41 results about "Pairwise comparison matrix" patented technology

The invention discloses a service selection method supporting cross-organization collaborative affairs, comprising the following steps: the main server CPU reads the information of each participant in this collaboration and stores it in a memory; obtains the information of each participant in this collaboration The list of services used in collaboration; analyze and obtain the shared services of the collaborative participants; determine the quality requirements of the collaborators for the shared services; construct the decision space for the candidate objects of the shared services; The weight of the quality requirement is transmitted to the master server; the quality requirement weight of each collaborator is normalized; the degree of satisfaction of each shared service candidate object in the decision space for the quality requirement of each collaborator is calculated; the master server selects the appropriate Serve. The invention starts from establishing the service description, and the whole selection process is automatically calculated by the computer, which improves the credibility and operating efficiency.

The embodiment of the invention discloses a monocular depth estimation algorithm, system and device, and a storage medium. The method comprises the steps: firstly estimating an original depth image and a plurality of common depth images with different resolution levels for a monocular image through employing a convolutional neural network; secondly, recovering a relative depth map from the selective estimation data according to rank-1 properties of the paired comparison matrixes; and 3, decomposing the common depth map and the relative depth map into a plurality of depth components, and optimizing and recombining the depth components to reconstruct an optimal depth map. Experimental results show that the embodiment of the invention has good depth estimation performance.

The invention discloses an airport runway allocation decision method based on fuzzy hierarchical analysis and evidence theory, and belongs to the technical field of runway allocation. This method firstly analyzes the factors affecting the allocation of entry and departure runways, and establishes a hierarchical analysis model; then, according to the specific situation of the current runway allocation problem, defines the triangular fuzzy pairwise comparison matrix of the hierarchical model; then uses the triangular fuzzy pairwise comparison matrix and its Consistency information, construct the evidence and its uncertainty given by different controllers; comprehensively consider the conflict between the evidence and the prior reliability of the controller, apply evidence theory to fuse different evidence; calculate the confidence interval of the fusion result, Make a decision; finally, compare the decision result of each decision maker with the execution result, and update the prior reliability of the controller for the next runway allocation decision. This method fully considers the subjective factors in the runway allocation process and converts them into the uncertainty of evidence for decision-making, making the runway allocation results more realistic.

The present invention relates to the big data processing technology in the computer information system, in particular to an AHP-based method and system for quantifying the criminal tendency and risk degree, which realizes the efficient collection and extraction of business data by calculating the traffic scheduling optimization algorithm; and based on the The situation analyzer adopts the AHP algorithm to quantify and grade the tendency and danger of criminals. The present invention provides a method and system for quantifying the tendency and danger of criminals, effectively processes and utilizes prison business information, and avoids the destruction of the sorting due to a single mistake through a pairwise comparison matrix algorithm, and solves the problem of It is difficult to quantify the tendency and danger of criminals.