278 results about "Real time analytics" patented technology

The invention discloses a power distribution network power transmission line intelligent control and measurement system and method based on an AI algorithm, and the system comprises a data collection module, a data preprocessing module, a multi-modal data fusion module, an AI fault diagnosis module, a control execution module and a communication module. Comprehensive monitoring of the running state of the power transmission line is achieved, historical and current data are analyzed in real time based on an improved dynamic multi-mode adaptive optimization model DMAOM, and the self-learning capability is achieved. A flexible adaptive control strategy is introduced, and a control scheme is dynamically adjusted according to real-time data. Under the abnormal condition, the system can rapidly execute current limiting, load transferring or circuit breaking operation, and through a feedback mechanism, the system can continuously optimize monitoring and response strategies after each fault processing. According to the method, the safety, stability and operation efficiency of the power transmission line of the power distribution network are remarkably improved, and the method has important practical application value and wide market prospects.

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

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention relates to the technical field of loaders, in particular to a loader multi-sensor data fusion fault diagnosis and early warning method, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring running state data of a loader in real time; s2, multi-modal data fusion: carrying out multi-modal data fusion on the preprocessed operation state data to form comprehensive data; s3, energy flow fault propagation prediction: analyzing energy flow distribution of each component in real time, and identifying a potential fault component and a propagation path thereof; and S4, fault assessment and graded early warning: comprehensively assessing the fault risk of the loader, and generating a graded early warning signal according to a set multi-stage early warning rule. According to the invention, the operation efficiency and safety of the loader are improved, the distribution of maintenance resources is optimized, the operation cost is reduced, the overall reliability of the loader is further improved, and the service life of the loader is further prolonged.

The invention discloses a fault analysis method and system for computer room equipment, and particularly relates to the technical field of computer room equipment. Accurate fault traceability and equipment positioning are realized by acquiring operation data of machine room equipment, analyzing an abnormal data mode in real time by utilizing a machine learning model, generating preliminary fault early warning and adopting a multi-source data fusion technology in combination with a historical fault database and a correlation analysis algorithm, and meanwhile, based on an equipment topological relation and service correlation analysis, the fault positioning accuracy is improved. Quantifying the influence range of the fault on the whole machine room system, calculating the influence level, and automatically generating an optimized fault processing scheme in combination with a fault case library; after the fault is processed, the equipment is continuously monitored, whether the fault is thoroughly eliminated or not is verified, and stable operation of the system is ensured; according to the method, the fault detection accuracy, the traceability and the repair efficiency of the computer room are effectively improved, the risk of false report and missing report is reduced, and the service continuity and the data integrity are improved, so that the intelligent level of operation and maintenance of the computer room is enhanced.

The invention relates to a data security processing method and system based on distributed storage, and relates to the technical field of computer information processing. The method comprises the following steps: cutting data into encryption fragments with a configurable number by adopting a dynamic fragmentation strategy, and generating a physically isolated dynamic check block in combination with a timestamp to realize tampering prevention; a dynamic threshold value is dynamically calculated based on the data sensitivity index and the node load, and the node is optimized through the reliability score for cooperative decryption; a database table is divided into independent marshalling storage according to main foreign key association, foreign key fields are encrypted by adopting cross keys, and cross-marshalling access needs to meet a multi-key threshold condition; an intelligent threat perception engine is constructed, access logs and threat intelligence are analyzed in real time, and key rotation, fragment replacement and joint defense response mechanisms are dynamically triggered. According to the invention, full life cycle protection of data is realized, and the problems of key leakage risk and cross-table association attack are effectively solved.

The invention discloses a data security management method and system based on cloud computing, and relates to the technical field of data management, and the method comprises the following steps: S1, making a security policy; s2, performing security assessment on the cloud infrastructure; s3, performing identity authentication and access control; s4, data encryption and key management; s5, data classification and level-to-level management; s6, data leakage detection and defense; s7, performing auditing and compliance management; s8, abnormal behavior monitoring and threat response; and S9, an elastic backup and recovery mechanism. According to the data security governance method and system based on cloud computing, the elasticity and dynamic response capability of data security are remarkably improved by setting the distributed key management system and the intelligent monitoring model. The distributed key management system dispersedly stores the keys in different physical areas, so that the risk of single-point fault and attack is reduced; the AI-based intelligent behavior monitoring system analyzes user behaviors in real time, predicts abnormal operation and automatically responds.

The invention discloses a data access permission control method and system based on a block chain, and relates to the technical field of block chains, the system is composed of a plurality of function modules, and the system comprises a hierarchical permission module which adopts a role hierarchical strategy and defines the access granularity of a role through an intelligent contract; the dynamic permission adjustment module is used for dynamically adjusting the permission range by analyzing user behaviors in real time through machine learning according to the principle of following the minimum permission in the permission distribution process; attribute encryption and zero-knowledge proof are introduced, an access key is dynamically generated according to user attributes, and sensitive information is hidden in the verification process; the multi-chain collaboration module is used for coding an intelligent contract through an authority rule, automatically executing access request verification, storing encrypted data through a private chain, managing an index and an access strategy through an alliance chain, isolating a storage domain from an access domain, and carrying out process traceability on a data request through a cross-chain verification mechanism; and the key management module is used for generating a true random number private key by adopting a hardware security unit.

The invention relates to the technical field of data processing, and discloses a financial compliance risk assessment method and system based on big data, and a storage medium. The method comprises the following steps: collecting and standardizing multi-source data to form a structured data set; constructing a three-level evaluation system comprising risk fields, subclasses and indexes; training the stacked fusion network to obtain an evaluation model; analyzing and generating a risk report in real time; identifying risk points and performing quantitative evaluation; and an early warning threshold value is set, and an early warning signal is generated and sent to a risk management department when the threshold value is exceeded. According to the invention, efficient identification, accurate evaluation and timely early warning of compliance risks are realized, and the automation level and scientific decision-making ability of compliance risk management of financial institutions are improved.

The invention relates to the technical field of process data optimization, and discloses a traditional Chinese medicine extraction process optimization method and system based on dynamic countercurrent extraction and a medium. The method comprises the following steps: carrying out online monitoring data processing on a traditional Chinese medicinal material digital feature data set and a dynamic countercurrent extraction parameter initial data set to obtain a countercurrent extraction process real-time monitoring data stream and parameter associated data, and carrying out multi-dimensional parameter optimization processing; a dynamic countercurrent extraction process optimization parameter group is obtained and is analyzed and processed in real time, and an automatic parameter regulation and control instruction sequence and process abnormity early warning data are obtained; and performing quality characteristic data processing on the extract after execution of the automatic parameter regulation and control instruction sequence to obtain a process quality evaluation data set and a process tracing data set, and performing association analysis processing to obtain a dynamic countercurrent extraction process parameter optimization rule base. According to the invention, intelligent monitoring and automatic parameter optimization of the extraction process are realized, so that the accuracy and controllability of the traditional Chinese medicine extraction process are improved.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

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.

The invention discloses a multi-sensor data fusion control method for an automatic assembly line, and relates to the technical field of data fusion, and the method comprises the following steps: when multi-source data fusion is carried out, firstly, data information is obtained in real time through a multi-source sensor, the obtained data information is preprocessed, and the accuracy and integrity of data are ensured; by analyzing data quality and fusion performance indexes and system robustness and environmental adaptability indexes in real time, the system can accurately detect the influence of electromagnetic interference and environmental fluctuation on data and dynamically evaluate the state. And an early warning and adjustment strategy is automatically triggered in case of abnormality, so that data fusion continuity is guaranteed, and downtime and human intervention are reduced. And when the abnormity exceeds the self-repairing capability of the system, the system can give an early warning, so that the efficient and safe operation of the production line is ensured, and the capability of dealing with sudden problems is improved.

The invention relates to a precision marketing method and system based on real-time data flow. The method comprises the following steps: collecting customer behavior data in real time; preprocessing and dynamically marking the customer behavior data to generate preprocessed customer behavior data; performing real-time analysis on the preprocessed customer behavior data by using a deep learning algorithm, and generating a customer portrait in combination with historical behaviors, preference changes and situational features of the customer; according to the customer portrait and the customer real-time behavior, a marketing strategy is generated in real time through an intelligent decision routing technology, and recommendation content in the marketing strategy is dynamically adjusted based on the customer interaction behavior in the customer real-time behavior; according to the marketing strategy, the service recommendation corresponding to the marketing strategy is sent to the customer contact point, and through a real-time feedback mechanism, the marketing strategy is dynamically optimized according to the real-time response of the customer. The method has the effect of improving the marketing efficiency.

The invention relates to the technical field of signal switching control, in particular to a multi-network signal switching control system and method based on big data, and the system comprises a user track prediction module, a network coverage analysis module, a signal load evaluation module, a signal switching decision module and a switching execution monitoring module. According to the invention, accurate prediction of a user moving track is provided by integrating user positioning and moving speed data, a potential network switching point is effectively predicted, and a system is allowed to dynamically adjust a network switching strategy by analyzing user behaviors in real time, so that the continuity and the stability of network services are improved, and the user experience is improved. The system can predict the future network demand before the user state is stable by calculating the trajectory trend change rate of the user, the prediction mode combines the current data and the historical trend, the prediction accuracy is improved, the signal switching opportunity and the target network selection are optimized by analyzing the signal coverage and the real-time load of each base station, and the user experience is improved. And data packet loss and time delay are reduced.

The invention discloses a bridge health real-time monitoring system based on intelligent sensing and big data analysis. The bridge health real-time monitoring system comprises an intelligent sensing layer, a data transmission layer, an edge computing layer, a cloud computing layer and an application layer, the intelligent sensing layer deploys a multi-modal sensor on a bridge in a self-adaptive manner; the data transmission layer can perform data transmission; the edge calculation layer carries out preprocessing and real-time analysis on the data; the cloud computing layer is used for fusing multi-source data based on a constructed federal learning driven big data analysis platform and carrying out long-term performance prediction on a bridge; and the application layer provides a visual monitoring interface and intelligent decision support, and realizes autonomous optimization of a bridge maintenance strategy based on reinforcement learning. According to the invention, advanced technologies such as quantum sensing, dynamic digital twinning, federated learning and the like are integrated, real-time preprocessing and analysis of data are realized, and the response speed of a monitoring system is obviously improved; and multi-source data fusion and collaborative analysis are realized.

The invention provides a cross-border e-commerce big data analysis system and method, and the method comprises the steps: obtaining multi-dimensional original business data of a cross-border e-commerce platform, carrying out the real-time collection through a distributed data collection system, and obtaining a structured business data set; data collected in real time is cleaned, converted and structured, a unified data model is constructed, and integration of heterogeneous data sources is achieved; storing the processed structured data in a time sequence database to provide data support for real-time analysis; performing real-time aggregation and statistical analysis on the newly added data to generate business index and key performance index data; a Lambda framework is adopted, batch processing and stream processing are combined, the historical data and the real-time data are subjected to full-amount analysis, and an analysis result is obtained; based on an analysis result, a data mining algorithm is adopted to identify a service mode and predict a future trend; a multi-dimensional data cube is constructed, multi-dimensional and multi-granularity data exploration and interactive analysis are supported, and management decision making is assisted.

An AI-based training recommendation system (100) for personalized employee development, which includes the following: a module for creating user profiles that is configured to collect and analyze employee data, including job role, skills, learning history, performance indicators, and career aspirations; a module for real-time skills gap analysis that uses artificial intelligence to assess employee skills and identify areas for improvement; a training recommendation engine that uses machine learning algorithms to create personalized training suggestions based on employee data, company goals, and industry trends; a learning path optimization module configured to adaptively modify training recommendations based on employee progress, feedback, and evolving qualification requirements; an integration module for connecting to existing enterprise systems, including Learning Management Systems (LMS), HR platforms and performance management tools; a module for monitoring engagement and progress, designed to track employee participation in training, assess knowledge retention, and provide managers with insights; and an automated training planner configured to assign and prioritize training sessions based on employee availability and business requirements.

The invention relates to the field of wind power plants, in particular to a wind power cluster preventive maintenance strategy intelligent generation method and system. The method comprises the following steps: collecting multi-source heterogeneous data of a wind power cluster in real time, including SCADA system operation data, CMS monitoring data and environmental sensor data; performing standardization processing and abnormal value elimination on the collected data, and constructing a wind power equipment operation state database; a wind power equipment health state evaluation model is established based on a deep learning algorithm, the equipment operation state is analyzed in real time, and the potential fault risk is predicted; generating a preventive maintenance strategy by adopting a multi-objective optimization algorithm according to a fault risk prediction result in combination with historical maintenance records of the equipment and operation and maintenance resource scheduling information; and issuing the generated maintenance strategy to a wind power cluster operation and maintenance system to realize intelligent execution of preventive maintenance. The problems of high data fusion difficulty and low processing efficiency in the prior art are solved.

The invention relates to the technical field of user data processing, in particular to a user portrait enhancement method based on a dynamic tag, which is applied to a system method for designing data acquisition, data processing, data analysis and data classification of a user information and user portrait data use platform. By analyzing the user behavior in real time and dynamically adjusting the label in the user portrait, the more accurate and comprehensive user portrait can be constructed, and the real-time interest and long-term preference of the user can be captured. Based on the dynamically updated user portraits, the system can provide highly personalized recommendation services, commodity recommendation, content recommendation or service recommendation can be closer to the real demands of the users, and the satisfaction degree and participation degree of the users are improved.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention discloses a whole-process patient self-control analgesia management platform and method based on artificial intelligence, and relates to the related field of analgesia management, and the platform comprises a multi-source data interaction module which is used for building a time sequence standard data set; the information configuration module is used for starting the self-control analgesia pump; the AI pain evaluation module is used for generating a pain scoring result; and the identification early warning module is used for generating graded early warning. The technical problems of insufficient safety and unstable effect in the analgesia management process due to dependence on a fixed dose threshold and lagged artificial pain assessment in traditional patient self-control analgesia management are solved, the whole-process pain management platform based on artificial intelligence is constructed, multi-source data can be analyzed in real time, and the pain management efficiency is improved. And the pain state of the patient is dynamically evaluated, and the parameters of the analgesia pump are automatically adjusted, so that the technical effects of personalized analgesia management and safety and effectiveness in the analgesia process are realized.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention discloses a multi-modal sentiment analysis method based on a gating circulation hierarchical fusion network, which comprises the following steps: S1, multi-modal data preprocessing: collecting text, audio and video data, carrying out preprocessing and time sequence alignment, and constructing an annotation data set; s2, constructing a gating circulation hierarchical fusion network: designing a three-level network architecture comprising a modal feature extraction layer, a gating fusion layer and an emotion recognition layer; s3, model training and optimization: adopting an Adam optimizer, taking a cross entropy loss function as a target, carrying out iterative training on the labeled data set, and inhibiting overfitting through Dropout regularization and learning rate attenuation; s4, emotion analysis: inputting data acquired in real time into the trained model, analyzing the emotion state of the user in real time through a feature extraction layer, a gating fusion layer and an emotion recognition layer, and outputting an emotion analysis result; according to the method, information of different modes can be fully utilized in the sentiment analysis task, and the sentiment recognition accuracy is improved.

The invention provides a hospital operation monitoring analysis method and platform based on artificial intelligence, and the method comprises the steps: collecting data from a plurality of data sources of a hospital, and integrating the multi-source heterogeneous data into a unified data platform through a data access gateway; performing cleaning, standardization and feature extraction on the acquired data, and extracting key features by using automatic feature engineering; constructing an AI model according to a hospital operation scene, and training and verifying the model by using historical data; inputting the real-time data into an AI model for analysis, generating a prediction result or a classification label, and performing real-time early warning on an abnormal condition; key indexes and analysis results are displayed through a visual instrument panel, and an operation report is automatically generated. Through the artificial intelligence technology platform, hospital resource use conditions can be analyzed in real time, resource allocation can be dynamically optimized, medical staff scheduling and equipment use plans can be automatically adjusted, waiting time of patients is shortened, and scheduling efficiency and response speed are remarkably improved.

The invention relates to the technical field of data processing, in particular to a cloud computing-based software development application data processing method, which comprises the following steps of: collecting original data generated by software development application from a plurality of heterogeneous data sources in real time through a distributed data acquisition module; cleaning, filtering and formatting the original data, removing noise data and redundant information, and generating a standardized data set; the standardized data set is fragmented according to a preset fragmentation strategy; analyzing the stored data in real time by using the parallel processing capability of cloud computing to generate an analysis result; a result storage and updating step; a data visualization step; a security and authority management step; and an anomaly detection and fault-tolerant processing step. According to the method, the data processing speed and the throughput of the system are improved, and the requirement for real-time feedback in an agile development environment is met.

The invention discloses an intelligent data analysis method based on spatio-temporal big data, and belongs to the technical field of spatio-temporal big data, the method comprises the step of obtaining original spatio-temporal data of different sources in real time, and the method comprises the steps of performing feature extraction on preprocessed spatio-temporal data, including unification of a time format, spatial coordinates and a data unit, deduplication fusion and the like, so as to obtain the spatio-temporal data of different sources. According to the method, multi-source spatio-temporal data is formed, a spatio-temporal data analysis model is constructed based on deep learning, spatio-temporal dynamic changes in data can be captured, modes in the spatial data can be effectively extracted through models such as a convolutional neural network, classification is performed through cross entropy loss, high-precision spatio-temporal data analysis is achieved, and the spatial data analysis efficiency is improved through the trained spatio-temporal data analysis model. According to the method, new spatio-temporal data are analyzed in real time, abnormal modes and trend changes in the data are rapidly identified, an anomaly detection threshold value is set, model output is compared with the set threshold value, comprehensive judgment is carried out in combination with context information, and the accuracy and efficiency of anomaly detection are improved.

The invention relates to the technical field of data processing, and discloses a heat supply network heat supply system control method based on a prediction model, and the method comprises the steps: S1, collecting historical operation data and real-time operation data of a heat supply network heat supply system, S2, carrying out the preprocessing of the historical operation data and the real-time operation data, and generating a standardized heat supply data set; by constructing a prediction model based on historical data and real-time data, the thermal load demand change in a future time period can be dynamically predicted, the problem of adjustment lag caused by dependence on a static model in a traditional control method is solved, the response ability of a heat supply system to extreme weather and user demand sudden change is improved, and the heat supply efficiency is improved. The stability and reliability of heat supply quality are guaranteed, the time hysteresis between a regulation and control instruction and the actual heat supply effect is reduced by analyzing the heat transmission characteristic and the delay effect of each node of the pipe network in real time, the phenomena of insufficient heat supply and energy waste caused by untimely regulation of the system are avoided, and the energy consumption is reduced. And the operation efficiency and the energy utilization rate of the heat supply system are improved.

The invention discloses a data hierarchical storage method and device, computer equipment and a storage medium, and the method comprises the steps: collecting an access log of target data at a current moment, determining the data information of the target data according to the access log, carrying out the feature extraction of the data information, and obtaining a target access feature and a target data feature; inputting the target access features and the target data features into a trained deep learning model, outputting a predicted access mode within a preset time period after the current moment, and according to the predicted access mode, adjusting a distribution strategy of the target data in at least two storage layers of different read-write rates of a database to obtain an updated storage strategy, and migrating and storing the target data according to the update storage strategy. The data access mode can be analyzed and predicted in real time through the model, the distribution of the data among different storage layers can be automatically adjusted, and compared with an existing static storage strategy, the storage efficiency and the access efficiency can be effectively improved.