2341 results about "Network module" patented technology

The invention relates to the field of power equipment data processing, in particular to a steam turbine vibration fault diagnosis system fused with deep learning. Comprising a dynamic knowledge base construction module, a working condition adaptive data synchronization module, a knowledge-guided heterogeneous feature fusion module, a dynamic structure neural network module, an online self-optimization weight distribution module and a knowledge-enhanced coupling fault reasoning module. The dynamic knowledge base construction module updates the multi-modal knowledge graph through an incremental knowledge distillation mechanism, and generates an interpolation strategy template and a frequency band sensitivity matrix; the working condition self-adaptive data synchronization module dynamically calls an interpolation algorithm based on a rotating speed fluctuation mode to realize time sequence alignment optimization of multi-source sensor data; the system effectively solves the problems of time scale asynchronism and feature heterogeneity in multi-source heterogeneous data fusion through a knowledge-driven and data-driven closed-loop interaction mechanism, and realizes accurate diagnosis and early warning of steam turbine vibration faults under complex working conditions.

The invention provides an improved underwater target detection system and method based on RT-DETR, and belongs to the technical field of underwater target detection based on computer vision. The system comprises a backbone network module, a hybrid encoder module, an InerMPDIOU sensing module and a decoder module, the backbone network module performs large separable kernel attention calculation after extracting features based on the large separable kernel attention, and outputs a multi-layer feature map; the hybrid encoder module design comprises a deformable large kernel attention-based in-scale feature interaction unit and an improved cross-scale feature fusion unit; the sensing module introduces a scale factor and an auxiliary bounding box into IoU calculation of an original IoU sensing module, and target query initialization is improved by using query selection sensed by InerMPDIOU. According to the model improvement, adaptation is carried out aiming at the particularity of the underwater environment, the underwater operation efficiency is improved, and the accuracy is improved while the parameter lightweight is realized.

The invention, which relates to the technical field of structure health monitoring, discloses a sensing data-based on-line intelligent detection system for the whole body and the periphery of a house, comprising: a distributed sensor module, which is composed of a fiber grating sensor array, an MEMS environment sensor and an image acquisition unit, the system is arranged in a house bearing structure, a peripheral foundation, a drainage system and a greening area. The edge computing node module is configured to preprocess sensor data, including noise filtering, temperature compensation and abnormal data marking; the dynamic topology communication network module adopts a LoRaMesh and 5G hybrid networking technology and supports node self-repairing and bandwidth self-adaptive distribution; and the cloud analysis platform module integrates BIM model reconstruction, multi-source data fusion analysis and early warning decision, and outputs structure deformation prediction, an energy consumption anomaly map and a disaster risk assessment report.

The invention relates to the technical field of tunnel engineering construction, and discloses an intelligent material transportation and safety monitoring system and method for shield construction, and the system comprises a visual perception unit, a sensor network module, an AI analysis center module, a safety decision module and a human-computer interaction interface. According to the invention, data acquisition is carried out through the visual perception unit and the sensor network module, multi-target detection operation is carried out on image frames through the AI analysis center module after target identification and track prediction, target types, space coordinates, contour boundaries and confidence coefficients are identified and extracted, and safety judgment and early warning output are carried out. According to the invention, by integrating the multi-view camera equipment and the UWB, GNSS and other sensors and adopting a deep learning target detection algorithm, high-precision identification and continuous tracking can be carried out on construction site personnel, equipment, segments and other key objects, and accurate input is provided for subsequent risk analysis.

The invention discloses a cooperative monitoring device and method for a large deep foundation pit complex supporting system, and relates to the technical field of foundation pit supporting. The device comprises a multi-dimensional sensor network module, a BIM-GIS digital twin platform module, an intelligent analysis and early warning module, a construction collaborative decision module, a data backup and recovery module and a remote monitoring and management module. The method comprises the following steps: step 1, carrying out multi-source data space-time registration; step 2, dynamically constructing a digital twinborn model; step 3, coupling risk assessment; step 4, construction collaborative optimization; according to the technical scheme, the method comprises the following steps of data processing, data quality control and system performance evaluation, through three innovations of deep coupling of multi-source data, dynamic model correction and intelligent collaborative decision making, a'monitoring-analysis-decision-execution 'full closed loop is constructed, technical breakthroughs are achieved in complex working condition adaptability, early warning real-time performance and construction safety, and the method has remarkable engineering application value.

The invention discloses a lung X-ray image segmentation system based on deep learning, which comprises a data preprocessing module, a lung X-ray image segmentation network module, a training module and a segmentation result output module, and is characterized in that the lung X-ray image segmentation network module constructs a lung X-ray image segmentation network based on deep learning, and the lung X-ray image segmentation network is recorded as MSDA-Net; and the training module is used for training the constructed MSDA-Net. A multi-scale feature extraction module is introduced into the network, and expansion convolution and feature fusion methods with different expansion rates are combined, so that the adaptability of the model to a lung region in an X-ray image is enhanced, a key target region in the image is highlighted, irrelevant background noise is suppressed, the significant difference of the lung region in form and size can be effectively handled, and the accuracy of the model is improved. The method improves the recognition capability of a complex and irregular region, and improves the segmentation effect of a target region.

The invention relates to the technical field of aircraft intelligent detection and maintenance systems, and discloses an aircraft defect intelligent evaluation system and method based on multi-modal fusion, and the system comprises a multi-modal data collection module, a tensor construction and decomposition module, a meta-prototype relation network module, a multi-target game optimization module, and a closed-loop feedback module. The method comprises the steps of constructing a five-order feature tensor through multi-modal data synchronous acquisition and space-time alignment, extracting low-rank features through hypergraph block item decomposition, dynamically generating a defect prototype set in combination with meta-learning, generating a maintenance decision by adopting a Nash equilibrium strategy and fusing multi-constraint conditions, and optimizing system parameters through closed-loop feedback. The whole-process intelligentization of aircraft defect detection and maintenance is realized; according to the method, high-precision defect detection is realized through multi-modal data fusion and hypergraph modeling, an intelligent decision is generated in combination with dynamic prototype learning and multi-target game optimization, and continuous self-optimization is performed by means of a closed-loop feedback mechanism, so that the operation and maintenance efficiency and safety of the aircraft are improved automatically in the whole process.

Methods and systems for managing a data processing system are disclosed. To manage the data processing system, a management controller may be included in the data processing system. The management controller may perform managerial functions for the data processing system regardless of whether in band components are compromised, depowered, and / or otherwise non-functional. In addition, to communicate with remote entities, the management controller may utilize a network module of the data processing system. The network module may direct network traffic between in band and out of band communication channels and may operate independently from hardware resources of the data processing system.

The invention relates to the technical field of modern agriculture, and discloses an intelligent yak breeding monitoring system which comprises a sensor network module, a data processing center module, an intelligent analysis module and a remote monitoring terminal module. The sensor network module integrates various sensors and is used for monitoring physical signs, behaviors and breeding environment parameters of yaks in real time; the data processing center module is responsible for receiving, processing and storing original data transmitted by the sensor network; the intelligent analysis module establishes a health assessment model and an environment optimization algorithm based on the processed data, and realizes real-time assessment of the health condition of the yaks and intelligent optimization of the breeding environment; the remote monitoring terminal module provides a user interface and supports remote control and early warning notification functions. The intelligent yak breeding monitoring system aims at solving the problems that an existing yak breeding environment monitoring technology is single in data, insufficient in intelligent degree and difficult to meet the requirements of precise breeding and scientific management.

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 relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention relates to the technical field of energy, and discloses a distributed electric power energy storage system and method, and the system comprises a decommissioned battery sorting and modularization module which is used for carrying out the performance evaluation, grading and standardized modularization processing of decommissioned power batteries; the energy storage unit module is used for assembling the retired batteries into an energy storage unit and storing and releasing electric energy; the energy management module is used for monitoring the running state of the energy storage unit and optimizing energy distribution; the communication network module is used for carrying out data transmission and cooperative control between the modules; and the power grid and user interface module is used for interacting the energy storage system with a power grid and a user side. According to the method, the energy scheduling unit can optimize the charging and discharging strategy of the energy storage unit in real time by combining reinforcement learning and federated learning algorithms, and meanwhile, the adaptability of a scheduling model in multiple scenes is improved by utilizing cooperative training of distributed nodes.

The invention relates to the technical field of energy management and carbon accounting, in particular to an intelligent carbon emission monitoring system and method based on the Internet of Things, and the system comprises a distributed sensing network module which collects the energy consumption and emission data of an independent accounting unit in real time; the edge computing module is deployed at a local edge computing node to preprocess the data acquired by the distributed sensing network module; the cloud co-processing module receives the preprocessed data and carries out dynamic accounting and multi-source data fusion; the carbon accounting model module automatically matches accounting requirements according to the industry to which the enterprise belongs and generates a monitoring point layout scheme; and the intelligent visualization module is used for generating a multi-dimensional carbon emission data analysis chart and a compliance report meeting MRV requirements based on data of the edge calculation module, the cloud co-processing module and the carbon accounting model module. Full-process automatic management from data acquisition, real-time monitoring to verification is realized, and the accuracy, integrity and traceability of carbon emission data are ensured.

The invention provides a gas turbine exhaust gas temperature prediction and fault early warning system and method, and relates to the technical field of gas turbine state monitoring and fault diagnosis. The system comprises a data acquisition and preprocessing module used for acquiring and preprocessing data; the dynamic graph construction module is used for constructing graph structure data; the graph neural network module is used for extracting spatio-temporal features through learning; the improved auto-encoder module is used for realizing feature reconstruction by learning normal working condition data and then outputting reconstruction errors to the fault early warning module; the temperature prediction module is used for predicting a future exhaust temperature value; the fault early warning module is used for performing fault judgment and decision; and the result display and output module is used for summarizing analysis results. According to the system, the prediction precision of the exhaust temperature is remarkably improved, the fault detection and early warning capabilities are enhanced, the system has better feature representation capability and adaptability to complex working conditions, and close integration and mutual enhancement of prediction and early warning are realized.

The invention relates to the technical field of high-frequency signal modulation, and discloses a high-frequency carrier synchronization signal modulation system. A carrier distortion compensation module of the system constructs a model based on a historical transmission data set, captures a phase jitter parameter, a spectrum leakage component and a modulation pulse sequence of a transmitting end carrier in real time, and outputs a reconstructed baseband parameter; the multi-dimensional distortion analysis module compares the reconstructed baseband parameter with a receiving end demodulation baseband parameter through composite difference detection, and generates a channel-level distortion coefficient tensor; the topological positioning network module is used for positioning signal out-of-step physical nodes and generating a probability distribution thermodynamic diagram by combining impedance characteristics of transmission nodes and signal path delay information; and the adaptive modulation strategy module starts a multi-band carrier injection mode for the high-probability out-of-step nodes according to the abnormal probability gradient of the thermodynamic diagram, and applies a phase disturbance test to adjacent transmission links. The system can improve the adaptability and accuracy of high-frequency carrier signal synchronous modulation.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on a hierarchical reinforcement learning strategy. The unmanned aerial vehicle autonomous navigation system is suitable for a three-dimensional flight task in an unknown environment. The system comprises a state sensing module, a hierarchical strategy network module, a control execution module, a data classification module and a data playback module. The state sensing module extracts obstacle position information based on a deep neural network, and fuses the target, the obstacle position and the flight state to generate a state vector and a time sequence. The hierarchical strategy network adopts a high-layer DQN to generate a navigation intention, and a low-layer LSTM and PPO are combined to output a continuous control action; the control execution module adjusts the attitude of the unmanned aerial vehicle according to the instruction and performs closed-loop correction. The system introduces a double dynamic memory mechanism (DDM), improves strategy training efficiency and stability through experience classification and proportional sampling, and adopts a multi-target award function guide strategy to optimize convergence among task completion, obstacle avoidance safety and flight rationality. The system has good environmental adaptability and generalization ability, and is suitable for autonomous navigation tasks in complex scenes.

The invention relates to a pig image BDE reconstruction system and method based on differentiatable image rate filtering, and belongs to the field of image processing, and the system comprises an image preprocessing module which is configured to process an original low-bit image; the neural network module acts on the processing and reconstruction tasks of low-bit pig images, the characteristics of each image are collaboratively optimized in a task dividing and conquering mode, and finally a high-quality image is obtained; the loss function module is configured to optimize the network model performance by minimizing the difference between the network output image and the real image; the training module is configured to optimize network model parameters and a learning process by adopting a staged training strategy; and the test and evaluation module is configured to evaluate the image quality through the peak signal-to-noise ratio and the structural similarity index and measure the reconstruction effect of the model. According to the method, a two-stage training strategy is adopted, an image reconstruction task is decomposed into two sub-tasks which cooperate with each other, and spatial resolution enhancement and bit resolution improvement are achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as medical health and financial science and technology, and discloses a model quantitative reasoning acceleration method, device, equipment and medium. The method comprises the steps that an input text is divided into a plurality of processing blocks, importance scoring is conducted on the non-first processing block, and calculation precision formats are distributed according to scoring results; determining a unified quantization configuration of each processing block; dividing the network modules into configuration sharing groups, and sharing quantitative configurations of corresponding processing blocks in the groups; and executing block-level quantization inference according to the unified quantization configuration, and generating a model inference result. The quantitative configuration of each processing block is uniformly determined on the basis of the token importance score, and the configuration is multiplexed in the network module group, so that block-level precision distribution and parallel quantitative reasoning are realized, the video memory overhead and the configuration time overhead are greatly reduced while the reasoning precision is guaranteed, and the reasoning efficiency is improved. And the execution efficiency and the video memory utilization rate in the long text reasoning task are effectively improved.

The invention relates to the technical field of water conservancy system design, in particular to a water conservancy informatization data collection and transmission system and method based on a distributed ad hoc network, and the method comprises the steps: deploying monitoring nodes at each monitoring point of a water conservancy project to collect hydrological data and environmental data; peripheral monitoring nodes are automatically found and connected through a distributed ad hoc network module, a multi-hop wireless network is formed, and communication between the monitoring nodes and gateway nodes is achieved. Specifically, a dynamic routing algorithm is adopted to monitor the delay and signal stability of a link in real time and monitor node residual energy, and an optimal transmission path is calculated; when a node or a link has a fault, a routing path is automatically recalculated according to a fault processing strategy, and disaster recovery transmission is realized; the energy consumption of the monitoring node is reduced through a sleep wake-up mode and an energy-aware routing algorithm; and converging the data of each monitoring node at the gateway node, and uploading the data to a cloud data center. And the flexibility and reliability of data transmission are enhanced.

The invention relates to the technical field of intelligent control of the welding process, and discloses an online monitoring system and control method for the friction stir welding process, and the system comprises a multi-mode sensor array module which is used for collecting temperature, pressure and displacement data in the welding process; the edge computing node module preprocesses the collected data and extracts spatial-temporal characteristics; the space-time diagram convolutional network module is used for mining a space-time association relationship between the sensors; the hierarchical reinforcement learning controller generates a control instruction according to the associated features; the execution mechanism module receives the control instruction and outputs a welding signal; the digital twinborn verification module performs simulation feedback on the control effect; and the cloud model optimization module fuses the multi-source feedback information to continuously optimize the control strategy. The welding process self-adaptive control technology based on reinforcement learning is adopted, the technical effect of adjusting the welding parameters in real time to optimize the welding quality is achieved, and the problem that in a traditional method, the welding quality is unstable due to working condition changes is solved.

The invention belongs to the technical field of AI vision, and particularly relates to a universal six-degree-of-freedom grabbing pose detection method and system for a robot. According to the detection method, the point cloud feature extraction process is optimized by introducing an attention mechanism, so that the prediction precision of the detection head on the six-degree-of-freedom grabbing pose is improved. The method comprises the following steps: firstly, carrying out serialization processing on unstructured, sparse and non-uniform point cloud data generated from an RGB-D image, and then designing a multi-encoder-decoder cascaded backbone network based on a Transform architecture so as to effectively extract rich features of the point cloud. On the basis, a network module special for six-degree-of-freedom grabbing pose detection is designed, and the predicted six-degree-of-freedom grabbing pose is optimized in combination with antipodal grabbing constraints, so that it is ensured that the six-degree-of-freedom grabbing pose conforms to actual physical rules. Through verification of a grabbing experiment on an actual Franka robot, the general grabbing pose detection performance of the method on any object in actual application is further proved.

The invention relates to the technical field of hydropower engineering, and discloses a single-core self-healing type looped network networking method and system, and the method comprises the steps: dividing a whole network into a plurality of sub looped networks; each sub-ring network operates independently; the light switch in each sub-ring network adopts an A / B end connection mode; reversely connecting the B end of the last measuring point to the A end of the initial measuring station by using an optical fiber to form a closed physical structure; configuring a protocol on the switch, and controlling a data flow path through the protocol; different sub-ring networks are connected through RJ45 ports, and a CAT5E shielded twisted pair or an Ethernet interface is used; when a certain node or link fails, the protocol automatically triggers a switching mechanism; the master station optical transceiver monitors the states of all nodes in real time and displays fault points through a network management interface. The system comprises a sub-ring network module, a control data flow module and a fault point display module. The problems that a traditional networking method of hydropower engineering is poor in reliability, and self-healing cannot be achieved after an intermediate node is disconnected are successfully solved.

The invention discloses a knowledge graph multi-hop reasoning-based complex manufacturing process intelligent planning decision method, which comprises the following steps of: 1, constructing a manufacturing process knowledge graph to obtain a triple set, generating a query-path pair sequence, and mapping entities and relationships in the query-path pair sequence into low-dimensional vectors through an embedded layer; 2, performing expansion causal convolution calculation on the query-path pair sequence through a shallow feed-forward convolution network module, extracting key process features and potential correlation between processes, and generating a feature vector fusing the potential process correlation; 3, performing rotation position coding and causal mask processing on the feature vector output in the step 2 by adopting a parallel multi-query attention module, and realizing cross-process dependency relationship modeling through a multi-head attention mechanism; 4, generating a global optimal process scheme through beam search; and on the basis of a rule-guided dynamic aggregation iteration strategy, mixing a process path generated by the model with an original training set, and updating parameters.

The invention discloses an AI oT-based cloud network side-end federated learning system, and relates to the technical field of cloud network side-end federated learning, comprising the combination of Internet of Things, cloud computing and artificial intelligence technology, and through intelligent cooperative computing of cloud-side-end three-layer computing resources, the cloud network side-end federated learning is realized. The AI oT-based cloud network side-end federated learning system further comprises a cloud module, a network module, an edge module and a terminal module, and has the advantages that local model aggregation is firstly carried out on edge nodes, a part of calculation tasks can be completed at an edge end, a model result subjected to primary processing and aggregation is uploaded to the cloud, and the cloud network side-end federated learning is realized. Different edge nodes and terminal devices have different computing capabilities, the cloud end dynamically distributes training tasks to match tasks with different complexity degrees with devices with corresponding computing capabilities, and the edge nodes with strong computing capabilities can undertake more complex model training sub-tasks. And the terminal equipment with relatively weak computing power is responsible for some simple tasks such as data preprocessing and the like.

The invention discloses an intelligent control system for a diamond grain curing sintering furnace, belongs to the technical field of industrial furnace control, and aims at solving the problems that existing sintering control depends on experience, process monitoring is not comprehensive, real-time optimization is difficult to achieve, and product quality consistency is difficult to guarantee. The system comprises a sensor module, a multispectral imaging device, a data preprocessing module, a liquid neural network module, an attention fusion module, a user interaction module, a thermal structure coupling module and a real-time feedback control module. A liquid neural network is used for processing time sequence dynamics, multi-dimensional features such as temperature, humidity, spectrum and mechanical force are fused through a multi-head attention mechanism, and in combination with an internal state result of user input and thermal structure coupling analysis, a real-time feedback control module adopts a closed-loop strategy to calculate optimal firing parameters and generate a control signal; and the actuating mechanism is driven to perform self-adaptive adjustment.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

The invention discloses a safety monitoring system of a high transverse support system for cable-stayed bridge man-shaped tower column construction, and relates to the technical field of bridge construction monitoring, the system comprises a creeping formwork integrated sensing module used for generating a point cloud model by using a mechanical arm integrated on a hydraulic creeping formwork platform and a laser radar scanning tower column curved surface, combining with a preset building information model coordinate, adaptively adjusting the mounting posture of the sensor, and outputting the mounting coordinate position of the sensor; the optical fiber sensing network module is used for collecting original strain and temperature data by using a distributed optical fiber sensor deployed along a main stress path of the support truss; an inertial navigation fusion positioning module; a digital twinning early warning module; and an edge calculation relay module. According to the invention, a full-process monitoring chain from data acquisition to risk early warning is constructed through cooperation of multiple modules, full-period and multi-dimensional dynamic control of construction of the human-shaped tower column high transverse support system is realized, and timely perception and overall control of potential risks are ensured.

The invention relates to the technical field of artificial intelligence, and particularly provides a human behavior recognition system and method based on multi-dimensional and multi-scale feature extraction. The system comprises a human body behavior data acquisition module, a human body behavior data transmission module, a human body behavior data storage module, a human body behavior data preprocessing module, an LGAM simple human body behavior recognition network module, an MPFEFN complex human body behavior recognition network module and a human body behavior information application module. The MPFEFN complex behavior recognition network module comprises an initialization unit, a residual error multi-parallel multi-attention feature extraction unit, a residual error multi-parallel channel enhancement and extraction unit, a feature fusion unit and a complex behavior judgment output unit, and the residual error multi-parallel multi-attention feature extraction unit and the residual error multi-parallel channel enhancement and extraction unit are connected in parallel. The recognition accuracy of the easily-confused behaviors is improved, effective recognition of complex human body behaviors is achieved, and the recognition performance of an existing model is improved.

The invention relates to the technical field of intelligent distribution, in particular to a power grid load distribution system and method based on intelligent control, and the system comprises a data collection and processing module which carries out the real-time collection and data processing of all node data and meteorological data of a power grid, and obtains the actual power grid load data; the communication network module is used for transmitting the actual power grid load data; the intelligent regulation and control module is used for constructing a rural power grid load model to obtain a load prediction value; the monitoring alarm module is used for monitoring the actual rural electricity consumption condition in real time according to the load prediction value; the system verification module is used for verifying the stability of the power grid after regulation and control configuration, verifying the resource utilization efficiency of the power grid according to the load rate of the transformer, and adjusting the regulation and control configuration; and the execution feedback module is used for performing control strategy command execution on the power grid equipment. The power supply and demand balance of the rural power grid is realized, and the utilization efficiency of power grid resources is improved.

The invention discloses an urban traffic flow space-time distribution prediction method based on multistage road operation information, and relates to the technical field of traffic flow prediction, and the method comprises the steps: S1, traffic flow information preprocessing, S2, employing channel attention and space attention mechanisms, constructing a space convolution network module, extracting space features, and carrying out the prediction of the spatial and temporal distribution of the urban traffic flow. The method comprises the following steps: S1, constructing a time cycle network module for capturing long and short term time dependence characteristics, and extracting time sequence characteristics, S4, constructing a connection layer module for regression prediction, and realizing grid area traffic flow space-time distribution prediction, and S5, constructing a traffic flow characteristic clustering and residual regression prediction module, and realizing traffic flow space-time distribution prediction of each road section in a grid. The urban traffic flow space-time distribution prediction method based on the multistage road operation information can flexibly adapt to the requirements of different cities, different road network scales and different prediction granularities, and is suitable for various intelligent traffic management systems such as intelligent traffic scheduling, congestion early warning and green travel guidance.