103673 results about "Control system" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention relates to the technical field of industrial robots, in particular to an industrial robot walking control system based on obstacle recognition. Comprising an environment sensing unit; the obstacle analysis and decision-making unit is used for processing the multi-dimensional data output by the environment sensing unit based on a deep reinforcement learning framework, accurately identifying static obstacle and dynamic obstacle types, motion trails and interaction influences, constructing a two-dimensional decision-making model of static obstacle avoidance and dynamic obstacle avoidance, and carrying out obstacle avoidance and obstacle avoidance on the basis of the two-dimensional decision-making model. A differential obstacle avoidance strategy is triggered; the path planning unit is based on a dynamic game path algorithm under space-time constraint; and an instruction transceiving unit. Through the multi-modal fusion sensing technology and the adaptive parameter adjustment module, space-time alignment and feature fusion of multi-source data such as three-dimensional point cloud, texture features and vibration spectrum are realized, a high-dimensional environment state model is constructed, and the problem of insufficient data fusion depth in the prior art is effectively solved.

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

The invention discloses a method, a system and a device for monitoring multifunctional parameters of a direct-current drilling machine, and relates to the technical field of manufacturing of industrial automatic control system devices. The method, system and device for monitoring the multifunctional parameters of the direct-current drilling machine comprises the steps that S1, various data are collected and subjected to standardization and normalization processing, and a standardized working condition data set is constructed; s2, multi-dimensional disturbance characteristics are analyzed, and the stability level of a drilling system is quantified; s3, evaluating a dynamic evolution trend of a working condition, and updating a risk level, a response strategy and a monitoring priority; and S4, identifying an abnormal state based on the key disturbance value and the trend evolution value, and generating a monitoring report. The problems that an existing direct current drilling machine display device is insufficient in key working condition feature extraction capacity, deep understanding and trend analysis of the equipment operation state are difficult to support, and then the early warning timeliness and judgment accuracy of the abnormal state are limited are solved.

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

The invention discloses a remote monitoring and intelligent management and control system for fire-fighting equipment, and belongs to the technical field of fire-fighting early warning, and the system specifically comprises the steps: deploying a multi-parameter sensor array, and enabling each sensor node to form a redundant monitoring network through a honeycomb topological structure; carrying out real-time fusion analysis on the sensor network data through an edge computing gateway, and triggering an abnormal region positioning module when detecting that any monitoring parameter exceeds a preset dynamic threshold value; the abnormal region positioning module constructs a three-dimensional thermodynamic diagram model based on the parameter gradient change of adjacent sensor nodes, and dynamically calculates the diffusion direction and the potential risk level of an abnormal region; the mobile inspection robot cluster receives the coordinates and risk parameters of the abnormal area, a multi-stage response path is automatically generated based on a building digital twin model updated in real time, a primary path quickly approaches along a preset main road, and a secondary path is dynamically adjusted according to field obstacle distribution; and the tail end path adopts a contact type detection module to recheck the abnormal area.

The invention relates to the technical field of operation and maintenance management and control, and discloses an intelligent building remote operation and maintenance management and control system based on a large model and a cloud edge collaborative architecture, and the system comprises a configuration subsystem which is used for carrying out the reverse proxy and persistent connection configuration of edge gateway equipment in an intelligent building, and obtaining a TLS encryption bidirectional communication channel; the construction subsystem is used for constructing a cloud-edge-end three-layer cooperative computing architecture; the conversion subsystem is used for performing unified semantic abstraction and conversion on the heterogeneous protocol data to obtain semantic data in a standard JSON (JavaScript Object Notation) format; the rule processing subsystem is used for performing rule processing through a near-end decision engine of the edge gateway to obtain a local intelligent response result; the multi-dimensional analysis subsystem is used for carrying out multi-dimensional analysis through a cloud large model engine to obtain an equipment health score, a fault prediction result and an energy optimization strategy, seamless fusion and interoperation of heterogeneous system data are achieved through the system, and then global optimization and linkage control of the intelligent building are achieved.

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

The invention belongs to the technical field of circuit breaker management, and discloses a control system for managing a primary and secondary fusion circuit breaker, and the system collects the electrical parameters, mechanical states and environmental conditions of the circuit breaker through multiple channels, and constructs a standardized operation data set; establishing a circuit breaker health characteristic spectrum based on deep characteristic learning; establishing an environmental adaptability control parameter library through environmental factor correlation analysis and multi-scene simulation; performing fault mode identification and predictive diagnosis in combination with the health characteristic spectrum, and generating a fault risk early warning matrix; optimizing a multi-circuit-breaker cooperative control strategy based on the early warning matrix, and generating an optimal control instruction sequence; and reliable execution and effect feedback of the control instruction are realized through security encryption verification and a hierarchical execution mechanism. The problems that a traditional circuit breaker control system is difficult in data integration, insufficient in environment adaptability, weak in fault prediction capacity, incomplete in cooperative control and the like are solved, and the safety and reliability of power grid operation are remarkably improved.

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention provides a patrol route self-adjusting method and system based on unmanned aerial vehicle patrol, and the method comprises the steps: firstly obtaining a multi-source environment perception data set of a target area, then carrying out the feature extraction of the multi-source environment perception data set, inputting a dynamic route planning model, generating an obstacle avoidance correction vector set and an environment adaptation parameter set, and carrying out the feature extraction of the multi-source environment perception data set; and carrying out spatial position offset compensation on the reference route based on the obstacle avoidance correction vector set to generate an initial correction path node set, carrying out path smoothness optimization processing on the initial correction path node set according to the environment adaptation parameter set to generate a final dynamic patrol route, and finally, carrying out dynamic patrol. The dynamic patrol route is converted into a waypoint control instruction stream which can be executed by the unmanned aerial vehicle flight control system, and the waypoint control instruction stream is transmitted to the airborne controller in real time to drive the unmanned aerial vehicle to execute the patrol task, so that dynamic self-adjustment of the unmanned aerial vehicle patrol route is realized, and the patrol flexibility and safety are improved.

The invention discloses a portal crane grab bucket control system and method based on multi-sensor fusion, and particularly relates to the technical field of portal crane grab bucket control. Image acquisition equipment and multiple types of sensors are arranged in a portal crane operation area, a multi-dimensional input data set under a unified time sequence is constructed, an optimized visual detection model and an attention mechanism are combined, high-precision recognition and abnormity judgment of the grab bucket state are achieved, and the system not only can automatically plan a grabbing path and a posture adjusting instruction, but also can automatically adjust the grabbing posture of the grab bucket. And control parameters can be dynamically optimized according to the grabbing effect and pose feedback, the grab bucket recognition accuracy, the control stability and the system intelligence level are remarkably improved, and particularly higher robustness and reliability are shown in the small-size, complex-pose or multi-target operation environment.

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention provides an intelligent factory monitoring method and system based on multi-sensor fusion, and the method comprises the steps: firstly obtaining real-time monitoring data streams of multiple types of sensors in an intelligent factory, including operation state data collected by an equipment state sensor and scene state data collected by an environment state sensor; performing basic synchronization processing on the real-time monitoring data stream to obtain a standardized monitoring data stream, calling a pre-trained multi-sensor association analysis model to perform cross-source feature fusion processing on the standardized monitoring data stream to generate a fusion feature sequence, and performing abnormal mode detection processing based on the fusion feature sequence to generate an abnormal detection result; and finally, generating a monitoring intervention instruction containing equipment positioning information according to an anomaly detection result, and sending the monitoring intervention instruction to a factory control system to trigger a state adjustment operation, thereby effectively improving the monitoring precision and anomaly processing efficiency of the intelligent factory.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention discloses a self-adaptive control unmanned aerial vehicle flight path correction method and system, and relates to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: collecting flight environment data through a sensor group carried by an unmanned aerial vehicle, constructing a three-dimensional environment model, and dividing the three-dimensional environment model into space units, the real-time pose of the unmanned aerial vehicle is determined based on point cloud matching and pose estimation technologies, trajectory offset is predicted according to a preset flight path, flight trajectory deviation parameters are generated, and a triaxial compensation strategy of horizontal plane, vertical direction and lateral offset is analyzed through trend analysis of the deviation parameters. And finally performing dynamic compensation correction on the flight path of the unmanned aerial vehicle according to the three-axis adjustment amount. The technical problem that the real-time accurate correction efficiency of the flight path of an existing unmanned aerial vehicle in a complex environment is low is solved, and the technical effect of shortening the dynamic compensation response time of the flight path deviation of the unmanned aerial vehicle is achieved through an industrial automatic control system device manufacturing technology and an intelligent monitoring device.

The invention discloses an intelligent water quality regulation and control system and method based on multi-parameter real-time monitoring, and belongs to the technical field of water quality monitoring. According to the intelligent water quality regulation and control system, the water quality data of the water body is obtained in real time through the multi-parameter sensor array, and the monitoring regulation and control server can quickly generate an abnormal report and a water quality regulation and control scheme. The data processing module performs feature extraction on the water quality data to obtain target features; the water quality evaluation module is used for accurately evaluating the water quality by using a pre-trained deep neural network model; the abnormity identification module can timely judge whether the water quality has a pollution risk and generate an abnormity report; and the regulation and control module generates a water quality regulation and control scheme by adopting a multi-objective optimization algorithm. The system realizes real-time performance, accuracy and intelligence of water quality monitoring, can quickly respond to water quality changes, effectively reduces pollution risks, and improves the efficiency and effect of water quality regulation and control.

The invention relates to the technical field of hot working control, and discloses a hot working management and control method, system and device based on digital twinning and a storage medium. The method comprises the steps of obtaining technological parameters and quality data of smelting, casting, forging and pressing and heat treatment procedures through a multi-sensor fusion internet-of-things data acquisition system, constructing a hot working full-process data set and a multi-dimensional technological data platform, achieving parameter mapping and grid conversion through a self-adaptive interface algorithm, establishing a collaborative simulation platform, and obtaining a multi-dimensional technological data platform. And constructing a digital twinborn model and deploying the digital twinborn model to an edge computing gateway to generate a visual management and control system. And calculating a process parameter combination and a resource priority allocation scheme. According to the invention, on the basis of obtaining a large amount of process data in real time, cross-process collaborative optimization and intelligent decision can be realized, especially under the conditions of multi-physics field coupling and complex time-varying characteristics, how to effectively monitor and adjust process parameters in real time and ensure the optimization of product quality and production efficiency.

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention provides a thermal power generating unit performance optimization control system and method, and the system comprises a data collection module which collects various real-time data in the operation process of a thermal power generating unit; the load prediction module is used for establishing a load prediction model and predicting a load change trend by comprehensively considering a historical load condition, a load change rate, a load change acceleration and an external interference factor; the optimization control module is used for dynamically adjusting fuel, an air door and air quantity according to a control strategy in combination with the load prediction result and the unit operation state; the actions of the regulating valve, the air door actuator and the water supply regulating valve are correspondingly controlled; the monitoring feedback module is used for realizing closed-loop control and self-correction; according to the method, a load prediction model based on the combination of time sequence analysis and a physical mechanism and a control strategy comprehensively considering multi-parameter collaborative optimization are utilized, so that quick response to load change, balance consideration of steam quality and combustion efficiency, self-adaptive stable operation under complex working conditions and improvement of overall performance are achieved.

The invention relates to the technical field of motor control, in particular to a permanent magnet motor current control system based on predictive compensation, which comprises a thermal field identification module, a response offset identification module, a correction track generation module, an amplitude limiting interval decision module and a compensation instruction output module. According to the method, a dynamic prediction correction mechanism is established based on thermal field characteristics and a response error trend, real-time adjustment of a current track of the permanent magnet motor is realized, a three-dimensional space thermal load curved surface is constructed by means of a stator temperature rise curve, a permanent magnet temperature change rate and driving torque fluctuation, and transient influence of thermal working condition change on magnetic performance of the motor is reflected. A multi-dimensional prediction compensation path is integrally constructed through interaction among multiple thermal field parameters, electromagnetic response variables and time sequence feature points, the response precision and control stability of the permanent magnet motor under the dynamic working condition are improved, the magnetic performance attenuation influence caused by thermal drift is reduced, and meanwhile the tracking consistency under current track disturbance is improved.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

A system and method for AI-based dynamic pricing, mapping, and emissions control to optimize inner-city mobility. The system acquires real-time nitrogen dioxide data across a geographic area from sources including satellites, feeds this data into a decision support system along with road-specific weight matrices from authorities, and updates a dynamic penalty matrix for roads in the area. Automatic number plate recognition tracks vehicle movement, queries vehicle databases for emissions profiles, and updates the platform. Users input journey parameters and the system, using AI tools including a genetic algorithm economic and environmental dispatch (GA-EED) optimizer, generates an optimized route based on user-selected criteria such as time, emissions, penalty, and cost. The system enables dynamic, environmentally-aware route optimization for inner-city mobility.