9results about How to "Reduce decision risk" patented technology

The invention discloses a low-delay adaptive code rate control system for a meteorological monitoring video stream, and relates to the technical field of adaptive code rate control in video stream media transmission, and the system comprises a content importance analysis module which is used for carrying out the real-time analysis of a video block to be transmitted, generating a content importance score representing the meteorological information value; and the decision arbitration module is connected with the content importance analysis module and is used for receiving the content importance score, the network state information and the buffer area state information. According to the low-delay adaptive code rate control system for the meteorological monitoring video stream, bandwidth resources can be allocated according to the difference of meteorological information values of video contents, the quality of key monitoring pictures is guaranteed preferentially when network conditions fluctuate, unnecessary code rate switching is inhibited actively through internal look-ahead simulation and risk assessment, and the quality of the meteorological monitoring video stream is improved. Therefore, balance between continuous and clear transmission of high-value information and overall watching fluency is realized in a complex network environment.

This invention relates to the field of flight scheduling technology, specifically providing a method, system, device, and storage medium for collaborative scheduling of low-altitude aircraft. The method includes: edge nodes receiving real-time data from a swarm of drones and generating corresponding scheduling data using a pre-deployed scheduling model; if the edge nodes confirm that the real-time data is environmental mutation data, they upload the environmental mutation data to a regional cloud; the regional cloud matches the environmental mutation data with historical scenario data; if the match is successful, scheduling data is generated; if the match fails, the environmental mutation data is uploaded to a central cloud; the central cloud generates scheduling data based on the received environmental mutation data and returns the scheduling data to the regional cloud, which then distributes the scheduling data to the edge nodes. This invention constructs a low-altitude traffic management system that combines real-time performance, reliability, and self-evolution capabilities.

The application discloses a sintering machine operation and maintenance strategy simulation test and optimization method and system based on digital twinning, relates to the technical field of sintering intelligent operation and maintenance, and comprises the following steps: a simulation environment is constructed; real-time operation data of equipment is acquired, the real-time operation data of the equipment is mapped into a current equipment state snapshot through a digital twinning platform, and the current equipment state snapshot is converted into an initial current state vector; an operation and maintenance strategy to be tested is packaged into a decision function module and dynamically linked to the simulation environment; a simulation engine of the simulation environment is called, each single-strategy simulation deduction cycle in the operation and maintenance strategy is executed step by step with the initial current state vector as a starting point; a state trajectory, an action sequence and an event log are recorded; and a single-strategy comprehensive performance score corresponding to each single strategy is calculated. The method solves the technical problems that in the prior art, an operation and maintenance strategy lacks simulation verification, is directly put into production, results in high trial-and-error cost, and strategy reliability cannot be quantitatively evaluated.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.

This invention discloses a method and system for identifying equity structure diagrams based on data processing, relating to the field of data processing technology. The method includes: performing feature recognition on the equity structure diagram to obtain entity features and relationship features; constructing an identification model based on a graph neural network, using entity features as nodes and relationship features as edges; identifying nested relationships in the equity structure diagram through the identification model; wherein, nested relationships include relationships between equity entities; equity entities include shareholders and shareholders; and determining multi-level equity penetration paths, actual control paths, cross-shareholding relationships, and potential equity change relationships in the equity structure diagram based on the nested relationships.

The invention relates to the technical field of glass seal welding process optimization and defect prediction, in particular to a glass seal welding process optimization and defect prediction method based on deep learning. The method comprises the following steps: firstly, acquiring multi-source sensor electric signal data of a welding furnace in real time, and synchronizing and preprocessing to form a standard time sequence data sequence; and then, inputting the data sequence into a pre-trained multi-task deep learning model, and synchronously realizing dynamic process optimization and early defect prediction by the model through a shared feature extraction network and two parallel task branch networks. After the process is finished, the system associates an actual quality result with process data to form an incremental sample, and performs online fine adjustment on the model based on an intelligent trigger mechanism and an anti-forgetting algorithm, so that the system can adapt to changes of equipment and materials. According to the invention, the transformation of the glass seal welding process from fixed parameter control to real-time closed-loop intelligent optimization is realized, and the product yield, the process stability and the production intelligence level are effectively improved.

This invention relates to the field of coal mine gas geological disaster prevention and intelligent identification technology, and discloses a method and system for intelligent identification and graded early warning of underground gas geological anomalies in coal mines. The method includes: S1, collecting multi-source heterogeneous monitoring data in the target area of ​​the coal mine and performing standardization processing and spatiotemporal benchmark unification; S2, constructing a multi-dimensional feature fingerprint database based on historical or accumulated standardized datasets; S3, acquiring real-time multi-source monitoring data for the current tunneling or exploration process and extracting its features; based on the multi-dimensional feature fingerprint database, using evidence theory to fuse and calculate multiple extracted real-time feature evidences to obtain a comprehensive confidence assessment result for the existence of a specific type of gas geological anomaly in the undiscovered area ahead; S4, generating graded early warning information based on the comprehensive confidence assessment result and a preset risk level threshold. This invention achieves advanced identification, risk assessment, and targeted early warning of geological disasters such as underground gas outbursts.

The invention discloses a software development demand management system and method, and belongs to the field of computers. The system comprises a demand acquisition module used for receiving demand information input by a user and generating structured demand data based on a preset software customer demand right template and a demand obligation book template; and the intelligent analysis engine is connected to the demand acquisition module and is used for performing multi-dimensional feature extraction on the structured demand data. According to the method, user input is guided through a structured template (right / obligation book), semantic ambiguity is analyzed in combination with a natural language processing technology, accurate alignment of demand description and a business target is ensured, and understanding deviation is reduced from the source. A bidirectional tracking mechanism of the demand verification module realizes dynamic association of demand entries and function design, and eliminates demand omission or realization deviation. The intelligent analysis engine predicts the demand change probability and the influence range based on a machine learning model, automatically marks the technical feasibility and conflict dependency relationship, and assists the team in quickly adjusting the priority.

The invention discloses a method for inhibiting formation thickness interpolation oscillation based on an improved KNN, and belongs to the technical field of geological modeling. Aiming at the problem that a traditional interpolation method is easy to generate a pseudo stratum without geological basis near a stratum pinching line, the method comprises the following steps: firstly, extracting a drilling position, a stratum existence label and thickness; then, on the basis of an improved KNN algorithm, intelligently partitioning a to-be-predicted region into an interpolation effective region and an interpolation ineffective region by calculating a spatial anisotropy weight and constructing a weighted distance formula; then, spatial interpolation is only carried out on the stratum thickness in the effective area, and the thickness is forcibly set to zero in the ineffective area; and finally fusing results of the two parts to generate a final thickness model. According to the method, interpolation oscillation is inhibited from the source, a clear stratum boundary is ensured, a model result is more geologically reasonable, and the reliability of resource assessment is remarkably improved.