16 results about "Local evaluation" patented technology

The invention discloses a concrete column axial deformation identification method and system based on a visual image, and relates to the technical field of building structure monitoring, and the method comprises the steps: collecting a continuous image sequence of a concrete column from different sides; edge detection and morphological processing are carried out on the continuous image sequence, the overall contour of the cylinder is extracted, the axial center line of the cylinder is fitted, and global axial length change features are generated; executing adjacent frame tracking identification of the local evaluation center point, and establishing a first local deformation feature; performing local deformation feature extraction of the convolutional neural network, and establishing a second local deformation feature; and hierarchical fusion is carried out, and an axial deformation identification result is established. According to the invention, the technical problems of one-sided deformation information, insufficient monitoring precision and poor reliability caused by single feature extraction of the axial deformation of the concrete column in the prior art are solved, and the technical effects of realizing multi-source feature fusion identification of the axial deformation of the concrete column and improving the precision and reliability of deformation monitoring are achieved.

The invention introduces an Internet of Vehicles vehicle reputation dynamic evaluation method based on an evaluation graph. The method comprises the following specific steps: S1, constructing a global vehicle evaluation graph; s2, carrying out local evaluation and set division; s3, evaluation set selection; s4, performing weighted aggregation calculation; s5, performing iterative updating and amplitude limiting processing; s6, vehicle-trust center closed-loop interaction is carried out; s7, performing long-term reputation evolution; according to the method, evaluation control and collusion attacks are prevented, long-term and smooth evolution of the vehicle reputation is also realized, the robustness and credibility of an Internet of Vehicles system are remarkably improved, meanwhile, forward excitation on low-reputation vehicles is considered, and a dynamic and credible vehicle reputation management system is formed.

The invention provides a high-speed railway joint debugging and joint test evaluation method and device, and the method comprises the steps: determining a high-speed railway joint debugging and joint test detection index according to the operation data of a high-speed railway system; cloud processing is carried out on the detection indexes to obtain cloud feature parameters of the detection indexes; calculating the information entropy of each detection index based on an entropy weight method; determining the weight of each detection index according to the information entropy of each detection index; according to the cloud feature parameters of the detection indexes and the weight of each detection index, a high-speed railway joint debugging and joint test comprehensive evaluation cloud model is obtained through a weighted average method; according to the high-speed railway joint debugging and joint test comprehensive evaluation cloud model, determining a high-speed railway joint debugging and joint test evaluation result; according to the method, the transformation of the high-speed railway joint debugging and test from local evaluation to system overall comprehensive evaluation is realized, the fuzziness and randomness in the evaluation are effectively quantified through the combination of the cloud feature parameters and the weights, the reliability of the evaluation result is improved, and a data reference is provided for railway engineering acceptance.

A kind of stop method of space vehicle target access optimization task based on local search and exploration enhancement belongs to space vehicle control technical field.To solve the problem of optimization termination control with robustness and self-adaptation, the present application designs optimization termination criterion as when the maximum prediction standard deviation of Gaussian process regression surrogate model in current local area is lower than preset threshold, terminate the iteration optimization of bayesian optimization;The design optimization termination criterion is embedded into BO;The prediction standard deviation of GPR surrogate model is used to measure the cognitive uncertainty of GPR surrogate model to objective function at a point;Set the judgment condition to judge whether the exploration enhancement ability of acquisition function is enhanced and whether local evaluation is triggered.The present application realizes the optimization termination control with robustness and self-adaptation.

The invention relates to the technical field of artificial intelligence security and natural language processing, and particularly provides a model security availability joint evaluation method based on dynamic multi-objective optimization. The method comprises the following steps: iteratively updating a test suffix by using an optimization signal provided by a local reference model to generate an evaluation sample; a local evaluation sample optimized based on a reference model is applied to a to-be-tested target model environment for verification and evaluation, and the method solves the problems that evaluation target coverage is insufficient, and sample naturalness and scene consistency are weak.

The invention relates to the technical field of platform management, and particularly discloses an enterprise AI intelligent platform management method based on edge computing, which comprises the following steps of: performing distributed processing on data divided according to service types on an edge side, extracting key service indexes by each edge node, calculating an index state score by comparing a current value with a dynamic statistical reference value generated based on historical data, and aggregating to generate a local evaluation result; on the center side, the central management platform gathers all edge data, constructs an enterprise operation association map reflecting complex association among all business indexes, simulates the influence of local fluctuation on the overall situation through map calculation based on the map, and finally generates an enterprise comprehensive score comprehensively considering the overall performance, the critical path health degree and the system toughness. Therefore, closed-loop management from local intelligence to global optimization is realized.

A hand-held device, such as a remote control, is provided for operation of a consumer appliance and is equipped with one or more biometric sensors to capture and report on the health condition of a user of the hand-held device. The data captured by such a hand-held device may be evaluated locally in the hand-held device itself or may be conveyed to a target consumer appliance either for local evaluation by that consumer appliance or for onward transmission to a central off-site monitoring service.

The invention discloses a multi-agent collaborative reinforcement learning method, device and equipment, a medium and a product. The method comprises the following steps: constructing an experience pool according to historical action parameters of multiple agents, selecting target experience data from the experience pool, and determining next-step execution strategy parameters of the multiple agents according to the target experience data and a multi-agent collaborative network; global return data and local return data are determined according to the next execution strategy parameters of the multiple agents; and performing parameter updating on the multi-agent collaborative network based on the global evaluation network and the local evaluation network according to the action parameters, the strategy parameters, the global return data and the local return data of the multiple agents. According to the method, the optimization efficiency of the intelligent agent strategy is maximized through the double-evaluation network architecture, the global evaluation network is responsible for capturing the macroscopic situation of multi-agent interaction, the local evaluation network focuses on strategy optimization of an individual behavior level, accurate updating of the strategy is achieved through the synergistic effect of the global evaluation network and the local evaluation network, and then the cooperation ability of the intelligent agents is improved.

The application provides an enterprise security comprehensive analysis method and system based on multi-modal data, and relates to the technical field of distributed processing. The method comprises the following steps: collecting local security data of each work area edge evaluation node and forming multi-modal security data, combining node resource constraint portraits to perform heterogeneous load distribution and processing depth self-adaptive adjustment to obtain standardized multi-modal security data segments, further generating a local security representation vector through feature space mapping and constructing a local risk relationship graph, calculating node evaluation confidence parameters and node drift correction parameters on the basis to complete confidence weighting and deviation correction of the local evaluation result, realizing semantic alignment and topological splicing of multi-node results by using a cross-node alignment mapping matrix to generate a global security evaluation graph, finally performing risk aggregation determination and iterative correction combined with dynamic updating parameters, and outputting a global security evaluation result.

The invention relates to the field of network security, and particularly discloses a multi-agent cooperative defense and evaluation method, which comprises the following steps of: independently acquiring original data in a monitoring range and generating local evaluation evidences through a plurality of agents deployed at different nodes; in order to solve the problem that evidence sources are different in credibility, an honor sub-mechanism is introduced to carry out weighted discount on local evaluation evidences. Meanwhile, a selective broadcasting strategy is designed, and broadcasting is carried out only when evidences meet the importance or variability threshold value. After all weighted and screened evidences are collected, heterogeneous and asynchronous evaluation information from different local perspectives is aggregated into a unified and reliable global consensus result through global evidence fusion and a consensus decision mechanism. And finally, the defense coordinator generates and issues a defense instruction according to the consensus result, so that the technical problem that accurate global threat cognition is difficult to quickly form due to view angle limitation, model heterogeneity and communication overhead in the traditional method is effectively solved.

The invention relates to the technical field of cultural tourism resource evaluation. The multi-dimensional cultural tourism resource comprehensive evaluation method and device, the equipment and the medium are provided, and the method comprises the steps that an initial local evaluation model is jointly trained through a preset federated multi-task framework, and a shared base layer and a personalized task layer are obtained; aggregating parameters of the shared base layer to generate a global consensus model; combining the personalized task layer and the global consensus model to generate a terminal exclusive evaluation model; anonymization weight vectors in the terminal exclusive evaluation model are extracted, and an anonymization weight vector set is obtained; analyzing conflict dimensions of the anonymized weight vector set, and generating a multi-dimensional value conflict graph; and performing group decision negotiation based on the terminal exclusive evaluation model and the multi-dimensional value conflict graph, and outputting a Pareto improvement strategy list, so as to achieve the technical effects of improving data privacy protection intensity, balancing global consensus and local characteristics, and optimizing conflict decision quality.

The invention provides an environmental protection big data supervision platform based on a block chain. The invention discloses an environmental protection big data supervision platform based on a block chain, and the platform comprises a monitoring module which is used for carrying out the atmospheric environment monitoring of a target region, and obtaining environment monitoring data; the data storage module is used for storing the environment monitoring data in a target block chain node; the data analysis module is used for evaluating and analyzing the environment monitoring data to obtain an analysis result; and the local evaluation module is used for calculating an evaluation value of a local area in the target area according to the atmospheric environment score and a preset score threshold. The atmospheric environment pollution degree of the target area is locally analyzed through the data analysis module, some special areas in the target area are further analyzed through the local evaluation module, deep mining of the monitored atmospheric environment data is achieved, and the utilization rate of the data is improved.

The invention discloses a multi-dimensional contribution degree-based knowledge sharing excitation method and device and a storage medium, and relates to the technical field related to the Internet, and the method comprises the following steps: constructing and distributing cross-scene adaptation state metadata; the local node executes local evaluation of the knowledge contribution degree and generates feedback data based on the metadata and the real-time demand data of the local scene, and writes the feedback data into a local adaptation result of the metadata; the aggregation node collects metadata of a plurality of local nodes, and performs federal aggregation on local evaluation related parameters in the metadata; performing full-link adaptation effect simulation verification of knowledge cross-scene circulation based on the updated global optimization parameters, and optimizing metadata according to a verification result; the optimized metadata are distributed to a local node and an aggregation node, the local node updates a knowledge contribution degree evaluation rule, and the aggregation node calibrates local model parameters used for parameter aggregation; the problem of mismatching of excitation resources is solved.

This invention discloses a knowledge graph-based method for evaluating human clinical information, comprising the following steps: collecting human clinical information of a target individual to generate a standardized clinical dataset; performing medical semantic extraction to generate a set of medical semantic elements; constructing a patient-level evaluation knowledge graph for the target individual to generate a clinical efficacy structure; identifying risk endpoint nodes, constructing an Even-Shiloach tree to form an Even-Shiloach forest; identifying a set of invalid edges, deleting the edges corresponding to the invalid edge set to obtain an updated Even-Shiloach forest; generating a hierarchical difference set and identifying the affected subgraphs, performing local evaluation updates to generate corresponding evidence chains; determining the initial evaluation result of the human clinical information, correcting the initial evaluation result to obtain a corrected evaluation result. This invention achieves dynamic updability and traceable evidence chains in the evaluation of human clinical information.

An object of a system according to an embodiment is to appropriately evaluate thinking skills and subjective learning.SOLUTION: A system includes an analysis part, an evaluation part, and an indication part. The analysis unit analyzes a sentence of a student. The evaluation unit evaluates thinking ability based on the sentence analyzed by the analysis unit. The pointing unit points out an error or an improvement point based on a result evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

The present application relates to the technical field of deep geothermal prediction and detection, and particularly relates to a multi-element fusion deep geothermal detection method, comprising the following steps: constructing a multi-element evaluation structure and mapping it into a score propagation network, obtaining multi-source basic data to generate element scores and uncertainty information, extracting time changes to construct time elements, introducing a nonlinear coupling relationship to dynamically correct the score propagation, triggering constraint rules based on uncertainty to regulate the propagation amplitude and path, recording the influence path and contribution ratio through score propagation, and finally forming a comprehensive score corresponding to the target area comprehensive node, realizing the structured, dynamic and interpretable evaluation of the deep geothermal system. In the present application, by constructing a multi-element evaluation structure and mapping it into a score propagation network, the system integration and step-by-step transmission of multi-element information are realized, the one-sidedness of traditional single-parameter local evaluation is improved, and the comprehensive reflection ability of deep geothermal characteristics is improved.