902 results about "Planning algorithms" patented technology

The present invention provides an intelligent robot for power inspection and an inspection method thereof, comprising a quadruped robot, wherein a laser radar, a voiceprint sensor and an infrared sensor are provided at the front end of the quadruped robot; a pan-tilt base is provided at the middle of the quadruped robot, and a camera is provided on the pan-tilt base; a six-degree-of-freedom mechanical arm is also provided at the tail of the quadruped robot, and a mechanical claw is provided at the end of the mechanical arm. The robot flexibly plans tasks based on the layout of the substation, sets inspection cycles for different equipment according to their importance, and automatically generates accurate task plans, thereby avoiding the tediousness and omissions of manual planning and improving planning. The advanced path planning algorithm enables the robot to quickly plan the optimal path, integrates multiple positioning technologies to ensure accurate navigation, and effectively reduces travel time. Multiple inspection modes are carried out in parallel, non-stop inspections are dynamic screening, routine inspections are comprehensive inspections, and non-preset inspections are flexibly targeted at specific points, which greatly improves the coverage efficiency of the inspection and avoids detection blind spots.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

The embodiment of the invention provides a document generation method and device based on artificial intelligence, and the method comprises the steps: extracting multi-modal data features of a historical multi-modal file through natural language processing and a computer vision algorithm, and constructing a corresponding knowledge graph according to the multi-modal data features, converting the multi-modal data subjected to feature extraction into high-dimensional vectors by using a vectorization algorithm, and storing the high-dimensional vectors to form a vector library; splitting a current document generation task into a plurality of sub-tasks by adopting a task planning algorithm, and executing the sub-tasks at the same time to obtain a first document; performing iterative optimization of document content according to the document first draft, the cue word and the language model to obtain an optimized first draft, acquiring related information from the vector library by using an attention mechanism-based retrieval algorithm, and fusing the related information and the optimized first draft through a text generation algorithm to obtain a final document; according to the invention, the accuracy and intelligence in the document generation process can be effectively improved.

The invention relates to the technical field of steel production plan optimization based on multi-dimensional constraint dynamic coupling, in particular to a molten iron operation plan arrangement method based on dynamic constraint modeling hybrid optimization, which comprises the following steps: acquiring order data, equipment state data and constraint rule data to generate structured input parameters comprising order priority labels; constructing a multi-objective optimization model based on the molten iron distribution constraint and the maintainable time length of the equipment, extracting the maintenance conflict relationship between the equipment groups, constructing a maintenance rule knowledge graph, and generating a dynamic constraint condition set; generating an initial molten iron distribution and maintenance plan through global search by adopting a genetic algorithm, and outputting a molten iron distribution path and an equipment maintenance time sequence through local optimization by combining a linear programming algorithm; according to the method, dynamic collaborative optimization of molten iron distribution and equipment maintenance is achieved, and the resource utilization rate, the order delivery rate and the production system stability are improved.

The invention relates to the technical field of artificial intelligence, can be applied to the field of medical health, and discloses a navigation method, device and equipment in an unexplored environment and a medium. Analyzing a pre-trained large language model into a structured semantic representation at least comprising target entity information, spatial constraint information and contextual intention information; inputting the target entity into a pre-trained large language model to generate a standardized task description question, inputting the standardized task description question and real-time environment image data into a pre-trained visual language question and answer model, and outputting a semantic answer result about whether the target entity exists or not; and if yes, generating a navigation strategy through a pre-trained large language model based on the structured semantic representation, and controlling the robot to execute a navigation action through a preset path planning algorithm. The method does not need to depend on an environmental map and scene training, realizes navigation in an unexplored environment by utilizing cooperation of a large language model and a visual language question and answer model, solves the problem of strong dependency of a traditional scheme on prior data, and has good generalization ability and environmental adaptability.

The invention belongs to the technical field of path planning of unmanned equipment, and relates to a medical rescue unmanned aerial vehicle and robot dog cooperative linkage method, which comprises the following steps: integrating LiDAR point cloud, visual images, IMU inertial data and compensated UWB positioning data into a multi-modal data stream subjected to space-time alignment through time alignment and space alignment technologies; respectively extracting LiDAR and visual feature poses through parallel SLAM (Simultaneous Localization and Mapping) calculation, and carrying out graph optimization and joint optimization by utilizing a GTSAM library, so as to construct a globally optimized pose and a global semantic map; the path planning algorithm provides dynamic channel routing for the unmanned aerial vehicle and the robot dog based on a global semantic map, the feature fusion weight is adjusted by considering the environment illumination intensity and the point cloud density, the reverse iris control mechanism detects whether the robot dog enters a shielding area through a UWB signal intensity attenuation value, the unmanned aerial vehicle is switched to a UWB signal follower, and the UWB signal follower is switched to the unmanned aerial vehicle. And through combination with a Kalman filter, penetration positioning in a shielding area is realized, so that stable execution of a rescue task in a complex environment is ensured.

The invention discloses a pitch angle dynamic compensation method based on multi-radar space-time synchronization, and relates to the technical field of wind power generation. In order to solve the problem that the pitch angle control precision is insufficient due to the fact that three-dimensional wind field space-time characteristics cannot be captured in traditional single-point wind speed measurement, a wind wheel plane three-dimensional space-time speed field is synchronously constructed through a horizontal-vertical radar array, and blade segmented aerodynamic load distribution is calculated in real time in combination with a flow field-structure coupling model; establishing a vorticity transport equation under a rotating coordinate system based on a vorticity-shearing force transfer function, mapping dynamic load deviation through a neural network to generate a reference variable pitch instruction, and fusing the tower shadow interference feature vector to perform pre-compensation; a quadratic programming algorithm is adopted to resist saturation optimization, so that the variable pitch rate strictly follows a dynamic constraint curve of a hydraulic system. According to the method, the problem of aerodynamic load phase delay is effectively solved, periodic load fluctuation caused by the tower shadow effect is reduced, and technical guarantee is provided for stable operation of a large wind turbine generator under the complex wind condition.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention discloses an emergency plan making method based on an industrial park, and the method comprises the steps: achieving the data collection and preprocessing through a distributed sensor network and edge calculation, building a park dynamic model through the multi-source heterogeneous data fusion and digital twinning technology, analyzing a risk propagation path in combination with a graph neural network, and obtaining a risk plan. And a preliminary emergency suggestion is generated based on a knowledge graph matching historical case. According to the invention, a resource scheduling optimization and path planning algorithm is further adopted to formulate a dynamic response scheme, effects of different schemes are evaluated through a situation evolution prediction model, and finally an optimal emergency disposal scheme is generated and a structured instruction is issued. According to the method, intelligent formulation of the emergency plan of the industrial park is realized, the accuracy of risk identification and the timeliness of emergency response are improved, and powerful support is provided for safety management of the park.

The invention discloses a double-station robot sorting optimization method and system based on digital twinning and a terminal, double improvement of sorting efficiency and safety is achieved by constructing a digital twinning driven collaborative operation system, and the method comprises the steps that firstly, a laser radar and a polarization camera are used for forming a composite sensing unit; geometric morphology, material reflection characteristics and spatial pose data of a target object are synchronously obtained, and are input into a digital twin engine after time synchronization processing; an engine constructs a virtual sorting scene containing a material attribute database based on a physical rendering technology, sub-millimeter-level space registration is achieved through feature fusion of a binocular stereoscopic vision depth map and geometric parameters, high-precision digital twin stations are generated, a system plans a space-time constraint trajectory of a double-station robot in a virtual environment, and a target object is obtained. And an integrated discrete event simulation engine performs operation time sequence conflict prediction, and when a space overlapping risk is detected, an alternative path containing a dynamic obstacle avoidance strategy is generated through a trajectory re-planning algorithm.

The invention discloses an intelligent logistics scheduling method and device based on a dynamic weight and a medium, and the method comprises the steps: carrying out the fusion processing of the operation environment data of a logistics carrier, so as to construct a dynamic environment model, and carrying out the fusion processing of the operation environment data of the logistics carrier based on a reinforcement learning model according to a real-time environment state and a multi-target optimization demand. Dynamically adjusting the weight vector of each logistics parameter in the logistics carrier; predicting the space-time trajectory of each logistics carrier based on the kinematic model of the logistics carrier, mapping the space-time trajectory to a three-dimensional grid, identifying a potential conflict area between the logistics carriers, and calculating a corresponding conflict severity index; based on the dynamic weight vector and the conflict severity index, generating an obstacle avoidance path of the logistics carrier by adopting a global path re-planning algorithm, and optimizing the obstacle avoidance path through a local track to generate a corresponding smooth track; and executing a scheduling instruction corresponding to the smooth trajectory, and collecting an execution feedback result in real time to update the reinforcement learning model parameters and the dynamic weight strategy.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The present invention discloses a city planning simulation system based on real-scene three-dimensional (3D) and relates to the technical field of city planning. The system comprises the following components: a data acquisition module, a real-scene three-dimensional (3D) and disaster simulation module, a comprehensive planning module and a comprehensive evaluation module. The present invention plans the optimal emergency evacuation route by utilizing a path planning algorithm and taking into account the area affected by the damaged infrastructure and the recovery time of the infrastructure, thereby not only ensuring the safety and feasibility of the evacuation route but also greatly improving the evacuation efficiency. At the same time, according to the emergency evacuation route planning results and the infrastructure resilience evaluation results, the type and layout of the rescue facilities are determined, the service scope and coverage of the rescue facilities are analyzed by a geographic information system, the layout of the rescue facilities is optimized, and it is ensured that the rescue resources can efficiently and accurately reach the disaster-stricken area.

The invention discloses a code spraying identification optimal path planning method based on an intelligent optimization algorithm, and relates to the technical field of code spraying control, and the method comprises the steps: dispersing a code spraying region into a three-dimensional space, and constructing a dynamic environment model of marking parameter constraint; based on the code spraying task priority queue, a hybrid optimization algorithm is adopted to carry out collaborative optimization of marking parameters and path nodes, and an optimal path candidate set is generated; an obstacle avoidance model is constructed based on the optimal path candidate set, a collision-free path is generated, the pose of a nozzle is adjusted in combination with visual feedback, and an anti-interference code spraying action sequence is output; monitoring the operation feedback of the code spraying equipment, and carrying out the iterative updating of the marking parameter library and the algorithm rule. According to the method, the core advantages of multiple intelligent optimization algorithms are fused, a mixed collaborative optimization architecture is constructed, the limitation of a single algorithm in a complex scene is effectively broken through, and a traditional path planning algorithm is easily troubled by a local optimal solution and is difficult to meet multi-dimensional constraint conditions at the same time.

The invention relates to the technical field of intelligent maintenance of high-speed rail stations, and discloses a control system of a composite intelligent maintenance robot for a high-speed rail station, and the system comprises a visual detection module which is used for carrying out defect detection and fault recognition on high-speed rail station equipment through a deep learning algorithm; and the navigation and positioning module is used for carrying out environment mapping based on a synchronous positioning and map building technology of data fusion of the laser radar and the visual sensor, and realizing autonomous navigation and accurate positioning of the robot by combining a path planning algorithm. And by adopting the remote monitoring module, real-time monitoring, anomaly detection and remote control of the robot are realized, and the intelligence and safety of maintenance operation are improved. Through multi-sensor fusion, the state and environment of the robot are accurately sensed, and task scheduling and execution monitoring are optimized. And in combination with 5G and MQTT, stable and efficient data transmission is ensured, and the reliability of remote control is improved. Bayesian reasoning is adopted for anomaly detection and fault prediction, the fault rate is reduced, and the operation and maintenance efficiency is improved.

The invention discloses an intelligent three-dimensional warehouse storage management method and system, and relates to the technical field of warehouse management, and the method comprises the steps: receiving a task instruction from an upper system through a communication network, and distributing specific execution equipment according to an equipment operation state and real-time inventory data; calculating a logistics path based on the equipment position information, the reservoir area environment data and the task demand, and generating a control instruction to drive the execution equipment to execute operation according to the path; according to the goods attributes and the storage rules, goods allocation distribution is optimized through a dynamic programming algorithm, and goods storage and taking operation is completed; the operation state of execution equipment is collected in real time through a sensor, abnormity is recognized based on collected data, an alarm signal is triggered through an upper system, and meanwhile a task execution strategy is adjusted. According to the intelligent three-dimensional warehouse storage management method provided by the invention, comprehensive monitoring of the equipment health state is realized, abnormity can be found in time, an alarm mechanism can be triggered, and the equipment fault risk is remarkably reduced.

The invention discloses a humanoid robot dynamic environment adaptation and behavior optimization method based on artificial intelligence, the humanoid robot dynamic environment adaptation and behavior optimization method has excellent environment adaptation capability, the robot can comprehensively and accurately perceive and understand environment change through a multi-modal environment perception and knowledge graph real-time construction and updating mechanism, and the robot can be used for optimizing the environment change. The self behavior strategy is rapidly adjusted, and the strong adaptive capacity far better than that of a traditional technology is shown in a complex dynamic environment; through fusion of reinforcement learning and a planning algorithm and combination of rich information of a knowledge graph, the robot can quickly make scientific and reasonable behavior decisions when facing various uncertainties and emergencies, and decision errors are effectively avoided; the robot has continuous evolution learning ability, and a continuous learning and optimization mechanism enables the robot to continuously accumulate experience in the operation process, continuously improve and optimize a knowledge system and a decision model of the robot, gradually improve the performance and ability, and better adapt to diversified environments and complex task requirements.

The invention discloses an AI-driven personalized foreign language learning content generation platform, and belongs to the technical field of artificial intelligence. Comprising the following modules: a data center and user portrait module used for comprehensively presenting personalized learning features and demands; the language ability evaluation module predicts the learning ability in combination with historical learning data and generates a standardized ability evaluation report; the intelligent content service module preferentially recommends the matched existing content, automatically generates personalized learning resources and dynamically adjusts the content difficulty; the learning path planning module continuously optimizes a path planning algorithm through a feedback closed loop to realize long-term formulation and dynamic adjustment of a learning strategy; the real-time monitoring and diagnosis module monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes the learning effect based on data driving; and the multi-modal interaction module is used for providing scene-based adaptive interface display and ensuring interaction experience consistency and fluency among the functional modules.

The invention provides an AI agent emergency order insertion dynamic decision production scheduling method, a medium and a system, and belongs to the technical field of industrial agents. A dynamic weight adaptive optimization model is adopted to calculate a target weight coefficient and construct a multi-target function set, an improved non-dominated sorting genetic algorithm is adopted to solve and output a Pareto optimal solution set, and a delay risk assessment correlation matrix is combined to start an incremental re-planning algorithm to generate a local adjustment scheme. The Pareto optimal solution set and the local adjustment scheme are combined to generate a final production scheduling scheme, a real-time monitoring module is started to track the execution deviation condition, and when it is detected that the deviation degree exceeds a threshold value, a rapid rescheduling mechanism is triggered to conduct scheme correction; the technical problem of poor production scheduling scheme quality caused by low multi-agent cooperation efficiency in the emergency order insertion dynamic decision process is solved.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses an intelligent fire evacuation system and method based on dynamic environment perception, and relates to the technical field of fire fighting and safety protection, and the method comprises the steps: collecting real-time data in a fire scene; dividing the evacuation area into a plurality of grid units by using a two-dimensional rasterization technology to form a real-time updated environment map; in combination with dynamic modeling data, an optimal evacuation path is generated by using an improved path planning algorithm; an evacuation path is transmitted to trapped people in real time in a visual and auditory mode, and the visual and auditory mode comprises the use of a dynamic indicator lamp, a marker lamp, an electronic screen and a voice broadcast system; the guidance content is adjusted in combination with real-time data, and the evacuation efficiency is optimized; fire fighting equipment is linked, and the evacuation path environment is optimized; and the evacuation state is monitored through a dynamic data feedback mechanism. According to the invention, an evacuation path is calculated and updated in real time by integrating a sensor network, fire scene dynamic modeling and an intelligent path planning algorithm, and a safe and efficient evacuation scheme is provided for trapped people in a fire.

The invention relates to a simulation-based satellite scheduling task dynamic planning method, device and equipment, and the method comprises the steps: carrying out the modeling according to various time-varying environment dynamic parameters in an environment where a satellite is located, and obtaining a dynamic environment model which comprises an environment factor model and an emergency triggering mechanism; the method comprises the following steps: constructing a satellite resource dynamic model according to various resources of satellites, resource consumption during task execution and resource supply, constructing a multi-satellite collaborative model according to an interaction relationship among a plurality of satellites and related information of each satellite, and constructing a multi-satellite collaborative model under a dynamic environment model, the satellite resource dynamic model and the multi-satellite collaborative model. According to the method, tasks in a to-be-executed task data set containing single-satellite and cooperative tasks are dynamically scheduled by adopting a task planning algorithm, a task execution planning table of satellites is obtained, satellite task planning can be carried out aiming at emergencies and multi-satellite cooperative tasks, and the planning is closer to the actual situation.

The present invention provides an unmanned ship laser ranging optimization method and system based on dynamic environment perception, which relates to the field of navigation and obstacle avoidance technology of unmanned ships. The method includes: sensing the surrounding environment data in real time through sensors to obtain obstacle position, speed and heading information; based on the dynamic characteristics of the environment, using an adaptive algorithm to dynamically adjust the measurement frequency and angle of the laser rangefinder to optimize the measurement accuracy; combining the environmental data with the laser ranging results, using a path planning algorithm to optimize the obstacle avoidance route, and adjusting the heading in real time; adjusting the laser ranging parameters according to dynamic feedback information to ensure the stability of navigation and obstacle avoidance performance. The present invention can effectively improve the laser ranging accuracy of unmanned ships in complex dynamic environments, optimize navigation safety and obstacle avoidance capabilities, and has broad application prospects.

The invention relates to an intelligent fish blocking and ship passing system based on multi-mode AI fusion and self-adaptive control, and belongs to the field of water conservancy projects and intelligent control. Aiming at the problems that a traditional electric fish blocking device is low in ship communication efficiency, poor in environmental adaptability and high in maintenance cost, a multi-mode sensor (laser radar, sonar, infrared thermal imaging and the like) fusion technology is adopted, and a reinforcement learning optimization proportion-integral-differential (PID) control parameter and a graph neural network (GNN) path planning algorithm are combined. All-weather high-precision sensing and dynamic gate regulation and control are realized; and equipment faults are predicted through a long short-term memory (LSTM) model, so that the shutdown risk is reduced. According to the system, the ship dredging efficiency is improved by 25%, the accident rate is reduced by 70%, the maintenance cost is reduced by 40%, the system adapts to extreme environments, and intelligent water conservancy construction is supported.

The invention discloses an energy consumption optimization path planning method for an underwater robot. According to the method, an ocean current model is established by using a Navier-Stokes equation, and complex water flow in the ocean is simulated; a mountain peak model is used for simulating complex submarine topography, and a sphere model is used for simulating random obstacles in the ocean; the outer layer of the path planner adopts a particle swarm optimization algorithm, the inner layer of the path planner adopts a sequential quadratic programming algorithm, and the optimal speed of each section of path can be obtained while path points are obtained; fitting path points by using a B-spline curve to generate a smooth path; and a special fitness function calculation mode is adopted, and a re-planning strategy is combined, so that the calculation efficiency of the algorithm is improved. According to the method, the optimal navigation speed of the robot in different environments can be calculated, the energy consumption of the planned path is reduced on the basis of ensuring the navigation time and safety of the robot, and the efficiency and robustness of path planning are effectively improved through a multi-algorithm combined double-layer path planner and a dynamic re-planning mechanism.

The invention discloses an intelligent PCB sorting process and system based on an unmanned factory, and the process comprises the steps: obtaining surface images and internal structure parameters of PCBs in real time through an image collection module and a scanning module on sorting equipment, and generating a standardized feature data set; the lightweight classification model performs classification and quality grade division on the PCBs in combination with a process grade judgment rule, and dynamically allocates sorting priorities based on MES system order demands to generate a task queue; the path planning algorithm calculates the optimal grabbing path of the mechanical arm and the AGV conveying path according to the priority queue, and a sorting instruction set is formed and issued to the execution unit through the industrial communication network; the mechanical arm completes PCB grabbing and placing operation according to the instruction, and meanwhile, the visual verification module detects a sorting result in real time and outputs a sorting state report and quality data; according to the process, high efficiency, accuracy and unmanned operation of PCB sorting are realized through automatic data acquisition, intelligent classification decision and a closed-loop optimization mechanism.

The invention relates to the technical field of underground pipe gallery distributed intelligent control, and discloses an underground pipe gallery intelligent illumination and energy consumption optimization system, which comprises a dynamic credibility evaluation module for analyzing multi-modal environment sensing data in real time and generating a dynamic credibility score; the edge-cloud collaborative optimization module is used for realizing distributed collaborative learning through local model fine tuning and global knowledge distillation; and the event-driven resource allocation module dynamically optimizes a lighting strategy and a communication priority based on credibility scores and emergency information, and through multi-modal data cross validation and adaptive weight adjustment, the robustness of anomaly detection is significantly improved, and misjudgment caused by sensor failure or environmental interference is effectively suppressed; the dynamic planning algorithm is used for synchronously optimizing energy consumption and equipment service life, efficient balance of safety and energy efficiency is achieved, the system is particularly suitable for complex underground environments and can automatically adapt to sudden anomalies, and the intelligent level and the energy utilization efficiency of pipe gallery illumination are remarkably improved.

The invention discloses an electric vehicle V2G micro-grid energy storage capacity optimization method based on dynamic planning, particularly relates to the technical field of micro-grid energy storage optimization, and aims to solve the problem of energy storage capacity optimization defects caused by spatial dynamic characteristics caused by mobility of an existing electric vehicle. Performing geographic grid division on the node position, generating a space-time matrix by combining with a timestamp, and identifying a high-frequency access node; constructing a state transition equation based on the inter-node impedance matrix, and setting a node-level safety boundary by taking a line capacity change rate as a dynamic constraint; a staged reverse dynamic programming algorithm is adopted, spatial dimension optimization is preferentially executed on the high-frequency access nodes, and charging and discharging constraints fusing voltage deviation and capacity overrun penalty terms are generated; carrying out global optimization by taking the minimization of the total operation cost as a target after the constraints are integrated, and outputting an energy storage capacity configuration strategy of each time period; and finally generating an energy storage deployment scheme.

The invention discloses an intelligent forest fire prevention method and system, and aims at solving the problems of monitoring failure, information updating lag and low prediction precision of a traditional method in a severe environment by integrating multi-source real-time data and adopting deep learning and a dynamic probability model to realize accurate identification of a fire disaster and fire spreading prediction. Through an intelligent path planning algorithm and an unmanned aerial vehicle autonomous fire extinguishing technology, real-time response to dynamic changes of a fire scene and flexible adjustment of a fire extinguishing strategy are realized, the fire extinguishing efficiency and safety are improved, a fire extinguishing effect data acquisition and feedback mechanism is established, and the defect that a traditional system lacks effect evaluation after fire extinguishing is overcome. And a reliable basis is provided for subsequent model optimization and decision improvement, so that the forest fire early warning and emergency response capabilities are integrally improved.