "gongyi_kongxilv", and "gongyi_yasuohuitan" which contain some predefined values.
The code defines the objective function "obj(X)" which takes a vector X as input and calculates the objective value based on the given formulas.
The code defines four constraint functions "con1(X)", "con2(X)", "con3(X)", and "con4(X)" which represent the constraints on the variables.
The code creates a list "mycons" which contains dictionaries specifying the type of each constraint and the corresponding function.
The code initializes the initial guess for the optimization variables in the list "x0".
The code defines the bounds for the optimization variables in the list "bound".
The code creates an empty list "xcall" to store the intermediate results during the optimization process.
The code defines the objective function "myobj" as a lambda function which calls the "obj" function.
The code uses the "minimize" function from scipy.optimize to perform the optimization. It takes the objective function, initial guess, bounds, constraints, and options as inputs.
The code saves the intermediate results in separate CSV files every 10 iterations.
The code prints the optimal objective value, success status, optimal solution, and any error message.