3437 results about "Intelligent algorithms" patented technology

The invention discloses a wide-area landslide rapid identification method based on an interpretable intelligent algorithm, and relates to the field of remote sensing science and technology, and the method comprises the steps: building a dual-channel feature extraction architecture through multi-source spatio-temporal data fusion and knowledge graph dynamic weighting: capturing image local textures through a lightweight CNN, modeling geological spatial correlation through a graph convolutional network, and carrying out the recognition of the landslide. Combining the SHAP value and causal reasoning to generate an interpretable contribution degree thermodynamic diagram and a rule chain; a knowledge graph bidirectional verification system is introduced, spatial logic contradictions are verified by using prior rules, and co-evolution of a model and a rule base is triggered based on misjudgment samples; outputting a multi-dimensional credibility report, quantifying uncertainty by Monte Carlo Dropout, and customizing interpretation granularity according to roles; a terrain-adaptive block-stream processing architecture is adopted, and edge lightweight deployment and federated learning are combined, so that wide-area real-time early warning and model dynamic updating are realized. According to the scheme, the limitation of a traditional black box model is broken through, and a disaster prevention closed loop with physical driving, transparent decision and second-level response is formed.

The invention discloses a railway track damage detection method, and belongs to the technical field of railway track detection. According to the method, an image acquisition module and an ultrasonic detection module are installed at the bottom of a track detection vehicle, the detection vehicle is controlled to run, and track top face and side face image sequences and ultrasonic reflection signals are acquired; preprocessing the image, and respectively inputting the image into a deep convolutional neural network model and a support vector machine classifier to obtain a crack identification result and a wear level; processing an ultrasonic reflection signal, and judging a layering defect; and finally fusing the data, marking a damage position and generating a structured detection report. According to the method, the problems of incomplete detection, low precision and the like in the existing railway track damage detection are solved, efficient detection of track surface cracks, side abrasion and internal layering defects is realized through collaborative acquisition of the multi-modal sensor, intelligent algorithm processing and data fusion, and the comprehensiveness and reliability of detection are improved.

The invention discloses a storage battery abnormity early warning system based on an intelligent battery gateway, and relates to the technical field of battery monitoring. The system comprises a sensor module used for collecting voltage, internal resistance, temperature, thermal runaway state and floating charge voltage parameters of a single battery in real time; the intelligent battery gateway comprises a data processing module and an early warning module, and the data processing module carries out fusion analysis on sensor data through an intelligent algorithm, calculates the battery remaining capacity SOC and the state of health SOH and executes an intelligent equalization strategy; and the early warning module generates a multi-stage early warning signal according to the parameter threshold and the anomaly detection model. According to the invention, through a multi-dimensional fusion sensing technology and a dual-drive intelligent algorithm, the aging sign of the internal microstructure of the battery is accurately captured, and high-precision estimation of the residual electric quantity and the health state of the storage battery is realized; and in the aspect of early warning performance, a multi-dimensional linkage detection model and an intelligent early warning mechanism are established, so that the accuracy and timeliness of fault early warning are effectively improved, and the early warning time is greatly advanced.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention relates to the technical field of virtual power plants, in particular to a multi-target scheduling optimization method and system for a virtual power plant to participate in a power market, and the method comprises the steps: carrying out the dynamic integration of distributed energy, an adjustable load and an energy storage unit in the virtual power plant, and constructing a resource model; establishing a distributed resource identity authentication and data storage mechanism; constructing a multi-objective optimization function, comprehensively considering a plurality of objectives such as resource utilization rate, carbon emission and load balance, and designing a dynamic weight adjustment mechanism; calculating a multi-objective optimization result, determining the optimal output and operation state of virtual power plant resources, and generating a virtual power plant optimization scheduling strategy; and cooperative optimization of internal resource scheduling and external power grid scheduling of the virtual power plant is realized by combining a game theory and a swarm intelligence algorithm. According to the method, the utilization efficiency of distributed resources can be improved, source-network load-storage coordination and new energy consumption are promoted, and the flexibility and comprehensive benefits of a power system are enhanced.

The invention relates to a multi-product-line-oriented robot welding integration method, device and equipment and a medium, and the method comprises the steps that three-dimensional geometrical characteristics and material attribute data of multiple types of workpieces are collected, and geometric parameters and material mechanical properties of weld joints are extracted; an order demand template is intelligently matched, and initial welding parameters and a clamp configuration scheme are generated; the health state of production equipment is evaluated in real time, and the collision-free movement track of the mechanical arm is planned; dynamically analyzing the joint angle sequence to generate a high-precision servo control signal; the welding process is monitored in real time, and current and molten pool temperature parameters are corrected; and iteratively optimizing the algorithm weight of the process knowledge base to improve the adaptive capacity. Through cooperation of multi-source data fusion and an intelligent algorithm, rapid switching of multiple kinds of workpieces, self-adaptive compensation of the equipment operation state and closed-loop optimization of welding process parameters are achieved, and the equipment utilization rate and the welding quality stability in a flexible production scene are remarkably improved.

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

The invention relates to the technical field of mobile energy storage vehicle energy management, and discloses a mobile energy storage vehicle energy management method based on an intelligent algorithm, and the method comprises the steps: collecting the charging and discharging rate of a battery pack, the environment temperature, the power grid load fluctuation and other multi-dimensional energy state data; a heterogeneous layered architecture of edge computing nodes, a cloud collaboration platform and a vehicle-mounted control terminal is constructed, energy partitions are divided according to peak valleys of a power grid, and low-delay response, global optimization and local closed-loop control modes are configured; fusing the multi-source heterogeneous energy data streams and removing abnormal values; a dynamic optimization decision model is constructed through a battery degradation model and a power grid supply and demand balance equation, and a multi-parameter collaborative constraint relation is solved through simultaneous solving of a multi-target iteration solver and a parallel gradient descent algorithm; and generating a three-level adaptive response instruction sequence of local battery overload alarm, regional power grid frequency modulation early warning and global energy scheduling imbalance pre-judgment based on constraint boundary triggering conditions. According to the method, the accuracy, the collaboration and the robustness of energy management are improved.

The invention discloses an attention state recognition neural network modeling and reasoning method based on an electroencephalogram sequence. The core is to construct and train a deep neural network model suitable for electroencephalogram signals so as to realize intelligent recognition and classification. Firstly, wavelet transformation and time-frequency feature extraction are carried out on electroencephalogram time sequence signals, and a multi-dimensional input tensor is generated in combination with channel position information; and inputting the feature into a deep network fusing spatial convolution, gating circulation and a residual connection structure, and extracting spatio-temporal joint features. A cross-time-step attention mechanism and a dynamic loss adjustment strategy are introduced in a training stage, so that the discrimination capability of the model on an alertness state transition region is improved. The final model can conduct reasoning on electroencephalogram data of any length, and a classification label and a confidence score are output and used for measuring classification reliability. The method focuses on construction and optimization of a specific calculation model, reflects application characteristics of an intelligent algorithm in cognitive state recognition, and belongs to an intelligent calculation method with a neural network as a core.

The invention discloses a self-adaptive grouting control plugging method based on mining-induced fracture real-time monitoring, and relates to the technical field of mine safety engineering and hydrogeology. Through cooperative work of the grouting mechanism, the data monitoring system, the data collecting and processing module and the self-adaptive control module, automation and intellectualization of grouting protection can be achieved, and through introduction of the fracture roughness coefficient and the time-dependent viscosity, rough fracture flow resistance and grout rheology and time-varying characteristics are quantified, and the grout diffusion radius calculation error is reduced. The data weight is dynamically adjusted through a micro-seismic travel time residual error and sound wave velocity field joint inversion algorithm in combination with a weighted robust LM algorithm, and accurate fracture positioning is achieved. And a PID gain compensation and saturation function mechanism is adopted, so that self-adaptive safe and efficient grouting is realized. The fracture dynamic state is monitored in real time through the multi-source data fusion technology, grouting parameters are dynamically optimized in combination with an intelligent algorithm, complex geological interference is effectively restrained, slurry waste is reduced, and efficient and accurate plugging of mining-induced fractures is achieved.

The invention provides a weather forecast learning system based on an artificial intelligence algorithm, and the system comprises a data access collection module which collects a multi-source data set; the data fusion and processing module is used for carrying out data fusion and processing to obtain a multi-source fusion data set; the AI model design module is used for constructing a multi-model collaborative architecture and carrying out multi-model parallel training and optimization; the model fusion module is used for carrying out multi-model weighted fusion to obtain an AI model; the real-time prediction module is used for updating a prediction result according to the real-time data; the visualization and interpretability module is used for designing a visualization interface and carrying out interpretability verification; and the evaluation and iteration module is used for carrying out comprehensive evaluation and continuous improvement on a prediction result in combination with evaluation indexes. According to the method, accurate and reliable observation data can be obtained, massive meteorological data are efficiently processed by combining an artificial intelligence algorithm, trend analysis and prediction are automatically carried out, and a reliable prediction result is generated.

The invention discloses a circuit board AOI detection result analysis method based on an intelligent algorithm, and the core of the method is to carry out the high-precision synchronous collection, denoising normalization processing and time sequence alignment of a detection image and multi-dimensional process parameters, and extract joint features through an attention mechanism. A process parameter-detection result correlation distribution model under each process scene is established, model output abnormal drift detection and tracing reasoning are realized, the detection model and the process parameters are dynamically optimized in combination with interpretability AI, the method realizes efficient tracking, attribution and adaptive optimization of detection misjudgment caused by process parameter variation, and the detection accuracy is improved. And the stability of the production process of the SMT detection system is improved.

A high-altitude electrode discharge voltage prediction method and system, a device and a medium, relating to the technical field of electrode discharge. The method comprises: acquiring a measured dataset of a high-altitude true electrode, and performing normalization processing and data partitioning on the measured dataset in a preset selection ratio to generate an initial training set and a test set; on the basis of the degree of influence between a feature variable and a target variable in the initial training set, constructing a target training set; carrying out model construction on the basis of a support vector regression method and an ant lion optimization algorithm in combination with the target training set, to generate an intermediate intelligent model; using the test set to test the intermediate intelligent model, and determining a target intelligent model; and inputting the measured dataset into the target intelligent model for discharge voltage calculation, to generate discharge voltage prediction data corresponding to the high-altitude true electrode. By means of the ant lion optimization intelligent algorithm and the target intelligent model, the accuracy of prediction results can be improved.

The invention relates to the technical field of equipment monitoring, in particular to an equipment corrosion evaluation and life prediction method and application, and the method comprises the following steps: deploying a sensor network in an easily-corroded area of coal chemical equipment, and collecting multi-dimensional data; carrying out abnormal value elimination, data compression, time synchronization and space-time alignment preprocessing on the collected multi-source data; image features are extracted through a convolutional neural network, processed data are analyzed through an LSTM-attention model, and a fuzzy comprehensive evaluation matrix is established to evaluate the corrosion level; a physical model based on the Faraday electrolysis law and a data driving model based on the Transform network are constructed, and the residual life is predicted through Bayesian network fusion output and Monte Carlo simulation. Through fusion of multi-source data and an intelligent algorithm, accurate evaluation of the corrosion state of the equipment and accurate prediction of the residual life are realized, and safe and efficient operation of the coal chemical equipment is guaranteed.

The invention relates to a building material mining life cycle process carbon emission improvement potential quantification method comprising the following steps: constructing a full life cycle carbon footprint digital model covering mining, transportation, processing and restoration, and integrating a digital twinning and BIM technology dynamic correlation carbon emission factor library; key carbon emission influence factors are identified through Monte Carlo simulation and regression analysis; establishing a multi-objective optimization model, generating an optimal process parameter combination by adopting an intelligent algorithm, and quantifying the emission reduction potential; model parameters are dynamically calibrated in combination with real-time data, and a closed-loop optimization mechanism is formed; space-time distribution visualization and block chain evidence storage technologies are introduced, and accurate monitoring, intelligent decision making and credible tracing of carbon emission are achieved. According to the method, the problems of data lag and single optimization in a traditional method are solved, the accuracy of carbon emission and the feasibility of an emission reduction scheme are remarkably improved, meanwhile, carbon sink offset evaluation is supported, and a whole-process technical support is provided for mine green transformation.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention belongs to the field of microgrid resource capacity optimization. The invention provides a power distribution network wind and light storage capacity optimization method considering multi-microgrid energy storage cooperation. The method comprises the following steps: step 1, establishing a wind and light combined operation power information data set considering spatial correlation during multi-microgrid source load fluctuation; and step 2, multi-microgrid wind and light storage capacity configuration optimization is realized by using a reinforcement learning algorithm. And step 3, complementing the wind-light fluctuation scene with few samples by using a transfer learning algorithm. Based on deep fusion space-time correlation modeling, multi-agent reinforcement learning and cross-domain transfer learning, a multi-microgrid energy storage collaborative optimization framework with dynamic adaptive capacity is provided. According to the method, the deep association rule of the multi-dimensional operation data of the micro-grid group can be analyzed, global optimal capacity configuration is realized through a coevolution mechanism of an intelligent algorithm, and a brand new solution is provided for solving the problem of power distribution network optimization under high-proportion new energy access.

The invention relates to the technical field of energy storage systems, in particular to a power dispatching method of an energy storage system and the energy storage system.The power dispatching method comprises the steps that by deploying edge computing nodes and building a cloud global optimization engine, data support is provided for follow-up precise regulation and control, and timeliness and integrity of data acquisition are guaranteed; energy storage equipment capacity and power grid requirements are analyzed by means of an intelligent algorithm, a reasonable response priority is set, tasks are dynamically allocated, and efficient utilization of resources and accurate execution of the tasks are ensured; an MPC algorithm is adopted to take power grid frequency deviation as an optimization target, output instructions are optimized in a rolling mode, prediction errors are corrected, power grid frequency fluctuation adjustment of the energy storage system is achieved, and power grid stability is enhanced; and the long-time energy storage charging and discharging plan is optimized by taking the full life cycle cost minimization as the target, so that the stability of the power grid and the operation economy of the energy storage equipment are effectively improved.

The invention discloses a distributed electrochemical energy storage fire alarm system based on an intelligent algorithm, and relates to the technical field of energy storage battery safety monitoring. Technical processes of multi-source data acquisition and preprocessing, key health factor extraction, multi-modal feature fusion, depth model detection, cross-system intervention and adaptive strategy optimization are provided; a high-quality data stream is generated through real-time synchronous sampling and noise filtering, weak fault symptoms such as internal short circuit or material deterioration are highlighted by applying a multi-dimensional feature structure, a deep learning model is utilized to accurately recognize and output early warning, and then load reduction, isolation and temperature control measures are carried out in cooperation with a BMS, an EMS and a fire fighting system. An evolutionary algorithm or a reinforcement learning continuous iteration intervention strategy is introduced in a simulation environment, high-sensitivity capture and rapid suppression of latent risks under multi-scene complex working conditions are achieved, the rate of missing report and false report can be remarkably reduced, and the overall safety of the system is remarkably enhanced.

The invention relates to the technical field of power adapter temperature management, and discloses a gallium nitride power adapter temperature management system comprising a dynamic thermal model module used for accurately predicting the temperature change trend of each key component; the multi-level temperature monitoring network module is used for forming a complete temperature distribution diagram; the multi-device collaborative sensing module is used for generating a sensing result of the whole heat dissipation environment; the self-adaptive air duct structure module is used for optimizing an air flow path; the heat dissipation path module based on swarm intelligence is used for calculating and generating an optimal multi-device collaborative heat dissipation path by applying a swarm intelligence algorithm; the self-adaptive heat dissipation control strategy module is used for realizing multi-target balance optimization of temperature, noise and energy consumption; the problem that heat dissipation is difficult when multiple adapters are used in a centralized mode is solved, heat dissipation efficiency is improved, energy consumption is reduced, and noise is reduced.

The invention discloses a photovoltaic energy storage equipment health state intelligent diagnosis system and method based on the Internet of Things. Comprising a photovoltaic module monitoring module, an energy storage battery health monitoring module, an inverter operation monitoring module, an environmental data acquisition and analysis module, a fault early warning and prediction module, an energy management and optimization module, an equipment remote control and management module and a system self-learning and optimization module. The photovoltaic module monitoring module is used for monitoring the working state and the performance index of the photovoltaic module in real time, efficient and stable operation of the photovoltaic energy storage system is ensured through the comprehensive functions of data acquisition, intelligent analysis, fault early warning, optimal scheduling, remote control, self-learning optimization and the like, and the system can be used for monitoring the working state and the performance index of the photovoltaic module through data driving and an intelligent algorithm. The operation state of each device can be accurately monitored and optimized, potential faults are warned in advance, the occurrence frequency and the operation risk of the device faults are reduced, meanwhile, the energy utilization efficiency is improved, the operation and maintenance cost is reduced, and the reliability and the adaptability of the system are enhanced.

The invention discloses an unmanned aerial vehicle autonomous obstacle avoidance system and method based on millimeter wave radar and multi-mode vision fusion, and the system comprises a millimeter wave radar module which is used for obtaining the distance, speed and point cloud data of an obstacle; the multispectral vision module comprises an RGB camera and an infrared camera and is used for extracting the texture, the category and the thermal characteristics of the obstacle; the embedded AI calculation unit is used for real-time data processing and fusion; the time-space synchronization module is used for aligning timestamps of radar and visual data through hardware trigger signals and unifying space coordinates based on joint calibration and an SLAM (Simultaneous Localization and Mapping) technology; and the feature level fusion module is used for fusing the radar point cloud features and the visual semantic features by adopting a lightweight network. Aiming at single sensor defects, complex environment challenges and hardware computing power limitation, a more efficient unmanned aerial vehicle autonomous obstacle avoidance system is constructed through multi-modal hardware fusion, lightweight feature processing, a dynamic adaptive mechanism and intelligent algorithm optimization, and all-weather and full-scene autonomous operation requirements are met.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention discloses an intelligent detection method for fault detection of an outdoor power line, and the method comprises the following steps: 1, carrying out the collection and preprocessing of multi-modal data, and carrying out the collection and preprocessing of the multi-modal data through an unmanned plane cluster, a distributed optical fiber sensor, a laser radar and meteorological monitoring equipment; visible light image data, infrared image data, laser point cloud data, vibration waveforms, temperature distribution and environmental parameters of the power line are synchronously obtained, and multi-source image data are processed, namely the visible light image data, the infrared image data and the laser point cloud data are processed; and 2, intelligent fault diagnosis: inputting the data acquired in the step 1 into a multi-task neural network model, and outputting a fault positioning and type identification result. According to the novel detection method based on multi-modal data fusion, an intelligent algorithm and closed-loop optimization, the fault identification precision, the dynamic decision-making capability and the comprehensive protection efficiency are improved, and the intelligent operation and maintenance requirements of a modern power grid are met.