5603 results about "Multiple device" patented technology

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention provides a real-time transmission method and system based on internet-of-things sensing data in a digital twinborn scene, and the method comprises the steps: collecting and processing vibration spectrum data, pressure gradient change data and temperature distribution data of equipment in an industrial production line, generating a preprocessing data package, carrying out the analysis to obtain an equipment state fingerprint feature and an environment working condition label, and carrying out the real-time transmission of the environment working condition label. A target virtual control parameter and a target error compensation amount are generated by using a dynamic parameter correction loop of the digital twin, an actual material flow state is generated in combination with material flow rate data, an optimization strategy sequence of multi-device collaborative operation is generated by using a distributed computing architecture, and the optimization strategy sequence is converted into a track compensation parameter of an end effector in the device; in combination with the target error compensation amount, data closed-loop transmission is carried out; according to the invention, millisecond-level time domain matching of vibration, pressure and temperature data and material flow velocity is realized through a dynamic timestamp alignment mechanism of 5G edge computing nodes, and the problem of synchronization error accumulation of traditional internet-of-things sensing data is solved.

The invention discloses a task integrity judgment method for multi-device cooperative work under complex constraint conditions. The method comprises the steps that a comprehensive data collection system is established, multi-dimensional data of cooperative devices are collected in real time and preprocessed, constraint modeling is combined, and the complex constraint conditions are quantified into a computable constraint condition model; a multi-agent collaborative evaluation model is constructed, each device participating in collaborative work is regarded as an agent by the multi-agent collaborative evaluation model, and evaluation of the integrity of the whole task is achieved by simulating the collaborative process between the devices; automatically extracting advanced features from the multi-dimensional data by using a deep learning technology; and a distributed real-time analysis system is designed, and large-scale data processing and continuous model optimization are realized in combination with an adaptive optimization mechanism. According to the method, the reliability and efficiency of the cooperative task can be improved, powerful support can be provided for equipment task decision making, and the method plays an important role in improving the intelligent level of application guarantee and the decision making accuracy.

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention discloses a BIM (Building Information Modeling)-based building energy efficiency optimization system and method, particularly relates to the field of building energy efficiency management, and is used for solving the problems of dynamic optimization and collaborative maintenance of building equipment aging and energy efficiency reduction. Initial attenuation data, historical operation data and environmental influence parameters of the equipment are embedded into the data, and dynamic adjustment and real-time tracking of the aging state of the equipment are realized in combination with a sensor network and edge calculation. The maintenance period of the equipment is predicted based on a random forest regression algorithm, a collaborative maintenance plan is generated in combination with physical position information in the BIM, and load optimization and time sequence coordination during multi-equipment maintenance are ensured. By calculating the dynamic adaptability nonlinear response coefficient and the performance regeneration residual nonlinear deviation coefficient, the load adjustment opportunity is accurately judged, the load distribution between the equipment is optimized, the overall energy efficiency of the building is effectively improved, and the full-life-cycle management and energy efficiency optimization of the building equipment are realized.

The invention discloses a hydropower station equipment fault analysis method based on state data mining, and relates to the technical field of hydropower station equipment intelligent fault diagnosis, and the method comprises the steps: collecting key operation parameters through deploying multiple types of sensors, and constructing a unified state time series data set; carrying out supervised training by adopting an LSTM network, extracting a dynamic feature vector, and constructing an AI state analysis model; introducing a micro-fluctuation abnormal coefficient WBYX, and evaluating the operation stability of the equipment; a coupling disturbance collaboration coefficient OHRD is calculated, and a fault conduction relation among multiple devices is identified; and calculating a trend evolution coefficient QSYH based on the state vector included angle offset, and analyzing whether the equipment operation trend is abnormal or not. By setting a multi-level threshold value, generation of a hierarchical early warning mechanism and a response strategy is realized, and the operation safety and the fault prediction capability of hydropower station equipment are effectively improved. The method is suitable for hydropower station key equipment state monitoring and intelligent operation and maintenance management in a complex environment.

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention relates to a multi-modal large model dynamic compression and reasoning optimization method based on a MoE architecture. The method comprises the following steps: establishing an edge computing system conforming to medical equipment specifications, constructing a medical image analysis network based on an improved hybrid expert MoE architecture, and adopting a three-layer cascade structure of a feature coding layer, a dynamic routing layer and an expert execution layer; executing expert module dynamic loading and video memory optimization; executing knowledge graph compensation and domain knowledge injection; executing hardware instruction level optimization and calculation acceleration; executing multi-expert feature fusion and decision weighting; performing diagnosis result generation and confidence evaluation; performing real-time data return and model iterative optimization; executing multi-device cooperation and load balancing; executing system security monitoring and exception handling; and generating a structured diagnostic report. The problem that the precision loss of a multi-modal large model is difficult to meet actual requirements is solved, and medical feature adaptive dynamic compression, medical hardware collaborative energy efficiency optimization and cross-modal compensation of medical knowledge enhancement are realized.

The invention relates to an intelligent equipment access system and access method based on an AI large model, and relates to the field of artificial intelligence and Internet of Things. According to the technical scheme, a dynamically adaptive universal object model access framework is constructed, functional attributes, control instructions and data formats of intelligent devices of different brands and protocols are mapped into a standardized logic model, meanwhile, a multi-mode AI large model engine is combined, high-precision voice recognition, voiceprint feature detection and multi-level semantic intention analysis capabilities are integrated, and the intelligent devices of different brands and protocols can be obtained. And an intelligent interaction closed loop of end-cloud collaboration is formed. Semantic analysis adopts a mixed strategy, a natural language instruction is converted through function mapping, a local knowledge base is utilized to match user scene requirements, and external data is queried by means of cloud networking to optimize instruction logic. According to the scheme, the multi-device access and operation and maintenance cost is remarkably reduced, the semantic understanding accuracy and scene adaptability are improved, and the method is widely applied to the fields of smart home, smart medical treatment and industrial Internet of Things.

The invention relates to the technical field of charging pile power dispatching, in particular to a service area new energy charging management method and system based on multi-device data analysis. The method comprises the following steps: acquiring real-time operation monitoring parameters of all charging piles in a service area; multi-dimensional feature perception and charging behavior time popularity distribution analysis are carried out, and a dynamic charging behavior hotspot map is constructed; carrying out multi-time-point power sampling according to the dynamic charging behavior hotspot map, carrying out power cooperation path topology evolution, and constructing a power cooperation path network; performing charging pile power trend prediction and power peak congestion evolution according to the power cooperation path network, and generating a power peak congestion state feature of the charging pile; and obtaining new energy traffic flow data of the service area, carrying out unit time traffic flow average calculation, and carrying out traffic flow prediction to obtain a traffic flow prediction thermodynamic diagram. Charging power scheduling is carried out through demand prediction, and the operation efficiency and stability of charging equipment are improved.

The invention relates to the technical field of multimedia interaction, in particular to an intelligent interaction control method and system for digital exhibition hall multimedia equipment, and the method comprises the following steps: S1, carrying out the arrangement of a sensor, and carrying out the intelligent sensing and data collection; s2, generating an intelligent interaction strategy; s3, equipment cooperative control and execution; and S4, performing real-time feedback and system optimization. According to the method, user behaviors and environment data are collected in real time through the multi-modal sensor, user requirements are accurately speculated in combination with a deep learning algorithm, the optimal interaction strategy and task priority are generated, individuation and interaction efficiency of user experience are remarkably improved, multi-device cooperation is optimized through distributed task scheduling, and user experience is improved. According to the method, display content and environment parameters are dynamically adjusted, self-optimization of the system is realized by utilizing user feedback, real-time performance, smoothness and reliability of interactive experience of the exhibition hall are ensured, rapid fault response and accurate maintenance demand prediction are realized through anomaly detection and equipment log analysis, and the stability and sustainability of operation of the exhibition hall system are greatly improved.

The invention relates to the technical field of mixed reality, in particular to a mixed reality multi-user interaction large-space positioning, aligning and collaborating system and method, and aims to solve the problems that in the prior art, the user interaction experience is improved only by collecting multi-aspect data for processing, efficient collaboration work of multiple users in a large space cannot be achieved, and the user experience is poor. Information sharing, task distribution and real-time interaction among users cannot be realized; information sharing, task distribution and real-time interaction among users are realized through the multi-user cooperation module, cross-device operation consistency is ensured through a multi-device sharing coordinate system, configuration is simplified, cooperation efficiency is improved, real-time data transmission is realized through multi-device network synchronization, delay is reduced, cooperation fluency is enhanced, and user experience is improved. The interaction reality sense is enhanced through physical interaction logic synchronization, the working process is optimized, and the cooperation experience is improved.

The invention provides an information transmission device and implementation method based on multi-protocol dynamic conversion and data fusion, and belongs to the technical field of information transmission, and the device comprises a multi-protocol communication interface module, a data analysis unit, a dynamic protocol conversion engine, a data fusion unit, a data assembly reporting unit and a log storage module. According to the method, information transmission is realized by initializing an interface, analyzing multi-protocol original data, dynamically converting the multi-protocol original data into a uniform format, fusing de-duplicated data, packaging, reporting and recording logs. The device and the method solve information interaction obstacles of different protocol devices, support bidirectional conversion of multiple industrial protocols, realize cross-protocol device collaboration and centralized management, improve the system intelligence level, and are suitable for scenes requiring multi-device collaboration, such as electric power, intelligent transportation, intelligent buildings and the like.

Enhanced user authentication in cellular networks This invention describes a method, apparatus, and system for authenticating, authorizing, and managing a connection in a cellular system to allow a user to retrieve / send some data, e.g., from / to an application function (AF) or to communicate with another remote user over multiple networks in an optimized / secure manner. The user may have one or multiple devices (UEs) using (e.g., connected to) multiple / different networks, e.g., a device with multiple subscriber identity modules (SIMs) and / or different radio access technologies.

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention relates to a training method, a data processing method, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The training method comprises the steps that a training sample is input into a to-be-trained model, forward propagation is executed, output of the to-be-trained model is obtained, the to-be-trained model comprises a plurality of expert layers, and a specific activation value is not stored in the forward propagation process; determining the value of a loss function according to the output of the to-be-trained model; according to the value of the loss function and the loss function, back propagation is executed, in the back propagation process of each expert layer, a recalculation task of a specific activation value and a first communication task are executed in an overlapping mode, and for each expert layer, the first communication task comprises a communication task of data interaction among multiple devices corresponding to the expert layer.

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention relates to the technical field of sequence control, in particular to a sequence control test system based on the Internet of Things, which comprises a task decomposition module for analyzing equipment load, decomposing tasks according to equipment operation capability, acquiring task requirements and equipment parameters, judging operation conditions of equipment stations, refining task dependency relationships and performing task scheduling on the basis of a system task sequence; and associating the tasks with the equipment parameters to generate an equipment task sequence table. According to the method, efficient coordination of multiple devices under different loads is ensured by adopting a distributed coordination algorithm, task scheduling is more flexible and efficient by analyzing task dependency and state changes of the devices, optimizing the task execution sequence and arranging the execution time and stations of the devices, and the task scheduling efficiency is improved by judging synchronous execution conditions and obtaining time nodes. The synchronous execution sequence of the tasks is coordinated, repeated occupation or idling of equipment resources is avoided, and the stability and task processing capacity of a sequence control system are improved.

The invention discloses a multi-device cooperative control method applied to deployment of an embedded real-time operating system, and relates to the field of embedded real-time operating systems, which comprises the following steps of: S1, constructing a real-time dependency relationship graph of a task link, a multi-device cooperative task is disassembled into sub-task link units including an execution path, a real-time constraint threshold and a cross-device communication dependency relationship. According to the multi-device cooperative control method applied to the deployment of the embedded real-time operating system, the problems of task priority conflict and resource utilization imbalance in a multi-device heterogeneous environment are effectively solved through a dynamic priority transmission mechanism and a distributed resource elastic perception model. The system can sense equipment load, communication link stability and task real-time requirements in real time, dynamically adjust sub-task weights and execution paths, ensure conflict-free transmission of end-to-end delay constraints, optimize the global resource utilization rate and avoid the problem of equipment overload or idle caused by traditional static scheduling.

The invention relates to the technical field of power adapter temperature management, and discloses a gallium nitride power adapter temperature management system comprising a dynamic thermal model module used for accurately predicting the temperature change trend of each key component; the multi-level temperature monitoring network module is used for forming a complete temperature distribution diagram; the multi-device collaborative sensing module is used for generating a sensing result of the whole heat dissipation environment; the self-adaptive air duct structure module is used for optimizing an air flow path; the heat dissipation path module based on swarm intelligence is used for calculating and generating an optimal multi-device collaborative heat dissipation path by applying a swarm intelligence algorithm; the self-adaptive heat dissipation control strategy module is used for realizing multi-target balance optimization of temperature, noise and energy consumption; the problem that heat dissipation is difficult when multiple adapters are used in a centralized mode is solved, heat dissipation efficiency is improved, energy consumption is reduced, and noise is reduced.

The invention provides a distributed energy access-oriented intelligent electric meter multi-device collaborative optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the interpolation of a unified electric meter, an inverter and energy storage data granularity, building a joint time error model, obtaining calibration reference data, and completing the multi-device time synchronization calibration; based on the reference data, acquiring operation data of three devices, and using a joint energy consumption model to distinguish electric meter hardware faults and multi-device collaborative anomalies to complete fault diagnosis; calculating local line loss by combining an inverter-to-ammeter line and electric quantity data, verifying residual electricity metering deviation, and tracing and correcting settlement data to complete line loss-bidirectional metering linkage verification; associating the fault result with the corresponding relation between the long-term statistical fault type of the metering data and the deviation rate and the line loss to establish a model to predict deviation trend early warning; and determining a multi-device cooperative abnormal source and transformer area load interference relation, calculating an interference coefficient, feeding back a scheduling system, and marking a line reconstruction priority. By adopting the method, the distributed energy consumption efficiency and the operation reliability of the power distribution network are improved.

The invention discloses an intelligent online learning system and method for multi-device energy consumption optimization, particularly relates to the field of multi-device energy consumption optimization, and is used for solving the problems of energy consumption fluctuation and low operation efficiency caused by non-optimization of a feedback path. Extracting feedback path weights and action chain features, and analyzing a dynamic interaction relationship between devices; clustering analysis and reinforcement learning are combined to optimize classification boundaries, a high-risk feedback mode is marked, an optimization template is generated, and a basis is provided for path adjustment and strategy optimization; by dynamically adjusting the feedback path weight, generating an equipment adjustment instruction and combining with a feedback optimization template, realizing efficient control of equipment cooperative operation; therefore, the negative influence of a high-risk feedback path on operation is effectively inhibited, the energy consumption cumulative effect caused by feedback enhancement between equipment is reduced, the equipment cooperation relation is optimized, and the energy consumption distribution efficiency and the operation stability of the system in a dynamic load environment are improved.

The invention discloses a multi-device cooperative management system based on cloud edge cooperation, and relates to the technical field of Internet of Things communication, and the system comprises a device management module which is used for connecting edge devices, carrying out the centralized management of the edge devices, and carrying out the optimal scheduling based on the functions and distribution positions of different edge devices; and the data acquisition module is connected with the equipment management module. According to the multi-device collaborative management system based on cloud edge collaboration, a comprehensive and accurate data basis is provided for multi-device collaborative management, and information exchange and collaborative work between devices are promoted; the optimization decision-making unit can formulate an equipment optimization management strategy according to the operation scheme generated by the big data analysis unit and the prediction result obtained by the prediction analysis unit, and continuously performs optimization adjustment according to the real-time operation condition of each piece of edge equipment; therefore, the system can adapt to continuously changing equipment operation environments and task requirements, and more efficient multi-equipment collaborative management is realized.

The invention relates to the technical field of equipment cooperative control, and discloses a multi-equipment cooperative control method and system under an autonomous cooperation algorithm, and the method comprises the steps: collecting the physical attribute data, real-time operation data, production task data and a smart factory scene graph of operation equipment under a multi-equipment cooperative task in a smart factory application; based on the physical attribute data and the real-time operation data, identifying a space conflict mode and a time conflict mode of the operation equipment to obtain collaborative conflict information; according to the production task data and the collaboration conflict information, carrying out collaboration conflict decoupling on the operation equipment to obtain a feasible collaboration mode; performing obstacle avoidance reinforcement learning on the operation equipment by using the smart factory scene graph to construct an obstacle avoidance global path of the operation equipment in the smart factory; and based on the feasible coordination mode and the obstacle avoidance global path, constructing a target coordination control scheme of the operation equipment so as to perform coordination control on the operation equipment. According to the invention, the reliability of multi-device cooperative control under the autonomous cooperation algorithm can be improved.

The invention relates to a multi-device cooperative control method and device based on a user state and an air conditioner. The method comprises: monitoring a sleep state of a user; and determining a sleep stage of the user based on the sleep state, and performing cooperative control on each device according to a control strategy matched with the sleep stage. The method and the device can adapt to the difference of dynamic sleep stages, and control the devices to operate cooperatively according to the control strategies matched with the different sleep stages, so that the self-adaptive adjustment and parameter adjustment precision of the devices is improved, and the problems of poor sleep mode adjustment effect and low adaptability in the prior art are solved.

The invention relates to the technical field of intelligent locks, in particular to an electric power facility intelligent lock remote control and authority management system which comprises an intelligent lock module, an identity verification module, an authority management module, a multi-device collaborative optimization module and a data recording and analyzing module. The intelligent lock module is physically connected with a lock of an electric power facility through a prefabricated mounting interface; the identity verification module is used for carrying out multi-mode identity verification on an operator; the authority management module dynamically allocates authority levels of operators; the multi-device collaborative optimization module optimizes operation and resource allocation among devices; and the data recording and analyzing module records an operation log of the intelligent lock in real time, analyzes the operation log, detects an abnormal behavior and automatically performs permission adjustment. According to the invention, high-efficiency cooperation of equipment and safety, flexibility and operation efficiency of the system are ensured, and complex electric power facility management requirements are effectively met.

The invention relates to the technical field of equipment collaboration, and discloses a multi-equipment collaboration control method and device, computer equipment and a storage medium, and the method comprises the steps: building a session channel with collaboration equipment according to the authentication information of main control equipment; acquiring operation state parameters of the collaborative devices based on the session channels, and generating a permission allocation matrix according to the operation state parameters of the collaborative devices; and processing task requirements of the main control equipment by utilizing the permission allocation matrix, generating a differential instruction set, and executing synchronous control on the collaborative equipment according to the differential instruction set. According to the invention, the problems of authority conflict, inflexible task allocation and incapability of fast and automatic switching after the main control equipment fails when multiple devices cooperatively control the cloud mobile phone instance in the prior art are solved.

The invention discloses a platform and a monitoring method for monitoring the home safety of old people based on decision engine multi-device linkage, and the method comprises the steps: carrying out the data preprocessing according to the human body home data collected by an edge computing device; according to the human body home data, multi-modal data fusion is carried out to generate behavior identification data, and the multi-modal data fusion comprises one or more of time sequence alignment, modal completion and feature extraction; inputting the behavior recognition data into a local recognition model for real-time behavior analysis, and inputting the behavior recognition data into a cloud decision engine for multi-source data comprehensive deep analysis; and according to an output result of the local identification model and / or the cloud decision engine, performing operation of matching the smart home equipment and / or home behavior alarm processing. According to the invention, multi-device data integration and cooperative linkage measurement can be adopted to monitor the home safety of the elderly.

The invention provides a network security perception early warning method and system for a smart power plant, and relates to the technical field of power plant network security early warning, and the method comprises the steps: reading a multi-source monitoring data set of a first time zone, carrying out the abnormality judgment of the multi-source monitoring data set, and carrying out the deviation calculation of the multi-source monitoring data set through employing a parameter reference value if an abnormality index is 0, and performing space-time mapping distribution on the plurality of parameter deviation values according to the position coordinates and the timestamps, constructing a network attack sequence diagram, performing abnormal association identification based on the network attack sequence diagram, determining a global attack chain, performing historical risk backtracking according to the global attack chain, and outputting a risk early warning level. The technical problem that the potential network security risk of the power plant is difficult to warn in time due to the lack of comprehensive monitoring and analysis of the overall network security state of various devices and systems in the power plant in the prior art is solved. The technical effects of overall network security situation awareness and timely potential risk early warning of multiple devices and systems in the smart power plant are achieved.

The invention relates to the technical field of lithium ion battery charging, and discloses a multi-device compatible intelligent fast-charging lithium ion battery system and a protocol matching method, the system comprises a charging device, the charging interface of which is used for being connected with a target device to charge a lithium ion battery pack of the target device; the battery management equipment acquires an initial communication signal of the connected target equipment in real time when the charging interface is connected with the target equipment, so as to extract a voltage fluctuation feature, a handshake time sequence feature and a protocol identification feature for generating an equipment feature vector; performing similarity matching on the equipment feature vector and the protocol feature parameter to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load state of the target equipment, obtaining an optimal charging protocol and a corresponding charging curve template; and charging the lithium ion battery pack according to the optimal charging protocol and the charging curve template. And efficient, flexible and safe charging control can be realized.