14results about How to "Improve decision accuracy" patented technology

The invention provides an enterprise space appeal matching method based on an AI model, and the method comprises the following steps: collecting the multi-source sensing data of an enterprise space, and building a structured time-space information matrix through an access control, energy consumption, position and personnel label sensing terminal; constructing a causal relationship graph by using a structural causal modeling algorithm, and identifying key causal factors directly related to the spatial appeal; causal invariant characterization is extracted based on a multi-environment invariant risk minimization method, and an intervention effect model is established in combination with controllable intervention variables such as partition adjustment, capacity limitation and use strategies; generating an anti-fact sample of the candidate space unit by using an anti-fact reasoning mechanism, and calculating a matching adaptation score; establishing a matching optimization model according to constraint conditions such as topology, capacity, safety and moving lines; according to the method, high-precision and interpretable matching distribution under the conditions of complex space resources and multi-dimensional appeals is realized, and the method has the effects of flexible deployment, high matching stability and sustainable optimization.

The invention relates to the technical field of network security, in particular to a WEB application mimicry judgment system and method.The WEB application mimicry judgment method based on runtime data is characterized in that a parameter collection module is used for implanting instrumentation codes in key functions of WEB application programs and collecting runtime parameters of the WEB application programs in real time; the event conversion module is used for converting the acquired runtime parameters of the plurality of heterogeneous execution bodies into structured runtime events; the sequence alignment module is used for carrying out time sequence alignment on runtime events from different heterogeneous executors according to the business operation labels; and the risk judgment module is used for vectorizing the aligned text data of the runtime events, calculating a plurality of judgment factors and performing weighted fusion to generate a final confidence score, and performing feedback control to judge whether to trigger an execution body rotation operation or not according to the confidence score. According to the method, the WEB application runtime parameters are collected, through event conversion, sequence alignment and multi-factor weighted decision, the WEB application abnormal attack recognition accuracy is greatly improved, and the defect of traditional flow decision is effectively overcome.

The invention discloses a progressive upgrading method for an SDN (Software Defined Network). An SDN controller periodically acquires a historical traffic matrix of a hybrid network in a hybrid acquisition mode through management connection; performing order polynomial fitting on the traffic time sequence by using a least square method to generate a predicted traffic matrix; taking a prediction traffic matrix and a network topology as input, constructing an optimization model taking maximization of an SDN controllable traffic scale as a core target and taking upgrading budget and a link utilization rate as constraints, and efficiently solving and outputting an upgrading priority list of traditional nodes through a dual rising method of dual weights; and executing upgrading and evaluating the effect according to the list to form closed-loop optimization. According to the method, the characteristic of partial path controllability of the SDN is fully utilized, the contribution of node upgrading to the controllable flow is quantified, the SDN control range is rapidly expanded on the premise of ensuring the network performance, and the core value of progressive migration is improved.

The application provides a UAV group sensing method for city monitoring data time delay guarantee, comprising the following steps: a main process of a scheduling center creates a data time delay and a trajectory pool; the scheduling center starts a plurality of sub-processes for simulating city monitoring data generation and time delay updating; a UAV group collects simulated trajectories according to a current sensing strategy; the simulated trajectories are evaluated; the scheduling center optimizes the sensing strategy by using an asynchronous backtracking algorithm according to data of the data time delay and the trajectory pool, and synchronizes the optimized sensing strategy to each sub-process; until the UAV group sensing strategy no longer changes, the optimal sensing strategy is obtained; the UAV group is dynamically scheduled to guarantee city monitoring data time delay; the method is suitable for participating in a task sensitive to a time threshold, and therefore, compared with a traditional guarantee task, the UAV can be used to participate in city monitoring data time delay guarantee tasks to improve the completion satisfaction of the task.

This invention provides a multi-agent traceable decision-making system and method for environmental data mining, belonging to the interdisciplinary field of artificial intelligence and environmental informatics. The system includes: a task complexity assessment module, a multi-agent conditional collaboration module, an arbitrator triggering and adjudication module, an environmental knowledge graph module, a feedback learning optimization module, and a decision tracing and visualization module. The method includes the following steps: Step 1: Task complexity assessment and collaboration mode allocation; Step 2: Parallel processing and dynamic collaboration of two intelligent analysts; Step 3: Consistency check and arbitration triggering; Step 4: Arbitrator adjudication and weight integration; Step 5: Human expert correction and weight optimization feedback; Step 6: Decision tracing and weight visualization. This invention enables more accurate decision optimization, significantly improves the professionalism and accuracy of decision-making, and possesses the ability to proactively identify and prevent potential problems.

The application uploads the data obtained by the vehicle-mounted camera sensor of the unmanned mine car to the cloud server, carries out feature recognition on the cloud data through the cloud server, then selects the road curvature, the slope, whether there is an obstacle in the front road, whether there is a fork in the front road as the characteristic parameters according to the characteristics of the mine area to realize the data screening of the vehicle-mounted camera, so that the data input into the decision model of the unmanned mine car is reduced, and at the same time, the speed information of the unmanned mine car is referred to in the vehicle-mounted camera data screening process, and the screening weight is dynamically determined, so that the decision time of the decision model is reduced, and according to the characteristics of the mine area, the sensor data screening and the dynamic determination of the screening weight are considered, most of the interference garbage data is considered, the decision time is reduced, and the decision accuracy of the decision layer is improved.

The invention discloses an AI-driven dynamic decision and process linkage method and system, and the method comprises the following steps: S1, receiving business data of an enterprise system through an API interface, and carrying out the preprocessing of the business data; s2, inputting the preprocessed service data into a double-algorithm model, wherein the double-algorithm model outputs a path switching value according to the switching times, the switching time consumption and the switching deviation value of a historical process; s3, if the path switching value reaches a first preset value, judging a risk level according to the grading decision value to generate a flow adjustment decision instruction; and S4, generating a new flow through a flow adjustment decision instruction, calculating a flow matching degree, and feeding back the flow matching degree to the double-algorithm model, so that the double-algorithm model adjusts a path switching value according to the flow matching degree. According to the method, a double-algorithm model of the GBDT model and the RL model is adopted, and the advantages of the GBDT model in the aspects of feature mining and regression prediction and the characteristics of the RL model in the aspects of dynamic decision optimization and feedback learning are exerted.

This application discloses a system and method for matching new Chinese-style clothing based on virtual try-on and style matching, belonging to the field of e-commerce. This application obtains basic user data, behavioral data, feedback data, and information on currently purchased items from an e-commerce platform. Based on the multi-dimensional characteristics of the new Chinese-style clothing items purchased by the user, it maps the clothing wearing scenario space, evaluates the user's corresponding wearing scenario, and selects a set of new Chinese-style clothing candidates that meet the user's scenario needs from the e-commerce platform's product library. It generates matching effects between purchased items and candidate items based on virtual try-on, evaluates the matching of purchased items and candidate items, assesses the user's focus on clothing attributes based on historical user behavior and evaluation feedback data, and comprehensively scores candidate items based on the user's wearing scenario, matching evaluation, and focus evaluation, and makes recommendations based on the comprehensive score, thereby improving matching coordination and the accuracy of user decision-making.

The invention relates to a rice nitrogen fertilizer application amount prediction method, which comprises the following steps: firstly, constructing each rice test data sample based on multi-point nitrogen fertilizer gradient test data, then constructing a secondary mixing effect yield response model containing a fixed effect coefficient and a random effect item, and fitting to obtain a yield-nitrogen application amount response model; then, based on a yield-nitrogen application amount response model and an economic objective function, solving the highest yield nitrogen application amount and the economic optimal nitrogen application amount of each test point, then, carrying out Monte Carlo resampling and training to obtain a highest yield nitrogen application model group and an economic optimal nitrogen application model group, and finally, aiming at each position of a target to-be-tested area, carrying out optimal nitrogen application on each position of the target to-be-tested area. According to the method, the nitrogen application amount related to the highest yield target and the nitrogen application amount related to the economic optimal target are determined, then the high-resolution regional optimal nitrogen application map is formed, the design scheme has both the interpretability of an empirical model and the generalization ability of machine learning, and the decision precision of farmland nitrogen fertilizer management recommendation can be remarkably improved.

This application discloses a machine learning-based 6G terahertz communication optimization method, relating to the field of communication technology. It employs a reinforcement learning framework for communication performance optimization. Through continuous interaction with the environment and trial-and-error learning, the policy network can continuously adapt to channel changes, user movement, and sudden interference. The experience replay mechanism based on importance sampling prioritizes learning high-value experiences, greatly accelerating the learning process. Furthermore, the policy network is updated in two layers using an importance sampling strategy and a hyperparameter optimization strategy based on the orthogonal vortex balance optimization algorithm, further improving decision accuracy and the policy network's robustness in complex scenarios.

This invention discloses a liquid crystal display (LCD) driving system with an intelligent switching drive mode strategy, belonging to the field of intelligent driving technology. It aims to solve the problem of making the selection and switching of display drive modes more convenient. This invention introduces a transition mechanism to prevent abrupt changes during switching, and uses closed-loop feedback to correct signals in real time to maintain accuracy. It monitors system status at high frequency across all dimensions, processes data in layers, and uses log-related feedback for decision-making, ensuring display quality and system stability. During decision-making, data is synchronized and aligned, and dynamic weight allocation combined with three-dimensional adaptation calculations increases hardware verification and optimization, improving user experience and the accuracy of drive mode decisions.

The invention discloses a dynamic replenishment method and system based on consumption trend prediction, and relates to the technical field of inventory management. The method comprises the following steps: step 100, constructing an inventory full-link digital twin, synchronizing physical inventory dynamic data in real time and identifying potential bottlenecks; step 200, collecting multi-dimensional core data, and constructing a consumption behavior inventory state external environment causal link graph based on a knowledge graph technology; step 300, inputting the causal link map and an inventory constraint factor into a causal reasoning model, and outputting a consumption trend prediction result and an inventory feasibility verification report in combination with quantum lightweight calculation; and step 400, generating an initial replenishment scheme based on a federated learning driven inventory health degree scoring system, and outputting an optimal replenishment scheme after the digital twinborn scene rehearsal optimization. According to the invention, accurate matching of dynamic replenishment prediction and inventory actual bearing capacity is realized, real-time response and cross-scene self-adaption characteristics are realized, and the feasibility of replenishment scheme landing is greatly improved.

The invention discloses a block chain account book establishment method and system for intelligent agriculture, and the method comprises the following steps: collecting agricultural data through a 5G-6G network and intelligent agricultural equipment, and carrying out the primary processing and transmission through edge calculation and an intelligent network slicing technology; the collected agricultural data is stored through a block chain technology, a block chain edge computing node is deployed in the Internet of Things equipment, and decentralized data processing and storage are carried out; the stored data provides information and support for each application of the smart agriculture, the data collection efficiency and processing capability of the smart agriculture are improved, the decision accuracy is improved, and the development of intelligent precision agricultural equipment is promoted; meanwhile, the problems of popularization and actual effects of intelligent agricultural application are solved, the security and privacy protection of data are improved, and the problem of data sources is solved; in addition, the system also solves the challenge of processing large-capacity data, and optimizes the operation efficiency and safety of the whole intelligent agricultural system.

The application discloses a traffic emergency command retrieval enhancement generation method based on static and dynamic double-layer knowledge base enhancement. In view of the problems of existing RAG technology, such as static and dynamic knowledge splitting, lack of regulation constraints and strategy verification, a static knowledge base including a basic regulation sub-library and a historical case sub-library, and a dynamic knowledge base including a real-time situation sub-library and a prediction and deduction sub-library are constructed; a four-level knowledge graph of events, scenes, strategies and effects is established; after receiving an emergency event, multi-dimensional timeliness activation weights including emergency degree, freshness, spatial correlation and historical similarity are calculated to activate dynamic knowledge, and cross-layer attention mechanism is used to associate static knowledge for multi-target fusion sorting; the fused knowledge is input into a large language model, candidate strategies are generated by combining regulation Trie tree constraint decoding; and the optimal strategy is output after sand table deduction and evaluation by a traffic digital twin model, and feedback closed-loop optimization is carried out based on actual disposal effect.