969 results about "Intelligent decision" patented technology

This invention provides a rapid rewarming and circulatory support system for frostbite and hypothermia in high-altitude environments, relating to the field of treatment device technology. It includes: a first temperature acquisition module for acquiring and preprocessing the current first ambient temperature, the first frostbitten skin surface temperature, and a first skin surface image; a control module connected to the first temperature acquisition module for analyzing the preprocessed first ambient temperature, the first frostbitten skin surface temperature, and the first skin surface image to determine the corresponding first target temperature and a first target warming rate; and a temperature regulation module connected to the control module for gradually warming the skin to the optimal preset temperature according to the warming rate. This invention achieves dynamic temperature control, continuous protection, and intelligent decision-making for frostbite and hypothermia in high-altitude environments.

The application discloses a kind of based on multi-source data fusion's catering intelligent decision-making method and system, it belongs to site selection decision-making technical field, by using big language model to carry out knowledge extraction to multi-source data, and to this construction catering site selection knowledge graph, based on knowledge graph automatically calculates geographic position index, traffic accessibility index, consumption potential index and the objective weight of competitive environment index, simultaneously using big language model to the qualitative description of the region to be selected is retrieved to obtain membership vector, according to objective weight and membership vector calculation comprehensive suitability degree score, the recommended site selection region is obtained by ordering comprehensive suitability degree score, it quantifies index weight by catering site selection knowledge graph, avoids subjective dependence on expert experience, quantifies index using big language model, significantly improves the accuracy and efficiency of catering site selection, adapts to large-scale multi-source heterogeneous data scene, and in the face of dynamic factors can respond to update in time, convenient for application and promotion.

This invention discloses a multifunctional home robot, comprising: a robot body; a central control system disposed on the robot body, the central control system being configured to perform multimodal data fusion and intelligent decision-making; a multimodal sensing unit electrically connected to the central control system for collecting user physiological data, behavioral data, and environmental data; and a life-assistance execution unit connected to the central control system for performing various operational tasks in the home environment according to instructions from the central control system. This multifunctional home robot solves the problem of single-function home service devices by introducing a dual-core architecture of a "life-assistance execution unit" and an "adaptive physiotherapy execution unit." The robot can not only perform household chores such as floor cleaning and tidying up, but also provide professional physiotherapy services. More importantly, the central control system enables the scenario-based linkage between the two.

The application discloses a park energy-carbon coordination early warning management and control method, relates to the technical field of energy saving and emission reduction and intelligent decision support, and comprises the following steps: establishing a unified atomic time reference, setting a unified time reference for a plurality of energy subsystems, generating a cross-source time anchor set under the unified time reference, and using the time anchor set to calibrate the sampling start time of each energy subsystem. The application realizes time alignment and real time sequence restoration of energy consumption data of multiple energy subsystems by establishing a unified atomic time reference and a cross-source time anchor set, and improves energy consumption fusion accuracy. By introducing conflict isolation and breathing time domain patching mechanism, the time distribution and load balance of peak value aggregation section are dynamically controlled, the energy consumption superposition amplification effect is inhibited, and the accuracy and stability of carbon emission reduction simulation and economic analysis results are ensured.

This invention provides a method and system for intelligent directional aging of baijiu (Chinese liquor) based on quantum sensing and multi-physics field synergistic regulation. The method includes: S1, using quantum sensors to detect dynamic information at the molecular scale of the liquor; S2, intelligent decision-making and flavor path planning reaction pathways based on artificial intelligence and quantum chemical simulation; S3, applying a preset energy excitation to the liquor through multi-physics field synergistic regulation to guide the aging process. This invention transforms "passive aging" into "active brewing": from an uncontrollable natural process to a designable, programmable, intelligent production process. By precisely lowering the target reaction energy barrier, the formation cycle of core flavor substances is shortened from "years" to "months" or even "weeks," achieving an order-of-magnitude efficiency improvement and breaking down the "time barrier." Based on market demand, products with different flavor emphases can be produced by adjusting the "target flavor fingerprint" and corresponding field regulation "prescription."

The present application relates to a kind of intelligent interview evaluation and feedback system based on multi-modal data fusion, specifically relates to data processing field, by meta-learning mechanism dynamic perception interview scene and generate initial fusion weight, subsequently utilize the complex interaction relationship between modalities modeled by graph neural network to carry out fine-grained correction to weight, so as to significantly improve the accuracy and scene adaptability of multi-modal evaluation, further introduce reinforcement learning, link evaluation decision and long-term performance of talents, continuously optimize weight generation strategy, ensure that evaluation standard and business goal are aligned, finally, through closed-loop iteration mechanism, make the whole scheme can be updated automatically according to new data and performance feedback, continuous evolution, with strong self-optimizing ability and long-term robustness, realize the fundamental change from static rule to dynamic intelligent decision.

This application discloses a multimodal temporal causal intelligent decision-making method, apparatus, device, and medium for complex scenarios, relating to the field of artificial intelligence technology. The method includes: constructing an integrated intelligent decision-making framework from multimodal data; achieving shared semantic representation of multi-source data through unified encoding; obtaining reliable fusion causal strength through dual-channel causal discovery; constructing and dynamically updating a causal knowledge graph based on this strength; and generating compliant decisions by combining constraint projection and counterfactual verification. The beneficial effects are: realizing dynamic causal knowledge updating and generating constraint-compliant and reliable decisions based on multimodal data; ensuring that decisions strictly comply with physical and business rules; and improving the reliability, robustness, and real-time performance of decisions in complex scenarios.

This invention discloses an adaptive cleaning method and system for photovoltaic panels using drones, relating to the field of photovoltaic power plant operation and maintenance technology. The method includes the following steps: S1: Constructing a digital twin 3D model of the target photovoltaic power plant, which at least includes the spatial location, geometry, and electrical connections of the photovoltaic panels; S2: Acquiring multi-source pollution sensing data of the photovoltaic panels and mapping the pollution information to the digital twin 3D model to generate a dynamic pollution map with pollution type, pollution level, and spatial distribution; S3: Based on the dynamic pollution map, weather forecast information, and resource constraints, generating an adaptive cleaning task plan for at least one drone using an optimization algorithm. Through the dynamic pollution map and a real-time sensing intelligent decision-making system, drone-based photovoltaic cleaning achieves a paradigm shift from extensive spraying to precise, adaptive operation, significantly reducing resource consumption and operation and maintenance costs while ensuring efficient cleaning of complex arrays.

The system comprises a data access module, a power grid flood disaster field knowledge graph, a digital twin body environment, and a digital twin body agent (DTA), a monitoring and early warning agent (MWA), a fault diagnosis agent (FDA) and a case handling agent (CHA). The DTA provides simulation deduction services. The MWA generates a flood field and a device-level risk list using a physical model chain. The FDA predicts device failure probability using a GWO-BPNN model. The CHA retrieves cases based on the knowledge graph to generate a scheme, and requests the DTA to perform virtual deduction and iterative optimization. The application interaction layer integrates visual output. The application realizes a full-process closed-loop intelligent decision from disaster perception, risk diagnosis, scheme generation to virtual pre-performance, solves the problems of insufficient early warning accuracy, static risk evaluation and lack of forward-looking verification of the disposal scheme, and improves the emergency response capability and decision scientificity of the power grid in response to flood disasters.

The application discloses a kind of arid region reservoir water and sand prediction and coordinated regulation potential evaluation method and system, belong to water resources, silt treatment and reservoir optimization scheduling technical field.The technical problems of traditional model prediction accuracy insufficient caused by arid region hydrology data scarcity, water and sand prediction process is fragmented and reservoir desilting and silt reduction and water storage benefit difficult to be synergistically optimized, its gist is that: long short-term memory network is used to predict future runoff sequence by fusing attention mechanism, and the sediment concentration is calculated by combining the water and sand relationship formula;By setting the critical sediment concentration threshold, the high sediment concentration period is identified and the available water for dredging is calculated;At the same time, the flood resource potential is evaluated by combining reservoir capacity and water demand constraints;Finally, the coordinated scheduling scheme is generated by comparing the two potentials.The application realizes the integrated accurate prediction of water and sand process in arid region and the simultaneous quantitative evaluation of regulation potential, and provides intelligent decision support for multi-objective optimization scheduling of reservoir.

The application relates to the field of intelligent driving of rail transit, and discloses a real-time monitoring and early warning system for the safety state of a rail car based on deep learning, which comprises the following modules: a multi-modal data acquisition module, which is used for deploying sensors on key components of the rail car and collecting time series data including vibration, temperature, pressure and operation parameters; a cross-modal feature conversion module, which is used for encoding the collected time series data into a three-channel color image; a safety state evaluation module, which is used for deep modeling and feature learning on the three-channel color image, real-time prediction of a current state classification result of the rail car, and obtaining of a safety state evaluation result of the rail car; and an early warning and intelligent decision module, which is used for performing abnormality detection and alarm prompting on the rail car when potential risks exist according to the safety state evaluation result. The application realizes high-precision early fault identification and automatic early warning.

The application relates to the technical field of industrial automation and network communication, and particularly discloses a digital twin industrial switch remote state monitoring management system, which comprises an edge agent module, a digital twin modeling and synchronization engine module and a visual interaction module. The edge agent module collects and evaluates the state of a physical switch, dynamically decides the data sampling frequency, compression algorithm and redundant sending strategy in combination with the network condition fed back by the cloud. The cloud module updates a high-fidelity digital twin model and feeds back the network quality, and simultaneously performs predictive analysis. Through adaptive synchronization and intelligent decision-making, the application realizes reliable and real-time synchronization of key state data under network fluctuation, and improves the real-time performance and early warning capability of state monitoring.

This invention belongs to the field of ecological and environmental protection technology. It discloses a regional ecological protection intelligent decision support and dynamic management system based on big data. The system acquires multi-temporal ecological monitoring data, constructs an ecological factor correlation network through time-delay mutual information analysis, uses virtual intervention experiments and counterfactual comparisons to perform causal inference, and identifies real ecological action paths. It calculates lag propagation time and signal attenuation rate to establish a multi-scale time anchor sequence; identifies feedback loops and calculates loop gain and stability domain boundary parameters; constructs a dynamic evolution model containing a system of time-delay differential equations, and simulates and generates a hierarchical set of decision schemes; and performs residual analysis through real-time monitoring of ecological responses to trigger adaptive correction of model parameters and dynamic adjustment of schemes. This invention achieves a leap from correlation analysis to causal identification, and from static planning to dynamic management, improving the scientific rigor and accuracy of ecological protection decision-making.

This invention discloses an intelligent decision-making system for precise water allocation in agricultural irrigation districts based on crop water requirement coefficients. The system comprises three main modules: hardware acquisition and execution, software algorithms and modeling, and digital twin simulation and decision-making. These modules operate in a closed-loop logic of acquisition, processing, simulation, decision-making, execution, and feedback. The hardware module employs a four-dimensional collaborative acquisition, dual-mode transmission, and edge computing scheme. The software module incorporates algorithms and adaptation rules such as dual-factor coupling and dynamic water requirement coefficient correction. The digital twin module completes instruction issuance and parameter correction through full-element modeling, LOD optimization, and multi-objective decision-making. This invention, through dual-path feedback, digital twin collaboration, and multi-algorithm coordination, combined with dynamic water requirement coefficient correction, terraced field spatial adaptation, and soil water-salt coupling modeling, accurately matches plot requirements, balancing water conservation, crop adaptation, and energy consumption, thereby reducing irrigation district management costs and adapting to complex irrigation district operations.

The application relates to the technical field of intelligent decision-making, can be applied to a business system platform such as financial technology and medical health, and discloses a navigation decision generation method and device based on visual imagination, equipment and a medium, which comprises the following steps: acquiring a navigation instruction and initial environment data of a navigation scene, and determining a navigation imagination mode according to the navigation instruction and the initial environment data; extracting semantic information of the navigation instruction, converting the semantic information into a visual imagination image, and extracting target region features of the visual imagination image; acquiring real-time environment data, extracting a semantic alignment region matched with the target region features from the real-time environment data, and generating a spatial attention map of the semantic alignment region; converting the spatial attention map into a spatial text abstract of the semantic alignment region, performing chain thinking reasoning on the spatial text abstract, and obtaining a structured decision sequence; and generating a navigation decision according to the structured decision sequence. The application can improve the accuracy of a navigation decision generation result in visual language navigation.

This application discloses a dynamic decision-making method and system for flash floods and debris flows based on multi-agent and LLM (Limited Language Model), belonging to the field of emergency management and intelligent decision-making technology. This method constructs an intelligent decision-making architecture based on a large model and multi-agent collaboration. It solves the execution challenges of complex disaster tasks through the division of labor and cooperation among multiple agents, and overcomes the decision-making limitations of traditional systems by leveraging the deep reasoning capabilities of a large language model. Specifically, the multi-agent collaborative framework decomposes disaster prevention and control decisions into sub-tasks such as intent parsing, contingency plan retrieval, spatial analysis, data acquisition, and analysis and prediction. Each functional agent focuses on its area of ​​expertise, improving the efficiency and accuracy of task execution. The agent communication protocol ensures efficient information interaction and collaborative scheduling between modules, realizing fully automated processing from task input to data output.

The application relates to the technical field of decision management, and provides a phosphorus mine industrial internet intelligent decision method and system based on a knowledge graph. Production data in a phosphorus mine industry is collected through intelligent sensors, and decision information is generated by performing edge processing on the production data through an edge computing node; knowledge tuples are generated based on the production data and the decision information; entity devices and their relationship information are mapped into device nodes and edges of a Bayesian network, a knowledge state code and a hidden state at a set time are generated based on a confidence weight of the device nodes; the knowledge state and probability of the device nodes at a next time are predicted based on the hidden state at the set time; and a dynamically evolved knowledge graph is generated based on the hidden state at the set time and the knowledge state and probability at the next time, so that the phosphorus mine industrial production is decided through the knowledge graph. The dynamically evolved knowledge graph generated by the scheme can assist decision making, and the efficiency and accuracy of dynamic decision making in the phosphorus mine industrial production are improved.

This invention discloses a seismic acquisition decision-making system and method. The system includes a data retrieval subsystem, a receiving control subsystem, and an excitation control subsystem. The data retrieval subsystem includes node acquisition units and a data retrieval terminal. The node acquisition units periodically acquire seismic signals and node status data, and the data retrieval terminal wirelessly retrieves and uploads the status data of each node. The receiving control subsystem acquires the SPS file of the construction area and the status data of each node, determines the arrangement of the preset nodes, and sends it to the excitation control subsystem. Based on the arrangement and the SPS file, the excitation control subsystem determines the excitationable shot points from the preset shot points and executes the excitation, generating an excitation report which is transmitted to the receiving control subsystem. Based on the excitation report, the receiving control subsystem determines the retrieval arrangement nodes and the retrieval task. This achieves collaborative intelligent decision-making in seismic acquisition, ensuring the effectiveness of shot point excitation and the integrity of seismic data.

The application relates to the technical field of metal processing, and discloses a surface nanometer strengthening treatment system for metal pieces, which comprises a data acquisition module, an intelligent decision module, a process simulation module, a verification and optimization module and an instruction output module, extracts initial state data of the surface of the metal pieces, carries out nanometer strengthening decision analysis on the initial state data to obtain a nanometer strengthening process parameter sequence of the surface of the metal pieces, simulates an electrochemical deposition process on the nanometer strengthening process parameter sequence to obtain nanocrystalline layer growth prediction data of the surface of the metal pieces, carries out stability verification on the nanocrystalline layer growth prediction data, adjusts weight parameters in the nanometer strengthening decision analysis when the verification fails, obtains an optimized process parameter sequence of the surface of the metal pieces, encodes the optimized process parameter sequence to obtain nanometer strengthening treatment instructions, and can improve the process research and development efficiency and production implementation efficiency of the surface nanometer strengthening treatment of the metal pieces.

This invention discloses a resource collaborative scheduling and demand response optimization system and method for virtual power plants in industrial parks. It achieves global collaborative scheduling through a two-layer Actor-Critic network architecture, and uses a fusion of particle swarm optimization and fuzzy control to achieve grid-connected stability control. A three-layer fusion optimization architecture is constructed to complete the continuous amplitude calculation of demand response and the generation of equipment-level instructions. A unified algorithm support is provided by an intelligent optimization module. This invention effectively solves the problems of weak adaptability of existing global optimization models for virtual power plants in industrial parks, low level of intelligent decision-making in specialized sub-scenarios, and difficulty in coordinating multi-timescale scheduling and refined execution. It can achieve robust optimization and refined control in uncertain scenarios, balancing system economy, stability, and operational reliability. It is suitable for virtual power plants in industrial parks to efficiently participate in the power market and resource collaborative scheduling.

The present application relates to the technical field of bridge nondestructive testing, and particularly relates to a multi-modal guided wave steel bridge deck fatigue crack intelligent fusion detection method and system, the intelligent fusion detection method comprising: scanning a to-be-detected area of a steel bridge deck by using ACFM detection technology, positioning a suspicious area, and merging the suspicious area and a high-risk area to form a fine detection target set; based on the fine detection target set, outputting an EMAT detection instruction for guiding EMAT detection of each fine detection target by using a hybrid intelligent decision model, and performing targeted fine detection on the fine detection target to obtain EMAT characteristic parameters; combining the ACFM characteristic parameters and the EMAT characteristic parameters of the fine detection target into a fusion feature vector, and inputting the fusion feature vector into a PINN evaluation model comprising a second classifier, and outputting a final defect classification result of the fine detection target and a quantitative physical parameter of the defect by the PINN evaluation model. Through efficient cooperation of ACFM and EMAT, the present application realizes integrated intelligent detection of "screening-decision-fine detection-evaluation" from surface to internal fatigue cracks.

This invention discloses an adaptive monitoring and early warning system for ship main engines, comprising a multi-source heterogeneous data acquisition and cleaning module, a dynamic operating condition self-identification module, an adaptive condition monitoring model library, an operating condition matching and model scheduling unit, a real-time monitoring and anomaly triggering unit, a multi-dimensional contribution tracing and decision support module, and a model adaptive update engine. The system performs anomaly detection by calling PCA-based sub-monitoring models to calculate and statistically analyze data through data cleaning, online operating condition identification and matching scheduling. When an anomaly is triggered, the system locates the suspected root cause by calculating the contribution of variables and generates maintenance suggestions based on a fault case library. If no anomaly is triggered, the system uses health data to continuously update model parameters and control limits through a sliding window mechanism. This invention achieves dynamic adaptive tracking of main engine operating conditions, self-learning evolution of monitoring models, and a closed loop from anomaly detection to intelligent decision support, improving the accuracy and reliability of ship main engine condition monitoring.

The application provides an intelligent analysis method and system based on a WMS system and an AI large model, relates to the field of warehouse management, and comprises the following steps: converting WMS business data into description text, identifying and labeling abnormalities by using an AI model, and constructing a causal knowledge graph associated with risk weights. Based on the graph, a candidate decision scheme is generated by multi-path reasoning, and the feasibility and risk exposure of the candidate decision scheme are evaluated to select the optimal scheme. Finally, the scheme is simulated for risk propagation and a blocking measure is generated. The application realizes the automatic identification, quantitative evaluation and intelligent decision of warehouse operation risks, and improves the timeliness and accuracy of risk response.

This invention discloses a hierarchical coordinated control method and system for gas-cooled microreactors using deep reinforcement learning, belonging to the field of nuclear reactor control technology. The method constructs a three-layer architecture comprising an intelligent decision-making layer, a signal processing layer, and a PID controller layer. It utilizes a SAC (Smart Agent Control) agent to optimize the incremental output of electrical power and outlet temperature based on the gas-cooled microreactor's thermal parameters. The signal processing layer performs rate limiting and low-pass filtering on the optimized incremental output to generate a smooth setpoint command. The bottom-layer PID controller then outputs turbine speed and airflow signals to perform load tracking. This invention integrates the advantages of deep reinforcement learning in handling strong coupling and nonlinear dynamics with the robustness of traditional PID control. Through a physical constraint layer, it effectively suppresses oscillations and overshoot of the AI ​​control signal, achieving fast, smooth, and safe decoupled control of the gas-cooled microreactor across the entire power range, significantly improving load tracking performance and extending actuator life.

This invention relates to the field of unmanned aerial vehicle (UAV) swarm control technology. To address the problem in existing UAV control methods where the inability to synchronize states between UAVs leads to a lack of inter-UAV collaboration, this invention provides a UAV swarm control method and system. The method executes the following steps: Step S1, collecting the local state of each UAV to generate standardized local state information; Step S2, performing distributed full-state synchronization processing on the standardized local state information to generate globally consistent state information; Step S3, performing local intelligent decision-making processing on the globally consistent state information to generate local control commands; Step S4, performing execution control processing on the local control commands to generate UAV actions; Step S5, performing effect feedback processing on the UAV actions to generate updated local state information, and using the updated local state information as the new local state information, returning to step S2.