3205results about How to "Improve intelligence" patented technology

The invention discloses a small unmanned aerial vehicle inspection data management method, relates to the technical field of unmanned aerial vehicle inspection, and is used for solving the problem that unmanned aerial vehicle inspection data management is not clear. According to the invention, through a high-precision positioning technology and timestamp synchronization, multi-modal data collected by an unmanned aerial vehicle is associated with an equipment position, space-time consistency of inspection data is realized, multi-modal feature fusion and dimension reduction processing are carried out, unified feature representation is generated, the precision and efficiency of data analysis are improved, historical data and real-time collected data are combined, and the accuracy and efficiency of data analysis are improved. Equipment states are monitored in real time, fault modes are identified and classified, risks are evaluated, fault development trends are predicted through a time sequence prediction model, maintenance suggestions are generated, the timeliness of fault diagnosis is improved, based on fault detection results, routing inspection task priorities and path planning are dynamically optimized, an optimal routing inspection path is generated, and a flight path is corrected; the intelligence and the precision of the inspection task are improved, and the efficiency and the safety of the unmanned aerial vehicle inspection system are remarkably improved.

The invention provides a cross-domain network security policy automatic generation and protection policy collaboration method and system, and relates to the technical field of network security, and the method comprises the steps: collecting multi-domain security event data; constructing a security threat map; generating a cross-domain security risk score by using a deep learning algorithm; determining an inter-domain access control rule set and an intra-domain protection strategy according to the score; forming a cross-domain collaborative protection strategy; deploying a strategy on the multi-domain security device; establishing an inter-domain information sharing channel; monitoring the strategy effect; optimizing the strategy by using a reinforcement learning algorithm; and updating to each domain security device. According to the method, the effectiveness of cross-domain network security protection is improved.

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention provides a shield tunneling machine fault detection method and system based on edge computing, and relates to the technical field of industrial Internet, and the method comprises the steps: deploying edge computing nodes on a shield tunneling machine, and carrying out the time-space alignment and feature level fusion of data collected by different sensors, and obtaining unified feature data; inputting the fused feature data into an online learning model pre-deployed in an edge computing node to obtain a fault detection result; the edge computing node uploads the fault detection result and feature data related to the fault to the cloud server, generates a fault analysis report and feeds back the fault analysis report to the edge node and the user terminal; and the edge node periodically pulls the updated online learning model and knowledge base from the cloud to realize cloud-edge collaborative model updating and knowledge evolution.

The invention discloses a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool, and relates to the technical field of numerical control machine tool operation. A multi-axis linkage state, a gas cylinder stress state, vibration frequency, cutting force and deformation data are acquired in real time through a data acquisition module; the dynamic modeling module constructs a digital twinborn model by using finite element analysis and real-time sensing data, and accurately simulates stress dynamic change and deformation trend; the intelligent error compensation module dynamically evaluates the multi-axis linkage precision by analyzing the machining data, generates an error compensation instruction and optimizes the operation parameters of the machine tool; the closed-loop feedback control module adjusts the cutting speed, the feeding depth and the clamping force in real time through a fuzzy logic algorithm, a closed-loop adjusting mechanism is formed, the machining precision and stability are greatly improved, the machining error and the workpiece surface damage risk are reduced, and the machining quality and efficiency of complex workpieces are effectively improved.

The invention relates to the field of inductive control of illuminating lamps, in particular to an inductive control system of an inductive LED illuminating lamp. The method comprises the following steps: the lighting scene module is used for identifying all LED lighting lamps in an area; calculating node positions of all the LED lighting lamps in the area one by one, and identifying use scenes in the area to construct a plurality of LED lighting scene sub-areas; the ambient light feature module is used for acquiring ambient light parameters of a scene sub-region; performing light spatial distribution tracking on the ambient light parameters of the scene sub-regions, and performing ambient light state feature mining to generate ambient light comprehensive state features; and the multi-area lighting cooperation module is used for carrying out dynamic light source output parameter adjustment and multi-area lighting cooperation smoothing on the plurality of LED lighting scene sub-areas based on the environment light comprehensive state characteristics so as to generate multi-area lighting cooperation control parameters. According to the invention, rapid and accurate induction control of the LED illuminating lamp is realized.

The invention discloses an intelligent management method and system for IT equipment asset maintenance, and relates to the technical field of intelligent management. During system operation, multi-dimensional parameters of IT equipment are collected in real time and transmitted to a data processing module, the collected data are preprocessed, the current state of the equipment is comprehensively analyzed, and the health state coefficient Hstate of the equipment is calculated; the method comprises the following steps: acquiring a current equipment operation state, performing baseline establishment on historical operation data of the equipment, comparing the current state with the historical data, analyzing the performance trend and change of the equipment, calculating to obtain a trend analysis coefficient Ttend, and identifying potential problems and performance reduction trend of the equipment through long-term data accumulation. And calculating a final fault prediction probability index Pfault, comparing the final fault prediction probability index Pfault with a set threshold value, obtaining a grade prediction scheme, generating early warning information, prompting a system administrator to sort and distribute according to the urgency and priority of tasks, automatically generating a comprehensive report by the system, and providing decision support for the administrator.

The invention discloses a control method and a control system of a data center liquid cooling heat dissipation system, and relates to the technical field of liquid cooling heat dissipation, the system realizes real-time state monitoring of server operation through a data acquisition and monitoring module, integrates multiple data such as temperature, flow velocity and pressure difference, forms a detailed comprehensive heat dissipation data set, and improves the heat dissipation efficiency. And a database is constructed for storage. And meanwhile, a cooling liquid flow velocity analysis control module of the system generates a server heat dissipation coefficient CN through the server operation environment parameter C1 and the server operation parameter C2, the server heat dissipation coefficient CN is compared with a preset heat dissipation threshold C for evaluation, and the cooling liquid flow velocity is adjusted in real time. Therefore, when the load of the server is increased, the cooling intensity can be automatically increased, the chip temperature is reduced, and stable operation of the server is kept; and when the load is reduced, the cooling intensity is reduced, the energy consumption is reduced, and the cooling efficiency and the energy utilization rate of the system are remarkably improved.

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention discloses a robot pose estimation method based on laser point cloud and visual SLAM, and relates to the technical field of robot sensing and positioning, and the method comprises the steps: in a data collection and integration link, installing a specific camera and a radar to collect and compress data; a data preprocessing and feature strengthening stage: processing the image and the point cloud to improve the quality and extract the features; a cross-modal network is constructed through feature fusion and accurate matching, and a special matching mode is adopted; space-time calibration and coordinate unification are realized by means of a high-precision circuit; multi-modal fusion and reinforcement learning are applied to loop detection and intelligent optimization; correcting filtering and visualizing uncertainty by combining pose smoothing and intelligent output with a prediction model; according to the method, the pose estimation precision, reliability and environmental adaptability of the robot are effectively improved, resource utilization is optimized, the pose uncertainty is visually evaluated, and the overall performance and the intelligent level of the robot are improved.

The invention discloses an algorithm for automatically extracting and identifying a photovoltaic abnormal fault, and the algorithm comprises the steps: S1, importing photovoltaic power station data, collecting and classifying power station design information data, power station historical working data and power station historical meteorological data in a database, so as to obtain first data; s2, photovoltaic power station data preprocessing is carried out, integrity and rationality checking is carried out on first data, and type conversion and normalization processing are carried out on the checked first data to obtain actual photovoltaic system operation condition data; and S3, identifying the fault of each link of the photovoltaic system. According to the algorithm for automatically extracting and identifying the photovoltaic abnormal faults, an abnormal fault sequence is rapidly and accurately extracted through data acquisition and analysis and intelligent fault prediction and diagnosis, and digital, intelligent, remote and unmanned operation and maintenance of a photovoltaic power station are achieved through accurate positioning and calculation judgment.

The invention belongs to the technical field of Linux kernel schedulers, and particularly relates to a Linux kernel scheduler parameter optimization system and method.The Linux kernel scheduler parameter optimization system comprises a data collection module, a large model training and optimization module, a kernel scheduler module, a code generation and verification module, a small model training module and a small model reasoning module; the large model is responsible for periodically providing a visual angle of global optimization, generating an initial weight and a scheduler prototype of the small model, extracting a rule from long-term accumulated data, and ensuring that an optimization strategy has a global visual field. Meanwhile, the small model serves as a local adapter to process the dynamic scheduling requirement of a kernel mode in real time. The layered optimization mechanism can balance short-term adaptability and long-term performance improvement.

The invention relates to the technical field of water quality and water level monitoring, and discloses a water quality and water level monitoring method for a water conservancy project, which comprises the following steps: S1, arranging a multi-parameter water quality and water level monitoring sensor at a key position of the water conservancy project; s2, performing real-time processing on the data acquired by the multiple sensors based on a data fusion algorithm, wherein the data fusion algorithm is based on a Kalman filtering model; s3, acquiring external data through an environment sensor, and dynamically adjusting a water level and a water quality monitoring threshold value based on environment change; s4, predicting the water level and the water quality by using a deep learning algorithm based on LSTM (long short-term memory network); and S5, if the monitoring data exceed the dynamic threshold value, automatically triggering an early warning mechanism. By arranging the multi-parameter water quality sensor and the water level sensor at the key positions of the water conservancy project, the water quality and the water level can be comprehensively monitored, the coverage range and the precision of the monitoring system are greatly improved, and the complex monitoring requirements of multi-source pollution can be effectively met.

The invention discloses an intelligent inspection monitoring method and system based on electronic vision, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: collecting multi-mode inspection data, and carrying out the preprocessing of the data; target identification classification and anomaly detection are carried out based on a convolutional neural network; and optimizing the inspection path by adopting a graph theory model and a shortest path algorithm. According to the intelligent inspection monitoring method based on electronic vision, the image obtained in the inspection process is analyzed through the convolutional neural network, the fault detection precision and the equipment classification accuracy are improved, the flexibility and the accuracy of anomaly detection are greatly improved through the auto-encoder model, and the detection accuracy is improved. The equipment position is regarded as the node of the graph, and the optimal inspection path is calculated by adopting the graph theory model, so that the inspection efficiency is remarkably improved, the inspection scheduling is optimized, the inspection coverage rate is improved, and better effects are achieved in the aspects of fault identification accuracy, inspection efficiency and adaptability.

The invention discloses a method for dynamically adjusting a tea fermentation environment by utilizing fuzzy logic control. The method comprises the following steps: S1, collecting and obtaining environment parameter data of each layer in real time; s2, fuzzy processing is carried out on the temperature, humidity and oxygen concentration in each time period through a fuzzy logic controller, and fuzzy control output of each time period is generated; s3, combining a hybrid neural network architecture and a meta-learning technology to construct an environment parameter change model; s4, optimizing the fuzzy rule base by using the environment parameter change model; s5, converting the fuzzy control output of each time period into a control instruction; s6, optimizing the fermentation environment through a cross-layer parameter equalization strategy to obtain the optimal state of the environmental parameters in the fermentation process; and S7, displaying environmental parameters of each spatial level and time stage in real time, and supporting switching between a manual mode and an automatic mode. The method combines fuzzy logic control and a deep learning technology, realizes accurate dynamic adjustment of the tea fermentation environment, and has the advantages of high adaptability and the like.

The invention discloses a wind power plant equipment fault intelligent diagnosis method and system. The method comprises the following steps: acquiring multi-dimensional sensor data of wind power plant equipment; carrying out preliminary screening on the data by utilizing an edge analysis algorithm, and determining data needing to be further diagnosed; the screened data are input into a pre-constructed fault diagnosis model, the data are fused through a multi-modal deep learning model in the pre-constructed fault diagnosis model, and then a fault occurrence area is determined through transfer learning, domain adaptation and a multi-agent reinforcement learning framework; meanwhile, the causal inference engine and the knowledge graph enhanced neural symbol inference module are responsible for deep processing and fault diagnosis. And finally, inputting the determined fault area and the diagnosis result into a dynamic system health index calculation module to generate a final fault diagnosis result. According to the method, the diagnosis efficiency and accuracy of the equipment fault of the wind power plant are remarkably improved, and a powerful guarantee is provided for stable operation of the wind power plant.

The invention relates to the field of intelligent titration control, and discloses a high-precision intelligent titration control method and system.The method comprises the steps of sample information input and preprocessing planning, sample automatic preprocessing, multi-sensor system calibration and preparation, intelligent titration parameter setting and scheme optimization, and multi-sensor data real-time collection and fusion in the titration process. Titration end point prediction and judgment based on machine learning, result calculation, verification and storage, system self-cleaning, maintenance and state monitoring. Through multi-module collaborative operation and a series of intelligent technical means, the accuracy, the reliability, the automation degree and the adaptability of high-precision titration in each link are comprehensively improved, and the high requirements on the titration precision and the titration efficiency in a complex and diversified chemical analysis scene are met.

The invention provides an interconnection communication system based on multiple mesh circuits, and belongs to the technical field of communication network management, and the system comprises a tensor obtaining module which collects basic parameters of network nodes, constructs a multi-dimensional tensor, and expands the multi-dimensional tensor to obtain an expanded multi-dimensional tensor; the model building module is used for decomposing the extended multi-dimensional tensor and building a global network topology model based on a decomposition result; the model analysis module is used for analyzing the global network topology model and determining an initial path candidate set based on an analysis result; the path determination module is used for selecting an optimal path from the initial path candidate set based on a preset algorithm, and taking the optimal path as a main path for communication resource transmission; and the fault processing module is used for deploying a preset monitoring tool at each network node to monitor the operation parameters of each link in real time, and triggering a fault report mechanism and starting a fault processing flow when an agent detects that the link is abnormal. And the intelligence and reliability of network management are improved.

The invention discloses a container tracking management system and method. The method comprises the following steps: S1, carrying out dynamic encryption binding and multi-dimensional data acquisition on an RFID chip; s2, realizing real-time position data acquisition of the container under different environmental conditions through a multi-source signal fusion and error correction algorithm; s3, various sensor modules are installed inside and outside the container, and data processing is carried out through distributed edge nodes; s4, performing on-chain storage on total data generated in the transportation process through a Hash encryption algorithm, and realizing automatic data verification, state tracking and abnormity alarm in combination with an intelligent contract; s5, carrying out real-time dynamic modeling analysis on the transportation path and resource allocation, and dynamically adjusting the transportation plan by using a distributed scheduling strategy; and S6, integrating the real-time state data, the path optimization model and the transportation scheduling decision, and providing a report for a user. The method has the advantages of high positioning precision, high data security, high transportation efficiency and good real-time performance.

The invention provides a plastic product quality detection method and system based on machine vision, and relates to the technical field of optical intelligent detection.The method comprises the steps that information extraction is conducted on an image data set through a contour recognition channel, and the contour of an identification component is determined; performing fitting filling of the point cloud data stream by taking the contour of the identification component as a constraint, generating an identification component point cloud, and fusing and building a plastic grid model; comparing based on the plastic grid model, and positioning size deviation information; and performing defect extraction on the plastic grid model based on the defect identification elements, determining abnormal defect information, and generating a quality detection report. Through the method, the technical problems of poor detection precision, comprehensiveness and detection efficiency caused by insufficient precision, accuracy and integrity of surface defect and dimensional deviation detection and low automation degree of an existing method can be solved, the automation of quality detection can be improved, and the detection efficiency is improved. And the effects of improving the detection accuracy, comprehensiveness and detection efficiency are achieved.

The invention provides a live pig health monitoring method based on multi-source information fusion, and the method comprises the steps: collecting the sound data of a live pig in a natural environment, carrying out the classification and preprocessing of the sound data, forming a sound sample data set, inputting an LSTM neural network model, and carrying out the training, and obtaining a sound detection model; acquiring a live pig attitude image to obtain an attitude sample data set, and inputting the live pig attitude sample data set into the YOLOv5s model for training to obtain an attitude detection model; inputting live pig sound data collected in real time, preprocessing the data, and inputting the data into the sound detection model to obtain sound features; inputting live pig attitude images collected in real time into the attitude detection model to obtain attitude features; measuring the body temperature of the live pig by using an infrared temperature sensor, measuring the weight of the live pig by using a weight sensor, and fusing the sound feature, the posture feature, the body temperature and the weight to obtain a fusion vector; and inputting the fusion vector into a random forest classifier for decision making, and determining the health state of the live pig. According to the method, multi-modal data are fused, comprehensive evaluation and diagnosis of the health condition of the live pigs are facilitated, and the accuracy and robustness of the whole monitoring system are further improved.

The invention relates to the technical field of power-carbon collaborative comprehensive energy systems, in particular to a virtual power plant robust collaborative optimization method considering uncertainty demand response. According to the method, a multi-dimensional feature fusion demand response model is established, and the characteristics of four dimensions of time delay, adjusting speed, tracking power and response energy and the influence of the characteristics on demand response precision are identified and analyzed. And fitting a normal distribution function through historical data, estimating a confidence interval of uncertainty features, constructing a multi-dimensional coupling deviation model, and accurately depicting a demand response dynamic contour. A two-stage optimization strategy is adopted, the first stage is an instruction decomposition model, the second stage is an instruction tracking model, and the operation cost and the total penalty cost are minimized through a mixed integer linear programming and energy storage system deviation compensation strategy. According to the method, the uncertainty in demand response can be effectively managed, and the flexibility and economy of the virtual power plant are improved.

The invention discloses a flight control method and system of an unmanned aerial vehicle tracking technology, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the following steps: before an unmanned aerial vehicle takes off, starting an airborne sensor in advance, executing parameter initialization and calibration operation, and installing a magnetic marker on a tracking target; acquiring the body position of the unmanned aerial vehicle in the flight process, and sensing the target position of the tracking target in real time by adopting a multi-sensor data fusion algorithm; obstacles existing in the flight path are detected in real time, a three-dimensional environment model is integrated, and the motion state of a tracking target is captured in combination with a follow-up visual tracking algorithm; target tracking data are stored and fed back in real time, and the battery state and the energy consumption speed of the unmanned aerial vehicle are monitored. By combining the magnetic guidance technology, multi-sensor data fusion and a follow-up visual tracking algorithm, high-precision target tracking can be realized in a dynamic complex environment, meanwhile, obstacles are effectively avoided, and the flight stability and tracking precision of the unmanned aerial vehicle are ensured.

The invention discloses an intelligent monitoring method and a monitoring system for an IDC (Internet Data Center) machine room environment, particularly relates to the field of IDC machine room environment monitoring, is used for solving the problems of multi-parameter relevance and real-time early warning, and aims at solving the problems of multi-parameter relevance and real-time early warning by constructing a high-dimensional topological structure, a Bayesian network model and a dynamic threshold adjustment mechanism of the IDC machine room environment. The relevance and the causal relationship among the multi-dimensional parameters are effectively captured, and the analysis and prediction capability on the complex environment data is remarkably improved. And by utilizing a persistent coherence algorithm and random forest regression, nonlinear correlation of parameters is accurately identified, and the limitation of a traditional method in multi-parameter analysis is overcome. Through Granger causal relationship test, a parameter causal sequence is determined, and a basis is provided for accurate prediction of environmental changes. A dynamic threshold adjustment mechanism flexibly adapts to environment changes, false alarm and missing alarm are reduced, it is ensured that the system rapidly identifies abnormities and triggers alarms in a complex environment, and the intelligence and stability of the system are remarkably enhanced.

The invention discloses an electroporation ablation voltage control method and system based on a pulse sequence, and relates to key technologies such as intelligent target area identification, electric field modeling, dose control, real-time monitoring and feedback. Tumors are accurately identified and treatment is planned through the AI technology, electric field distribution is optimized to ensure the accurate effect of electroporation pulses, pulse parameters are dynamically adjusted through closed-loop control, and treatment effect evaluation is provided through the sensor fusion technology. The system integrates data analysis and machine learning, intelligently adjusts treatment strategies, realizes personalized treatment and complication prediction, and improves the treatment effect and patient safety. The system is suitable for precise tumor treatment, and healthy tissues are protected while the curative effect is ensured.

The invention relates to a self-adaptive screen brightness adjustment method and device, equipment and a storage medium, and the method comprises the steps: obtaining ambient light data in real time, analyzing the ambient light data through a preset display adjustment model, and dynamically adjusting the display setting according to an analysis result; obtaining a current use scene, analyzing the current use scene according to a preset use mode database, and optimizing the adjusted display setting according to an analysis result; and obtaining a user adjustment action, collecting user adjustment parameters and ambient light data corresponding to the user adjustment action, and updating the preset display adjustment model and the preset use mode database according to the user adjustment parameters and the ambient light data. The method and the device have the effect of improving the efficiency and the accuracy of screen brightness adjustment.

The invention, which relates to the technical field of intelligent logistics management, discloses an AI-based intelligent logistics management system comprising a warehouse management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehouse management unit is used for managing warehouse-in and warehouse-out information and cargo information of a warehouse, reminding inventory risks, and carrying out demand prediction and inventory optimization; the data fusion acquisition unit is used for acquiring personnel, goods, equipment and environment information of a warehouse; the digital twin supervision unit comprises an intelligent AI recognition unit and a data analysis processing unit, the intelligent AI recognition unit is used for carrying out recognition analysis and risk reminding on personnel, goods, equipment and environment of a warehouse through an AI intelligent algorithm, and the data analysis processing unit is used for mapping data of an entity warehouse to a digital twin warehouse. And visual interaction management and intelligent scheduling of personnel, goods and equipment are carried out on the digital twin warehouse, so that the warehouse can be comprehensively monitored, and the goods storage and management efficiency is improved.

The invention discloses a regional power grid new energy consumption prediction and decision support system, which relates to the technical field of smart power grids and new energy, and comprises a data acquisition unit, a characteristic analysis unit, a prediction model construction unit, a decision generation unit and an interactive display unit, multi-source data such as new energy power generation, meteorological information and power grid operation are collected in real time through the data collection unit, key features are extracted through the feature analysis unit, a solid foundation is provided for prediction model construction, the prediction model constructed through the quantum fusion algorithm can accurately predict the new energy power generation capacity and the power grid load, and the prediction efficiency is improved. Meanwhile, the decision generation unit can quickly generate an optimal new energy consumption decision scheme based on a dynamic fuzzy neural network technology, effectively cope with power grid fluctuation and ensure stable operation of a power grid, and the functions jointly improve the utilization rate of new energy and the overall efficiency of the power grid.