42 results about "Preference relation" patented technology

A collaborative filtering recommendation method for integrating preference relationship and trust relationship includes the following steps: digging preference relationship between users according to a project grading datum of users and building a preference relationship network; integrating preference relationship and trust relationship and building a preference and trust relationship network; positioning similar neighbors of a target user in the Markov random walk method and on the basis of the preference and trust relationship network; and forecasting grading values of corresponding projects for the target user on the basis of the grading value of the similar neighbors on a certain project. By adopting the method, a recommendation system can forecast grades of the projects made by the users efficiently in a novel mode. Compared with the prior art, the collaborative filtering recommendation method has the advantages of being simple, easy to achieve and capable of generating accurate grading forecasting values; and being convenient to select due to the fact that the method has only one parameter and recommended results are insensitive to the parameter.

The invention provides a method based on fuzzy preference relation and D-S evidence theory. The first step is to obtain the basic probability distribution function of multi-sensor information in mechanical equipment monitoring; In the second step, the credibility of the evidence is obtained by the cosine angle function, that is, the credibility matrix. In the third step, we construct the uncertainty matrix based on Deng entropy variance, measure the uncertainty of evidence, and obtain the preference relationship between the evidence. In the fourth step, the fuzzy preference relation matrix isconstructed by using the preference relation obtained in the previous step, and the ranking value instead of BPA is generated. In the fifth step, the weighted average evidence is obtained by modifyingthe weight vector through the confidence matrix and the ranking value. In the step 6 Fusion of weighted average evidences by Dempster combination rule for n-1 times, decisions are made once the results of the fusion are obtained. The present invention proposes a novel conflict evidence correction technique for obtaining modified weighted average evidence.

The invention relates to a store recommendation algorithm based on user-store interactive behaviors in network-physical spaces. The store recommendation algorithm comprises 1) an off-line user-store preference relation learning stage, in which user-store interactive behaviors in the physical space are extracted from Wifi logs generated by the users, a User-Store preference relationship is obtainedaccording to the Wifi logs, and modeling is performed on the relation between stores and store attributes; and 2) a tripartite graph and preference relationship based store recommendation stage, in which a tripartite graph about the users, stores and store attributes is built so as to predict the relationship strength between a predicted user u3 and an accessed store s1. The store recommendationalgorithm has the beneficial effect that the store recommendation model can obtain the best recommendation performance for all kinds of store recommendation, and has an advantage in digging shopping preferences of the users by comprehensively considering the interactive behaviors between the users and stores in the physical space and the network space.

The invention provides a photography teaching system and a photography teaching method. The method comprises the following steps: presetting a movement track of a photographic device, respectively regarding a scene component as a main body object at each coordinate point, acquiring photos formed by combining composition parameters with shooting parameters of the scene components, and transmitting the photos to an upper computer to be stored in real time; starting a teaching simulating operation device, selecting coordinates on the device interface, and respectively defining the scene components; establishing the selection range and preference relations of the composition parameters and the shooting parameters of the scene components at the coordinates by the upper computer; according to the defined selection range and the preference relations, selecting the composition parameters and the shooting parameters, performing picture synthesis, and displaying the synthesized picture on the interface; submitting the finally selected picture and the corresponding shooting parameters to the system to be saved. The photography teaching method disclosed by the invention allows students to perform practical operation after teaching by teachers through simulating outdoor photography in a classroom, so that the teaching efficiency can be greatly improved.

The invention discloses a scheduling method and system, and the method comprises the steps: collecting performance parameters provided by a plurality of working nodes, enabling the working nodes to beservers which bear a load balancing work task, enabling the plurality of servers to be arranged in a load balancing server cluster network, and obtaining the load state of the server cluster network;And according to the performance parameter set and the load. Missing scheduling preference information occurring after newly adding working nodes into a server cluster network under a plurality of performance parameters can be complemented; Through the construction of the multi-working-node scheduling preference relationship, all performance parameters of the working nodes are integrated, and a fuzzy relationship matrix is established for the scheduling preference of each parameter to each working node through the load balancing scheduling node to calculate missing information.

The invention relates to a competition score evaluation method based on the social selection theory and belongs to the competition evaluation and colony decision field. According to the method, firstly, competitor preference relations of referees are acquired, and a competitor pairing preference matrix is established on the basis of the competitor preference relations; secondly, according to the pairing preference matrix, a digraph is constructed, and strongest paths are calculated; and lastly, a strongest path matrix is generated, and the final competitor sequence is acquired through comparing the strongest paths of competitors. Through taking consideration of the competitor preference relations of the referees, the digraph is constructed, the strongest paths are further calculated, maneuverability in an ordering process is weakened essentially, and a new method for score evaluation on most subjective competitions is provided.

The invention discloses an image rapid movement estimation method based on a self-adaptive mechanism, which is implemented as following steps: step 1, classifying movement vectors; step 2, counting coefficients a and b in a formula; step 3, selecting a search manner, and adopting a SDSP (Small Diamond Search Pattern) and a SSP (Square Search Pattern) which are parallel in preference relation; and step 4, estimating the rapid movement. According to the image rapid movement estimation method provided by the invention, the self-adaptive search selection is performed according to the local variation degree of the movement vectors; based on the relation between the LMSD and the MVVD, the LMSD is adopted to classify the movement vectors; and according to the experimental result on an MPEG-4 check platform, compared with other algorithms, the algorithm in the invention has improved movement estimation speed, similar or better performance, and favorable effectiveness and practicability.

The invention belongs to the technical field of data mining and recommendation systems, and discloses a user behavior recommendation model establishment method and a position recommendation method based on spatio-temporal information. Firstly, time level influence degrees of different position types are learned; secondly, the sign-in data is mapped into a plurality of time periods and constructing a behavior data set fusing time correlation; then, the preference relationship between the user and the behavior is updated in real time; and finally, the attraction degree of the aggregation phenomenon of the same type of positions on the geographic space to the users is researched, a personalized position recommendation model is constructed by fusing the spatio-temporal information of the user behaviors, and the most suitable position is recommended to each user. The method is novel in that the model considers the time correlation of user access behaviors, in addition, the model provides a real-time behavior prediction method and provides a new angle to research the attraction degree of a position aggregation phenomenon to the user, and finally the accuracy of personalized position recommendation is improved by fusing spatio-temporal information.

To enable efficient abduction even for observations that are faulty or inadequately modeled, a relaxed abduction problem is proposed in order to explain the largest possible part of the observations with as few assumptions as possible. On the basis of two preference orders over a subset of observations and a subset of assumptions, tuples can therefore be determined such that the theory, together with the subset of assumptions, explains the subset of observations. The formulation as a multi-criteria optimization problem eliminates the need to offset assumptions made and explained observations against one another. Due to the technical soundness of the approach, specific properties of the set of results (such as correctness, completeness etc.), can be checked, which is particularly advantageous in safety-critical applications. The complexity of the problem-solving process can be influenced and therefore flexibly adapted in terms of domain requirements through the selection of the underlying representation language and preference relations. The invention can be applied to any technical system, e.g. plants or power stations.

The invention provides an emergency task planning scheme evaluation method and system based on personalized consensus, a storage medium and electronic equipment, and relates to the technical field of emergency task planning scheme evaluation. The method comprises the following steps: according to a plurality of given emergency task planning schemes, obtaining an individual decision preference matrix determined after each evaluation expert compares the different emergency task planning schemes in pairs, and adopting a language distribution preference relationship as a decision opinion expression form in the individual decision preference matrix, by quantifying the linguistic items, not only can the linguistic items in the decision matrix have operability, but also personalized individual semantics of evaluation experts can be obtained; according to the initial group consensus degree, an automatic feedback algorithm is adopted to adjust personal preferences of the evaluation experts, the adjusted interval fuzzy preference relation of each evaluation expert is obtained, and evaluation information of the planning scheme with the low consensus level corresponding to the evaluation experts with the low consensus level can be automatically adjusted, so that the group consensus level is improved.

The invention provides a conflict game analysis visualization method, device and equipment based on a graph model, and the method comprises the steps: determining feasible states in a conflict game and a transition relation between the feasible states, and constructing a conflict state transition graph; determining a preference relationship between the feasible states, and constructing a conflict state preference graph; determining the stability condition of each decision maker in each feasible state, and constructing a global game state diagram; constructing a game state evolution diagram by taking any feasible state as a starting point; respectively taking any two feasible states as a starting point and an ending point, and constructing a state reachable network diagram. By analyzing the stability, balance and accessibility of the states, the preference and stability conditions of decision makers about the states and all reachable paths between any two specified states are visually displayed, and the method has the advantages that the visualization of the conflict game analysis process is improved, and analysts are assisted in dynamically tracking and understanding the conflict game evolution process.

The invention discloses a network intrusion detection method based on a Plackett-Luce model,belonging to the technical field of network security. The method comprises the following steps: generating network traffic data in a network according to users; obtaining preference values of a user for network traffic at different times by utilizing a Packett-Luce model; determining the preference relationship of the user to different network flows and the network flow domination times according to the preference value; then, according to a logarithm likelihood function of a Plackett-Luce model, obtaining a logarithm likelihood function of the Plackett-Luce model; constructing an iterative function by using a maximum likelihood estimation method; and finally, standardizing the domination times of the network traffic and substituting into an iterative function to calculate the observation weight of the network traffic each time, andtaking the result as a detection result to detect the network flow.The technical problem that whether the network flow of the user is abnormal or not is judged by adopting the Plackett-Luce model on the basis of the network flow data of the user is solved, inconsistency of behavior habits of different users is fully considered, and whether an intrusion behavior exists in the network or not can be better judged.

The embodiment of the invention provides a satellite observation scheme screening method and system based on intuitive language preference relationship particles, and belongs to the technical field of satellite observation method decision making. The method comprises the following steps: acquiring an evaluation information matrix of each evaluation system; inserting a plurality of segmentation points in the language item set of the intuitionistic language preference relationship to obtain an information granule of each language item; setting the flexibility to determine information granules corresponding to the membership degree and the non-membership degree; optimizing the segmentation points and the flexibility by adopting a PSO (Particle Swarm Optimization) algorithm to obtain an optimal information granule; updating each evaluation information matrix according to the optimal information particle of each evaluation information; aggregating the evaluation information matrixes of all the evaluation systems to obtain an aggregated fusion evaluation information matrix; obtaining evaluation information of each satellite observation scheme according to an intuitive language induction ordered weighted average operator, and obtaining a score function value corresponding to each piece of evaluation information; and selecting the satellite observation scheme with the maximum score function value as an optimal scheme.