1779 results about "Predictive analytics" patented technology

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention provides an equipment group collaborative fault prediction analysis method and system for a complex industrial process, and relates to the technical field of fault prediction, and the method comprises the steps: collecting equipment group data, and reconstructing a multi-scale time sequence feature; constructing a quantum walking enhanced dynamic association map; extracting spatial-temporal features and mapping the spatial-temporal features to a Riemannian manifold space; identifying an abnormal mode and generating hidden variable probability distribution; constructing a fault evolution model driven by a non-equilibrium state statistical fluctuation theory; and solving the cooperative field equation set to obtain a control parameter sequence. The method can effectively improve the accuracy and reliability of equipment group collaborative fault prediction and reduce the maintenance cost.

The present application discloses an underground water real-time monitoring method and system, and a medium. The method comprises: acquiring historical environment data and historical monitoring data in an underground water monitoring area for spectral clustering data partitioning, and importing related data into a pollution monitoring model for predictive analysis and model training; acquiring pollution monitoring data in monitoring sub-areas in real time, importing the pollution monitoring data into the pollution monitoring model for pollution prediction, and obtaining pollution diffusion directions and pollution source prediction areas; and on the basis of the pollution source prediction areas and environment data of the monitoring sub-areas, calculating a monitoring demand degree of each monitoring sub-area, and generating a monitoring scheme on the basis of the monitoring demand degree. By means of the data spectral clustering method in the present application, accurate grouping and acquisition of training data can be effectively implemented, and model prediction precision is improved. In addition, the present application can realize dynamic adjustment of monitoring areas, effectively conserve human and material resources in underground water monitoring areas, and realize the cost reduction and efficiency improvement of underground water pollution monitoring.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

A method and system for early prediction and warning of mine disasters through on-site monitoring is provided. [Solution] The method involves installing sensors at the mine site to monitor surface water, groundwater data and underground rock data in real time, so that mine safety management personnel can obtain timely and comprehensive data to better understand the state of the mine environment. The predictive analysis module uses deep drilling and computational learning technology to analyze the surface water, groundwater data and underground rock data to predict the risk assessment index Fpzs, which is helpful in predicting potential disaster risks, and comprehensively takes into account various factors such as the number of past disasters Lzs, the water level fluctuation coefficient Sbxs, and the rock stability coefficient Ywxs, which is helpful in quantifying the risk level, providing a more accurate disaster risk assessment, and reducing the casualties caused by mine disasters.

The invention discloses a data real-time analysis and decision-making system based on an edge intelligent Internet of Things, belongs to the technical field of Internet of Things, and aims to solve the technical problems of insufficient real-time performance, high delay, large bandwidth occupation and high dependence on a central server in data processing of Internet of Things equipment. Comprising an edge device layer, an edge computing node layer, a cloud edge collaboration module and a central server layer, each edge computing node is used for taking data after local analysis processing as input, performing prediction analysis through a local model, making a decision through a decision generation mechanism based on an analysis result, and performing cloud edge collaboration. Generating decision optimization through an adaptive decision optimization algorithm, and performing data exchange with the central server layer through a cloud edge collaboration module; and the central server layer is used for carrying out model updating based on the real situation and the analysis result corresponding to the analyzed and processed data uploaded by each edge computing node, and returning the updated model parameters to the edge computing node layer.

The invention relates to the technical field of digital twinning, in particular to a high-density urban ponding evolution and early warning method based on digital twinning. The method comprises the following steps: real-time ponding depth, real-time river water level, real-time meteorological conditions and real-time drainage pipe network water level conditions corresponding to a city are collected in real time through ponding depth, river water level, meteorological monitoring and drainage pipe network sensors installed in the city, digital twinborn construction is carried out, and an urban inland inundation digital twinborn model is generated; obtaining drainage pipe network distribution and carrying out drainage grid division to generate an urban drainage grid model; acquiring a historical ponding event rule, and performing ponding area prediction analysis and ponding evolution coupling analysis to generate an urban ponding evolution distribution field; performing ponding space-time evolution analysis and ponding early warning processing on the urban ponding evolution distribution field so as to execute corresponding urban ponding emergency response work. According to the invention, accurate ponding evolution prediction and effective early warning can be provided.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention relates to the technical field of greenhouse gas monitoring, in particular to a greenhouse gas collaborative monitoring and analysis platform. According to the technical scheme, the system comprises a cross-modal data fusion module, a causal reasoning analysis engine, a holographic dynamic visualization system, a distributed edge computing node and a self-adaptive decision optimization module, and the cross-modal data fusion module is used for integrating satellite remote sensing data, a ground sensor network, unmanned aerial vehicle mobile monitoring data and industrial Internet of Things emission source data in real time; a dynamic weight distribution algorithm is adopted, the multi-source data fusion weight is automatically adjusted according to environmental parameters, and a space-time continuous greenhouse gas concentration field is generated; a causal reasoning analysis engine is based on a hybrid architecture. According to the method, data privacy is guaranteed and delay is reduced through multi-source data fusion, causal reasoning analysis, holographic visualization and edge calculation, and comprehensive and accurate monitoring, scientific prediction analysis, efficient decision execution and risk prevention and control of greenhouse gas are realized based on decision optimization of dynamic games and block chain smart contracts.

The invention relates to the technical field of pipeline maintenance, and discloses a pipeline inner wall corrosion detection method and system suitable for hydraulic engineering, and the method comprises the steps: obtaining a plurality of sensor data from a sensor assembly arranged in a to-be-detected pipeline; performing feature extraction to obtain detection data features; performing time-space synchronization fusion to obtain fusion features; acquiring historical corrosion data and fusion features of the inner wall of the pipeline and inputting the historical corrosion data and the fusion features into the long-short-term memory network model to obtain a first corrosion prediction result; generating a second corrosion prediction result through a random forest algorithm; generating a third corrosion prediction result by a gradient boosting tree algorithm; and inputting the three prediction results into a preset detection model to obtain a detection report. According to the method, multiple sensor data are obtained and fused to obtain the fused features, multiple factors influencing corrosion are comprehensively referenced, prediction analysis is carried out through multiple algorithms according to the fused features to obtain three prediction results, the detection report is generated, and the accuracy of the detection result is effectively improved.

The invention relates to the technical field of health state analysis, in particular to an intelligent health state analysis system and method based on artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal health data; s2, data preprocessing and anomaly detection: preprocessing the collected multi-modal data; s3, multi-modal data fusion and feature selection: carrying out feature fusion on data of different modals through a deep learning model, and carrying out feature extraction on time sequence data by adopting a convolutional neural network (CNN); s4, health state prediction and trend analysis: performing prediction analysis on the health state of the user based on a long and short term memory network (LSTM), and generating a health state trend chart of the user in combination with historical health data and real-time data; and S5, personalized health intervention suggestions are generated: personalized health management suggestions are automatically generated, a more comprehensive health assessment result is provided, the user can better know the own health condition, and personalized management is carried out.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The present invention relates to a system and method for cross-domain predictive modeling using Bedrock-based foundation models and blockchain-anchored data. The invention integrates large-scale foundation model reasoning with distributed ledger-based data provenance to enable verifiable, secure, and explainable predictive analytics across heterogeneous domains such as finance, healthcare, logistics, and environmental systems. The system comprises a data ingestion unit for receiving and normalizing multi-domain datasets, a blockchain anchoring unit for generating cryptographic hashes and recording data provenance into a distributed ledger, a cross-domain harmonization processor for aligning heterogeneous feature representations into a unified latent space, a foundation model processor configured to execute Bedrock-based predictive inference with adaptive domain contextualization, a verification processor for validating predictions against blockchain-anchored ground truths, and a governance processor for maintaining immutable audit trails of model evolution.

The invention relates to the technical field of new energy power station control, and particularly discloses a new energy power station perception control method and system, and the method comprises the steps: S1, data collection, S2, state perception, S3, monitoring and early warning, S4, decision and optimization control, S5, execution and feedback, S6, intelligent operation and maintenance, and S7, self-healing scheduling. The deep neural network DNN is utilized to process a large amount of sensor data, so that the system can learn modes from complex historical data, accurately identify conditions such as power fluctuation and voltage abnormity, and timely discover potential abnormity through real-time updating of the model, so that the possibility of misinformation and missing report is reduced, and the reliability of the system is improved. The system monitors the operation state of the power station in real time, can quickly recognize and classify abnormal conditions, provides instant data support for decision making, can take measures in advance through abnormal prediction, prevents problems from being further expanded, and can recognize equipment needing to be maintained and prevents faults from being expanded through patrol inspection and prediction analysis of the unmanned aerial vehicle.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of digital information transmission, and provides a communication network security situation prediction method and system based on big data, which break through the limitation of single data in the aspect of data fusion, integrate four large classes and 12 subclasses of multi-source heterogeneous data, and combine a dynamic weighting mechanism to make feature extraction more comprehensive and accurate; on the aspect of model architecture, CNN-Bi-LSTM-Attention three-level fusion and PSO optimization are adopted, spatial and temporal features are effectively captured, the convergence speed is increased, the prediction accuracy is high, and the false alarm rate is low; in the risk assessment aspect, a layered assessment system is constructed, and early warning is realized in combination with a dynamic threshold value and Monte Carlo simulation; the model training module innovatively adopts a federated learning mode, and the model generalization ability is improved while data privacy is guaranteed; the prediction analysis module deploys an optimization hybrid model, supports high-concurrency prediction and is short in response time; the visual decision-making module provides three-dimensional visualization and geographical drilling functions, and output data can be seamlessly connected with a third-party platform.

The present application relates to the field of warehouse management technology, and provides an information management method, device, electronic device and storage medium for intelligent warehousing. By preprocessing the target data set to realize a standardized spatiotemporal fusion data stream, a virtual space is dynamically mapped based on the standardized data stream to generate a virtual-real synchronized digital twin warehouse model, and multi-target scheduling is performed according to the real-time status data in the digital twin warehouse model to generate an AGV path matrix, and the AGV path matrix is ​​dynamically replanned according to the real-time status data to generate anti-disturbance scheduling instructions. At the same time, root cause analysis of the product data set is performed to obtain predicted improvement data, and finally, material allocation analysis is performed on the anti-disturbance scheduling instructions and predicted improvement data according to the preset optimization method to obtain warehousing and logistics decisions. This application realizes flexible scheduling of warehousing and logistics based on the current status of warehousing and combined with the predictive analysis of the product cycle, which can accurately respond to actual needs and adjust the operation strategy in time.

A system for monitoring and optimizing the performance of an e-commerce platform, which includes the following: a) a data collection and integration module configured to retrieve real-time data from e-commerce platforms, marketing channels, inventory systems and user interactions using APIs, tracking scripts and server-to-server communication; b) a data preprocessing and filtering module for cleaning and structuring the collected data by removing inconsistencies, normalizing formats and detecting outliers; c) a machine learning and predictive analytics machine that uses deep learning, regression models and clustering algorithms to analyze historical and real-time data to predict sales trends, customer behavior and demand fluctuations; d) a performance monitoring dashboard configured to visualize key performance indicators, sales trends and marketing effectiveness, and to provide custom reports and real-time alerts; e) an automated module for optimizing marketing campaigns, which dynamically adjusts advertising strategies, budget allocations, target audience approach and creative elements based on real-time data insights; f) an automated decision and control system configured to execute AI-driven optimization strategies, including bid adjustments, price changes and inventory reallocations without manual intervention; (g) a fraud detection and compliance monitoring module that uses anomaly detection algorithms to identify fraudulent transactions, fake reviews and advertising fraud while ensuring compliance with legal regulations; the system continuously monitoring, analyzing and optimizing the performance of the e-commerce platform through automated data-driven decision-making.

The invention relates to the technical field of industrial safety, in particular to a factory safety intelligent management and control method based on a heterogeneous multi-system cross service fusion technology, and the method comprises the steps: collecting factory multi-source heterogeneous data, and carrying out the time-space alignment and fusion; constructing a risk assessment model based on a deep learning algorithm, and performing quantitative risk analysis on personnel behaviors, environmental parameters and equipment states; a dynamic grading early warning mechanism is established, and warning and automatic handling are achieved according to the risk grade; introducing risk trend prediction, and analyzing a risk evolution trend; and a safe closed-loop optimization mechanism is constructed, and self-adaptive adjustment of the management and control strategy is realized. According to the method, a full-process and dynamically-optimized intelligent safety management and control scheme is constructed, and comprehensive perception, quick response and continuous optimization of factory safety management are realized.

The invention belongs to the technical field of power grids, and discloses a user behavior data mining and prediction analysis system in a smart power grid environment, which comprises a data acquisition module, a multi-dimensional feature extraction module, a behavior pattern recognition module, a demand prediction and trend analysis module, a personalized service recommendation module and a data storage and security module. According to the method, the accuracy of user behavior pattern recognition is improved, complex features can be extracted from the power consumption behaviors of the users through combination of multi-dimensional feature extraction and a deep learning algorithm, and therefore the behavior patterns of the users are recognized more accurately. Compared with simple clustering of traditional single-dimensional features, the system can recognize multi-level behavior features such as power utilization peak and valley periods and seasonal preference, and the comprehensiveness of user behavior portraits is improved. Through a demand prediction technology combining time sequence analysis and an integrated learning model, accurate short-term and long-term power consumption prediction can be carried out, and a demand change trend is judged.

Predictive Maintenance Digital Twin with Carbon Footprint Optimization System, consisting of: a digital twin framework for real-time asset monitoring, performance simulation, and failure prediction; an IoT-enabled data acquisition layer with sensors to collect operational and environmental data; an edge computing module for low-latency anomaly detection and diagnostics; a cloud-based AI engine that uses deep learning for predictive maintenance and remaining useful life (RUL) estimation; a carbon footprint optimization engine for tracking energy consumption, emissions, and sustainability metrics; an LLM-supported NLP interface for analyzing maintenance data, generating prescriptive recommendations, and optimizing CO2 efficiency; a predictive analytics engine for early fault detection, energy optimization, and automatic alerts; an interactive dashboard for visualizing asset health, predictive insights, and tracking carbon impacts; a hybrid cloud-edge architecture for secure, scalable, and efficient real-time processing.

A system for real-time data aggregation and analysis within an Internet of Things (IoT) network, consisting of: a multitude of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a data acquisition unit for capturing data in real time and a local processing unit for preprocessing the captured data by performing data filtering, normalization and compression prior to transmission; a centralized aggregation server communicating with the multitude of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize pre-processed data streams from the multitude of sensor nodes, thereby enabling consolidated data storage; a timestamped metadata indexing unit coupled with the data aggregation module and configured to add metadata to each data entry, including a unique identifier, a geographic location, and a timestamp; an analysis engine integrated into the aggregation server, which includes the following: a data processing unit configured to perform real-time analysis of the aggregated data by applying pattern recognition and anomaly detection techniques and to generate processed results based on predefined parameters; a predictive analytics module that is connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from historical and real-time data; a communication interface connected to the aggregation server, configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit embedded in the aggregation server, configured to dynamically distribute computing resources between the data processing unit and the predictive analytics module depending on data volume and complexity.

A healthcare supply management system that uses machine learning for predictive supply chain optimization, comprising: a) a data collection and integration module configured to collect and integrate real-time and historical data from multiple sources, including electronic health records, vendor databases, hospital inventory logs, and external market trends. b) a predictive analytics and demand forecasting module that uses machine learning algorithms to analyze data and generate accurate demand forecasts for medical supplies. c) an inventory optimization and automation module that dynamically adjusts inventory levels based on predictive insights and automates orders. d) a supplier management and logistics module that can be used to evaluate supplier performance, track shipments and predict delivery delays. (e) a risk management and disruption mitigation module that identifies potential risks in the supply chain and proposes emergency strategies. f) a decision support and reporting module that provides real-time insights, AI-driven recommendations, and customizable reports through a user-friendly dashboard. (g) a processing unit and a cloud-based database to store and process supply chain-related information and to facilitate seamless data access and communication between the modules.

The invention provides a ground surface settlement prediction and early warning method based on a neural network, and relates to the technical field of foundation pit settlement prediction, and the method comprises the steps: obtaining the structure data of a foundation pit and a soil body in a preset range around the foundation pit, obtaining the monitoring data of a plurality of preset point positions in the foundation pit in real time, and summarizing and outputting the initial data; selecting historical data matched with the initial data, outputting historical reference data, performing comprehensive analysis on the initial data and the historical reference data in combination with a preset analysis model, and outputting a real-time analysis result; and performing prediction analysis on the initial data and the historical reference data in combination with a preset prediction model, outputting a prediction analysis result, obtaining a real-time state and a prediction state of the foundation pit structure in a result-state comparison table in combination with a real-time analysis result, outputting a state analysis table, and performing early warning judgment according to a preset threshold condition. According to the method, prediction and risk early warning can be carried out on the ground surface settlement data around the foundation pit in the construction period in real time, and corresponding precautionary measures are taken in time.

The invention provides a logistics distribution demand prediction analysis method and system based on big data, and relates to the technical field of logistics distribution, and the method comprises the steps: dividing a distribution region into hexagonal grid units, building an order density evaluation model based on historical order data, extracting order propagation features through combining with a graph attention network, and carrying out the prediction analysis of the logistics distribution demand. And an external factor encoder is introduced to obtain an influence factor, probability correction is carried out by adopting kernel density estimation, and finally order demand prediction distribution with a confidence interval is obtained. The logistics distribution demand can be accurately predicted, the distribution efficiency is improved, the operation cost is reduced, and intelligent scheduling is realized.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

The invention relates to the technical field of power grid planning, in particular to a charging pile planning method based on dynamic analysis. The method comprises the following steps: obtaining electric vehicle use data in a target area, and carrying out charging demand prediction analysis according to the electric vehicle use data to obtain a charging demand prediction model; based on the charging demand prediction model, performing multi-scene simulation to obtain charging demand distribution map data, multiple scenes including different traffic flows, different driving modes and different charging demand scenes; obtaining geographic information data of the target area; and constructing a spatial analysis model according to the geographic information data, and evaluating the candidate position of the charging pile according to the charging demand distribution map data, thereby obtaining position suitability score data. According to the method, different scenes are simulated, so that the adaptability and robustness of planning are enhanced.

The invention relates to a self-adaptive pressure regulation vacuum pump closed-loop control system and method. The system comprises a model building module which is used for acquiring real-time data flow of a vacuum pump and building a nonlinear dynamic model to obtain a pressure-flow-power feature mapping relation; the predictive analysis module calculates a pressure-flow predictive value based on the relationship and compares the pressure-flow predictive value with the real-time power data for analysis. And generating a parameter adjustment instruction once the deviation value exceeds a preset threshold value. And the control scheme generation module corrects the control parameters according to the parameter adjustment instruction and generates an optimized power adjustment scheme. And if the data is abnormal, generating a state update value. And the control scheme updating module fuses the state updating value and the pressure-flow prediction value to obtain an optimized model parameter so as to adjust a weight coefficient of an adaptive control strategy and generate a control scheme. The system effectively improves the accuracy of vacuum pump pressure control, greatly enhances the ability of the system to deal with abnormal conditions and equipment performance changes, and ensures long-term stable operation of the vacuum pump.

The invention discloses a multi-device cooperative management system based on cloud edge cooperation, and relates to the technical field of Internet of Things communication, and the system comprises a device management module which is used for connecting edge devices, carrying out the centralized management of the edge devices, and carrying out the optimal scheduling based on the functions and distribution positions of different edge devices; and the data acquisition module is connected with the equipment management module. According to the multi-device collaborative management system based on cloud edge collaboration, a comprehensive and accurate data basis is provided for multi-device collaborative management, and information exchange and collaborative work between devices are promoted; the optimization decision-making unit can formulate an equipment optimization management strategy according to the operation scheme generated by the big data analysis unit and the prediction result obtained by the prediction analysis unit, and continuously performs optimization adjustment according to the real-time operation condition of each piece of edge equipment; therefore, the system can adapt to continuously changing equipment operation environments and task requirements, and more efficient multi-equipment collaborative management is realized.