292 results about "Time series database" patented technology

The invention relates to the technical field of wind power generation equipment data processing. The invention provides a time sequence database data compression method, system and device and a storage medium. The method comprises the steps of dynamically partitioning time sequence data based on a preset time window; hybrid coding compression is performed on each data block, and a composite strategy of difference coding and run length coding is adopted for numerical data; a metadata index of the compression blocks is established, the time range, the data feature statistics and the compression parameters of each data block are recorded, and the metadata index comprises extreme value distribution, variance features and data fluctuation frequency indexes; and dynamically adjusting a compression strategy according to a historical data feature analysis result, predicting data fluctuation modes of different equipment sensors through a machine learning model, and automatically selecting an optimal coding combination and compression granularity for subsequent data blocks. The problems that when wind power plant time sequence data are processed through an existing method, time sequence characteristics are difficult to adapt, the storage cost is high and the query efficiency is low are solved.

The invention discloses a garden landscape plant health assessment method, and belongs to the technical field of resource assessment. Comprising the following steps: step 1, constructing a time sequence database of environmental factors and plant growth states; step 2, according to different plant species and growth stages thereof, dynamically calculating and generating a health reference value adapted to the current environmental condition; step 3, in the optimal evaluation time window, performing preliminary evaluation on the plant health condition, extracting original health indexes, and performing correction in combination with an environment disturbance compensation factor; step 4, performing double comparative analysis on the corrected health assessment result, eliminating false anomalies caused by environmental fluctuation, and calculating an anomaly confirmation credibility index; step 5, grading the plant health conditions, visually displaying the plant health conditions in a thermodynamic diagram form, and marking the environmental interference degree; and step 6, based on an evaluation result, automatically generating a targeted maintenance suggestion, and designing a corresponding maintenance effect verification scheme.

The invention discloses an industrial data real-time acquisition monitoring system fusing edge computing and 5G, and belongs to the technical field of industrial Internet of Things. The system is composed of a multi-source heterogeneous data acquisition module, an edge computing node cluster, a 5G communication network and a cloud analysis platform, an edge-cloud collaborative architecture is innovatively adopted, a multi-protocol adapter is integrated to realize unified access of heterogeneous data of industrial equipment, a low-delay transmission channel is constructed by using a 5G network slicing technology, and the heterogeneous data of the industrial equipment is transmitted to the cloud analysis platform. And transmitting the preprocessed data to the edge computing node in parallel. And the edge layer realizes dynamic resource scheduling by adopting a containerization technology, and realizes millisecond-level response and local decision feedback. And meanwhile, through an edge-cloud data synchronization mechanism, a distributed time sequence database is constructed, and visual monitoring and deep analysis of multi-dimensional data are supported. The system improves the real-time processing capability of industrial field data and the reliability of the system, and has the technical advantages of low time delay, high concurrency and optimized resource utilization rate.

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 an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention discloses a switch fault diagnosis and intelligent analysis management method and device, equipment and a storage medium, and relates to the technical field of network equipment fault diagnosis and intelligent analysis, and the method comprises the steps: obtaining monitoring container index information, carrying out the data preprocessing based on the monitoring container index information, synchronizing a preset time sequence database, and determining synchronous data; extracting time sequence common characteristics based on the synchronous data, matching a corresponding fault mode, and determining a root cause probability sequence; and positioning a fault propagation path based on the root cause probability sequence, triggering topology to generate a marked target fault path, determining a display topological graph, and completing switch fault diagnosis and intelligent analysis management based on the display topological graph. According to the method, the time sequence features are extracted, the fault modes are matched to determine the root cause probability sorting, and the fault paths are reasoned to dynamically mark the topological graph, so that data islands are broken, multi-source data association analysis is realized, the diagnosis time is shortened, the real-time performance and accuracy are improved, and the visualization effect is optimized.

The invention discloses a transaction processing mechanism optimization method and system for time series data, and belongs to the technical field of databases, and the method realizes a differentiated ACID support mechanism, strengthens atomicity and durability guarantee, and weakens consistency and isolation requirements; comprising the following steps: designing a collaborative architecture of a transaction system and a pre-write log WAL, and establishing a binding relationship between a transaction state and WAL writing; generating a WAL record only for the successfully submitted transaction operation, and performing persistent storage by adopting a batch group submission strategy; transaction scheduling is realized through a lightweight transaction manager, and the life cycle of a transaction request is managed; the functions of efficient writing of transaction granularity, log storage and rapid recovery are realized through a pre-writing log manager; through intelligent Checkpoint background service, based on a periodic incremental check point mechanism, compression merging and space recovery of the WAL file are realized. According to the invention, the performance breakthrough of the time sequence database in a high-concurrency write-in scene can be realized.

The invention provides an IoTDB and AI collaborative driving welding robot parameter self-optimization method, which comprises the following steps: performing data alignment processing on an obtained original data set of a welding robot, generating time-space alignment features, and storing the time-space alignment features in an IoTDB time sequence database; performing cross-modal feature fusion and quality prediction through a pre-trained neural network based on all space-time alignment features in the I oTDB time sequence database, generating a quality prediction value, and writing the quality prediction value into the I oTDB time sequence database; and on the basis of the current space-time alignment features and the corresponding quality predicted values in the IoTDB time sequence database, optimized welding parameters are generated through an incremental reinforcement learning algorithm, and the optimized welding parameters are used for indicating the welding robot to conduct parameter adjustment. By adopting the method, the dynamic response capability and the process stability of intelligent manufacturing can be improved through long-term feature extraction of the time series data and collaborative optimization of an incremental learning mechanism.

The invention discloses an intelligent elevator host bearing fault diagnosis system based on AI and fault physical fusion, and belongs to the technical field of elevator diagnosis. Comprising an edge end which is connected with a cloud end; when fault diagnosis is carried out, the edge end firstly collects a high-frequency signal through a sensor, sequentially executes signal preprocessing, feature extraction, anomaly detection and data compression, and finally sends compressed feature data to the cloud end; after the cloud end receives and verifies the integrity of the data, the feature data is stored in a time series database, deep analysis is executed through a data analysis engine, a lightweight model is optimized based on historical data, an analysis result is displayed through a remote monitoring platform, and an alarm notification is sent when abnormity is detected; meanwhile, the cloud end issues the optimized lightweight model to the edge end to form a continuously optimized closed loop. Through the feature extraction and data compression technology of the edge end, the data volume needing to be transmitted is greatly reduced, and the network bandwidth requirement and the transmission delay are remarkably reduced.

The invention discloses a power grid equipment anomaly detection method and system, and the method comprises the steps: obtaining node-level power grid equipment state time sequence data from a local distributed time sequence database of each power grid partition, and constructing a topological graph corresponding to the power grid partition based on the power grid equipment state time sequence data, wherein the power grid equipment state time sequence data are written into the distributed time sequence database in parallel according to equipment nodes and time windows in advance; constructing a graph neural network based on the topological graph corresponding to each power grid partition, and modeling a spatial dependency relationship among equipment nodes in the topological graph based on a graph attention mechanism; the graph neural network corresponding to each power grid partition is trained on a locally deployed distributed time sequence database through a joint optimization loss function of point prediction and quantile prediction, and model parameter optimization is carried out through a federated learning algorithm and a federated server; and obtaining a trained graph neural network for performing equipment anomaly detection in a corresponding power grid partition.

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 relates to a time series data acquisition method and device based on a dynamic time window and fingerprint deduplication. The method comprises the following steps: establishing a sliding time window with a variable length by taking the current system time as a reference, and only querying incremental time sequence data in the window; adaptively zooming the window by calculating the ratio of the collected data volume to the expected volume in real time; setting the trigger interval as half of the length of the sliding time window to form a time overlap, thereby capturing out-of-order data; key features of each piece of time series data are extracted, and data fingerprints are generated; a bloom filter is used for primary screening, and accurate duplicate removal is carried out in a key-value storage system; compared with an existing fixed polling scheme, the method has the advantages that resources can be dynamically adjusted along with data flow rate, system load is reduced, real-time performance is improved, data integrity is guaranteed by overlapped windows, zero repeated acquisition is guaranteed by fingerprint two-stage duplicate removal, and the method is applicable to efficient incremental synchronization scenes of transactional log-free time sequence databases such as InfluxDB and the like.

The invention discloses a server non-perception resource scheduling method and system based on cluster telescopic self-adaptive reinforcement learning. The method comprises the following steps: step 1, using a monitoring assembly based on a time sequence database to collect various operation load indexes of a cluster server; and step 2, constructing a function resource scheduler by using a Kubernetes scheduling framework. And step 3, constructing a scheduling decision agent by using a cluster telescopic adaptive reinforcement learning algorithm, inputting a variable-length observation sequence, and outputting a node deployment action. And step 4, using a historical cloud service load data set to construct a load generator to simulate a production environment to train and strengthen an intelligent agent deep neural network. And 5, using the trained scheduling decision agent to make a production environment scheduling decision. According to the method, the existing reinforcement learning algorithm is subjected to elastic cluster-oriented algorithm optimization, the expansibility and robustness of the scheduling algorithm when the cluster executes node scaling are improved, and the method has practical application value.

The invention discloses a time sequence database architecture, a data writing method and electronic equipment, and the time sequence database architecture comprises a distributed storage module which enables data of the same equipment to be continuously distributed in a physical storage layer based on an equipment dimension fragmentation strategy; the independent index layer is decoupled from the data storage module and maintains a mapping relationship between the equipment identifier and the corresponding physical fragment through a metadata structure; the migration module is used for performing rolling updating on the hot data layer; and the query engine module is used for positioning the target physical fragment according to the equipment identifier and executing predicate push-down calculation on the compressed data. The data writing method comprises the following steps: receiving a data writing request, and analyzing a device identifier in the request; positioning a target physical fragment according to the equipment identifier in cooperation with a Hash algorithm; and adding the data to the column type storage file in the corresponding physical fragment according to a timestamp sequence, and synchronously updating the metadata mapping relationship of the independent index layer. According to the method, the data query performance and query efficiency can be improved, and the data storage cost is reduced.

The invention relates to the technical field of data compression and storage, and discloses a monitoring data compression and storage method driven by a time sequence database, comprising the following steps: S1, preprocessing multi-source monitoring data, and constructing a dynamic tensor comprising a timestamp, a numerical index and a multi-dimensional label; s2, performing dynamic dimension reduction processing on the dynamic tensor to generate a core tensor and a multi-dimensional factor matrix; s3, based on the core tensor and the multi-dimensional factor matrix, determining a compression parameter, a decompression parallelism degree and an index granularity through a joint optimization model; and S4, performing hierarchical coding on residual data generated by the dynamic dimension reduction processing to generate a lightweight residual coding result. Through a dynamic tensor decomposition and incremental updating technology, low storage overhead and real-time dimension expansion capability of streaming monitoring data are realized, the problems of calculation redundancy and storage expansion caused by the fact that the streaming monitoring data cannot adapt to dynamic newly-added tags are solved, and meanwhile, frequent reconstruction cost caused by data dynamic expansion is avoided.

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.

A performance monitoring system includes a metric collector configured to receive, via metric exporters, telemetry data comprising metrics related to a network of computing devices. A metric time series database stores related metrics. An alert rule evaluator service is configured to evaluate rules using stored metrics. The performance monitoring system may include a machine learning module and is configured to determine optimized metric collection sampling intervals and rule evaluation intervals, and to automatically determine recommended alert rules.

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 provides a non-market power load dynamic prediction method and system based on a dimension separation mechanism, and relates to the field of power transaction, and the method comprises the steps: constructing a multi-scale time sequence database; screening low-correlation features through segmented regression, carrying out reverse processing on the low-correlation features, and carrying out re-evaluation and extraction in a condition subspace to obtain multi-dimensional condition features; predicting an annual total amount and a monthly decomposition scheme based on macroeconomy, policy characteristics and history monthly; separating the trend from the seasonal component by using a deep decomposition architecture, and outputting a monthly score daily outline and a daily load total amount; calculating a characteristic similarity matching historical daily curve, and weighting to generate 15-minute-level time-sharing prediction data to obtain daily time-sharing prediction data; combining the prediction data step by step, dynamically adjusting the feature weight, optimizing the model, and outputting multi-scale load prediction. The method is used for overcoming the defects that in the prior art, the power output change of each power supply is large due to the fact that the power supply characteristics are different according to the fixed-point policy.

The present invention relates to an artificial intelligence-based energy and power data asset evaluation method, comprising the following steps: S1: acquiring multi-source heterogeneous data from the energy and power industry; S2: constructing a database based on a time series database and distributed storage to store the multi-source heterogeneous data; S3: constructing an energy knowledge graph based on the multi-source heterogeneous data; S4: constructing a multidimensional value assessment model to quantify the economic benefits, quality indicators, usage, and potential mining capabilities of data assets; and S5: establishing a dynamic monitoring system to conduct real-time evaluation of the value of data assets throughout their life cycle based on the multidimensional value assessment model and the energy knowledge graph. This invention effectively improves the utilization efficiency of energy data assets.

The invention discloses a water supply pipe network monitoring point arrangement method based on a risk assessment and optimization algorithm, and relates to the technical field of intelligent pipe network monitoring, and the method comprises the steps: collecting water supply pipe network data, carrying out the preprocessing, and storing the data in an InfluxDB time sequence database; constructing a multi-dimensional risk assessment model, and generating risk scores of the pipe sections; a greedy algorithm and genetic algorithm hybrid optimization strategy is adopted to generate a water supply network monitoring point layout scheme; the method comprises the following steps: deploying an edge computing gateway, distributing water supply network data to the edge computing gateway through an MQTT protocol, generating a pipe section risk probability by using a pipe section risk probability model, setting a four-level judgment threshold, and triggering a hierarchical alarm mechanism; a DBSCAN algorithm is used for identifying a water supply network risk high-incidence area, water supply network monitoring points are increased, and the monitoring radius of the water supply network monitoring points is dynamically adjusted. According to the method, through a greedy-genetic hybrid optimization strategy, a global optimal solution and rapid convergence of monitoring point layout are realized.

The invention discloses an intelligent blasting platform which comprises a physical layer, a data layer, a business logic layer, a function module layer and a user interaction layer. The physical layer is provided with an unmanned aerial vehicle, a GNSS receiver, a blasting recorder and Internet of Things sensing equipment, and blasting engineering three-dimensional geographic space information and a BIM model are generated through three-dimensional live-action modeling and BIM forward modeling; the data layer integrates GIS data, BIM data, monitoring data and engineering data, and constructs a data resource center comprising a time sequence database and a knowledge graph database; the business logic layer comprises a GIS + BIM + IoT information sensing module, a multi-source data fusion module, a blasting design module, an intelligent optimization module, a monitoring identification module and a blasting effect evaluation module; the functional module layer develops a 3D visual query module, a dynamic monitoring module, a blasting design module, an intelligent optimization module, a statistical analysis module, a forecasting and early warning module and a system management module through a front-end and rear-end separation architecture; and the user interaction layer realizes multi-terminal cooperative interaction based on WebGL, Vue3 and a mobile terminal technology.

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 provides an intelligent prediction method and system based on a thermal working area and a maintenance range. The method comprises the following steps: S1, acquiring point location data of a thermal working area in real time, and uploading the point location data to a server through a data transmission protocol; s2, storing point location data by using a time sequence database, and performing data management based on a timestamp, a tag field and a measurement value; s3, performing incremental training on historical data and real-time data based on a machine learning model, and dynamically predicting a maintenance loss risk prediction result; s4, dynamically loading the thermodynamic point location data according to the zoom level of the user interface map; s5, generating a resource allocation strategy based on a prediction result, and dynamically scheduling maintenance resources according to risk levels; the invention has the following advantages; 1, the real-time performance of data acquisition and processing is enhanced; 2, the intelligence and accuracy of the prediction algorithm are improved; 3, high efficiency of thermodynamic point location data display; and 4, the expansibility and the performance of database design are optimized.

The invention discloses a time sequence database LSM-Tree-oriented merging frequency optimization method and system, and relates to the technical field of database optimization. The invention aims to solve the problems of low system throughput and long response time caused by extra delay of a system due to excessive consumption of memory and disk IO (Input / Output) in the existing LSM tree merging method. The method comprises the steps that a database receives external time sequence data and stores the external time sequence data in MemTable; the MemTable sorts the time sequence data and judges whether the MemTable reaches a preset capacity limit or not, if the MemTable reaches the preset capacity limit, the MemTable is newly built, the delay processing data volume N is determined, the last N pieces of data in the MemTable are moved into the newly built MemTable, and the rest of data are written into the SSTable from small to large according to timestamps; otherwise, continuing to receive the time sequence data; finally, judging whether the timestamps of the time sequence data in different SSTtables are overlapped or not, and if the timestamps are overlapped, combining the SSTtables with the overlapped timestamps; and otherwise, directly linking the adjacent SSTable. The method is used for optimizing the LSM merging frequency.

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 invention discloses a cross-platform operation and maintenance efficiency intelligent evaluation method and system oriented to a multi-cloud environment, and the method comprises the steps: collecting operation and maintenance data of a plurality of cloud platforms in the multi-cloud environment, the operation and maintenance data comprising a resource use condition, a task distribution record and network performance data; performing standardization processing on the collected operation and maintenance data, and storing the operation and maintenance data in a time sequence database in a unified format; performing data preprocessing on the standardized data based on a distributed computing framework, and extracting key indexes of operation and maintenance efficiency; constructing a multi-dimensional evaluation index framework based on resource use conditions, task allocation records and network performance data; based on the multi-dimensional evaluation index framework, generating an operation and maintenance efficiency evaluation report; according to the method, unified integration of the operation and maintenance data in the multi-cloud environment is realized, and the problems of data dispersion and inconsistent formats in the multi-cloud heterogeneous environment are solved. Meanwhile, an operation and maintenance efficiency evaluation report is generated based on a multi-dimensional evaluation index framework through resource use conditions, task allocation records and network performance data, and analysis and evaluation of operation and maintenance data are automatically completed.

The invention relates to the technical field of battery monitoring, in particular to a battery health state estimation method based on a battery aging system, which is characterized in that an upper computer and a plurality of test nodes are connected through an industrial field bus communication protocol, charge-discharge cycle test is performed on a battery pack, and battery data are acquired in real time; and processing the data by using a protocol analysis engine and a sliding time window technology, calculating a battery capacity fading rate and an internal resistance change rate, and generating a standardized data set. And a health state evaluation model is constructed through a machine learning model, real-time evaluation and visual display of the health state of the battery are realized, and early warning is given out when the health state is lower than a threshold value. The method supports dynamic parameter configuration, adopts a time sequence database to store data, and facilitates full-life-cycle data tracing. According to the invention, multi-dimensional data acquisition, intelligent analysis and safety protection are integrated, the efficiency, reliability and intelligent level of the battery aging test are improved, and a scientific basis is provided for battery maintenance and management.

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.