115 results about "Real-time web" patented technology

The invention discloses a network security defense method and system based on incremental network attack analysis learning. The method comprises the following steps: collecting initial network flow data, and extracting a feature vector; and collecting real-time network flow data, performing segmentation processing based on a sliding time window, extracting time sequence association features from the segmented data, and matching the time sequence association features with the feature library to identify potential attacks or abnormal behaviors. And when the time sequence correlation feature is not matched with the feature library, marking the time sequence correlation feature as a candidate novel attack feature, calculating a mahalanobis distance between the time sequence correlation feature and a known attack feature to determine the attack variability, and dynamically adjusting the weight of the time sequence correlation feature. And inputting the adjusted feature weight and the real-time flow feature into a deep reinforcement learning detection model, and generating and updating a network security defense strategy. The scheme of the invention can effectively identify novel attack behaviors, dynamically respond and optimize defense strategies, and improve network security.

The invention relates to the field of network security, and discloses a problem line determination method based on IP address classification and identification, which comprises the following steps of: collecting public network IP behavior data in a network flow log, extracting multi-dimensional characteristics such as a time regularity entropy value, a protocol diversity proportion, target port dispersity and a request rate change rate, and determining a problem line according to the extracted multi-dimensional characteristics; constructing an IP association graph fusing feature similarity, time synchronism and physical position constraints; an abnormal IP group is identified by adopting a community division algorithm, and the detection sensitivity is adaptively optimized according to a real-time network load and a historical false alarm rate in combination with a dynamic threshold adjustment mechanism based on a PID feedback control model; the attack type is judged through protocol-port mapping and Mahalanobis distance statistics, alarm information containing an abnormal IP list, a time window and a classification result is generated, and the alarm information is linked with safety equipment to execute a defense strategy. According to the method, the defects of a traditional method in the aspects of multi-dimensional attack recognition, dynamic environment adaptation and cooperative attack detection are overcome.

The invention relates to a network security vulnerability intelligent detection method based on artificial intelligence and big data, and the method comprises the steps: carrying out the dynamic time synchronization processing of multi-modal network security data, and generating a multi-source data flow with aligned time sequences through cross-modal correlation analysis; extracting cross-modal features from the data stream, and performing semantic fusion on the cross-modal features in combination with a vulnerability knowledge graph to generate a multi-dimensional feature vector; training the multi-dimensional feature vector through a hybrid model to obtain a vulnerability detection model, detecting real-time network behavior data by using the model, and outputting a vulnerability probability and an abnormal risk score; and performing automatic vulnerability verification according to the vulnerability probability and the abnormal risk score to obtain a verification result, and updating the vulnerability detection model according to the result. The system can effectively improve the accuracy, timeliness and stability of network security vulnerability detection and reduce the false report and missing report rate through multi-modal data collaboration, hybrid model dual detection and closed-loop optimization mechanisms.

The invention relates to the technical field of audio playing optimization, and discloses a low-delay multi-room audio playing optimization method and system, a storage medium and equipment, and the method comprises the steps: building a global time synchronization system based on a precision clock protocol in a playing equipment cluster, and enabling all playing equipment to operate based on a unified time reference; monitoring network state parameters in real time, dynamically calculating and adjusting the audio buffer depth of each playing device based on the monitored network quality, and obtaining audio data in advance according to the playing progress and the network state; generating a global playing timestamp according to the global time synchronization system, and controlling all playing devices to perform synchronous starting and progress control of audio playing based on the global playing timestamp; a transmission strategy of audio data is dynamically optimized based on real-time network state parameters, packet loss recovery and bandwidth adaptation are achieved, sub-millisecond multi-room audio synchronous playing is achieved, and meanwhile performance parameters can be automatically optimized according to different network environments and hardware configurations.

The invention discloses an eSIM service cost intelligent optimization method based on different operator information sources, and particularly relates to the technical field of operator network management. The method comprises the following steps: collecting eSIM tariff rules of a plurality of operators and real-time charging data of eSIM terminals, extracting tariff terms and conditional expense trigger mechanisms hidden in charging behaviors of different operators by using an association rule mining algorithm, and constructing a multi-level cost structure model; on the basis of a network registration behavior log of the eSIM terminal, predicting the probability that each operator signal source has an abnormal registration event, and evaluating the risk that each operator signal source triggers a hidden cost trap in combination with a multi-level cost structure model; and according to the real-time network signal parameters of the eSIM terminal, a signal source recommendation strategy is generated in combination with the risk assessment result, and the eSIM terminal is driven to execute a signal source switching operation, so that real-time adjustment and cost optimization control of communication network selection are realized, and the service stability of the eSIM in a cross-network use scene is improved.

The invention discloses a zero-trust data access control method and system and a storage medium, and the method comprises the steps: carrying out the identity authentication of a user logging in a client, and obtaining the current terminal equipment information of the client, the current network information and the historical access information of the user after the authentication succeeds, the trust level of the authorization request and the current access permission matched with the trust level are obtained; acquiring a real-time access action of a user and real-time terminal equipment information and real-time network information of a client to perform threat detection on the authorization request, and adjusting the current access permission so as to generate a real-time access strategy corresponding to the client according to the adjusted real-time access permission; and acquiring a data access request uploaded by the client according to the real-time access strategy, acquiring access data required by the client according to the access request, and sending the encrypted access data to the client, thereby improving the security of data access.

The invention relates to a large language model reasoning enhancement method and device based on a multi-source retrieval and summary module, and the method comprises the steps: taking Qwen2.5-32B as a large language model, and carrying out the fine adjustment of the large language model through a preset retrieval data set; multiple rewards are introduced through reinforcement learning to improve the self-cognition ability of the large language model; constructing a vector database and a real-time network retrieval system based on the FAISS, and performing multi-source retrieval on the text data through the vector database and the real-time network retrieval system; text information of the text data is summarized through a preset small language model, and the text information is output to the large language model. According to the method, the self-cognition ability of the big language model is improved by adopting reinforcement learning (GPRO), and the multi-source retrieval module is constructed, so that the data with similar semantics can be quickly queried to assist the big language model in deeply understanding the semantic connotation of the data, and the accuracy and rationality of retrieval are improved.

The invention discloses a hydrological data synchronization method and device based on distributed storage and a medium, and the method comprises the steps: dividing hydrological data collected by different hydrological monitoring point sensor devices in real time into a plurality of different grades according to the data emergency degree, and adding a space-time label to the hydrological data; fragmenting the hydrological data based on the space-time dimension to obtain a standard data block, and determining a regional storage node corresponding to the standard data block; according to the real-time network state, determining an optimal transmission path corresponding to different levels of hydrological data; and based on the consistency transmission rules corresponding to the multiple different levels, synchronizing the standard data blocks to the corresponding regional storage nodes through the optimal transmission path. Through level-to-level management and intelligent routing decision making of hydrological data, an optimal transmission path is selected according to characteristics of different levels of data and network states, resource waste and transmission delay caused by a traditional unified synchronization strategy are avoided, and the data synchronization efficiency is remarkably improved.

The invention relates to the technical field of data processing, in particular to a vCPE resource allocation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining system operation data of each monitoring point, inputting the system operation data into a preset anomaly detection model, outputting an abnormal data point, determining a vCPE resource allocation strategy according to the abnormal data point, inputting the system operation data into a preset target prediction model, and obtaining a vCPE resource allocation result. And outputting predicted operation data in a future time period, adjusting a resource allocation strategy according to the data, and adjusting vCPE resource configuration according to the adjusted strategy. According to the method and the device, the limitation that traditional vCPE resource allocation is based on static configuration or simple rule scheduling is broken through, the resource allocation strategy can be dynamically adjusted according to the real-time network condition and the user service requirement, the vCPE resource utilization rate is effectively improved, the network performance is optimized, and the problems of low resource utilization rate and limited network performance caused by an existing resource allocation mode are solved.

The invention discloses a storage resource allocation method and device, electronic equipment and a storage medium, and relates to the technical field of resource scheduling, and the method comprises the steps: determining the priority weight of each storage volume based on network state information and performance index information at intervals of a preset time period or if the network state information represents that network congestion occurs at present, allocating a corresponding bandwidth to each storage volume based on the priority weight; if the current to-be-processed task does not need to be operated across the heterogeneous storage device, determining a target transmission path and a target storage volume of the current to-be-processed task according to the priority of the current to-be-processed task and the bandwidth corresponding to each storage volume; and processing the current to-be-processed task based on the target transmission path and the target storage volume. The technical problem of storage performance jittering caused by the fact that the storage resource distribution cannot be dynamically adjusted according to the real-time network state is solved, and the technical effects of reducing the sudden delay or lagging phenomenon and improving the system stability are achieved.

The embodiment of the invention discloses a video safety playing method and device based on multi-dimensional protection and a medium, and relates to the technical field of video transmission, and the method comprises the steps: obtaining terminal device information under the triggering of a video playing request of a target playing terminal, performing two-factor authentication on the target broadcast terminal based on the terminal equipment information and terminal registration information pre-stored in a block chain node, and determining an equipment authentication result; collecting a real-time network index of each transmission path through a pre-deployed network probe, and screening a target transmission path from the plurality of transmission paths based on the real-time network indexes; and when the device authentication result is passed, acquiring watermark fragment data corresponding to a target video stream in a pre-constructed block chain node, so as to transmit the watermark fragment data to the target broadcast terminal through the target transmission path, and monitoring the transmission process in real time to realize safe broadcast of the target video stream.

The invention discloses a power production OTN resource scheduling method based on a particle swarm optimization algorithm, and the method comprises the following steps: 1) constructing a digital twinborn model of a power production OTN network through a digital twinborn modeling method of multi-class model fusion; and 2) modeling is carried out on a power production OTN resource scheduling problem, a swarm intelligence optimization method based on a particle swarm optimization algorithm is adopted, a neighborhood search strategy and a global search strategy suitable for power production OTN resource scheduling are designed, and the power production OTN resource scheduling method based on the particle swarm optimization algorithm is realized. The improved particle swarm optimization algorithm is adopted to dynamically adjust the position and speed of the particles according to the real-time network state, self-adaptive optimization is achieved, the method adapts to the changing network environment, multiple optimization targets can be considered at the same time, the scheduling scheme is comprehensively optimized, and the method has higher global search ability, high convergence speed and high robustness.

The invention relates to the technical field of industrial networks, in particular to a time-sensitive service flow resource adaptation method for large-bandwidth multicast flow awareness, which comprises the following steps: S1, a controller collects registered service flow information and real-time network state information; s2, after service flow priorities are divided according to the information, a mapping relation between service flows and multiple queues is established, and a forwarding rule is established according to the mapping relation; s3, establishing an optimization target and a resource constraint according to the mapping relation and the forwarding rule; and S4, designing a Markov decision according to the optimization target and the resource constraint, training the neural network model according to the Markov decision and the double-delay depth deterministic strategy gradient so as to output a scheduling decision, and obtaining a multi-dimensional resource adaptation result by a solver according to the scheduling decision. According to the invention, a basic queue model support is provided for end-to-end deterministic transmission of the unicast stream and the multicast stream through the mapping relation and the forwarding rule, the utilization rate of network resources and the number of service streams capable of being subjected to incremental scheduling are improved, and the worst end-to-end delay of the large-bandwidth multicast stream is reduced.

The invention discloses a flow filtering method and device for extracting quintuple flow characteristics, and the method comprises the steps: obtaining a quintuple characteristic sequence of a historical network communication data sample; in a plurality of time windows, extracting joint probability distribution among different quintuple features in the quintuple feature sequence to obtain time sequence correlation features; constructing a communication prompt word according to the time sequence association feature; inputting the communication prompt word into a preset large language model for training to obtain a target large language model; performing anomaly analysis on the real-time network communication data through the target large language model to obtain an analysis result; determining an exception type based on an analysis result; and determining a traffic filtering strategy according to the exception type and a preset strategy library, and performing traffic filtering through the traffic filtering strategy. According to the invention, the accuracy of abnormal traffic filtering can be improved.

The invention provides a hybrid congestion control method and system based on dynamic adjustment of a monitoring time period, and relates to the technical field of communication, and the method comprises the steps: obtaining a real-time network state, and calculating a round-trip delay change according to the network state to obtain network state data; performing normalization processing on the network state data to standardize the network state data, comparing the standardized network state data with a preset threshold to classify the network state data after normalization processing, and calculating a monitoring time period value according to a classification result to dynamically adjust the monitoring time period value; judging whether the current time reaches a monitoring time period value or not; and if yes, switching the current congestion window to a congestion window which is generated based on deep reinforcement learning and is adjusted by an optimal action according to the monitoring time period value. Compared with a traditional fixed monitoring time period scheme, the method not only has higher environment generalization ability, but also can quickly adapt to network condition changes through real-time state classification.

The invention belongs to the technical field of internet online conference scheduling, and particularly relates to an internet-based online conference scheduling system and method. According to the method, a space-time weight mechanism ensures that a high-priority scheme is generated in one step, multiple rounds of back-and-forth communication are reduced, overlapping and absent risks in a selected time period are effectively avoided through historical behavior prediction and conflict verification, an interactive feedback mechanism quickly responds to newly-added conflicts, the stability of the scheme is improved, an audio and video transmission strategy is dynamically reserved, and the user experience is improved. Resource allocation and historical QoS data driving are intelligently adjusted according to real-time network conditions, delay, lagging and interruption caused by network problems in a conference can be remarkably reduced, in combination with personalized prediction of a topological structure, an internal cooperation habit, a visual time selection topological graph and an online negotiation tool are better matched and organized, transparency and sense of participation are enhanced, and the network quality is improved. And the multi-dimensional conflict and the space-time weight are fused, so that the balance optimization of the comprehensive cost and the efficiency is realized.

The invention provides a power distribution network evaluation method and system, and relates to the technical field of power system operation control, and the method comprises the steps: obtaining an extreme event index of a power distribution network in a current time period according to a measurement data flow, and judging whether an extreme event exists in the power distribution network or not according to the extreme event index; the measurement data streams are classified through a data quality grading mechanism, and then an observation weight matrix of the extreme events is constructed; supplementing missing data for the missing data segment according to the observation weight matrix, dynamically updating topological information of the power distribution network in combination with the supplemented missing data, and obtaining a real-time network model corresponding to the extreme event according to the topological information; and according to the real-time network model and the extreme event index, obtaining a state evaluation result of the power distribution network in the current time period. According to the invention, the evaluation precision of the power distribution network under extreme conditions is improved.

The invention discloses a multifunctional AR glasses and mobile phone interaction method and device, and relates to the field of equipment interaction. In the method, a plurality of communication channels are established between an AR glasses end and a mobile phone end through a bidirectional protocol, a protocol encapsulation layer is overlaid on the plurality of communication channels, and a mapping relation between a data type and the plurality of communication channels is established; mapping the user operation into a cross-device instruction stream, and selecting an optimal channel according to the data type and the mapping relationship of the cross-device instruction stream; determining a compression and encryption strategy according to the data type and the real-time network condition, processing the cross-device instruction stream by using the compression and encryption strategy, and transmitting the processed cross-device instruction stream through the optimal channel; and obtaining interaction data and an equipment state between the AR glasses end and the mobile phone end, and when the interaction data or the equipment state is abnormal, switching the optimal channel to the target channel. By implementing the technical scheme provided by the invention, the interaction efficiency of the AR glasses and the mobile phone is improved, and the data transmission reliability is ensured.

The invention discloses a cross-platform advertisement putting strategy collaborative management method and system, and relates to the technical field of advertisement putting, and the method comprises the steps: uploading a plurality of advertisement test samples to a putting platform test library for testing, obtaining real-time network information, and carrying out the evaluation of a test result, selecting the advertisement test sample with the highest advertisement putting return rate data as a main push advertisement, setting an advertisement putting strategy according to the hot time period of the platform, then pre-putting the main push advertisement on each platform according to the advertisement putting strategy to evaluate the pre-putting effect, establishing a revenue prediction model according to a deep reinforcement learning algorithm, and predicting the revenue of the main push advertisement. The method comprises the steps of predicting the income of advertising on each platform in real time, dynamically distributing a budget weight according to the predicted income data of the advertising on each platform to obtain an advertising strategy distribution table, and formulating an advertising income distribution contract according to a preset advertisement click rate and a conversion value. And according to the advertisement revenue distribution contract, calculating and distributing revenue to an account corresponding to the platform.

The invention discloses a network flow prediction method based on a decoupling dynamic space-time diagram neural network D2STGNN. The method comprises the following steps: constructing a network flow data processing and representing module, and collecting and preprocessing flow data; then, a prediction model is constructed based on D2STGNN, a space-time framework is decoupled to decompose a network traffic space-time signal into a node-traffic association signal and a network-traffic time trend signal, and by means of a space-time local convolution layer, a gating circulation unit and a multi-head self-attention layer, an inter-node association process and a capture time sequence dependency relationship are respectively modeled; and meanwhile, a time attenuation mechanism is introduced, and the real-time dynamic capturing capability of the model on the network flow is enhanced in combination with adaptive attenuation parameter adjustment and a layered attenuation mechanism. And finally, predicting real-time network flow data by using the trained model, and evaluating a prediction effect through related evaluation indexes. According to the method, the change trend of the network flow can be efficiently and accurately predicted, and powerful support is provided for reasonable distribution of network resources, network performance optimization and the like.

The invention provides a power system network security detection method, device, equipment and medium, and relates to the technical field of network security detection.The method comprises the steps that the node importance of each network node of a network topology structure is determined according to network flow data, respectively extracting a delay time node curve, a fluctuation time node curve, a packet loss rate time node curve and connection failure time node data of each network node, and constructing a network traffic time graph; inputting the graph structure, the time sequence feature vector and the log semantic embedding vector into a trained security detection model to obtain a security detection score; and generating a power system network security detection score according to node importance and the security detection score of each network node, dynamically adjusting an anomaly judgment standard according to real-time network performance parameters based on an adaptive threshold mechanism, and generating a security detection result according to the power system network security detection score and the anomaly judgment standard. The accuracy of safety detection can be improved.

The invention relates to the technical field of multimedia data processing, and particularly discloses an interactive media intelligent preloading and seamless switching method and system based on a dynamic narrative engine, and the method comprises the steps: carrying out multi-step logic deduction according to a predefined narrative logic structure and real-time playing context data of interactive media, calculating the selected dynamic probability of the media segment corresponding to each subsequent narrative node, outputting a high-probability target media segment list arranged in a descending order according to the probability, and deciding the pre-loading content and the loading priority according to the high-probability target media segment list, the attributes of the segments and the real-time network state; the high-probability target media segment is preloaded to a local cache; in response to a user selection instruction, obtaining a target media segment from a local cache or a network; performing decoder management and / or timestamp alignment and / or visual transition processing on the target media segment to obtain a seamless playing switching result; and the narrative logic can be deeply understood, the user behavior can be intelligently predicted, and zero-perception switching can be realized.

The invention discloses a dynamic hierarchical data encryption and decryption method, system and device based on the Internet of Things, and a medium, belongs to the technical field of Internet of Things security, and aims to solve the technical problem of how to dynamically adjust an encryption and decryption mode in an Internet of Things data transmission process, provide high security and flexibility of Internet of Things data transmission, and improve the security and reliability of Internet of Things data transmission. According to the technical scheme, the method comprises the following steps: data layering: dividing data into a plurality of hierarchies through a data analysis and classification algorithm according to the sensitive degree and transmission requirements of the data; dynamic encryption and decryption: dynamically adjusting the encryption and decryption hierarchy and key of the data according to the transmission demand of the data and the current network environment by means of a real-time network monitoring technology and a data transmission demand analysis model; key management: storing the key on a block chain by adopting a block chain technology to realize secure storage and distribution of the key; and formulating encryption strategies: formulating different encryption strategies according to the hierarchy and transmission requirements of the data.

The invention relates to the technical field of inventory replenishment, and discloses a dynamic inventory replenishment method and system based on big data, and the method comprises the steps: collecting inventory, historical delivery, supplier performance and social media data. A periodic rule is determined through time sequence analysis, and demand fluctuation is analyzed through a sliding window method. And the delivery time and success rate of the supplier are extracted, and a performance quantized value is determined by combining demand fluctuation and a weighted average method. Analyzing the social media data, generating a hot commodity table, and performing cross validation to determine the replenishment priority. According to the demand fluctuation and the priority, the replenishment amount is optimized through linear programming, and a preliminary plan is generated; and reducing the risk by combining the performance quantized value and Monte Carlo simulation to obtain a low-risk plan. And finally, dynamically and finely adjusting the low-risk replenishment schedule to form a final replenishment execution scheme. According to the method, real-time network hotspot commodities and the supply capacity of suppliers are taken into consideration, so that the accuracy and adaptability of dynamic inventory replenishment are improved.

The invention discloses a network security detection method and system based on a firewall, and belongs to the field of network security, and the method comprises the steps: discovering an abnormal mode and an outlier of traffic, and determining a potential malicious traffic cluster according to a clustering result; for the suspected malicious traffic clusters, representative samples of the suspected malicious traffic clusters are extracted, similarity matching is carried out on sample traffic and a known malicious traffic feature library through a threat intelligence docking interface, and if the matching degree exceeds a preset threshold value, it is judged that the malicious traffic is of a known type; and deploying the trained malicious traffic classification model into an online detection module of the firewall, predicting and classifying real-time network traffic, and if the real-time network traffic is judged to be malicious traffic, triggering a blocking and isolating strategy of the firewall to intercept the malicious traffic in real time.

The invention discloses a real-time network intrusion prevention method based on multi-source data fusion, and particularly relates to the technical field of network defense. According to the method, the sampling frequency is determined according to the time granularity difference of different data sources, flexible time windows are divided, the abnormal behaviors in the network are accurately identified by extracting network behavior characteristics such as granularity matching degree and time synchronization degree and combining a machine learning model for analysis, and the abnormal behaviors in the network are accurately identified after the abnormal behaviors are identified. And the system triggers a corresponding alarm mechanism according to the severity of the behavior and takes corresponding defense measures, so that effective detection and response to complex network intrusion are realized, and the network security protection capability is remarkably improved.

The invention discloses a network resource allocation method and a network resource allocation system. The network resource allocation method comprises the following steps: acquiring real-time network state information between a digital twin module and any physical entity; acquiring historical network performance information between the digital twin module and the physical entity; utilizing the prediction model to obtain predicted network state information according to the real-time network state information and the historical network performance information; obtaining an optimal access mode according to the predicted network state information and the real-time network state information; obtaining the required extra bandwidth according to the predicted network state information; and if the network resources between the digital twin module and the physical entity need to be adjusted, adjusting the access mode and the network bandwidth between the digital twin module and the physical entity according to the optimal access mode and the required additional bandwidth before a preset time point. The network bandwidth and the access mode are prevented from being passively adjusted when the network state is poor, and the network use experience of a user can be improved.

The invention provides an ant colony algorithm initialization method and system based on an ISIS protocol, and the method comprises the steps: collecting network topology information in real time based on the ISIS protocol, including a node set, a link set and cost parameters of each link, the cost parameters including transmission time delay, bandwidth utilization rate and packet loss rate; constructing a comprehensive cost evaluation model based on weighted summation of the cost parameters of the links; a pheromone matrix of the ant colony algorithm is initialized based on the result of the comprehensive cost evaluation model, and the link comprehensive cost is inversely proportional to the initial pheromone value; constructing a heuristic information model according to the network topology information, wherein the heuristic information model is the reciprocal of the comprehensive cost; and based on the pheromone matrix and the heuristic information model, initializing a node selection probability function of an ant colony algorithm, and executing the ant colony algorithm to iteratively solve an optimal routing path. According to the method, the real-time network information obtained by the ISIS protocol is used for the initialization of the ant colony algorithm, so that the initialization precision can be improved.

This invention discloses a big data-based system for predicting and evaluating scientific and technological achievements, relating to the field of scientific and technological achievement prediction and evaluation technology. Specifically, it includes: a research and development process data collection module, an information technology achievement prediction module, and an information technology achievement comprehensive evaluation module. The research and development process data collection module collects historical research data, feature data, and network transmission performance data of scientific research projects in real time during the research and development process. The information technology achievement prediction module establishes a prediction model for the feature data of the research process of scientific and technological achievements, sets research process status indicators based on the feature data, and selects network transmission performance data within a stable time range for further training and optimization of the LSTM prediction model. The information technology achievement comprehensive evaluation module comprehensively evaluates the stability of the research process of information technology achievements by predicting real-time network transmission performance data and setting information research fluctuation indicators to predict the stability of the research process.

The application discloses an aeronautical data conversion unit test system, which can complete complete parsing and import of an ICD in an XML source format, configuration and editing of the ICD, and test of an aeronautical data conversion unit; automatically realize ICD management test, that is, based on a complete ICD parsing and import configuration tool chain, automatic management of the ICD can be realized, and automatic test of the aeronautical data conversion unit can be realized; based on the editable configuration of the ICD management, management and editing of a signal interface in a test process can be realized, and various test configurations can be realized; based on a test tool chain composed of a digital simulation environment module and an integrated test environment module, data interaction based on a real-time network can be realized, and commands such as test issuing can be completed.