1032 results about "Cloud management" patented technology

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention discloses a self-adaptive cloud management platform system based on intelligent resource scheduling and container arrangement, and relates to the field of computer information management. The system comprises a refined resource scheduling and adaptive optimization module, a containerized application life cycle management and dynamic container arrangement module, a high-precision operation and maintenance monitoring and self-healing mechanism module based on big data analysis, and a dynamic resource allocation and elastic scaling strategy module of an intelligent scheduling engine. The system takes a containerization technology as a core, realizes centralized management and monitoring of cloud computing resources, can realize dynamic intelligent resource scheduling and optimization, accelerates application deployment, improves system flexibility, strengthens operation and maintenance monitoring and platform safety guarantee, and comprehensively improves resource optimization and cost effectiveness. The resource scheduling efficiency is improved, the application deployment is simplified, the operation and maintenance monitoring is enhanced, and efficient resource management is realized through an adaptive optimization technology.

The invention provides a distributed collaboration-based computing network intelligent agent system and a resource dynamic scheduling method thereof, and belongs to the technical field of computing network integration. The system comprises an edge agent used for sensing local computing power, network bandwidth and task load in real time, predicting task demand fluctuation by using a lightweight neural network, adjusting resource allocation weight in real time in combination with network topology change, and executing a preliminary task scheduling decision; the regional collaborative agent is used for aggregating multiple edge node states based on federated learning, generating a cross-node resource scheduling strategy, verifying the credibility of a computing power transaction smart contract and determining a cross-domain resource allocation scheme; and the cloud management agent is used for constructing a global resource portrait model according to the information provided by the edge agent and the regional collaborative agent, performing long-term strategy optimization, issuing global strategy information, and constructing and updating a computing power transaction smart contract based on a preset computing power transaction smart contract template. According to the invention, multi-level refined scheduling of computing network resources is realized.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

According to the grid-connected line electric energy quality on-line detection device, the system comprises a broadband sensing module which is deployed on a grid-connected line to form an original analog signal; the conditioning module forms a standardized digital sampling signal; the edge calculation module receives the standardized digital sampling signal, analyzes a voltage effective value, a harmonic distortion rate, a sag depth and an event duration index in real time through a parallel calculation architecture, and forms an electric energy quality characteristic data set; the intelligent analysis module receives the electric energy quality characteristic data set to form an abnormal event report; and the communication interaction module receives the abnormal event report, encapsulates the abnormal event report into a standardized data frame through a configurable protocol stack, synchronously pushes the standardized data frame to the local monitoring terminal and the cloud management platform, receives an external control instruction and feeds back the external control instruction to the intelligent analysis module to optimize an analysis strategy. The grid-connected line electric energy quality on-line detection device can solve the problem that complex disturbance cannot be recognized in real time and cannot be accurately traced to the source.

The invention relates to the technical field of distributed energy storage and intelligent power grid control, in particular to an intelligent distributed energy storage cluster collaborative management system. Comprising a state acquisition module used for acquiring local power grid state parameters of an edge computing node in real time; the power grid state parameters comprise power grid frequency, power grid voltage and a heartbeat communication state with the cloud management platform; the risk calculation module is used for calculating quantified island risk indexes; the mode judgment module is used for determining a current operation mode decision; the mode switching module is used for switching the system from a vertical cooperative mode to a horizontal autonomous mode when the island risk index is greater than an island switching threshold value; and when the island risk index is smaller than or equal to the island switching threshold value, the system is maintained to operate in the vertical cooperation mode. According to the system, misjudgment and missed judgment of a traditional single threshold value mode are overcome, it is ensured that the mode switching module can be switched to the horizontal autonomous mode from the vertical cooperative mode in time under the real risk, and decision making is accurate and reliable.

The invention discloses an intelligent water level monitoring system and a data transmission method thereof, and belongs to the technical field of Internet of Things sensing. The system comprises a distributed water level sensing node, an edge computing gateway and a cloud management platform, wherein the sensing node is integrated with a piezoresistive water level transmitter and an adaptive Kalman filtering module; the data transmission method comprises a dynamic threshold triggering mechanism and a compression algorithm based on improved Huffman coding, and low-power-consumption and high-reliability transmission is realized by optimizing transmission frequency and a data packet structure. The edge computing gateway is internally provided with an LSTM water level prediction model, historical data are analyzed through an LSTM neural network, and an early warning signal is generated in real time. And the cloud management platform stores the key data by adopting a block chain technology. Through multi-sensor data fusion and layered encryption transmission, the water level monitoring error rate is reduced to + / -0.5 cm, the data transmission energy consumption is reduced by 40%, and the monitoring efficiency and the data safety are remarkably improved in flood early warning and reservoir management scenes.

The invention relates to the technical field of livestock breeding, in particular to a cow manure automatic scoring system and method.The cow manure automatic scoring system comprises an image acquisition unit, a physical property sensor, a chemical component detection module and a data processing unit.The image acquisition unit is used for capturing the form, color and fiber residue of manure; the physical property sensor is used for measuring the water content of excrement and evaluating the viscosity and hardness of the excrement; the chemical component detection module comprises a near infrared spectrum analysis unit and an electrochemical sensor unit; and the data processing unit is used for integrating an AI algorithm, realizing multi-source data fusion and scoring, and uploading the data to the cloud management platform through the communication module. Therefore, the problems that in the prior art, manual scoring is subjective, low in efficiency and difficult to quantify, diseases are difficult to find early due to the lack of rapid component detection means, and accurate nutrition regulation and control are difficult to achieve due to the fact that scoring data are difficult to integrate into a breeding system are solved.

The invention discloses an automatic fare collection method and system based on multi-modal identity recognition, and relates to the technical field of rail transit automatic fare collection systems and multi-modal biological recognition fusion, and the method comprises the following steps: 1, enabling a user to approach a recognition area, carrying out the multi-modal data collection, and carrying out the quality detection and preprocessing; 2, feature extraction and credibility preliminary calculation are carried out; 3, environment data are collected and subjected to standardization processing; step 4, adaptively predicting a weight through machine learning, and constructing weight smoothing and time domain constraints; step 5, carrying out D-S evidence step-by-step fusion, and calculating comprehensive credibility; and step 6, comparing the comprehensive credibility with a preset threshold value, if the comprehensive credibility is greater than or equal to the preset threshold value, determining that the identification is successful and the gate is opened for passing, otherwise, determining that the credibility is insufficient, and performing alternative / manual processing. The system comprises a front-end identification terminal, a read-write industrial control all-in-one machine and a cloud management platform.

The invention discloses a new energy automobile battery health prediction method and system, and relates to the technical field of monitoring analysis, and the method comprises the steps: carrying out the real-time collection of the operation state parameters of a power battery pack corresponding to a new energy automobile, obtaining a battery state data set corresponding to the new energy automobile, carrying out the adaptive noise suppression processing of the battery state data set, and obtaining a new energy automobile battery health prediction result. Generating a de-noised battery state feature map, performing feature extraction on the de-noised battery state feature map to obtain a multi-dimensional coupling feature set, constructing a dynamic weight distribution matrix, performing feature fusion on the multi-dimensional coupling feature set, determining a battery health state comprehensive index, and triggering a grading early warning mechanism based on the battery health state comprehensive index; and constructing a battery health prediction model, carrying out long-term modeling on the battery health state comprehensive index, predicting a battery health state evolution trend in a future charging and discharging period, generating a balance maintenance strategy, and pushing the balance maintenance strategy to the vehicle-mounted terminal and the cloud management platform. The method has the effect of improving the accuracy of battery health prediction.

The invention belongs to the technical field of cognitive disorder rehabilitation, and particularly relates to a dynamic self-adaptive middle-aged and elderly cognitive and mental health intervention system which comprises a VR interaction module, a physiological data acquisition and processing module, a dynamic self-adaptive module and a cloud management platform. Interaction scenes of the VR interaction module comprise an occupational scene and a nostalgic scene; the physiological data acquisition and processing module acquires and processes multi-modal physiological data of a user in real time; the dynamic self-adaptive module is used for calculating a cognitive load index and an emotion pressure index based on physiological data, dynamically adjusting occupational task complexity through a fuzzy logic controller, triggering restalgic scene switching, and embedding a mild cognitive task in a restalgic scene to maintain training continuity; and the cloud management platform stores user historical data, and optimizes a scene recommendation strategy through a machine learning model. A personalized intervention strategy can be customized according to the actual situation of the user, and the user experience is improved.

The invention provides a small carrier for manned and logistics and an unmanned intelligent driving system of the small carrier. The system is applied to the technical field of automatic driving and comprises a sensor module used for collecting the information of a carrier and the environment of the carrier, a path planning module used for performing target detection, road recognition, obstacle detection and positioning and map construction according to the information of the carrier and the environment of the carrier by using a multi-modal sensor fusion method, and obstacle avoidance and automatic path navigation are realized based on an improved path planning algorithm. The cloud management platform is used for monitoring the state of the carrier in real time, storing navigation data and vehicle health information through the cloud and supporting multi-vehicle collaborative scheduling; and the voice interaction module supports voice instruction input and is used for analyzing the voice instruction of the user, dynamically responding and re-planning the path by using the path planning module. According to the invention, the transportation efficiency and the intelligent level of the small-sized carrier for manned and logistics are improved.

The invention provides a data driving and multi-element cloud edge cooperative computing method for intelligent workshop construction, which comprises the functions of multi-protocol heterogeneous equipment sensing, edge node adaptive load balancing, lightweight real-time edge computing processing, cloud centralized management control, industrial intelligent application service deployment scheduling, cloud edge task unloading and the like. The system comprises a data acquisition and transmission middleware, an edge stream processing engine and a cloud service center. The method has the characteristics of real-time performance, low delay, flexibility, expandability, loose coupling, low resource occupancy, high availability and privacy security.

The invention provides an intelligent charging pile control system based on a new energy automobile. The intelligent charging pile control system comprises a communication module, a data processing module, a charging power adjusting module, a safety protection module, a user interaction module, a cloud management platform, a power supply checking and recording module, an information backup and transmission module and a second-hand power supply monitoring module. The power supply checking and recording module is connected with a new energy automobile power supply system, detects and records parameters such as battery capacity and internal resistance, generates a power supply state report, realizes comprehensive detection and recording of the power supply parameters, provides data support for battery maintenance and fault diagnosis, assists an automobile owner to know the battery condition, and improves the safety of the automobile. A basis is provided for reasonable use and maintenance of a battery, basic data is also provided for subsequent battery management, the information backup transmission module locally backs up a power supply state report and transmits the report to a vehicle merchant service platform, the module realizes backup and transmission of power supply information, and a vehicle merchant can timely obtain detailed information of a vehicle power supply.

The invention discloses a PEDOT sensor nonlinear response compensation optimization system, and relates to the technical field of PEDOT sensors, the system comprises a cloud management center, and the cloud management center is in communication connection with a data acquisition module, a feature extraction module, a response compensation analysis module, a cloud compensation module, a compensation deviation analysis module and a feedback adjustment module. The method combines the neural network model through the cloud, precisely learns the nonlinear characteristics of the sensor, carries out precise compensation, carries out initial linear compensation at the end side, reduces the data amount transmitted to the cloud, improves the real-time performance of the system, carries out the model training and optimization at the cloud through a large amount of historical data, and improves the real-time performance of the system. According to the PEDOT sensor, the error introduced by nonlinear response is remarkably reduced, and the measurement precision and accuracy of the PEDOT sensor are effectively improved in an end-cloud cooperation mode, so that the sensor can provide high-reliability measurement data under different environmental conditions, and the requirement of high-precision measurement is met.

The invention discloses a jewelry flaw detection and grading system based on machine vision and a manipulator. The system comprises a multi-mode imaging module; the image processing module is used for carrying out preprocessing operation on the original image; the edge processing module comprises a GPU acceleration processor and is used for deploying a flaw detection model to detect flaws; training data are automatically generated through a semi-supervised learning algorithm; the mechanical execution module comprises a six-axis mechanical arm, a path planning algorithm and a collision detection model for a bounding box and a directed bounding box of the six-axis mechanical arm; the cloud management module is used for storing detection data based on a block chain technology and supporting third-party verification; and dynamically updating the expert knowledge base. According to the jewelry sorting method and system, the types and grades of the jewelry are divided through the DINOv2 defect detection model, meanwhile, the PnP algorithm is adopted to be fused with the depth information, the jewelry is sorted according to grade division, meanwhile, consumers can rapidly and accurately obtain relevant information (such as materials, authenticity and value evaluation) of the jewelry, and the shopping experience is greatly improved.

The invention relates to the technical field of electrical equipment insulation performance detection, in particular to an insulating oil dielectric loss test system based on temperature compensation, which comprises a temperature acquisition module for acquiring insulating oil temperature data and environment temperature data; the dielectric loss measurement module measures a dielectric loss value and a dielectric loss angle through a self-adaptive anti-interference algorithm, and synchronously records voltage and frequency; the intelligent compensation module matches temperature characteristic parameters for different temperature intervals, and compensates a dielectric loss detection value through a segmented temperature compensation model; the state analysis module judges the aging degree and state of the insulating oil according to the compensated dielectric loss value and acid value, generates a multi-stage early warning signal and a diagnosis report by combining the breakdown voltage and the moisture content, and dynamically adjusts the test frequency according to the oil sample type and the operating age limit; and the cloud management platform stores historical data and compensation parameters, establishes a performance degradation model, analyzes a dielectric loss trend and generates maintenance suggestions. Therefore, the problems of insufficient test precision, poor environmental adaptability and the like in the prior art are solved.

The invention discloses a constructional engineering safety and quality inspection system and method.The constructional engineering safety and quality inspection system comprises an inspection robot, a BIM model database, a digital twin engine, an AI analysis module and a cloud management platform which are in data connection with one another. According to the method, the engineering safety and quality are ensured, synchronization of virtuality and reality is realized through a digital twinning technology and integration of a BIM model, analysis and rectification of problems are facilitated through an AI analysis model, industry big data are accumulated, rectification closed-loop management is supported, it is ensured that the problems are thoroughly solved, the rectification closed-loop period is shortened to be within 24 hours, and the method has high popularization value.

The invention discloses an intelligent building multi-dimensional monitoring system supporting edge computing and cross-terminal collaborative management, and relates to the technical field of intelligent buildings, the intelligent building multi-dimensional monitoring system comprises a multi-area edge monitoring node and an asynchronous communication cloud management platform, the edge node is provided with a lightweight dynamic collaborative engine, a node database and equipment management and control module, and a management and control terminal device; data are collected and standardized, simple / complex events are identified, risk levels are divided, and autonomous networking is carried out to form a temporary collaborative cluster when the risk is high; the data interaction module adopts UDP lightweight protocol low-delay transmission and local retransmission to guarantee data integrity, the cloud platform stores event processing abstracts, analyzes historical data optimization strategies and rules and silently issues in idle periods, the visual module assists monitoring and decision making, the system realizes distributed sensing and collaborative management and control, cloud dependence is reduced, and the real-time performance of the system is improved. The event response efficiency and the resource utilization rate are improved.

The invention discloses a dysarthria speech evaluation system based on acoustic analysis. The dysarthria speech evaluation system comprises a speech evaluation module, a system management module and a cloud management platform, the speech evaluation module is used for performing vowel composition evaluation, voice evaluation, definition evaluation and composition motion evaluation on the user; the system management module is used for carrying out login authentication management, authority management, data management and evaluation record management on a user, and maintaining the safety of system operation and the integrity of data; and the cloud management platform is used for performing real-time data transmission and data analysis on the speech evaluation module and the system management module to complete dysarthria speech evaluation. According to the invention, objectivity and standard consistency of dysarthria assessment are improved.

The invention discloses a cloud management and intelligent distribution method for classroom teaching resources, and relates to the technical field of cloud computing software. The classroom teaching resource cloud management and intelligent distribution method comprises the steps that S1, multi-source teaching resource data and auxiliary distribution data are acquired and preprocessed, and a knowledge content database is constructed; s2, performing semantic fusion analysis through text semantic association, structural consistency and professional term coverage data; s3, carrying out dependency evaluation by fusing pre-association, knowledge co-occurrence, sequence association and semantic fusion data; s4, performing resource adaptability distribution analysis through the maximum knowledge dependence value in combination with the learning state data and the historical feedback data; and S5, evaluating the teaching resource quality by collecting classroom feedback, teacher evaluation and resource use data, and implementing content optimization. The problems of semantic distortion, path breakage, distribution mismatching and resource updating inconsistency in the teaching resource data fusion and intelligent resource distribution process are solved.

The invention provides a resource recommendation method and related equipment. The method is applied to a cloud management platform. The method comprises the following steps: receiving first indication information of a tenant; determining at least one first resource recommendation scheme according to available resources in the at least one computing unit cluster and model information of a first AI model, the cost performance of the at least one first resource recommendation scheme is superior to that of candidate resource recommendation schemes except the at least one first resource recommendation scheme in a first candidate resource recommendation scheme set; and providing the at least one first resource recommendation scheme for the tenant. According to the method, at least one first resource recommendation scheme with relatively high cost performance can be provided for the tenant according to the model information of the AI model to be operated by the tenant and the remaining available resources in the computing unit cluster, so that the tenant can purchase proper resources, and the resource utilization rate in the computing unit cluster is improved.

The invention provides a cloud resource configuration method and device, electronic equipment and a computer program product. Relates to the technical field of cloud management. In view of the problem of complexity of the current cloud resource configuration process, the cloud resource configuration method provided by the invention comprises the following steps: acquiring a blueprint description file generated based on a cloud resource blueprint; the blueprint description file comprises cloud resource description information of to-be-configured cloud resources; analyzing the blueprint description file, and generating an abstract syntax tree for representing cloud resource description information; the abstract syntax tree records cloud resource description information through node information; generating a resource configuration file according to the node information of the abstract syntax tree and the mapping relation between the node information and the resource configuration information; based on the resource configuration file, executing a configuration operation on the to-be-configured cloud resource; according to the embodiment of the invention, the complexity of cloud resource configuration can be reduced, and the flexibility and convenience of cloud resource configuration are improved.

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 discloses a terminal-cloud collaborative air pollution monitoring system and a dynamic evolution and traceability method, the air pollution monitoring system comprises an unmanned aerial vehicle, a plurality of carried detection assemblies, a communication transmission module and a cloud management platform, and the system integrates high-precision sensors and advanced data transmission and processing technologies, so that the air pollution can be monitored more accurately and more accurately. The intelligent route planning capability is achieved, and the coverage range, the real-time performance, the accuracy and the response speed of air pollution monitoring are comprehensively improved; the unique system architecture and function optimization of the system not only solve multiple key problems in the prior art, but also show significant advantages in the aspects of monitoring efficiency, data processing, intelligent decision making and the like, and greatly enhance the capabilities of pollution source positioning and pollution prevention and control. And powerful technical support and guarantee are provided for environmental protection and public health guarantee.

The invention discloses a cloud-edge collaborative intelligent control edge computing terminal, and particularly relates to the technical field of edge computing, the terminal comprises an acquisition management module, a topology analysis module, a rule management module and a disconnection recovery module; the acquisition management module establishes a unified time base, performs event packaging on acquired data and control actions, and implements local sequential caching and uploading scheduling in a disconnection scene; the topology analysis module extracts consistency features and identifies topology change doubtful points; the rule management module realizes topology version configuration, versioning management of a linkage rule set and hardware resource mapping verification; and the disconnection recovery module completes disconnection detection, cache playback and progressive control recovery in the link interruption and recovery process. Therefore, under the condition that the network is unstable, the control state is kept consistent with the event time axis, synchronous reconciliation with the cloud management platform is carried out after the link is recovered, and the method is suitable for collection, in-situ linkage control and cooperative operation and maintenance of distributed sites such as water service pipe networks and agricultural trunk canals.

The invention discloses an oil and gas field three-dimensional visualization system based on digital twinning, and relates to the technical field of digital twinning, and the system comprises the following steps: S1, integrating GIS, BIM, IoT and GD multi-source data, and constructing a unified ground object entity data set; s2, carrying out sensor, camera and positioning beacon data fusion by using the ground object entity data set; s3, carrying out business system data integration and three-dimensional scene modeling by using the ground object dynamic state calibration set; s4, performing multifunctional application system development by using the three-dimensional scene business linkage model; s5, realizing efficient cloud management and redundant backup in combination with a SpringBoot distributed architecture; by setting a ground object dynamic state calibration model, a service system linkage model and a three-dimensional entity management model, multi-dimensional fusion modeling can be performed on geometric features, state parameters and service features of all ground objects in an oil and gas field, and the real-time performance and service adaptation capability of a three-dimensional visualization system are remarkably improved.

The invention discloses an intelligent cloud management high-definition card printer system, and relates to the technical field of card printers, the intelligent cloud management high-definition card printer system comprises a processor, a database and a printing module, the processor is connected with the database through signal transmission, and the printing module is connected with the processor through signal transmission. The printing module comprises an information acquisition assembly and a printing assembly, the information acquisition assembly is used for acquiring material information and environment information, the printing assembly is used for printing cards, and the processor controls the printing module to start by receiving a primary starting signal and a secondary starting signal. The processor constructs a color compensation module through information collected by the information collection assembly to output a color compensation value to adjust the printing assembly. By designing and constructing the color compensation model, the function of high-precision card printing is realized, the problems of dependence on off-line measurement, color gamut deviation and response lag are solved, the color deviation can be compensated in real time, and the quality and specialty of card printing are improved.

The invention provides an SDN (Software Defined Network) dynamic control system based on a cloud native architecture, and belongs to the technical field of SDN and cloud native. The system adopts a three-layer decoupling architecture of a service layer, a control layer and a data layer, wherein the service layer consists of a core network, a cloud platform and edge nodes and is used for issuing service requirements and executing a joint resource scheduling strategy; the control layer comprises four core modules, namely a micro-servitization SDN control module, a cross-domain collaborative engine module, an end-to-end service quality guarantee module and an edge collaborative optimization module, and is responsible for unified analysis, decision and scheduling to realize dynamic updating of a network strategy and multi-cluster collaborative optimization; the data layer is composed of an SDN switch and a router, and is used for executing a flow table rule and collecting network situation data. Through deep fusion of the SDN and the cloud native technology, the problems of upgrade interruption, low cross-domain collaboration efficiency and edge resource limitation of a traditional network architecture are solved, and the method can be widely applied to hybrid cloud management, industrial Internet of Things and intelligent city network scenes.

The invention discloses a non-sensitive management control system and method for a shared office. The system comprises a mobile APP, a cloud management platform, an intelligent access control terminal, environment sensing equipment and an electrical control unit, through the management system integrating the mobile APP, the cloud management platform, the intelligent access control terminal, the environment sensing equipment and the electrical control unit, non-sensitive management of the shared office can be realized. According to the system, a tedious and low-efficiency application process is simplified, so that the employees can quickly complete office reservation, and the consumption of office time is remarkably reduced. Meanwhile, the system can dynamically adjust the electrical control unit according to real-time data provided by the environment sensing device, it is ensured that device operation is automatically closed or reduced in the unmanned period, energy waste is effectively avoided, and the operation cost is reduced. In addition, the resource utilization efficiency is improved through the fine management mode, administrative expenses caused by idle or overuse of resources are reduced, and therefore the operation efficiency of an enterprise is overall improved.