The classical gradient flow optimization algorithm requires a valid initial point before starting the recursive algorithm,and the existing methods can’t guarantee that the initial values fully satisfy the friction cone constraints of contact point in the optimization process of gradient flow algorithm.In order to improve safety margin and prevent the finger from slipping at contact point,we present an iterative method of safe initial values with safety margin detection and develop a gradient flow optimization algorithm based on the safe initial values.Firstly,the safety margin is defined which more intuitively reflects the margin of the grasping forces at contact point.The resulting safe initial values can be achieved by the detection of desired safety margin at each iteration.Secondly,the safe initial values are usually not optimal,even with the valid initial values,and it can’t always ensure that the finger contact force always satisfies the friction cone constraints during the optimization.It is an effective way to eliminate the unreliable initial values in the optimization and obtain a safer initial values by increasing the safety margin.By transforming the safe initial values into an initial point of the gradient flow algorithm,the final optimized values of grasping forces can be generated efficiently by gradient flow iteration.Grasp examples of the soft multi-fingered hand indicate the effectiveness of the general solution of the force optimization algorithm based on safety margin detection.The method eliminates the shortcomings of the gradient flow optimization process caused by the initial value problem and provides a more accurate and reliable force optimization result for multi-fingered dexterous manipulation. The classical gradient flow optimization algorithm requires a valid initial point before starting the recursive algorithm, and the existing methods can not guarantee that the initial values ​​satisfy satisfy the friction cone constraints of contact point in the optimization process of gradient flow algorithm. In order to improve safety margin and prevent the finger from slipping at contact point, we present an iterative method of safe initial values ​​with safety margin detection and develop a gradient flow optimization algorithm based on the safe initial values. Firstly, the safety margin is defined which more intuitively reflects the margin of the grasping forces at contact point. The resulting safe initial values ​​can be achieved by the detection of the desired safety margin at each iteration. Secondarily, the safe initial values ​​are usually not optimal, even with the valid initial values, and it can not always ensure that the finger contact force always satisfies the friction cone constraints during the o ptimization. It is an effective way to eliminate the unreliable initial values ​​in the optimization and obtain a safer initial values ​​by increasing the safety margin. By transforming the safe initial values ​​into an initial point of the gradient flow algorithm, the final optimized values ​​of grasping forces can be efficiently incorporated by gradient flow iteration. Grasp examples of the soft multi-fingered hand indication the effectiveness of the general solution of the force optimization algorithm based on safety margin detection. method short for the shortcomings of the gradient flow optimization process caused by the initial value problem and provide a more accurate and reliable force optimization result for multi-fingered dexterous manipulation.