355 results about "Data cloud" patented technology

The invention discloses a battery full life cycle intelligent management method and system based on a large model, and the method comprises the following steps: S1, collecting the operation data of a battery, and carrying out the preprocessing; s2, inputting the pre-processed sample into a pre-trained Transform encoder model, and extracting a time sequence and cross-stage characteristics; s3, executing model reasoning, outputting a state index, and generating a battery state vector; s4, identifying an abnormal category and a position in combination with the working condition information; s5, generating a dynamically optimized battery management strategy based on the battery state index and the abnormity identification result; s6, deploying a lightweight model at the edge device, executing local reasoning and uploading data; s7, the cloud updates the model through self-supervised training and issues the model; and S8, repeatedly executing the steps S1 to S7, and carrying out optimized closed-loop management. According to the invention, large model modeling and an edge cloud cooperation mechanism are fused, and intelligent sensing and dynamic management of the whole life cycle of the battery are realized.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention discloses an intelligent fitness method and system. The intelligent bodybuilding method comprises the steps that the bodybuilding equipment receives a bodybuilding situation instruction and provides a corresponding motion mode for a user; the fitness equipment collects fitness data of the fitness equipment and feeds back the fitness data to the terminal; the terminal collects the fitness data and sends the fitness data to the cloud server; the cloud server processes the fitness data based on a pre-trained model and returns an interaction command to the terminal according to a processing result; and the terminal sends the interaction command returned by the cloud server to the fitness equipment, so that the fitness equipment updates the exercise mode of the fitness equipment. According to the technical scheme, efficient transmission and comprehensive analysis of the fitness data can be realized through interaction of the fitness equipment, the terminal and the cloud server, and a proper fitness situation instruction can be given by utilizing the pre-trained model in combination with the fitness condition of the user, so that the exercise mode is dynamically adjusted, and the user experience is improved. Personalized and intelligent fitness services and fitness guidance are provided for the user, and the fitness experience of the user is improved.

The invention relates to the technical field of lithium battery energy storage and temperature control, in particular to an electronic radiator intelligent temperature control system based on a lithium battery energy storage cabinet. The system comprises an edge temperature sensing module, a cloud thermal management strategy module, a cloud clustering analysis module, a thermal field topological optimization module, a multi-source thermal feature fusion module, a dynamic temperature control strategy generation module and a grading strategy issuing execution module. An edge temperature sensing module collects real-time temperature data of multiple monitoring points and an edge side preliminary temperature control scene judgment result; the cloud heat management strategy module receives battery pack topology connection data; the cloud clustering analysis module carries out fuzzy clustering on the data to obtain a temperature state and scene discrimination clustering center; the thermal field topological optimization module optimizes a temperature state clustering center in combination with topological data; the multi-source thermal feature fusion module fuses the two clustering centers to form multi-source fusion feature representation; the dynamic temperature control strategy generation module determines a temperature control strategy identifier accordingly, and the grading strategy issuing execution module matches and distributes a gradient temperature control strategy to a heat dissipation actuator.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention discloses a gas safety intelligent inspection method and system, and belongs to the crossing field of artificial intelligence technology and safety production management. The method comprises the following steps: acquiring image data through a field data acquisition terminal; a preliminary hidden danger identification module in the cloud server innovatively adopts a scene perception target detection model and a scene reasoning multi-label model for collaborative analysis; and the comprehensive risk assessment module performs deep correlation analysis on the hidden dangers based on a preset gas safety knowledge graph, and determines a quantitative comprehensive risk value by calculating a basic risk accumulated value of each hidden danger and a correlation risk increment generated by a correlated hidden danger pair. According to the method, the defects that traditional inspection depends on subjective experience and composite risks are difficult to quantify are overcome, amplification risks caused by concurrence of multiple hidden dangers can be accurately identified and quantitatively evaluated, continuous self-improvement of system capability is realized through a closed-loop mechanism, and the intelligent level of gas safety management is remarkably improved.

The invention relates to the technical field of bearing fault monitoring, and discloses a large-scale equipment bearing fault intelligent monitoring method, which comprises the following steps: S1, real-time data acquisition: deploying multi-source sensing equipment, acquiring bearing operation parameters in real time, setting a data cloud platform access layer, and integrating flaw detection data by adopting an OPC UA protocol; the invention further provides a large equipment bearing fault intelligent monitoring system which comprises a three-axis vibration acceleration sensing module, a temperature monitoring module, a lubricating oil online monitoring module, an installation state monitoring module, an environment auxiliary monitoring module and a data integration and intelligent analysis module. And bearing fault pre-judgment is realized, interpretation errors caused by manual intervention are reduced, complex working conditions can be adapted, the stability and reliability of a monitoring system are ensured, and thus the influence of equipment faults on production activities is reduced.

The invention discloses a distributed RID receiving system based on edge multi-source fusion, which relates to the technical field of low-altitude supervision, and is characterized in that a distributed RID receiving node cluster adopts a fixed node and maneuvering node collaborative grid deployment scheme to capture and preliminarily analyze low-altitude aircraft RID signals in real time, and node data is output after the low-altitude aircraft RID signals are processed by a primary fusion algorithm; the edge multi-source fusion processing unit is deployed in each management and control partition, converges node data in the area, completes calibration, de-duplication and noise reduction through a secondary fusion algorithm, and outputs structured RID data; the cloud management and control platform realizes global data overall planning, situation visualization, intelligent decision making and instruction issuing through a three-level fusion algorithm, and is linked with the urban low-altitude scheduling system to form closed-loop management and control; the clock synchronization module provides nanosecond time reference for the whole system. According to the method, the problems of incomplete coverage and insufficient data processing precision of a traditional RID receiving scheme are solved, and global sensing, accurate data processing and closed-loop management and control of the low-altitude aircraft are realized.

The invention relates to the technical field of new energy, in particular to an intelligent charging gun and a charging method. An off-line adaptation algorithm unit identifies CP signal characteristics of a vehicle through a built-in standard library of a CP signal identification module and outputs a vehicle type identifier, and a protocol compatibility algorithm library matches charging protocol characteristic parameters according to the vehicle type identifier; after the protocol conversion unit receives the parameters, voltage thresholds, PWM duty ratios and communication message formats among different protocol standards are converted in real time through a preset mapping table; when the off-line adaptation fails, the system triggers the on-line auxiliary adaptation algorithm unit, and the fault detection module monitors handshake failure, identifies protocol incompatibility characteristics and outputs a fault signal; the data recording and broadcasting module records data in the charging starting process, the cloud communication module uploads the data to the algorithm repairing platform and receives an updating package, and finally the protocol compatibility algorithm library is updated. According to the invention, intelligent conversion and continuous learning of the charging protocol are realized, and charging compatibility and system adaptability are significantly improved.

The invention discloses a fire-fighting smoke exhaust pipeline laying planning system based on an industrial cloud platform, and relates to the technical field of pipeline laying planning, and the system comprises a building information collection module which collects building data; the cloud edge cooperative computing module is used for constructing a smoke exhaust simulation model by using distributed computing resources of an industrial cloud platform and carrying out real-time parameter iteration through edge nodes; the pipeline path planning module is used for generating a plurality of candidate laying paths by using an improved algorithm and carrying out path optimization based on a pipeline resistance coefficient and a construction cost constraint condition; and the scheme verification and optimization module is used for carrying out smoke flow dynamics simulation verification on the optimal path scheme, identifying scheme defects and carrying out iterative optimization on path parameters through a genetic algorithm. According to the invention, through a cloud-edge collaborative intelligent computing architecture, a multi-dimensional constrained dual-target optimization mechanism and a dynamic adaptive path planning and verification system are fused, and collaborative improvement of fire-fighting smoke exhaust pipeline laying planning in accuracy, efficiency and compliance is realized.

The invention provides an air conditioner indoor environment parameter control method and system, and relates to the technical field of air conditioner control. External environment information and internal heat source information such as sunlight intensity data and cloud cover prediction data outside a building and the operation state and real-time power consumption data of large electronic equipment inside the building are obtained; the preset building physical attribute parameters are combined, the predicted total heat load increment of all the areas in the future preset time can be calculated in advance, an adjusting instruction is generated in advance before the indoor environment parameters actually change, the operation state of an air conditioner system executing mechanism is adjusted, accurate and active control can be achieved, and the control efficiency is improved. According to the method, local supercooling or superheating possibly caused by a traditional method is avoided, the comfort consistency of the indoor environment is remarkably improved, meanwhile, the energy utilization efficiency is optimized, and energy waste is effectively reduced.

The invention relates to the field of power transmission line operation and maintenance, in particular to a power transmission line tree obstacle intelligent supervision system and method based on the Internet of Things, and the system comprises an intelligent sensing module, a data cloud transmission module, a fusion prediction module, an analysis decision module and a linkage execution module. According to the coupling prediction, historical data are processed through an algorithm model, future tree growth and conductor wind deviation are predicted, and the scientificity and accuracy of prediction are ensured through cooperation of a state space model and a Kalman filtering algorithm, so that the prediction accuracy is improved through a sensor deployed at the front end and an intelligent algorithm at the cloud end. The device can continuously and automatically monitor for 7 * 24 hours, not only measures the current static distance, but also predicts the future growth trend of the tree based on historical data, and calculates the dynamic windage yaw of the lead by combining the real-time wind speed and wind direction, thereby being convenient to give out early warning before the tree actually approaches the safe distance, realizing the process from post-event disposal to pre-event prediction, and improving the working efficiency. And the preventive safety level of the power grid is improved.

The invention relates to the technical field of battery swap scheduling, and discloses a balanced optimization scheduling method and system for a battery swap network load. The method comprises the following steps of: acquiring the voltage, the temperature, the charge state and the health state of a battery cell in real time by each battery swap station hardware module through a BMS (Battery Management System) acquisition line to obtain battery cell state data; the cloud intelligent control platform performs multi-site fusion analysis on the battery cell state data to obtain a balanced scheduling strategy; the cloud intelligent control platform generates a scheduling instruction of each battery swap station according to the balanced scheduling strategy; and after receiving the scheduling instruction, the hardware modules of the battery swap stations execute multi-stage balancing processing to obtain a load redistribution result. According to the invention, coordination balance and load redistribution among multiple stations in the battery switching network are realized, and intelligent operation management of the battery switching network is further realized.

The invention provides a cooperative emergency disposal method, system and device for a new energy vehicle and a storage medium, and a vehicle end collects and uploads vehicle state data. The cloud server extracts the position of the vehicle, obtains associated external environment data and fire-fighting, traffic and other collaborative party data, then calculates the air pressure and temperature risk values of each monomer, and integrates the voltage data to identify the fault monomer with the highest risk. And based on the fault monomer risk value, the overall state of the battery pack, the environment temperature, the fire-fighting station distance and the traffic state, the cloud determines a vehicle dynamic risk index and divides risk levels, and generates a co-processing instruction in combination with fault monomer information and emergency resources. And the vehicle end and the collaborative party respectively respond to the instruction, the vehicle executes control operation adaptive to the risk level and the fault monomer, and the collaborative party carries out corresponding emergency operation. By the adoption of the method, timeliness and accuracy of new energy automobile safety risk early warning and pertinence and efficiency of disposal operation can be improved.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

The invention provides a photovoltaic module management system based on wireless communication, and the system comprises a plurality of photovoltaic modules, the interior of each photovoltaic module is integrated with an Internet of Things communication module, and the operation data of the corresponding photovoltaic module are collected through the Internet of Things communication module; the access point is in wireless connection with the Internet of Things communication module of each photovoltaic module so as to manage data acquired by each Internet of Things communication module; and the cloud server is connected with the access point so as to receive the operation data of the photovoltaic module or send a control instruction to the photovoltaic module. Wired communication of a cable is replaced by wireless communication, so that the cost is saved. And meanwhile, the power station cloud platform control center controls photovoltaic module controllers such as a turn-off device and an optimizer, the working mode of the photovoltaic module is optimized, and the working efficiency of solar photovoltaic is improved.

The invention belongs to the technical field of intelligent household appliances, and particularly relates to a control method and equipment of water purification equipment and a storage medium. The method comprises the steps that human physiological data of a target user is obtained through cloud equipment, the cloud equipment classifies the target user based on the human physiological data, and the cloud equipment determines a water purification scheme for the target user based on the classification of the target user and the human physiological data of the target user, the cloud equipment sends the water purification scheme to water purification equipment, and the water purification equipment adjusts the water purification equipment based on the water purification scheme and a raw water detection result; according to the method, the water quality of the water purification equipment is adjusted according to the intake requirements of different types of user groups on trace elements, the intelligence of the water purification equipment is improved, and the user experience is improved.

The invention relates to the technical field of compressor unit data safety management, in particular to a data safety management method and system for compressor unit operation state diagnosis, and the method comprises the following steps: collecting compressor unit sensor data in real time, and processing basic, sensitive and core three-level authority data at edge equipment; after receiving a cloud diagnosis request, dynamically opening a corresponding data channel and calling data; the cloud monitors the access behavior of the diagnostic tool and fuses unauthorized access in real time; and recording channel opening and tool operation logs, and associating real-time alarm events of the unit. Physical operation characteristics and safety logic are fused, edge preprocessing-cloud dynamic authorization-state linkage fusing-closed-loop optimization full-chain protection is achieved, fusing timeliness is guaranteed, sensitive data exposure is reduced through on-demand uploading, key characteristics are retained based on rotor dynamic characteristic desensitization, diagnosis accuracy is ensured, and the method has the advantages of being high in safety and high in reliability. And the high-priority diagnosis transmission delay is reduced through linkage of the bandwidth and the risk coefficient.

The invention provides an oil depot state low-cost monitoring method and system based on the Internet of Things, and relates to the technical field of oil depot detection, and the method comprises the steps: deploying a sensor group at the position of an oil depot storage tank, and collecting original monitoring data; preprocessing the collected original monitoring data, and then uploading the data to a cloud server through an Internet of Things gateway; based on the received data, the cloud server performs inversion calculation on the real-time liquid level height of the storage tank through an intelligent algorithm, and performs calibration calculation on the volume of the special-shaped storage tank; state fusion analysis and abnormity diagnosis are carried out based on the real-time liquid level height, the volume data and the multi-source environment data; and triggering graded early warning according to a preset rule based on results of analysis and abnormality diagnosis. According to the method provided by the invention, hardware deployment cost is revolutionarily reduced, and all-around efficiency leap from manual regular inspection to unmanned real-time monitoring, from single parameter measurement to multi-source fusion diagnosis, and from passive reading to active early warning is realized.

The invention provides a low-carbon strategy generation method based on carbon emission factor prediction. The method belongs to the technical field of power system low-carbon strategies. The method comprises the steps that a terminal layer collects power generation side output data, user side load data and environmental meteorological data of a power system; the cloud layer inputs power generation side output data, user side load data and environmental meteorological data into a dynamic carbon emission factor prediction model to generate a carbon emission factor prediction curve; the carbon emission factor prediction curve is input into a low-carbon regulation and control optimization model with the goal of carbon emission minimization, and a long-term low-carbon control strategy is obtained; and based on the peak valley characteristics of the carbon emission factor prediction curve, the long-term low-carbon control strategy and the user load characteristic spectrum, determining a low-carbon strategy meeting the power grid carbon emission reduction target and the user energy consumption demand. According to the method, the dynamic prediction of the carbon emission factor can be realized, the low-carbon energy consumption strategy is generated in combination with the user side demand, and the accuracy of carbon emission metering and the effectiveness of participation in emission reduction by the user are improved.

The invention relates to the technical field of automobile safety control, and provides an intelligent network connection automobile safety control system and method based on multi-source sensing fusion, and the system comprises a multi-source sensing module, a data processing module, and a cooperative control module. The multi-source sensing module comprises a sensor group and a data fusion unit, the sensor group senses information data and transmits the information data to the data fusion unit, and the data fusion unit performs fusion learning on the information data; the data processing module comprises a dynamic risk assessment module, an edge computing node and a cloud server, the dynamic risk assessment module analyzes and predicts data fused by the data fusion unit, the edge computing node is responsible for processing real-time data, and the cloud server performs global traffic flow optimization and historical accident data analysis; the cooperative control module comprises an active control layer and a passive control layer; the active control layer receives data processed by the edge computing node and performs active control; and the passive control layer receives data processed by the edge computing node and performs passive control.

The invention relates to a vehicle data processing system and a vehicle, the system comprises a vehicle end data module and a cloud service module which are in communication connection, subunits of the vehicle end data module comprise a gateway unit, a cabin domain controller unit, an intelligent driving domain controller unit and a vehicle-mounted communication unit, the cabin domain controller unit is in communication connection with the gateway unit, the intelligent driving domain controller unit and the vehicle-mounted communication unit. The vehicle end data module is used for collecting various initial vehicle data and processing and transmitting the initial vehicle data to obtain corresponding target vehicle data; the cloud service module is used for receiving and processing the target vehicle data transmitted by the vehicle end data module, generating a corresponding remote control instruction and sending the remote control instruction to the vehicle end data module; at least one subunit in the vehicle end data module is used for executing the received remote control instruction; the effectiveness and richness of cloud data can be improved, so that the recognition and solving efficiency of abnormal conditions of vehicles is improved.

The invention relates to the technical field of intelligent medical care, and discloses a drainage port nursing recommendation method and system for a patient with bile leakage. The system comprises a data acquisition module used for acquiring bile drainage data and state data of skin around a drainage port in real time; the cloud decision module is internally provided with a nursing scheme recommendation model associated with a bile leakage nursing knowledge graph, and is used for carrying out fusion analysis on the multi-modal data and generating a personalized nursing plan; the local execution terminal is used for receiving and displaying the nursing plan and guiding nursing personnel to execute and record actual nursing operation data; and the closed-loop feedback module is used for returning the actual execution data and the post-nursing state data so as to optimize the nursing scheme recommendation model and the bile leakage nursing knowledge graph. The technical problems that in traditional nursing, evaluation is subjective, decision-making depends on experience, execution is not standard, and knowledge is difficult to accumulate are solved, and standardization, individuation and continuous self-optimization of bile leakage drainage opening nursing are achieved.

The invention discloses an airplane icing early warning method, and relates to the technical field of icing early warning. According to the airplane icing early warning method, cloud picture data and cloud product file data of a set airplane are obtained, a cloud field operation state feature set of the set airplane is extracted based on the cloud product data of the set airplane, and a cloud liquid water path value of the set airplane is analyzed; based on a pre-trained cloud picture error mapping model and in combination with cloud picture data of the set airplane, analyzing a cloud type temperature correction factor of the set airplane, and in combination with the cloud field operation state feature set, analyzing an optimized cloud top temperature value of the set airplane; based on the cloud liquid water path value of the set airplane, the cloud top temperature value is optimized, and the ice accretion index of the set airplane is analyzed, ice accretion early warning processing is performed on the set airplane based on the ice accretion index, so that the finally generated ice accretion index result has higher environmental adaptability, dynamic correction and high-precision characterization of the ice accretion risk are realized, and the method is suitable for large-scale popularization and application. And thus, the accuracy of ice accumulation early warning is improved.

The invention discloses an ancient mural monitoring and repairing method and system and electronic equipment, and the method comprises the steps: a sensing layer deploys an intelligent camera carrying a lightweight edge intelligent segmentation model, collects a mural image in real time, and carries out the damage segmentation; the network layer adopts a low-power-consumption communication technology to transmit data; the cloud layer runs a generative artificial intelligence repair model to process a complex task; an edge intelligent segmentation model is optimized, the high-frequency feature capture capability is enhanced through an external injection dynamic feature modulation component, the segmentation precision is improved by combining internal fine tuning with residual connection, and the small damage detection sensitivity is improved by adopting a cross entropy and a Dice loss function; performing context sensing repair on small damage by directly utilizing a generative repair model fine-tuned by a parameter efficient fine-tuning framework; for large damage, image description is generated through a visual language understanding model, and a multi-modal large language model is combined to generate semantic prompts to guide a generative repair model to generate repair contents with consistent styles.

The platforms, systems and methods provided herein may provide a data-driven workflow platform. The method comprises: mapping selected data objects to a data storage model of the data-driven workflow platform, where the selected data objects are stored in a data cloud configuration that is operatively coupled to the data-driven workflow platform; and displaying a graphical user interface (GUI) for building a workflow that utilizes or manages the selected data objects, the workflow comprises at least one automated action triggered by a triggering event of the selected data objects and wherein the triggering event is a detected change of the selected data objects based at least in part on an output of a machine learning algorithm trained model.

The invention relates to the technical field of laying hen breeding, and discloses a laying hen breeding accurate feeding adjusting system based on big data cloud-side collaboration. The system comprises an environmental data acquisition module, a cloud edge collaborative analysis module, a multi-dimensional difference evaluation module, an abnormal area positioning module, a feeding strategy adjustment module and an encryption communication link module. The environment data acquisition module acquires chicken flock multi-source data streams through various sensors, and standard chicken flock data is generated after processing; a cloud-edge collaborative analysis module constructs a dynamic feed supply model according to the dynamic feed supply model and outputs theoretical feed demand; the multi-dimensional difference evaluation module performs multi-dimensional comparison on the theoretical value and the actual consumption value to generate a feed difference coefficient matrix; the abnormal area positioning module generates an abnormal feeding probability distribution diagram in combination with farm topological information; the feeding strategy adjusting module generates a self-adaptive feeding control signal based on the graph; the encryption communication link module transmits a control signal to an actuator through a quantum encryption channel. The system realizes accurate adjustment and safety control of feeding for laying hen breeding.

The invention discloses a real-time monitoring and analyzing method and system for underwater settlement deformation, and belongs to the technical field of marine surveying and mapping and deformation monitoring. The method comprises the following steps: calculating wave height, wave period and wave direction elements of waves through a water surface fluctuation inversion model; carrying out denoising processing on the obtained water depth data; a gyroscope and a GNSS buoy are used for dynamic wave measurement, and sea level and wave information is separated through low-pass filtering according to the frequency spectrum characteristic difference of the sea level and waves so as to correct the attitude of the buoy; the buoy measurement data and the water depth data collected by the pressure sensor are synchronized, the water surface height right above the measurement pile is calculated in combination with a water surface fluctuation inversion model, and the real-time elevation of the water bottom is obtained; and according to the water bottom real-time elevation and the water bottom settlement grade division standard, triggering early warning of a preset grade through the data cloud platform. According to the invention, real-time monitoring of underwater settlement deformation is realized, the monitoring period is shortened, the monitoring timeliness is improved, and the underwater settlement risk is found in time.

The invention relates to the technical field of glucometers, and discloses an intelligent multifunctional glucometer supporting data cloud storage and remote analysis, which comprises hardware, detection, data processing, wireless communication, a cloud server platform and an interactive terminal module to form a complete data closed loop. The hardware module is composed of an upper shell and a lower shell which are clamped, and the upper shell is provided with a detection port, an operation button and anti-skid lines. The detection module is integrated with an electrochemical sensor array and can synchronously detect blood glucose, uric acid, ketone body and blood fat. The data processing unit processes data in real time to generate a health report, and the wireless communication module supports multi-mode communication and encrypted transmission. The cloud platform stores data and outputs suggestions through machine learning, and the interactive terminal supports biological recognition and voice broadcast. The modules cooperate to realize multi-index detection, cloud analysis and intelligent interaction. The system has the advantages of multi-index synchronous detection, data security intelligent analysis and convenient interaction, and supports data cloud storage and remote analysis.

The invention relates to the field of battery health monitoring, and discloses a physical mechanism and deep learning fused power battery short circuit risk quantitative evaluation method, which comprises the following steps: acquiring real vehicle operation data from a data cloud platform, cleaning the real vehicle operation data to construct an equivalent circuit model, fitting model key parameters by means of a recursive least square method, and calculating the short circuit risk of a power battery. The processed data is converted into a time sequence format and standardized, a time window is divided to generate a target label, a time convolution network model containing structures of causal convolution, expansion convolution and the like is constructed, the model is trained by using a cross entropy loss function, evaluation is carried out by using indexes such as accuracy, and the trained model can receive multi-dimensional data of the battery in real time. The short-circuit risk probability is predicted, an alarm is triggered when a threshold value is exceeded, high-precision and real-time quantitative evaluation of the short-circuit risk of the power battery is realized, and the operation safety of the battery is guaranteed.