459 results about "Cloud database" patented technology

The invention discloses a big and small model cooperation-based power equipment fault knowledge graph construction method, which comprises the following steps of: preprocessing a power equipment fault document, storing image information into a cloud database, and screening high-quality text information from a big language model; summarizing different text information by using a large language model to generate an initial ontology framework; performing pre-labeling on text information by using a large language model, obtaining a training data set in combination with rule matching scripts and experts, optimizing a traditional deep learning small model based on data characteristics to obtain entities, and eliminating error entities based on a hierarchical verification strategy; utilizing a large language model to carry out relation extraction on association degrees between entities and different entity relations; and performing entity alignment by using the large language model, the SBERT model, a depth-first search algorithm and context information to form a final power equipment fault knowledge graph. According to the method, the problems of high labor cost consumption and knowledge redundancy of the knowledge graph in the existing power equipment fault knowledge graph construction process are solved.

The invention discloses an insurance clause knowledge base self-maintenance method, system and device based on a large model, and relates to the technical field of data processing. The method comprises the following steps: obtaining an associated segmentation term fragment set; carrying out vector conversion, and storing the vector into a cloud database to obtain a vectorized cloud knowledge base; after the vectorized cloud knowledge base receives a user retrieval request, performing knowledge base clause priority adjustment through a secondary retrieval mechanism to obtain an updated vector cloud knowledge base; responding to a user retrieval request based on the updated vector cloud knowledge base to obtain a clause fragment retrieval result; and inputting the Prompt construction result and the clause fragment retrieval result into a large language model constructed based on an RAG enhanced generation technology to perform retrieval enhanced generation, and obtaining a target retrieval interpretation analysis result. The technical problems that in the prior art, an insurance clause knowledge base is low in maintenance efficiency and poor in retrieval accuracy are solved, and the technical effect of improving the self-maintenance efficiency and the retrieval accuracy of the insurance clause knowledge base is achieved.

The invention relates to the technical field of energy storage battery control, in particular to a high-reliability low-attenuation energy storage battery management system based on intelligent control, and the system comprises a data collection module which is used for collecting battery temperature, environment temperature, SOC (state of charge), SOH (state of health) and external mechanical pressure information; the model construction module is used for constructing a battery attenuation risk dynamic coupling evaluation model and generating a battery safety and attenuation risk dynamic evaluation diagram; the self-adaptive adjustment module adopts a cross-dimension multi-stage cooperative control strategy algorithm, temperature compensation current control and SOH grading current limiting linkage are combined, and SOC dynamic charging optimization and mechanical pressure early warning are coupled; and the evaluation report module adjusts control parameters through a dynamic optimization algorithm, generates a battery health state evaluation report, synchronously uploads the battery health state evaluation report to a cloud database, and establishes a full-life-cycle battery attenuation risk file. Therefore, the problems that in the prior art, a risk assessment model is large in limitation, a control strategy lacks collaboration, and attenuation risk prevention and control lags are solved.

The invention discloses an intensive rehabilitation treatment decision method based on virtual reality, and relates to the field of medical monitoring, and the method comprises the steps: collecting user physiological state parameters in real time, creating a cloud database, and storing the user physiological state parameters collected in real time based on the cloud database; according to the method, the user physiological state parameters stored in the cloud database are traversed, the wooden barrel model is created based on the user physiological state parameters, and the standard wooden barrel model is synchronously created. The dynamic wooden barrel model is created by collecting the physiological parameters in real time, and accurate comparison with the standard model is achieved in a normalization processing and three-dimensional space alignment mode; the communication area and the risk area can be visually recognized, the abstract limitation of traditional data monitoring is broken through, and dynamic and accurate allocation of medical care resources is achieved in combination with a mobile equipment distance scheduling mechanism.

The invention discloses a power storage performance attenuation and variation detection method for an energy storage battery, and relates to the field of energy storage batteries. The method comprises the following steps: deploying a terahertz time-domain spectroscopy system and a microphone array on the surface of the energy storage battery, based on a terahertz time-domain spectroscopy system and a microphone array, acquiring a three-dimensional imaging map and a voiceprint signal of the energy storage battery at the beginning stage of charging the energy storage battery each time; creating a cloud database, and storing the three-dimensional imaging map and the voiceprint signal of the energy storage battery by using the cloud database; according to the method, terahertz time-domain spectrum three-dimensional imaging and voiceprint signal bimodal data are fused, battery global information acquisition is realized through distributed array scanning and matrix deployment, and a multi-dimensional attenuation evaluation model is constructed by combining dynamic storage and comparative analysis of a cloud database, so that the accuracy of battery attenuation evaluation is improved. A dynamic weight adjustment mechanism and ontology state information correction are introduced, so that the attenuation index estimation precision is remarkably improved.

The invention relates to the technical field of intelligent battery replacement recommendation, and discloses an intelligent battery replacement recommendation method and system based on battery historical data, and the method comprises the steps: a vehicle central control system carries out the vehicle identity recognition of a battery replacement request signal triggered by a user, and obtains the parameter information of a current vehicle; querying battery historical matching data corresponding to the parameter information from a cloud database, and calculating the suitability score of each battery model and the current vehicle; the battery replacement cabinet control system carries out multi-target collaborative recommendation decision making on the suitability score and the inventory battery to obtain an optimal recommended battery identifier; the battery replacement cabinet opens the corresponding bin door to execute the battery replacement process according to the optimal recommended battery identifier, and a battery replacement operation record is generated. The accuracy of battery suitability evaluation is improved, the performance association relationship between different battery models can be found, and the automation and intelligence level of the whole battery replacement process is improved.

The present disclosure provides techniques and solutions for more efficiently managing computing resources used for cloud database instances. Operational data for shared cloud database instances and dedicated database instances can be monitored, including on a continuous basis. The operational data can be compared with one or more thresholds. If the operational data indicates that an entity assigned to a dedicated cloud database instance is underutilizing its assigned resources, it can be migrated to become a tenant of a shared cloud database instance. If the resources of a shared cloud database instance are overutilized, a tenant can be migrated to a dedicated cloud database instance. Migration can include copying data from the original cloud database instance to a migration target cloud database instance. After migration, computing resources can be released that were assigned to the dedicated cloud database instance or to a particular tenant of the shared cloud database instance.

The invention discloses a hydropower engineering electromechanical equipment installation detail control method based on a three-dimensional laser scanning technology, and the method comprises the steps: S1, setting and determining the instrument parameters of a three-dimensional laser scanner, and collecting the point cloud data of a target region through the three-dimensional laser scanner; s2, remotely reading the point cloud data through a computer, uploading the point cloud data to a cloud database in a file stream form, and storing the point cloud data; s3, performing iterative fitting on the plane model by using a random sample consensus algorithm RANSAC and removing inner points, fitting a target ball model by multiplexing the RANSAC algorithm in the residual point cloud data after the plane model is removed, and screening target ball parameters with the maximum number of inner points; s4, constructing a combined reference model based on the electromechanical equipment to be installed and the virtual target ball, and performing coordinate deviation value comparison with the actually measured target ball model to obtain a deviation value; and S5, generating an installation detail adjustment scheme according to the coordinate deviation value, and sending the adjustment scheme to a mobile client of the field installation personnel.

The invention discloses a swimming pool water quality management system and method based on intelligent regulation and control of residual chlorine concentration, and belongs to the technical field of swimming pool water quality management. According to the system, water quality parameters are monitored in real time at the frequency of one second through residual chlorine, pH, turbidity and water temperature sensors, after filtering processing is conducted through a data acquisition module, the adding amount is dynamically calculated through an intelligent adding algorithm in combination with a preset residual chlorine target range, and then accurate adding is conducted through a high-precision metering pump. Meanwhile, the water quality comprehensive management unit automatically adjusts the pH value to a preset range, and a flocculating agent is regularly added to reduce the content of organic matters. When the residual chlorine concentration is lower than 0.3 mg / L or higher than 1.0 mg / L, early warning is triggered, and all monitoring data, adding records and early warning information are synchronously stored in a local server and a cloud database; water quality real-time monitoring and dynamic accurate regulation can be realized, and safety and stability of water quality of the swimming pool are guaranteed.

The invention relates to a medical data screening system and method based on AI auxiliary interrogation, the screening system at least comprises a client APP, an AI client and a cloud which are mutually connected, compared with the prior art, the system fully combines the advantages of AI, cloud storage, a user intelligent terminal and an offline diagnosis and treatment end, and realizes data acquisition, classification, fusion and interaction; the upstream of the AI client side can allow a patient to input basic data and image data, the downstream of the AI client side can call a cloud database, pathological data can be accurately screened through feature comparison, and pathological similarity suggestions are provided; in the high-incidence period of infectious diseases, the screening system or the screening method can be used for shunting hospitals, limited medical resources are fully allocated, and cross infection is avoided; by means of big data screening and online and offline diagnosis and treatment combination, the pathology screening accuracy is improved, accurate diagnosis and treatment suggestions can be given, and the medical diagnosis service experience is improved.

The invention discloses a chronic obstructive pulmonary disease high risk dynamic assessment model system based on an artificial intelligence algorithm, relates to the technical field of chronic obstructive pulmonary disease high risk assessment, and aims to solve the technical problem that the timeliness and accuracy of an assessment result of an existing assessment model are limited. The medical data acquisition module is used for acquiring medical data of a subject through a wearable device, an electronic medical record system or a manually input multi-source channel, generating standardized preprocessed medical data after performing format cleaning, missing value filling and abnormal value filtering on the data, and transmitting the standardized preprocessed medical data to the memory of the cloud database; and the artificial intelligence dynamic assessment model construction module is used for constructing an artificial intelligence assessment model fusing time sequence characteristics and a nonlinear relationship based on the preprocessed medical data, and generating an intermediate result and a final parameter of risk assessment through multi-level calculation. The method has the advantage that the high risk of chronic obstructive pulmonary disease can be dynamically evaluated in real time.

An elevator health state online monitoring method includes: acquiring parameters such as a position, a three-axis acceleration, an inclination angle, and a noise of a car in a running state of an elevator, left and right brake temperatures of a traction machine, and an environment temperature. Parameter measuring devices are installed on a top of the car and at the brake side of the traction machine. The parameters are collected to a cloud database through a wireless network after being acquired, and a judgment result is given through background automatic analysis. The method is mainly used for monitoring whether key mechanical parts of the elevator are abnormal or not, including guide rail deformation, guide shoe abrasion, car sliding, brake abrasion, adhesion, and the like. This way the health state of the elevator can be automatically monitored, and early warning can be carried out in time when abnormity is found.

The invention relates to the technical field of big data, and discloses a teaching big data resource management system based on a cloud database, the system integrates a data servicing unit, a cloud base unit and an intelligent center unit through a layered distributed architecture, a closed-loop service system is constructed, and the system depends on the elastic storage capability of the cloud database to realize resource management. A teaching scene load is sensed in real time through an elastic scheduling module, database computing resources and storage space are dynamically adjusted by using a storage resource expansion model and a resource dynamic allocation function, automatic capacity expansion in a high-load period and accurate capacity reduction in a low-load period are realized, the problems of performance bottleneck and resource idleness of a traditional scheme are avoided, and the teaching efficiency is improved. And meanwhile, data security is guaranteed through cross-regional synchronization, encrypted storage and disaster recovery backup, intelligent analysis and recommendation of teaching big data are realized in combination with a knowledge graph and machine learning, elastic and efficient cloud database service and big data resource service management capability are provided for a teaching scene, and resource utilization efficiency and teaching service quality are improved.

The invention discloses a battery cell abnormity determination method and system based on BMS data driving, and the method comprises the steps: collecting multi-dimensional battery cell operation data through employing a plurality of sensors and a BMS, and carrying out the preprocessing of the collected data, and forming a basic data set; performing data dimension reduction and feature extraction on the obtained basic data set, comparing an extracted feature value with a dynamic adaptive threshold obtained based on a dynamic threshold adjustment mechanism to obtain a cell health state evaluation result, generating an abnormal early warning signal according to the cell health state evaluation result, and positioning a potential fault point; and performing exception classification on the exception early warning signals, generating maintenance suggestions, generating exception records and storing the exception records in a cloud database. According to the invention, accurate identification and rapid response to the abnormity of the battery cell are realized.

Systems and methods for integrating generative artificial intelligence (AI) with real-time data streaming platforms in distributed computing environments are disclosed. A real-time streaming platform receives a natural language input from a client device, stores a corresponding text request in a topic, and generates a prompt using a processing engine. The prompt is provided to a generative AI system, which generates a structured query language (SQL) query. The SQL query is stored in the topic and executed on a cloud SQL database to obtain an SQL result. The SQL result is stored in the topic and a response based on the SQL result is transmitted to the client device. This approach leverages real-time data streaming, automated prompt generation, and AI-driven query construction to facilitate accurate and timely access to distributed data sources.

The present invention relates to a system method system based in a IoT (Internet of Things) platform for monitoring solids treatment machinery in fertilizer, mineral, or chemical sectors, which allows obtain information about the machines state and performance remotely and automatically, and in real time. This invention combines a set of sensors that measures different parameters such as position, vibration or temperature of different machinery component; with a network that receives that information and stores it into a cloud database which and an app to check said information.

The invention relates to the technical field of intelligent monitoring of production equipment, in particular to a convenient food production equipment monitoring management method and system. The method comprises the following steps: collecting multi-source data in real time through a multi-dimensional sensor; preprocessing the collected original data; the running state of the equipment is monitored in real time; the abnormal state of the equipment operation state is identified, grading is carried out according to the abnormal severity, and early warning is carried out; generating a maintenance plan, and automatically matching a maintenance work order according to the early warning level; storing equipment operation data, maintenance records and fault cases in a cloud database; the system comprises a data acquisition module, a data processing module, an equipment monitoring module, an abnormity early warning module, a maintenance module and a storage module. Through the above mode, the abnormal state can be recognized in time, and the graded early warning is performed according to the abnormal severity, so that the maintenance personnel can preferentially process the fault which has great influence on the equipment, and the fault processing efficiency and pertinence are improved.

The invention discloses a physical random anti-counterfeiting generation and identification method, which relates to the technical field of anti-counterfeiting, and realizes high-safety, low-cost and easily-integrated anti-counterfeiting and traceability management by generating a unique physical texture through double-overprint micro-dislocation and combining multi-light-source acquisition, phase feature extraction, self-supervised alignment and dynamic threshold judgment. The system can be seamlessly connected with production management systems such as MES, WMS and the like, and supports full-link digital tracing. A unique physical texture is automatically generated on the basis of digital printing double-overprint micro-dislocation, an exclusive fingerprint is injected into each hang tag, binding with a cloud database is carried out through a two-dimensional code, production end generation, circulation end tracking and consumption end one-key verification are achieved, and safety, economical efficiency and large-scale landing are considered; on the premise that the printing cost is not obviously increased, a unique physical fingerprint which cannot be transferred, is difficult to copy and can be quickly read by a machine is generated for each clothing tag, and credible data mapping which runs through factory delivery, circulation and terminal verification is constructed.

The invention relates to the technical field of Internet of Things, and discloses a multi-user equipment access and control method based on a smart cloud platform. The method comprises the following steps: the Internet of Things equipment sends an access request to a smart cloud platform through a network, distributes an access token after verification, and stores equipment information to a cloud database; after the user logs in for the first time, the platform loads a device list according to user account information and a device binding relation, and creates an independent account space; a user sends a control instruction through the terminal, and the platform verifies user permission and converts the instruction into a protocol format which can be recognized by target equipment; the target device executes an operation and feeds back a result, and the platform updates and pushes the result to the user terminal in real time; and the platform continuously monitors the state of the equipment, analyzes and evaluates the state, triggers an early warning mechanism when an abnormity occurs, notifies a user and provides a solution, and the overall efficiency and safety of the system are improved, and the compatibility of multi-user control and the user experience are improved.

The invention discloses an intelligent lighting dynamic operation and maintenance management system and method based on digital twinning, relates to the technical field of public safety monitoring and intelligent analysis, and solves the technical problem of lack of construction of a digital twinning model for outputting an operation and maintenance decision by fusing environment, operation and safety features. Comprising a data acquisition module, a data processing module, a data storage module, a data analysis module and an intelligent lighting digital twinning dynamic operation and maintenance decision module. Environment, operation state and image safety data are collected and transmitted to a cloud database through preprocessing and encryption, then an illumination environment comprehensive influence evaluation value is calculated by analyzing environment data, an operation state comprehensive influence evaluation value is calculated by analyzing operation state data, and an illumination safety comprehensive influence evaluation value is calculated by analyzing image safety data. And finally, constructing a digital twin model based on multiple evaluation values, outputting an operation and maintenance decision result after training, and dynamically adjusting a strategy according to real-time data.

The invention discloses an infant cry detection and translation system based on voice feature recognition in the technical field of voice recognition. The infant cry detection and translation system comprises a cloud database, an intelligent system and acquisition equipment, the intelligent system comprises an acquisition module, a preprocessing module, an analysis module, a translation module, a feedback module, an input module and an adjustment module. The acquisition module is used for acquiring environment sound and various indexes of a baby; the preprocessing module is used for preprocessing voiceprint information; the analysis module is used for calling the cloud database, analyzing and judging the infant crying reason and analyzing the environment sound condition; the translation module is used for translating the baby cry and sending suggestions to the guardian terminal; the feedback module is used for receiving feedback information; the input module is used for recording feedback information and various indexes of the baby; and the adjusting module is used for adjusting the system sensitivity. According to the system, sound information of the environment where a baby is located is collected through the collection module and is analyzed by the analysis module, and finally the sensitivity of the system is adjusted through the adjustment module.

Systems and methods include storage of database table shards on a plurality of storage nodes. Each shard may be stored as multiple replicas, where each replica of a given shard is stored on a different storage node. In case of a failure of a given storage node, all shard replicas which were stored on the given storage node are copied from other storage nodes to new or existing storage nodes. A storage node may store shards of database tables associated with multiple tenants.

The invention belongs to the technical field of child visual management, and particularly relates to a child and teenager visual development monitoring management system, which comprises a data acquisition module configured to acquire child visual parameters through intelligent terminal equipment, including diopter, naked eye vision, ocular axis length and corneal curvature; the data processing module is connected with the data acquisition module and is used for executing multi-modal data fusion processing and generating a comprehensive evaluation report containing a vision trend predicted value; the user terminal module is configured to provide hierarchical visual interfaces for parents and educational institutions and display vision evolution curves and intervention suggestions; a cloud database; and an early warning decision module. The monitoring management system can check vision changes and give an analysis chart according to changes of ages, heights and the like of children, parents can be reminded to carry the children to a hospital for special eye refraction examination by opening the system, and doctors can give a correction scheme in time.

The invention relates to internet data services such as databases and cloud database services, more particularly belongs to the field of internet and cloud computing and big data services, and particularly relates to a method and a system for realizing intelligent sorting of data assets based on a self-research algorithm, which comprises the following steps of: constructing an intelligent sorting link of a target database; generating an initial field description set of the target database through a field description generation module of the intelligent carding link; according to the semantic consistency consensus level and the field description accuracy of the initial field description set, establishing a quality evaluation matrix corresponding to the intelligent carding link; according to the quality evaluation matrix, reconstructing a dynamic increment Schema processing mechanism corresponding to the intelligent carding link; and performing vectorization storage on the data assets finally confirmed by the dynamic increment Schema processing mechanism to complete data asset sorting of the business system to be sorted. According to the method, the storage result of the data assets can be ensured, and database Schema dynamic change in a complex business scene can be accurately adapted.

The invention discloses a multi-cloud database consistency automatic inspection method, system and device, and the method specifically comprises the steps: configuring a detection process sequence through an integrated Blockly graphical designer based on early warning information; executing the detection process sequence in an encryption environment, and collecting the instance state, the connection number and the parameter configuration data of the multi-cloud database to form database instance data; analyzing database instance data by using an SHAP framework, and calculating feature importance to locate a root cause causing inconsistency of detection items; and establishing an incidence relation network between the database instance and the application service based on a knowledge graph technology, and positioning an abnormal root cause to a specific service module in combination with the positioned root cause. According to the invention, consistent automatic inspection of the multiple cloud databases is realized, connectivity risks are early warned in advance, abnormal root causes are accurately positioned to specific service modules, and stable operation of the multiple cloud databases is guaranteed.

The invention relates to an electromechanical system energy efficiency scheduling method based on edge-cloud collaboration, and relates to the technical field of computer processing, and the method comprises the following steps: S01, collecting motor operation parameters, environment data and energy consumption data in real time through edge devices deployed at all nodes of an electromechanical system to obtain original data at the same time bit, processing the original data by using an edge computing platform to obtain first processed data; s02, deploying a lightweight energy efficiency analysis model on an edge computing platform, calculating a current energy efficiency value of the motor in real time based on the first processed data, and if the energy efficiency is detected to be lower than a preset threshold value, triggering a local optimization strategy and recording optimization effect data; and S03, storing the optimization effect data uploaded by the edge device into a cloud database. According to the method, efficient, accurate and adaptive management of the energy efficiency of the electromechanical system is realized by constructing an intelligent scheduling architecture with edge real-time response and cloud global optimization collaboration.

This invention relates to the field of primary heating network technology, specifically disclosing a centralized heating primary heating network energy supply balance operation regulation monitoring and control system. The system includes a user area estimated heat demand analysis module, a user area comprehensive heat demand analysis module, an energy station heat distribution module, an energy station heat transmission detection module, an energy station transmission quality assessment module, a display terminal, a protection terminal, and a cloud database. This invention ensures the accuracy and precision of the actual heat supply analysis for user areas, thereby improving the heating efficiency and effect of energy stations. It safeguards the revenue of energy stations on one hand and ensures normal use on the user side on the other. This invention alleviates the pressure on energy stations while meeting the heating needs of users, thereby improving the relative balance of energy stations, reducing the deviation between heat supply and heat demand to a certain extent, and improving the efficiency of centralized energy supply.

The invention discloses an intelligent software management system of RFID, and relates to the technical field of intelligent software management. Comprising a login and safety module, a home page billboard module, a regional warehouse project management module, a data information management module, an inventory management module, a warehouse-in and warehouse-out management module, a warehouse checking module, a project inspection module, a user authority management module, a handheld terminal interface module and an intelligent abnormity identification and early warning module. By integrating the RFID scanning technology, the system can realize rapid identification and information input of the pipeline, automatically generate operation records and synchronize the operation records to a cloud database, so that the operation efficiency and the data accuracy are effectively improved; the intelligent software management system of the RFID comprises a mobile terminal operation interface and a cloud visual platform, efficient management, real-time monitoring, intelligent analysis and safety early warning of the whole life cycle of the pipeline are achieved by integrating a plurality of functional modules, and the management efficiency and decision scientificity are remarkably improved.

The invention discloses a method for realizing lossless switching of agent tasks between basic large models, and relates to the technical field of artificial intelligence. Then analyzing and extracting information from the agent tasks of which the hierarchical categories are determined through an associated metadata template, and carrying out standardization processing to obtain a plurality of standardized agent tasks; processing the standardized agent tasks by a pre-constructed basic large model, recording a processing condition, evaluating and optimizing a basic large model sequence; when an agent task to be processed is processed, the state is processed and monitored according to the optimized basic large model sequence associated with the hierarchical category of the agent task, and the lossless processing flow of the agent task is realized in a mode of lossless switching of the model in the sequence when the state is abnormal.

The invention relates to the technical field of printing equipment, and discloses a control method of thermal printing or thermal transfer printing equipment, a storage medium and a system, and the method comprises the steps that the printing equipment obtains model parameters of a printing medium; the printing equipment obtains environment parameters around the printing equipment; the printing equipment sends the model parameters to the cloud service platform; if the hit item of the model parameter exists in the cloud database of the cloud service platform, the cloud service platform returns a cloud printing parameter corresponding to the model parameter to the printing equipment; if the printing equipment receives the cloud printing parameters, the printing equipment inputs the cloud printing parameters and the environment parameters into an individual correction model to obtain corrected printing parameters, and the individual correction model is obtained by training correction behaviors, collected by the printing equipment, of the user on the cloud printing parameters under different environment parameters; and the printing equipment sets the corrected printing parameters as actual printing parameters of the printing equipment. According to the method, the printing parameters conforming to user preferences can be automatically configured, and the printing experience is improved.