183 results about "Time series database" patented technology

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention discloses a mass data display method, device and equipment based on a browser client side and a medium, relates to the technical field of computers, is applied to a server side and comprises the steps that a data obtaining request sent by the browser client side is obtained; the data acquisition request carries a request time range and a target sampling frequency; obtaining original time sequence data in the request time range from a preset time sequence database, and determining the size of a sampling window based on the data frequency of the original time sequence data and the target sampling frequency; the data frequency is greater than the target sampling frequency; grouping the original time series data based on the size of the sampling window so as to obtain a plurality of data windows, and extracting a target feature point for representing data trend change in each data window; and merging the target feature points of the data windows to obtain a downsampled data sequence, and sending the data sequence to a browser client for display. According to the method and the device, effective visual display of mass data on the browser client can be realized.

The invention provides a time sequence data storage method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining time sequence data and metadata information of the time sequence data, the metadata information comprising a first point location identifier, a data source and a data type corresponding to the time sequence data; according to the data source and the data type, determining a storage strategy of the time series data, including a target storage period and a target storage position; according to the storage strategy and the first point position identification, a target storage instance corresponding to the time sequence data is matched from a storage instance set, all storage instances in the storage instance set are combined and constructed according to different storage strategies and the point position identification, and all the storage instances are parallel storage instances; and storing the time sequence data to a time sequence database through the target storage instance. By determining a dynamic storage strategy driven by metadata and a parallel storage instance, the problem of performance bottleneck during high-concurrency writing of massive time series data is solved, and the real-time performance and reliability of time series data storage are ensured.

The invention relates to the field of distributed storage, and discloses a distributed storage system, method and equipment for wind power plant data and a medium, which are used for realizing flexible adjustment of a storage strategy and meeting the storage requirement of dynamic change of a wind power plant data value. Comprising the following steps: collecting SCADA system data at edge nodes of a wind power plant, inputting an information entropy module and an access heat analysis module in parallel, fusing to generate a data value evaluation result, inputting the evaluation result into a cellular automaton engine, outputting a cellular state change instruction, summarizing collaborative strategy requests of at least two edge nodes to a regional center node, and carrying out collaborative strategy analysis on the regional center node. And a fair resource allocation scheme is output through the Sharpley value calculation module. And each edge node synchronously receives the scheme, the time sequence database controller generates a final storage instruction, and data distributed persistent storage is completed. According to the method, on the basis of the correlation between the long-term data value evaluation result and the actual use value, the entropy value and the popularity weight coefficient are optimized, and self-adaptive optimization of the system is achieved.

The invention discloses an Internet of Things equipment data analysis method and system supporting multi-protocol custom splicing, and relates to the technical field of communication protocols, and the method comprises the steps: constructing a multi-protocol equipment access link; in response to an original data message reported by the Internet of Things equipment through the multi-protocol equipment access link, determining a product model of the Internet of Things equipment based on the original data message; splitting the original data message into a plurality of functional paragraphs based on the attribute of the product model; analyzing each function paragraph to obtain function paragraph data, and mapping the function paragraph data to a corresponding attribute of the product model; and storing the mapped functional paragraph data into a time sequence database and a relational database. According to the method, the external data interface can be customized and created, the service system can conveniently obtain the equipment data, the operation data and the freezing data, the method has the functions of real-time reading control and the like, the data transmission safety can be guaranteed, equipment access under a large number of protocol concurrency scenes can be effectively handled, and the access efficiency is improved.

The application belongs to the technical field of seal ring life prediction, in particular to a seal ring life prediction method and system and a storage medium, comprising the following steps: obtaining seal ring material information, calculating the theoretical prediction life of the seal ring based on the seal ring material information, and synchronously storing it to the cloud; calculating the corrected prediction life of the seal ring based on the online detection data and the theoretical prediction life, and synchronously updating it to the cloud; performing aging test, calculating the limit prediction life of the seal ring based on the corrected prediction life, and synchronously updating it to the cloud; verifying the limit prediction life of the seal ring under the limit environment based on the aging test, writing / updating the data of each stage to the cloud time series database in real time, and constructing the material-production-aging data chain, so as to realize efficient prediction of the seal ring life, avoid low-life products from flowing into the market, and solve the problems of complicated after-sales and high after-sales cost of products.

The invention discloses a photovoltaic panel defect analysis method based on unmanned aerial vehicle carrying, and relates to the technical field of data processing, and the method comprises the steps: obtaining basic data of a photovoltaic power station according to different instruments, obtaining topographic data according to the basic data, obtaining an actual routing inspection path of an unmanned aerial vehicle according to the topographic data and the basic data, and carrying out the routing inspection of the unmanned aerial vehicle according to the actual path. The method comprises the steps of presetting a triggering photographing point, obtaining real-time feature data according to the triggering photographing point, judging the defect degree grade according to the real-time feature data, dividing the real-time feature data into historical data, current data and prediction data, establishing a corresponding relation, and obtaining a detection report according to the corresponding relation. According to the method, the optimal inspection route is automatically generated by integrating the multi-source data set and based on the topographic data, the occurrence probability of potential faults can be predicted through the corresponding relation by formulating the fixed inspection period and constructing the time sequence database, and the detection report is output.

The application discloses a water conservancy knowledge differentiation retrieval and query decomposition method based on a large language model, and the method comprises the following steps: constructing a standardized retrieval channel for four types of heterogeneous data sources, namely a graph database, a relational database, a document library and a time series database; secondly, establishing a sub-query type classification system for the water conservancy field and forming a template library; thirdly, collecting training data from historical queries and expert samples, and adopting a multi-round labeling mechanism to label query decomposition results, channel mapping relationships and dependency relationships; finally, adopting a low-rank adaptation method to fine-tune the pre-trained large language model, so that the model can intelligently decompose a user natural language query into a sub-query sequence, automatically route to the corresponding retrieval channel and plan an execution sequence. The application realizes intelligent processing of a composite query in the water conservancy field, significantly improves the efficiency and reliability of cross-data source knowledge retrieval, and is suitable for decision support scenes such as flood control scheduling and reservoir group joint scheduling.

The application discloses a time series data processing method and system, a storage medium and an electronic device. The method comprises the following steps: in response to a client request, obtaining time series data to be processed from a time series database, wherein the time series data is generated by a data source according to a time period and stored in the time series database; storing a sharding result corresponding to the time series data in a message queue, wherein the sharding result is used to enable the message queue to store a corresponding log file; and writing the sharding result read from the message queue into a memory. The application solves the technical problem of low data storage efficiency.

This invention relates to the field of experimental data storage technology for nuclear fusion devices, and discloses a method for large-scale EPICS data archiving based on a time-series database. The method involves acquiring process variables in an event-driven acquisition mode through an embedded channel access protocol data acquisition module and writing them into buffered memory channels. Through group aggregation and batch persistence, the process variables are grouped into fixed-size groups, each group corresponding to an aggregation coroutine. The latest event data is read non-blockingly from the buffered memory channels of each group at fixed intervals. Metadata mapping is used to convert the process variable names into device paths and measurement value names in the time-series database, assembling them into a sparse wide table. Null value columns in the sparse wide table are filtered, and the data is then batch-written into a distributed time-series database through a connection pool. This invention achieves stable high-throughput writing and low-latency retrieval of large-scale experimental data in the EPICS system, and provides an efficient data access interface for subsequent artificial intelligence training and inference platforms.

A server apparatus disclosed in the present document may comprise a communication circuit, a memory, a time series database, and at least one processor. The memory may store instructions that cause the server apparatus to: store, in a first table, a label included in time series data and a first hash value corresponding to the label; receive the time series data from an external device through the communication circuit; generate a second hash value by hashing the label of the received time series data; check whether the second hash value is included in the first table; if the second hash value is included in the first table, store, in a second table, the time series data from which the label has been removed and the second hash value; check data prior to a reference time, from among the time series data stored in the second table; and compress a designated data range from among the data prior to the reference time and store same in a third table.

The application relates to a dynamic rebalancing strategy of a time series database cluster, and belongs to the technical field of IT software development, and comprises the following steps: S1. taking a monitoring alarm system as a trigger source, and waiting for the triggering of cluster rebalancing; S2. dynamically selecting a rebalancing strategy; S3. performing a disk data rebalancing process; and S4. performing a cluster shard rebalancing process, wherein the monitoring system is used to monitor the key indicators of each node in real time, such as disk usage, the number of shards and the like, and then the disk data rebalancing and cluster shard rebalancing strategies are dynamically adjusted according to the indicators, so that the resource utilization is improved, the performance is optimized, the stability and availability of the system are improved, and when the node disk usage exceeds a preset threshold, the data is automatically migrated from the node with high usage to the node with low usage, so that the storage and query performance are optimized, the storage efficiency is improved, the storage pressure is reduced, and the write throughput of the time series database is improved.

The invention relates to a data analysis method based on a supply chain, in particular to the technical field of business management, and the method comprises the following steps: S1, collecting supply chain data from an internal system and an external source, the internal data comprising a production plan, a production schedule, an inventory level, a purchase order and a sales record, the external data comprises logistics transportation information, market demand indexes, consumer behavior characteristics, meteorological monitoring data, road traffic flow and policy and regulation change information; according to the invention, by establishing a multi-source data access channel, data of an ERP system, an MES system, a WMS system, an external data platform and an Internet of Things device are uniformly collected, and historical data and real-time data are combined to construct a multi-dimensional time sequence database, so that complete acquisition and structured storage of supply chain global information are realized; and the comprehensiveness and accuracy of data input are ensured.

The invention provides a satellite interface data list (IDS) version control method and system, and aims to guarantee the consistency and reliability of data exchange between satellites and a ground station, between satellites and between modules in the satellites. The version control method comprises the following steps: encoding a source data table to generate an initial version identifier; registering it to a monitoring service using an event trigger mechanism, automatically recording and generating a second version identifier when any change is detected; performing field-level difference calculation on the two versions to obtain a difference set; a difference file is generated after the difference set is subjected to additional write mapping; and finally converting the difference file into a full-amount historical log and storing the full-amount historical log into a time sequence database. The version control method can effectively prevent data errors or communication faults caused by version inconsistency.

This invention relates to an intelligent monitoring system and deployment method for building module data interfaces based on the Internet of Things (IoT), belonging to the field of building information monitoring technology. The system consists of distributed sensing units, an edge computing gateway, a cloud platform, and a visualization terminal. The edge computing gateway incorporates an adaptive filtering algorithm and an anomaly pattern recognition model. The cloud platform uses a time-series database to construct a multi-dimensional data warehouse and performs structural health assessment by fusing LSTM neural networks and random forest algorithms. The deployment method includes: optimizing sensor placement strategies based on BIM models, establishing a wireless mesh self-organizing network communication architecture, and configuring a hierarchical early warning mechanism and fault tracing function. The innovation lies in proposing a dynamic threshold adjustment algorithm and an interface performance degradation prediction model to achieve real-time monitoring and lifespan prediction of building module connection status. This system has the advantages of flexible deployment, high detection accuracy, and low maintenance costs, effectively improving the intelligent level of building structural safety monitoring.

The application discloses an intelligent operating room air quality prediction and supervision system based on AI empowerment, which comprises a multidimensional data perception layer, an AI analysis engine and an intelligent supervision platform. The multidimensional data perception layer comprises a particle monitoring network and a parameter acquisition module. The particle monitoring network is arranged in layers around the operating bed to collect multi-particle size particle concentration data through sensors. The parameter acquisition module acquires environmental parameters. The AI analysis engine comprises a time series database, a multi-factor correlation analysis model and a prediction and early warning module. The time series database constructs a four-dimensional data storage structure. The multi-factor correlation analysis model establishes the correlation between the air quality index and the environmental parameters. The prediction and early warning module predicts the particle concentration trend based on the four-dimensional data storage structure and triggers a hierarchical early warning. The intelligent supervision platform generates control instructions according to the hierarchical early warning and collects data to optimize the model. The application realizes accurate prediction, active control and closed-loop optimization of the air quality in the operating room, and improves the control efficiency and medical safety.

The invention relates to the technical field of database monitoring, in particular to a monitoring method and device for a time sequence database in a public computing environment. The method comprises the following steps: collecting all original index data of each deployment node on a time sequence database; performing calculation operation and / or cleaning operation on the original index data to obtain intermediate index data; converting the type of the intermediate index data into a target format which can be identified by a monitoring system to obtain target index data; and sending the target index data to a monitoring system to realize omnibearing monitoring of the time sequence database. According to the invention, index data in the time sequence database can be monitored in an omnibearing manner, operation and maintenance personnel are helped to find potential risks in advance, fault recovery time is shortened, and business loss is reduced.

A quantum computing device determines quantum characteristics of a quantum computing system, wherein the quantum characteristics comprise properties of qubits of the quantum computing system. The quantum computing device stores in a time series database information containing the quantum characteristics of the quantum computing system. The quantum computing device categorizes the information in the time series database, wherein the categorized information is associated with the qubits. The quantum computing device obtains from the time series database via an application programming interface (API) the categorized information. The quantum computing device presents on a display device real-time information about the quantum computing system based on the categorized information.

The invention provides a log analysis method, device and equipment based on a time sequence database and sequence modeling, and relates to the technical field of network security, the method comprises the following steps: obtaining original historical alarm logs from a plurality of heterogeneous security devices; analyzing and aggregating the attack index to obtain log feature data which takes the attack index as a main key and is aggregated according to a time window, and storing the log feature data into a time sequence database; extracting data of the same attack index in a continuous time window from a time sequence database to construct a spatial-temporal feature sequence of the data; inputting the spatial-temporal feature sequence into a sequence modeling model to obtain a sequence feature representation representing the comprehensive behavior pattern of the attack index; and inputting the sequence feature representation into a classification model, and outputting the threat category of the attack index. According to the method, the multi-source logs are efficiently aggregated through the time sequence database, and the time-space correlation characteristics of the attack indexes are deeply mined by using the sequence modeling technology, so that the recognition accuracy and efficiency of threat intelligence are improved.

The invention relates to a fault detection method of an energy storage system and the energy storage system. The energy storage system is applied to an energy storage side end and is composed of a plurality of equipment assemblies. The fault detection method of the energy storage system comprises the steps that basic operation data generated in the operation process of a plurality of equipment components are acquired, fault recognition is carried out on the basic operation data, fault event data are obtained, and the fault event data represent fault types of the equipment components; extracting target fault event data in a preset time window from a preset fault event time sequence database; according to the target fault event data, performing fault assessment on the plurality of equipment components in a preset time window to obtain fault assessment feature data, the fault assessment feature data representing fault occurrence conditions of the plurality of equipment components in the preset time window; and sending the target basic operation data and the fault evaluation feature data in the preset time window to the energy storage cloud. By adopting the method, the fault detection effect of the energy storage system can be improved.

The invention relates to a power grid investment optimization method based on big data analysis, and belongs to the technical field of power grid investment optimization. The method comprises the following steps: accessing multi-source heterogeneous data, performing edge preprocessing and cloud fusion alignment, and storing the data to a time sequence database after standardization processing is completed; an LSTM model is constructed based on the time sequence database, a future power load and new energy power generation curve is predicted, the energy storage demand is quantified, and the project economy is evaluated; generating an optimal investment scheme set through a multi-objective optimization algorithm based on the predicted power grid state, and quantitatively evaluating the risk and income of each scheme by adopting Monte Carlo simulation; and carrying out geospatial visualization display on the investment scheme, and dynamically feeding back and optimizing power grid planning based on real-time monitoring data. According to the method, demand scheduling and energy storage scale quantification are realized through power grid state prediction, so that multi-objective optimization and risk assessment are completed, and maximization of investment benefits and multi-objective balance are realized.

The application discloses a cross-region synchronous power monitoring system and method based on a time series database, and relates to the technical field of data analysis.The method comprises the following steps: acquiring historical abnormal events and storing them into a time series database, extracting first and second event features therefrom, calculating a perception response duration and a recovery treatment duration, and taking the two as third event features; step-by-step construction of corresponding event sequence models to obtain a complete event sequence model set of all abnormal events; screening of event pairs by traversing the set, analysis of causality based on the event pairs, determination of a causal relationship, and generation of a causal link pair set; traversal of the causal link pair set, marking of associated causal chain pairs and grouping; establishment of a mapping and storage into a knowledge base, marking of consequence types and average recovery durations of each situation element; real-time monitoring of situation element abnormalities, query of the knowledge base to find common consequences, calculation of an estimated maximum recovery time, and early warning if the estimated maximum recovery time exceeds a threshold value.The application realizes cross-region synchronous power monitoring.

The invention discloses an underground environment monitoring and early warning system. The system comprises a sensing layer which collects multi-source data in real time; the sensing layer data is distributed to the edge computing layer through the transmission layer after being subjected to protocol conversion; the edge calculation layer is used for executing real-time data preprocessing and lightweight reasoning; the cloud platform layer is used for constructing a time sequence database by utilizing InfluxDB and ClickHouse, dynamically calculating a regional risk value through a space-time cube model, predicting a vehicle retrograde driving path by utilizing an LSTM-HMM hybrid model, mining an association rule through an FP-Growth algorithm, setting a root cause analysis agent, and reasoning an event-cause-measure causal chain by the root cause analysis agent based on a graph neural network, a security knowledge graph is constructed to provide a basis for early warning decision making; and the early warning decision-making layer is used for generating targeted measures according to event types, environmental parameters and personnel identities based on a dynamic decision-making tree of reinforcement learning and by fusing multi-objective optimization.

The invention discloses an AI-based trachinotus ovatus parent fish culture water body temperature determination method and system, and the method comprises the steps: collecting culture environment data and parent fish physiological data in real time, and generating a multi-modal time sequence database according to the collected data; based on the multi-modal time sequence database, establishing a target variable data set representing the reproductive performance of the parent fish; an AI prediction model is constructed and trained, a multi-modal time sequence database is used as an input feature, and a target variable data set is used as a prediction target; based on the trained AI prediction model, an optimization target is set, future reproductive performance under different water temperature strategies is simulated within a water temperature preset range, and a water temperature value capable of enabling the optimization target to be optimal is selected as a recommended set temperature according to a simulation result; and sending the recommended set temperature to a temperature control system of the culture pond to execute water temperature regulation and control. According to the method, the temperature of the trachinotus ovatus parent fish culture water body is accurately and intelligently regulated and controlled by fusing the multi-modal time sequence data and the AI prediction model.

Systems and methods of improving the functioning of a streaming platform system by managing database change stream offsets using a time series database are disclosed. In some example embodiments, a computer system retrieves an offset value from a plurality of offset values stored in a time series database, with the plurality of offset values being indexed in the time series database in time order, and the retrieved offset value being retrieved using a time parameter, and then the computer system transmits a data request to a stream-processing platform, with the data request comprising the retrieved offset value, and the data request being operable to retrieve a data record stored in association with the retrieved offset value in a storage layer of the stream-processing platform using the offset value.

The application provides a method for extending time series database analysis capability, and relates to the field of data analysis, which comprises the following steps: constructing a time series data analysis system for providing time series data analysis service independently of a time series database system, and extending, upgrading and maintaining the time series data analysis system without affecting the normal operation of the time series database system; adding an adaptive interface in the time series data analysis system, and calling different time series data analysis models; establishing an analysis operator for the time series data analysis service in the time series database system, and the analysis operator calling the corresponding time series data analysis model through the adaptive interface to complete time series data analysis in the execution of an SQL query process, so that interaction between the time series database system and the time series data analysis system which are isolated from each other in the processing flow is realized, and the complexity of the analysis and processing process is shielded.

The application provides a system configuration verification method based on digital twinning, which is used for verifying and evaluating the performance of a structured and universal mechanical and electrical system, and is characterized in that: the verification method is used for associating the data between the MBSE design domain and the digital twinning verification domain by introducing a standardized data interface specification and a UDP / TCP communication protocol to form a data link between the design end and the simulation end; in the static analysis stage, a normalization processing and a weighted coupling degree model are introduced to unify the dimensions of different module parameters, so as to objectively quantify the complex system architecture, and at the same time, a relational database or a time series database is used to store dynamic simulation data, and a filtering and abnormal value elimination mechanism is combined to improve the quality and traceability of the verification data, then, a closed-loop optimization mechanism is constructed, and a nonlinear performance evaluation model is used to feed back the dynamic running performance to the design end in real time; and the application can realize efficient screening and iterative optimization of multiple configurations of unmanned aerial vehicles.

The invention discloses a transformer substation hole digging pile construction supervision method and system based on GIM and digital twinning, and relates to the field of transformer substation digital twinning. In the method, environment parameters, construction progress and position information are collected in real time by deploying an Internet of Things sensor network, a three-dimensional digital model is constructed based on a power grid information model standard, and the construction speed of a hole digging pile is improved; and mapping the acquired real-time data to a corresponding position of the model through a sensor identifier and a space coordinate to form a time sequence database, and continuously updating the state of the model by using a digital twin engine to realize dynamic supervision and risk early warning of the construction process.