3 results about "Jump function" patented technology

This application relates to an inline hooking method and apparatus for compiled languages. The method includes: responding to a received inline hooking request, determining the size of the target function to be inline hooked; determining whether the target function can be inline hooked based on the size of the target function and the difference between the address of the hook function and the address of the target function; a hook function, a function used to execute the functionality of the target function by calling a jump function; when it is determined that the target function can be inline hooked, copying the instruction code of the target function to a preset memory area, and revising the content of the copied instruction code to obtain the jump function of the target function; writing a target jump instruction at the beginning of the target function, and using the target jump instruction to redirect the call to the target function to the hook function. This method solves the problem that existing inline hooking methods are not applicable to compiled languages.

The invention relates to the technical field of micro-grids, in particular to a state change-oriented micro-grid load intelligent evaluation method, which comprises the following steps of: acquiring a load power data sequence of a micro-grid when the load state of the micro-grid changes; performing load change fitting on the load power data sequence by using an S-type jump function to construct a nonlinear fitting residual factor for representing a load jump process; calculating a data mean value of the load power data sequence to obtain a load fluctuation value sequence, and performing time axis inversion on the load fluctuation value sequence to construct a time domain reflection symmetry factor; and forming a two-dimensional feature vector by the nonlinear fitting residual factor and the time domain reflection symmetry factor, performing clustering identification on the two-dimensional feature vector, determining an event type corresponding to the load state change of the microgrid, and realizing adaptive classification identification of the load state change event type through clustering. And the load change evaluation efficiency and precision of the micro-grid are improved.

The invention provides a process routing method based on a multi-model optimization and rule backspacing mechanism. The method comprises the following steps: configuring a node with a jump judgment function in an intelligent process as an intelligent routing node; the node preferentially calls the main reasoning model to analyze the input context, generates a recommendation path and confidence, judges whether the recommendation path is adopted or not according to a confidence threshold, and enters multi-model parallel reasoning if the recommendation path does not reach the standard; calling at least one alternative model for parallel analysis, outputting candidate paths and confidence coefficients, constructing a comprehensive scoring function based on the confidence coefficients, model historical performance, response delay and resource consumption, and selecting a qualified model path with the highest score for skipping; if all model scores do not reach the standard or path conflicts are difficult to solve, rule rollback is triggered, and a rule engine is called to determine a path according to a preset service rule. The method can improve the accuracy, robustness and flexibility of process routing decision, guarantees stable progress of the process, and is suitable for complex intelligent process scenes in multiple fields of finance, customer service, e-commerce and the like.