89 results about "Local domain" patented technology

The invention provides an adaptive path selection system based on GNN and multi-agent DRL, which is based on a layered multi-domain SDN architecture and comprises a root server layer, a domain controller layer and a data layer. The root server layer comprises a root server and a root agent, and the root agent is used for calculating a global routing path strategy and optimizing and adjusting a routing path according to feedback of a local network domain. And the root server distributes the routing path to a domain controller layer according to a routing path strategy to carry out routing path forwarding and adjustment of a local network domain. The domain controller layer comprises a plurality of domain controllers and domain intelligent agents, the domain controllers construct a topological structure composed of boundary switches, and a routing decision is made in a local domain according to an optimal global routing path strategy. And the domain intelligent agent performs modeling analysis on the topological structure by using a deep reinforcement learning algorithm in combination with a graph neural network to realize intra-domain path selection. And the data plane layer feeds back the network topology information to the domain controller, and receives and executes a routing decision sent by the domain controller at the same time.

The invention discloses a Linux container-based Android and open source gap dual-system collaborative operation method, relates to the technical field of operation system collaboration, and aims to reduce resource occupation and improve collaboration and switching coherence, and the method comprises the following steps: step 1, by taking an Android as a host, creating a container isolation running domain, starting a namespace and managing resources by a control group; the method comprises the following steps: presetting a binder virtualization and three-state template; 2, deploying an open source gap in a container, mounting a shared directory in a directory binding manner, configuring bridging or address translation, and establishing copy-on-write and snapshot identifiers; and step 3, establishing a cross-system binder channel, and setting a service registration agent in a service manager and completing mapping of a handle and an authority domain by combining a shared memory and a local socket. And step 4, executing system switching without restarting, enabling one foreground and the other background through a foreground interface and continuous quota assignment, freezing a background session, limiting the speed of background heartbeat, and returning according to a snapshot when an exception occurs.

The invention relates to the technical field of artificial intelligence, in particular to a field adaptive large model fine tuning method and system based on enterprise private data, and the method comprises the steps: extracting the feature representation of local data by each participating client through a unified pre-training model, generating a prototype set representing local region category features by adopting a mean value, clustering or random sampling method; gaussian random noise is added to the prototype set, differential privacy protection is achieved, and then the prototype set is uploaded to a server; the server summarizes prototypes of all the clients, constructs a global prototype training set, performs global training of a single communication round on the adapter module by using supervised learning classification loss under the condition that similar prototypes of different clients are not averaged, obtains a global adapter model and issues the global adapter model to the clients; and the client initializes a local model based on the global adapter model, performs fine tuning by using a small amount of data with labels, and meanwhile, prevents catastrophic forgetting through knowledge distillation constraint by taking the global model as a teacher, so that balance between global knowledge retention and local performance improvement is realized. According to the method, the problem that the expression of the model in a cross-domain reasoning task is remarkably reduced due to the fact that domain characteristics of different enterprises cannot be effectively aligned in the prior art can be solved.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

The invention belongs to the technical field of computer man-machine interaction and automatic control, and particularly relates to an RPA mouse and keyboard control method and system for a Wayland desktop. The method comprises the following steps: S1, creating and registering a virtual input device supporting mouse and keyboard functions in a user space by accessing a virtual input device interface provided by an operating system kernel; s2, current activity display output is detected, and screen resolution information is obtained; s3, the server program monitors a preset local Unix domain socket path for receiving a control instruction from the client; s4, the client sends a control request to the local Unix domain socket path in a structured format, and the server receives and analyzes the control request; s5, the server side maps the analyzed control request into a corresponding kernel input event sequence according to the operation type; meanwhile, by writing event data into the virtual input device, an input event is injected into the system so as to simulate a control operation on the graphic desktop.

The invention discloses an intelligent diagnosis method and system for mechanical equipment faults under an open set. The method comprises the steps that a novel FCform encoder based on Fourier and convolution embedding is used for constructing a long-distance dependency relationship in a frequency domain, and more representative local domain invariant fault features are extracted; designing an open set dynamic sub-domain adaptive module based on DLMMD, and aligning condition feature distribution of known classes by covering potential unknown classes; in order to reduce the influence of empirical threshold setting on unknown class detection, decision boundaries of known classes and unknown classes are established by using an adaptive threshold learning method. And training a shared feature extractor and a fault classifier in the model through a minimum-maximum entropy game training strategy so as to suppress the problem of excessive confidence of the model possibly caused when the entropy value is minimized. According to the method, the problems of cross-domain knowledge migration of mechanical equipment faults and intelligent detection of unknown faults under the open set can be solved, and the method has a wide application prospect.

An improved computing system is arranged for cross-origin network communications on a single computing device. The system includes a processor, a networking module, and memory with software instructions arranged to operate a local computing server resource on a first local domain, instantiate a relay mechanism that has an iFrame and an invisible window, instantiate a local web server on a second local domain, install a service worker on the invisible window, receive a request for information at the local web server, verify a presence of the local computing server resource on the first local domain, communicatively connect the second local domain to the iFrame, and directly communicate, via the at least one networking module, at least one message between the local computing server resource on the first local domain and the local web server on the second local domain using the relay mechanism.

Systems and methods that allow a multihomed gateway to monitor its membership in individual domains in an Ethernet Virtual Private Network (EVPN) Data Center Interconnect (DCI) Network, and issue withdrawals from multihoming groups associated with those domains based on this membership, are disclosed. According to embodiments, links between a local domain and a gateway, and between the gateway and a remote domain, can be monitored at the gateway. When the link between the gateway and a domain goes down the gateway may withdraw itself from any multihoming group associated with the domain to which the link has been lost.

This invention discloses a method and system for generating real-time content in a specific domain based on AI technology, belonging to the field of artificial intelligence technology. The method includes the following steps: 1. Constructing a local material knowledge base RAG_src in the form of word vector storage using AI technology, whose materials include multimodal materials such as text, voice, audio / video, or H5; 2. Constructing a local domain knowledge classification knowledge base RAG_policy in the form of file storage using AI technology, whose content includes domain specification documents and technical classification documents categorized by domain knowledge and information; 3. Constructing PageIndex by combining a Large Language Model (LLM) with RAG_policy; 4. Receiving content generation requests from real-time software, calling RAG_src and PageIndex through LLM to generate real-time content that meets the requirements of the corresponding scenario; 5. Checking the generated content and outputting compliant content. This invention solves the problems of non-standard content generation, disconnection from relevant standards, low reuse rate of proprietary materials, and low retrieval efficiency in existing technologies, achieving accurate, efficient, and real-time content generation. It is adaptable to various scenarios such as real-time content generation, content compliance checking, and real-time domain assistance in specific domains. Application scenarios include, but are not limited to, oral practice and oral examinations according to teaching syllabi.

The invention discloses a traction motor cross-equipment fault diagnosis method and system based on partial adversarial domain adaptive migration, and relates to the technical field of fault diagnosis. And a global domain discriminator model and a local domain discriminator model which can dynamically adjust loss weights. When fault data category spaces of the source domain motor and the target domain motor are inconsistent, the fault data category space of the source domain motor and the fault data category space of the target domain motor are not consistent. The transfer learning model can accurately select samples of fault categories related to a source domain and a target domain to avoid the problem of negative transfer caused by paying attention to unrelated outlier categories, effective transfer of fault diagnosis knowledge between the two domains under label offset is achieved, and the adaptability of the fault diagnosis method to cross-equipment fault diagnosis of an actual running motor is improved; the method is easy to implement, extra hardware equipment is not needed, and the real-time operation health monitoring level of the motor and the train can be improved.

The invention relates to the technical field of data query, in particular to a bank data query intelligent question and answer interaction system based on a large model. Comprising a question and answer interaction end, a bank database, a data retrieval module, a local domain extraction module, a cross-domain extraction module and a reply integration module, in order to solve the technical problems of tedious cross-domain extraction, dependence on manual authorization and limited question and answer range caused by data dispersion, the invention constructs a large-scale model driving system fusing natural language understanding, intelligent permission routing and collaborative workflow. Inherent data islands in banks are converted into logically associated data networks and efficient intelligent collaboration, so that non-technical personnel can accurately obtain cross-system complex business data in a dialogue mode, automation and refinement of data security management and control and cross-department collaboration process reconstruction are realized at a bottom layer, and the data security management and control efficiency is improved. Therefore, on the premise that financial compliance and data security requirements are strictly met, fundamental improvement of data query efficiency, depth and a cooperation mode is realized.

The invention discloses a redundancy optimization-based power grid data consistency method, which comprises the following steps of: dividing a whole network into a plurality of local domains by utilizing reputation data and topological information of nodes, electing a global committee, further performing dynamic influence evaluation on a transaction request based on a network domain division graph, and according to an evaluation result, determining the consistency of the transaction request. High-frequency local transactions are limited in a local consensus domain for closed-loop processing, and only transactions related to global influences are submitted to a global committee. Through the mode, the communication redundancy of the whole network is remarkably reduced, so that the concurrent processing capability and expansibility of the system are greatly improved while the data consistency is guaranteed.

This invention discloses a generalized identification method and system for slope rock mass structural surfaces based on multi-level domain adaptation. This invention relates to the field of slope rock mass identification technology. By constructing four levels—global domain adaptation, regional domain adaptation, local domain adaptation, and sample-level adaptive adaptation—this invention achieves hierarchical and progressive evolution of model capabilities. A variational autoencoder is used to explicitly decouple features into domain-invariant and domain-specific parts, enabling the model to distinguish between the essential geometric features of rock mass structural surfaces and the weathering and unloading features of specific regions. Through regional meta-learning training, it can quickly adapt to new regions with only a small number of labeled samples.

The invention discloses a network access method and device in a DNS hijacked scene and a related product, and relates to the technical field of network security. The method comprises the following steps: firstly, checking a local domain name resolution cache of a client, preferentially multiplexing a valid IP address cache, if the valid IP address cache is invalid, acquiring an IP address list corresponding to a target domain name through an IP direct connection trusted service interface, and bypassing a DNS resolution link; an IP address set with timeliness is generated and stored persistently, and address validity is guaranteed through regular refreshing of a double-judgment mechanism; a main domain name, a standby domain name and a local cache IP address are obtained based on the set, and a domain name-first progressive failover access strategy is generated in combination with multi-network configuration; and when the access of the main domain name and the standby domain name fails, automatically switching to the local cache IP address to initiate the access. According to the method and the device, stable network access in a DNS hijacking scene is realized, and reliable network access guarantee is provided for various software which depends on domain name access.

The present application relates to the technical field of artificial intelligence, in particular to a domain self-adaptive large model fine-tuning method and system based on enterprise private data, the method comprising: each participating client extracts the feature representation of local data using a unified pre-training model, and generates a prototype set representing the category features of the local domain by using mean, clustering or random sampling methods; uploading the prototype set to the server after adding Gaussian random noise to realize differential privacy protection; the server aggregates all client prototypes to construct a global prototype training set, and performs global training of the adapter module in a single communication round using supervised learning classification loss without averaging the same prototypes of different clients, obtains a global adapter model and distributes it to the client; the client initializes the local model based on the global adapter model, fine-tunes it using a small amount of labeled data, and uses the global model as a teacher to prevent catastrophic forgetting through knowledge distillation, achieving a balance between global knowledge retention and local performance improvement. The present application can solve the problem that the existing technology cannot effectively align the domain features of different enterprises, resulting in a significant decline in the performance of the model in cross-domain reasoning tasks.

This application provides a data security sharing method, apparatus, and system based on a usable but invisible mechanism, applied in the field of cross-domain data sharing security technology. It constructs a secure sharing mechanism based on a three-dimensional collaborative approach of "spatiotemporal-object-rights," deeply integrating a spatiotemporal grid coding system with a digital object identification system to generate unique identifiers with spatiotemporal semantics. This is combined with a rights root mechanism to complete data ownership registration. During data circulation, relying on data network infrastructure and digital contract mechanisms, usage requests are routed to the data provider side, and a trusted execution environment bound to the contract is scheduled. This ensures that the original data completes secure computation without leaving the local domain. Simultaneously, traceable evidence information and measurable encrypted credentials are automatically generated along with the computation results. Ultimately, this achieves trusted sharing throughout the entire lifecycle of data—data that remains within the domain, usable but invisible, controllable, and measurable—providing fundamental support for a data-driven society.

A UMR auto-discovery mechanism which allows a gateway EVPN router to advertise a UMR route and to suppress redistribution of remote domain MAC-IP routes. In one embodiment, upon joining a local EVPN domain of a network, a network device (an EVPN router) advertises its UMR capability, which may include UMR installer capability or UMR originator capability. The UMR-capable devices in the network are also advertised to the newly added network device. A UMR originator (e.g., gateway) with knowledge of the UMR capabilities of the devices in the network then generates and sends a route list to the devices, where the route list includes a UMR route, or MAC / IP routes, or both, depending upon whether all of the network devices are all UMR-capable, none are UMR-capable, or the devices include both UMR-capable and non-UMR-capable devices, respectively.

The invention relates to an electroencephalogram self-adaptive classification method based on a dynamic united domain adaptation network and application. The method comprises the steps that firstly, EEG signals are collected and preprocessed and then transmitted to an upper computer; after receiving the preprocessed EEG signal, the upper computer performs signal feature extraction and classification based on a dynamic joint domain adaptation network DJDAN model, and the DJDAN model comprises a feature extractor, a classifier, a global domain discriminator and a local domain discriminator; the upper computer converts the motion intention label output by the classifier into a control signal required by rehabilitation equipment; the rehabilitation equipment generates feedback signals according to the training actions of the patient and transmits the feedback signals to the upper computer, and the upper computer records and evaluates the training progress of the patient. The motion intention recognition based on the electroencephalogram signals can be realized, the method is applied to an intelligent rehabilitation training system of a stroke patient, and the rehabilitation effect and the motion ability of the patient are improved.

The invention discloses an intelligent comparative analysis method and system for a network security situation, and relates to the technical field of network security, and the method comprises the steps: constructing a multi-source coupling index E and a disturbance anomaly index Ycz on the basis of obtaining a standard environment perception data set and a standard communication perception data set; quantitative judgment on whether environmental disturbance can reasonably explain communication abnormity or not is achieved. When the disturbance anomaly index Ycz is higher than a set threshold value, a node transverse behavior structure analysis mechanism is further triggered, the sensing node is divided into a local domain Dm based on physical adjacency and topological adjacency, and a behavior structure offset function Mm is calculated based on the disturbance anomaly index of each node in the local domain. According to the structural analysis mode, abnormal outliers, synchronous abnormal groups or potential attack source nodes in a local structure can be effectively recognized, the linkage recognition capability of a system on local explosive attacks or node out-of-control phenomena is improved, and comprehensive perception upgrading from single-point behavior abnormity to structure-level situation migration is achieved.

The application discloses a multi-working-condition industrial process fault diagnosis method based on a dynamic adaptive domain adversarial network, and comprises the following steps: standardizing process data and taking a sliding window to obtain dynamic data; training a dynamic adaptive domain adversarial network by using labeled dynamic data of a source working condition and unlabeled dynamic data of a target working condition; the dynamic adaptive domain adversarial network comprises a feature extractor, a label classifier, a global domain discriminator and a local domain discriminator; the global domain discriminator aligns the marginal distribution of the source working condition and the target working condition; the local domain discriminator aligns the conditional distribution of the source working condition and the target working condition; a kind of learnable parameter is proposed to adaptively evaluate the relative importance of the marginal distribution and the conditional distribution, so as to better extract domain-invariant features; a kind of common center loss is proposed to improve the inter-class separability and intra-class compactness of data, and further improve the fault diagnosis precision; and the label classifier is used to predict the fault category of a sample. The application establishes an accurate fault diagnosis model for the target working condition.

The invention discloses an open set domain adaptive blast furnace fault diagnosis method based on reciprocity point learning, and belongs to the field of industrial process monitoring and intelligent diagnosis. According to the method, an open set domain adaptation framework is provided for the problems of fault sample scarcity, data distribution drift and unknown fault category occurrence in the blast furnace production process. The method comprises the following steps: firstly, constructing a mixed CNN-ViT feature extractor, and fusing local and global features to capture the slow time-varying feature of a blast furnace fault; and secondly, designing a prototype-reciprocity double classifier, constraining the open space risk through reciprocity point learning, and realizing effective distinguishing of known and unknown faults. And an entropy-based adaptive threshold mechanism is further introduced to dynamically identify an unknown fault. And finally, domain invariant feature learning and cross-working-condition knowledge migration of known faults are realized through local domain adaptation and joint distribution alignment. The method has excellent fault diagnosis precision and robustness in multi-working-condition and multi-source-domain data of the blast furnace, and the recognition capability of unknown faults is remarkably improved.

The invention relates to a dynamically-configured VPN server multi-domain name shunting method and device, computer equipment and a medium, and the method comprises the steps: receiving the flow of a VPN client, and recognizing whether the flow belongs to DNS query flow or non-DNS query flow; when the flow belongs to DNS query flow, forwarding the flow to a local DNS query server, and when DNS response information returned by the DNS query server is obtained, extracting an IP address and domain name information from the DNS response information, and updating the IP address and domain name information to a local IP domain name mapping table; when the traffic belongs to non-DNS query traffic, performing domain name resolution; when the analysis is successful, executing a domain name shunting operation according to the domain name information obtained by analysis, and updating the target IP address of the traffic and the domain name information obtained by analysis to a local IP domain name mapping table; and when the analysis fails, querying a local IP domain name mapping table according to the target IP address of the flow, and executing domain name shunting operation according to the queried domain name information. According to the method, domain name shunting can be accurately and safely carried out on the encrypted traffic in real time.

The invention provides a GSM-R (Global System for Mobile Communications-Railway) cross-domain coordinated multipoint cluster wireless network simulation method and system, and the method comprises the steps: receiving a call request from at least one vehicle-mounted device for each domain, determining the call request as a circuit domain request or a packet domain request based on the call request, the calling request carries address information and identification information of the vehicle-mounted equipment, local domain address information and identification information of called target ground control equipment, or carries request content and address information of the vehicle-mounted equipment and a target domain name; if the call request is determined to be a circuit domain request, determining whether the call request is cross-domain communication based on identification information of the called target ground control equipment and address identification information stored in the domain; and after the address information of the local domain is reconstructed and the address information of the target ground control equipment of the target domain is determined through the central server, the reconstructed call request is forwarded to the target ground control equipment based on the address information of the target ground control equipment.

This invention provides a network traffic anomaly detection method adapted to multi-domain IoT federated domains. The method involves acquiring network traffic data from multi-domain IoT and processing it to generate a unified feature vector; establishing a multi-task labeling system for the IoT network traffic data based on attributes; deploying a shared backbone network and a local domain adapter on each federated client and performing local training; calculating a multi-task loss based on the multi-task labeling system; aggregating the parameter differences of the multi-domain IoT clients on the server side based on the multi-task loss and updating the global model; distributing the updated global model to each client of the multi-domain IoT and adjusting sampling, loss weights, and threshold calibration; performing multiple rounds of federated iterations until a termination condition is met, freezing the shared backbone network while retaining the local domain adapter; and performing anomaly detection and output on the real-time acquired multi-domain IoT network traffic data using the acquired shared backbone network and local domain adapter.

Disclosed are a cross-domain access method, apparatus, device and medium, which relate to the technical field of communications, comprising: a first terminal device configured with a non-transparent bridge in a source domain obtains a target data packet routed by other devices in the source domain in the domain through a local virtual function, determining a target domain index corresponding to the source domain address field of the first terminal device from the source domain address mapping table; different virtual functions correspond to different virtual machines; and packaging the target data packet based on the domain information including the target domain index and the routing information including the routing type, and transmitting the packaged data packet to a second terminal device configured with a non-transparent bridge in a target domain corresponding to the target domain index in a cross-domain manner, so that the second terminal device triggers a routing operation corresponding to the routing type, and routing the target data packet in the target domain by using the target identity determined from the target domain mapping table. According to the method, the mapping table is adopted, so that the terminal equipment serving as a cross-domain access initiator can perform cross-domain operation through the local domain identity.

The present invention relates to an electroencephalogram (EEG) adaptive classification method and application based on a dynamic joint domain adaptation network. The method first collects and preprocesses EEG signals before transmitting them to a host computer. After receiving the preprocessed EEG signals, the host computer extracts and classifies the signals based on a dynamic joint domain adaptation network (DJDAN) model, which includes a feature extractor, a classifier, a global domain discriminator, and a local domain discriminator. The host computer converts the movement intention labels output by the classifier into control signals required by rehabilitation equipment. The rehabilitation equipment generates feedback signals based on the patient's training movements and transmits them to the host computer, which records and evaluates the patient's training progress. The present invention can realize movement intention recognition based on EEG signals and is applied to intelligent rehabilitation training systems for stroke patients, improving rehabilitation effects and the patient's motor skills.

The application discloses an intelligent diagnosis method and system for mechanical equipment faults in an open set, which comprises the following steps: a new FCformer encoder based on Fourier and convolution embedding is used to construct long-distance dependency in the frequency domain and extract more representative local domain invariant fault features; a dynamic sub-domain adaptive module based on DLMMD is designed to align the conditional feature distribution of known classes by masking potential unknown classes; in order to reduce the influence of experience threshold setting on unknown class detection, an adaptive threshold learning method is used to establish the decision boundary of known classes and unknown classes. The shared feature extractor and fault classifier in the model are trained by a min-max entropy game training strategy to suppress the overconfidence problem of the model when the entropy value is minimized. The application can solve the problems of cross-domain knowledge transfer and intelligent detection of unknown faults of mechanical equipment in an open set, and has a wide application prospect.

The invention provides a cross-domain identification method for FeO content of sintered ore, which comprises the following steps of: acquiring cross-sectional images of a sintering machine tail under different working conditions, and dividing an image sample into a source domain data set and a target domain data set; converting the source domain image and the target domain image into a Token sequence through a patch embedding module, and mixing the source domain Token and the target domain Token at the same spatial position in a linear interpolation mode to obtain an intermediate domain Token sequence; a local domain adaptation module is arranged in the feature extractor, fine-grained feature alignment is carried out on a source domain Token, a target domain Token and a middle domain Token, and disturbance is added to a Token sequence in a randomly selected Transform Block hidden layer; a game strategy is adopted, coarse-grained feature alignment is executed through a domain mixing module, and a self-refining strategy is adopted to carry out iterative optimization on pseudo tags; and inputting a label-free target domain sample into the trained model, and outputting the FeO content category of the sintered ore. The method can effectively solve the common problems of inconsistent data distribution, label data scarcity and the like in actual industrial process working condition identification.

The present application relates to the technical field of communications. Disclosed are communication methods, a satellite system, an electronic device and a storage medium. In a communication method disclosed in the present application, when a first server has received a domain name resolution request sent by a terminal device, if the first server stores a first IP address corresponding to a first domain name in the domain name resolution request, the first IP address can be directly returned to the terminal device; and when the first server does not store the first IP address corresponding to the first domain name, the domain name resolution request is forwarded to a local domain name server by means of a second server deployed in a geostationary earth orbit satellite, and the first IP address is acquired from the local domain name server by means of the second server. In this way, satellite communication resources can be effectively saved, and the situation of network access failure of a terminal device can be effectively reduced, thereby improving the user experience.

The invention provides a domain name resolution method and device and related equipment, and relates to the technical field of communication. The method comprises the following steps: sending a domain name resolution request message of which the message type is a computing power resource record about a service end point to a local domain name server; a domain name resolution response message from the local domain name server is received, the domain name resolution response message comprises a first context record, and the first context record comprises the priority of the first context record, the domain name of the inquired service endpoint and a computing power resource record; under the condition that the first context record is in an alias mode, regenerating a domain name resolution request message based on the first context record, and sending the regenerated domain name resolution request message to a local domain name server; and under the condition that the first context record is in the service mode, determining that the first context record is a domain name resolution result of the service endpoint. Through the technical means, the problem that the computing power information of the service end cannot be obtained through DNS domain name analysis in the related technology is solved.