878 results about "Cloud storage" patented technology

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The invention discloses a human resource archive filing management system, and relates to the technical field of data management, the system comprises an acquisition module, a processing module and a management module, the acquisition module acquires original archive data of employees, transmits the data to the processing module, classifies the original archive data according to a preset first rule, and stores the classified data to the management module; the first archive data is obtained, controlled and managed, a calling permission and a distribution strategy are generated, the first archive data is recognized through a preset second rule, second archive data is generated, corresponding processing is conducted according to the processing priority, a processing log is reserved, third archive data are obtained and transmitted to the management module, and the management module is used for managing the first archive data. A distributed cloud storage cluster and a local storage node are constructed, an overall storage architecture and a retrieval path of third archive data are optimized, archive management is refined through three-dimensional classification, data integrity is guaranteed through field-level verification and automatic completion, scene-based dynamic authority control safety is achieved, compliance risks are intelligently cleared and avoided, and efficiency is improved through mixed storage. And active management of archive management is promoted.

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

The invention relates to the technical field of land parcel management, in particular to a land parcel full life cycle compliance management and operation monitoring system, which comprises a multi-source data acquisition module for acquiring multi-dimensional land parcel data such as spatial geographic ownership planning and approval from a plurality of heterogeneous data sources in real time; the data fusion and storage module performs data cleaning and standardized fusion, adopts a distributed cloud storage architecture and introduces data version management to support historical tracing; the intelligent compliance judgment engine generates a compliance state judgment result and a compliance risk report based on the dynamic compliance rule base and machine learning; the interactive visual monitoring module is used for dynamically displaying the compliance state of the land parcel space information life cycle stage and the key index trend; and the automatic early warning and intervention module is used for performing graded early warning and triggering a rectification or emergency intervention process. According to the invention, the problems of difficulty in compliance judgment, low efficiency, non-visual monitoring and slow risk response of existing land parcel management data integration are solved, and intelligent compliance management and operation monitoring are realized.

The invention relates to the technical field of agricultural environment monitoring, and discloses a cultivated land quality monitoring method. The method comprises the following steps: acquiring soil multi-parameter data, and carrying out sensor network deployment and anti-interference processing; performing exception filtering, standardization and missing value interpolation on the data; cloud storage data are generated through sliding window segmentation and encryption compression; generating a monitoring report based on the multi-dimensional analysis and the dynamic chart; adopting a plurality of intelligent algorithms to identify abnormal modes and constructing an early warning strategy library; and finally, real-time monitoring and early warning grade division are realized. According to the invention, full-process automatic monitoring and intelligent early warning of the cultivated land quality are realized, and the monitoring precision and the decision-making efficiency are obviously improved.

The invention relates to the technical field of government affair information management, in particular to a cloud-based government affair information management method, which comprises the following steps of: S1, adding a sensitivity label for each piece of government affair data in a cloud storage layer; s2, extracting data from the government affair database and generating a dynamic data view with an authority mark; s3, establishing a mapping relation table between routing keys and data source addresses; s4, acquiring a requester department function code, a business scene code and a target data identifier; s5, matching a target data source, and based on the authority mark in the dynamic data view, executing a desensitization operation on the sensitive field; and S6, if the genetic marker bit of the original data is true, superposing the data security classification in the original tag and the current security classification to form a new tag. According to the method, by constructing the sensitivity label and the dynamic authority control mechanism, safe inheritance and accurate access of government affair data in cross-department circulation are achieved, and the safety and compliance of data management are remarkably improved.

The invention provides an intelligent document management method and system, and the method comprises the steps: obtaining an uploaded file set, processing each file through a text analysis algorithm, an image analysis algorithm and a structured data analysis algorithm, generating a first feature set, and storing the first feature set in a temporary database; obtaining a distribution record from the first permission configuration file, writing the distribution record into an audit database through a log audit module, and generating a first operation traceability chain; according to the first operation tracing chain, a storage management module is called to perform fragmentation processing on the file and upload the file to cloud storage, a first storage index is generated, and the metadata table and the archive position are associated; and according to the second storage index, calling a disaster recovery backup algorithm to perform multi-region copying on the file, generating a first backup confirmation signal, and recording the first backup confirmation signal into an operation and maintenance log. According to the system and the method, intelligent classification, sensitive information protection and whole-course traceable management of the files are realized, the automation level and the safety of document management are improved, and a comprehensive solution is provided for document management.

The invention relates to the technical field of cloud storage, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a load and message queue regulation-based migration method, device, equipment and medium. Monitoring message backlog amount and consumption rate to generate message queue state information, generating migration regulation and control parameters based on the two types of state information, determining concurrency level and issuing rhythm by combining object size classification information, reducing issuing frequency and preferentially scheduling small objects when the message backlog amount is increased and oversize objects exist, and distributing the tasks to the working nodes according to the concurrency level and the issuing rhythm to execute migration, and adjusting migration regulation and control parameters based on execution state updating information. According to the method, concurrency and rhythm are regulated and controlled in real time, object size classification is combined, the problems that traffic is uncontrollable and throughput is affected by large files are solved, and efficient and stable data migration is achieved.

The invention discloses a data backup management system for communication, which belongs to the technical field of data management, and comprises an intelligent hierarchical backup module, a block chain security module, an AI resource scheduling module, a hybrid cloud backup module and an automatic fault processing module, the intelligent hierarchical backup module is used for dividing communication data into hot data, warm data and cold data according to the importance, use frequency and modification frequency of the data, and the block chain security module is used for generating a unique hash value for each backup data and recording the unique hash value on a block chain. The AI resource scheduling module is used for analyzing and learning historical backup data and operation records by using a deep learning algorithm, and the hybrid cloud backup module is used for combining a local data center and public cloud storage. And the automatic fault processing module is used for deploying the automatic fault detection module to monitor the running states of the backup server, the storage device and the network link in real time.

The invention provides a digital enterprise management data storage method and system based on cloud computing, and the method comprises the steps: obtaining a real-time management data set in an enterprise data flow, generating a virtualized storage node, determining a target encrypted fragment index matched with the virtualized storage node according to a preset storage path optimization model, and storing the target encrypted fragment index into a database; based on a dynamic storage path corresponding to the target encrypted fragment index, segmenting the structured data block and the unstructured data block into encrypted data units distributed across regions, and mapping the encrypted data units to corresponding cloud storage partitions; dynamically distributing storage resources of the cloud storage partitions through a load balancing algorithm, and adjusting priority weights of dynamic storage paths according to real-time flow characteristics; and when an access request of the encrypted data unit is detected, redistributing the concurrent connection number of the cloud storage partitions. According to the method, the collaborative optimization problem of resource allocation and safety efficiency can be solved.

The invention belongs to the technical field of cloud storage, is suitable for the financial field and the medical field, and discloses a data table migration method, device, equipment and medium, and the method comprises the following steps: obtaining the name of an original database, a connection character string and a configuration file of a to-be-migrated data table, and analyzing the configuration file of the to-be-migrated data table to obtain a data table to be migrated; obtaining a table name and a table owner of the to-be-migrated data table; connecting the original database according to the connection character string of the original database, generating a table existence verification statement through the table name and the table owner of the to-be-migrated data table, and verifying the validity of the to-be-migrated data table according to the generated table existence verification statement; renaming the to-be-migrated data table in the target database according to a preset naming rule and performing compatibility processing; the migration instruction of the data table is generated in the target database, and the compatible data table to be migrated is migrated into the target database according to the generated migration instruction, so that the migration efficiency of the data table is effectively improved.

The invention provides a cloud storage method and system for Internet of Things equipment data, and relates to the technical field of Internet of Things data storage and processing, and the method comprises the steps: collecting metadata of a plurality of Internet of Things equipment in an Internet of Things environment; performing tagging processing on the metadata to form a standardized data packet; performing hierarchical compression processing on the standardized data packet, and extracting multi-dimensional features from the data packet after the hierarchical compression processing; inputting the multi-dimensional features into a multi-modal neural network model based on deep learning, analyzing the multi-dimensional features through the multi-modal neural network model, generating a target storage priority index, and determining a corresponding security level according to the target storage priority index; and based on the security level, performing differential encryption processing on the data packet after hierarchical compression processing by using a lightweight encryption algorithm, and transmitting the encrypted data packet to a storage area corresponding to the cloud. According to the invention, the efficiency and security of Internet of Things data storage are improved.

The invention discloses a PDF (Portable Document Format) document structured extraction system based on a multi-modal language model, belongs to the technical field of document processing and optical character recognition, and aims at solving the technical problem of how to improve the existing OCR (Optical Character Recognition) technology to improve the analysis capability of a complex document structure and improve the recognition precision of handwritten forms and other non-standard fonts. According to the technical scheme, the system adopts a layered decoupling architecture and comprises an input layer, a preprocessing layer, a reasoning layer, an output layer and a monitoring and fault-tolerant module; wherein the output layer is used for multi-source data access and path management to realize a local file system or S3 cloud storage; the preprocessing layer is used for invalid document filtering and visual feature extraction; the reasoning layer is used for multi-modal model interaction and content processing; the output layer is used for outputting a content aggregation result; and the monitoring and fault-tolerant module is used for realizing real-time state monitoring, resource consumption analysis and exception handling.

The invention discloses a WebGL-based Unity asset cross-platform preview method and system, and a storage medium. The preview method comprises the following steps: 1) preprocessing an editor plug-in: scanning a target asset through the Unity editor plug-in, extracting a resource GUID,. Meta metadata and a dependency relationship, generating a lightweight AB packet and adding a dynamic thumbnail; step 2) cloud dependency analysis: the server side recursively constructs a dependency tree based on GUID, filters redundant resources, and generates a resource loading list which can be identified by the browser side; and step 3) a WebGL rendering engine: loading an AB packet in a browser, realizing particle effect regeneration, animation control and scene interaction by simulating a Unity Runtime environment, and adapting to a WebGL 1.0 / 2.0 rendering pipeline. The invention aims to provide a WebGL-based Unity asset cross-platform preview method and system and a storage medium, aims to solve the problem that a browser cannot directly preview Unity resource files, and provides a set of resource management workflow based on Unity-cloud storage-browser preview for the Unity resource files.

The invention relates to the technical field of cloud storage, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a hierarchical storage method, device and equipment for data files and a medium, and the method comprises the following steps: acquiring the data files in a storage unit, and extracting data characteristic parameters in the data files; performing multi-dimensional quantitative analysis on the data feature parameters to obtain a dimensional feature analysis value; identifying a dynamic weight of the data feature parameter, and performing weighted calculation on the data feature parameter according to the dimension feature analysis value and the dynamic weight to obtain a dynamic storage analysis value; identifying a data storage type label according to the dynamic storage analysis value; matching the data storage type label with strategies in a storage strategy library to obtain a target storage strategy; determining a storage hierarchy according to the target storage strategy; a storage address of the data file in the storage hierarchy is identified and the data file is stored in the storage address. The accuracy of hierarchical storage of the data files can be improved.

The invention discloses an intelligent fire-fighting inspection system and method for a main transformer of a transformer substation, and the method comprises the steps: collecting fire-fighting inspection related data of an automatic fire extinguishing system, and uploading the data to a cloud for storage; classifying the collected fire-fighting inspection related data of the automatic fire extinguishing system according to different time sequence change types; establishing a corresponding neural network prediction model according to fire-fighting inspection related data of the automatic fire extinguishing system and different classification results of time sequence change types; inputting the classified fire-fighting inspection related data of the automatic fire extinguishing system into a corresponding neural network prediction model, and calculating to obtain a fire-fighting inspection prediction result of the automatic fire extinguishing system; and comparing the fire-fighting inspection prediction result of the automatic fire extinguishing system with a preset fire-fighting inspection data threshold value, and judging whether the fire-fighting inspection prediction result of the automatic fire extinguishing system has a fault risk or not. The working state of the fire extinguishing system is intelligently predicted, and the automatic fire extinguishing system of the transformer substation main transformer is intelligently inspected.

The invention relates to the technical field of log detection, in particular to a real-time anomaly detection and tracing method and system for a cloud storage log, and the method comprises the following steps: calculating an authentication failure proportion based on a user identifier and an internet protocol address, constructing a proportion change sequence, comparing an abrupt change threshold, screening candidate sections, fitting a slope, and aggregating anomaly records; calculating a fluctuation difference sequence, extracting a causal fracture node, recombining a subsequence graph, extracting a cross-node record and correcting a timestamp, connecting a transmission edge based on the timestamp, and accounting time consumption to construct a traceability path. According to the method, the serious defect that a fixed threshold value cannot sense system load fluctuation is overcome by constructing a dynamic proportion change slope, a causal fracture node is established to drive log atlas recombination so as to break an information barrier of a cross-node distributed environment, and a median function is used for correcting time migration to eliminate disorder interference caused by clock asynchronization; and the accurate risk tracing is realized by connecting the correction timestamp with the access transmission edge.

The invention discloses an image secret type cloud storage method and device based on feature block encryption, and the method comprises the steps: a cloud server receives and stores an encrypted image, an encrypted image feature set and a feature inner product, and forms an image database; the cloud server receives a feature ciphertext query feature uploaded by the authorized user when the authorized user needs to query the image; the cloud server performs retrieval according to the received ciphertext query features, separately calculates the similarity between color and texture sub-features in the ciphertext query features and features in each encrypted image feature set in the image database by using a hierarchical earth moving distance algorithm, and calculates the comprehensive similarity corresponding to each encrypted image; and screening out a plurality of encrypted images according to the comprehensive similarity and returning the encrypted images to the authorized user. According to the method, safe and efficient search of the image can be realized, and the image retrieval performance is improved.

Embodiments of the present disclosure relate to migrating data from the cloud to on-premises storage. More specifically, a processing device obtains an indication of a plurality of cloud storage resources associated with a plurality of cloud storage providers and a plurality of geographic regions. The processing device generates an on-premises file structure based on the indication of the plurality of cloud storage resources and the plurality of geographic regions. The processing device migrates the plurality of cloud storage resources to on-premises storage based on the on-premises file structure.

The invention discloses a regional grid safety evaluation method and device based on smart gas and a medium, belongs to the technical field of gas safety supervision, and aims to solve the technical problem of how to overcome the defects of insufficient matching between grid division and gas facility characteristics and lagging leakage risk prediction in the prior art. In order to realize full-process intelligence from risk identification to closed-loop management and improve supervision efficiency and early warning precision, the technical scheme adopted by the invention is as follows: dividing a pressure sensitive grid: performing optimization division on a regional grid based on a gas pipeline pressure grade and facility distribution characteristics, and realizing accurate division of the regional grid where a pipe network is located by combining a GIS and an improved DBSCAN algorithm; acquiring three-level Internet of Things data: integrating sensor data of an unmanned aerial vehicle, a pipe network pressure device and a leakage monitoring device, and covering a full-link data system of a terminal sensing layer, an edge computing layer and a cloud storage layer; constructing a double-engine evaluation model; carrying out digital twinborn visualization; and scheduling the capability matrix.

The invention relates to the technical field of data processing, and provides a cloud data archiving method and system, and the method comprises the steps: obtaining a source data set of to-be-archived data, carrying out the grade division, obtaining a data grade label, dividing the source data set into a plurality of data blocks, carrying out the data encryption of different grades, obtaining a plurality of encrypted data blocks, and storing the encrypted data blocks in a database; analyzing and coding to obtain a multi-dimensional data storage structure, uploading the multi-dimensional data storage structure to a preset cloud storage node, and generating a storage index information set based on multiple data blocks in the uploading process; and when a data recovery request is received, acquiring the data block from the cloud storage node according to the storage index information set, and performing decryption and data recombination. Through the scheme, classification, hierarchical encryption and structured archiving of the source data set are realized, the security and management efficiency of the data in a cloud storage process are improved, and the problem that the encryption level cannot be dynamically adjusted by data sensitivity or content features when a large-scale heterogeneous data source is used is solved.

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

The invention provides an SM9-based identity-based searchable public key encryption method and an SM9-based identity-based searchable public key encryption system. The system comprises a key generation center, a cloud storage server and a file sending and receiving party. A secret key generation center generates system public and private keys by using an SM9 algorithm, and generates a private key for a user in combination with identity information of the user. The file sender uses the user identity of the receiver and the file keyword to generate a keyword index and uploads the keyword index to the cloud server; when a receiver retrieves the file, the auxiliary parameters in the index are firstly obtained from the cloud server, then a keyword trap door is generated through a private key of the receiver, and the keyword trap door is uploaded to the cloud server; and the server compares the index with the trap door through a matching algorithm, and returns a corresponding ciphertext file to a receiver for decryption if matching succeeds. The secret key generation method is consistent with an SM9 algorithm, the application field of SM9 is expanded, and the safe and efficient searchable public key encryption method which does not need certificate management and can effectively resist internal keyword guessing attacks is achieved.

Systems and methods for securely transferring Data Loss Prevention (DLP) incident data from a cloud-based DLP system to a tenant's cloud storage account in a multi-tenant environment include detecting a DLP incident by identifying a policy violation and generating an incident event and an associated request containing metadata and contextual information for the incident; processing the request, the processing comprising validating the tenant's configuration settings for storage access permissions and target storage details and determining routing information for the tenant's cloud storage account; and writing DLP incident data associated with the DLP incident into the tenant's public cloud storage account.

A data processing system and method provides automated extraction, translation, load (ETL) operations. A server coupled with the processor executes the instructions and extracts data from various sources, such as cloud storage, external APIs and direct uploads. The system may include a stream mode processing unit to process large data files in manageable blocks, thereby improving efficiency and reducing memory load. It performs integrity checks to ensure data accuracy and consistency. The system configures and applies predefined and custom transitions, which is facilitated by user-friendly interface and API integration. Custom conversion logic is integrated into the process, allowing adaptive data operations to be performed. The converted data is then verified and formatted to be loaded into different destination systems. The ETL process is efficient, extensible and user-friendly, and thus suitable for wide data processing applications.

The present disclosure provides a system and a method for performing real-time cloud-native fingerprinting and managing sensitive content within a cloud storage platform to prevent data leakage. The system comprises a cloud storage platform comprising a data leakage prevention (DLP) server. The DLP server configured for receiving and storing a sensitive document, receiving a sensitivity level of the sensitive document, fingerprinting the sensitive document based on the sensitivity level, indexing and storing the fingerprint, sharing the fingerprint to an endpoint security agent, receiving leak indication, performing leak analysis and notifying the leak to a document owner. The system and method further perform monitoring of the sensitive data that has been fingerprinted and stored under a security folder, for a predefined time and automatically moving the sensitive data from the security folder after the predefined time.

The invention relates to the technical field of security engineering, and discloses a panoramic monitoring video cloud storage processing method and system, and the method comprises the steps: constructing a grading processing strategy set corresponding to multi-source panoramic video data, and a streaming processing rule engine and a value density scoring matrix of the grading processing strategy set; carrying out pre-transmission information reinforcement processing on a high-value event in the multi-source panoramic video data to generate an encrypted data stream of the multi-source panoramic video data; combining the space-time-behavior feature vector of the encrypted data stream with the global information atlas of the global monitoring front-end equipment, and setting a differential storage mode of the encrypted data stream; and finally, according to the hierarchical processing strategy set, the encrypted data stream and the differentiated storage mode, generating a cloud storage optimization scheme of the multi-source panoramic video data. According to the invention, the resource utilization efficiency of the panoramic monitoring video storage system can be improved.

The invention discloses a pressure monitoring method and system based on a power transmission cable axis sensor, and belongs to the technical field of data transmission, and the method comprises the steps: S1, a sensor module collects cable traction data and self position data at a measurement time point, and transmits the data to a monitoring module through a relay module; the monitoring module generates adjustment data based on the traction force data, wherein the adjustment data comprises an adjustment numerical value and an adjustment time point; s2, the cloud storage module obtains the data sequence and verifies the data sequence, and if verification is passed, the step S3 is executed; s3, analyzing the adjustment data, if an analysis result does not meet a preset rule, extracting an adjustment time point, and defining the adjustment time point as an abnormal time point; and S4, a marking module obtains a measurement time point which is located after the abnormal time point and is closest to the abnormal time point, obtains corresponding position data based on the measurement time point, and marks an indication label. According to the invention, a manager can remotely supervise the cable laying process.

The invention relates to the technical field of containers and micro-service architecture, and discloses a K8s-based Juser Notebook container arrangement method, which comprises the following steps of: S1, packaging Notebook into a Docker mirror image file; s2, mounting a Notebook configuration file on the K8s cluster through a ConfigMap (Configuration Map); s3, deploying and defining the number of copies of the Notebook application through Deployment; S4, configuring a service network and an ingress network; S3, deploying and defining the number of copies of the Notebook application through Deployment; s5, a cloud storage mode is adopted, and calculation and storage are separated; and S6, K8s API automatic management is carried out. The deployment process is simplified, the maintenance and management difficulty is reduced, and the resource utilization rate is improved; a cloud storage mode is adopted, separation of calculation and storage is achieved, and environment configuration is simplified; the Ingress provides network functions such as flow management and reverse proxy, so that the Notebook service is easier to access and integrate; the K8s cluster can ensure the high availability of the Notebook service; and when a fault occurs, quick recovery is realized, service interruption time is reduced, monitoring and management are easy, and management and operation and maintenance efficiency is improved.