108019results about How to "Improve efficiency" patented technology

The invention provides a dynamic health adaptive monitoring method and system using artificial intelligence, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the noise reduction through a hierarchical diffusion probability model through multi-source heterogeneous health data, extracting preliminary features through combining with an improved attention auto-encoder, learning discriminative features through a contrast learning network, and carrying out the recognition of the discriminative features; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition, inputting the discriminative features into a dynamic graph neural network to obtain a health feature tensor, obtaining time sequence features by using a hierarchical memory enhancement network, and generating a health knowledge graph in combination with a dynamic routing capsule network and a causal reasoning network; and inputting the time sequence characteristics and the health knowledge graph into a deep Bayesian network for health risk assessment, and obtaining an adaptive monitoring scheme through a model prediction control network by using a hierarchical reinforcement learning network and combining a multi-agent collaborative optimization system and a deep deterministic strategy gradient network.

The invention discloses a small unmanned aerial vehicle inspection data management method, relates to the technical field of unmanned aerial vehicle inspection, and is used for solving the problem that unmanned aerial vehicle inspection data management is not clear. According to the invention, through a high-precision positioning technology and timestamp synchronization, multi-modal data collected by an unmanned aerial vehicle is associated with an equipment position, space-time consistency of inspection data is realized, multi-modal feature fusion and dimension reduction processing are carried out, unified feature representation is generated, the precision and efficiency of data analysis are improved, historical data and real-time collected data are combined, and the accuracy and efficiency of data analysis are improved. Equipment states are monitored in real time, fault modes are identified and classified, risks are evaluated, fault development trends are predicted through a time sequence prediction model, maintenance suggestions are generated, the timeliness of fault diagnosis is improved, based on fault detection results, routing inspection task priorities and path planning are dynamically optimized, an optimal routing inspection path is generated, and a flight path is corrected; the intelligence and the precision of the inspection task are improved, and the efficiency and the safety of the unmanned aerial vehicle inspection system are remarkably improved.

The invention discloses a decision-making method and model for off-line reinforcement learning and continuous on-line fine tuning, and the method comprises the steps: obtaining original text-image pair data and historical track data, and obtaining an enhanced state sequence through feature matching verification and standardization processing; performing sequence prediction by using a multi-level attention mechanism, and obtaining pre-training model parameters through knowledge distillation and task adaptability enhancement; on the basis of stability evaluation and plasticity control, online knowledge migration is carried out on the model, and optimized model parameters are obtained by combining experience playback optimization; and finally, performing multi-dimensional performance evaluation and resource optimization. According to the method, through multi-modal feature fusion, dynamic knowledge migration and an adaptive optimization mechanism, the problems of insufficient feature representation, disastrous forgetting, low optimization efficiency and the like are effectively solved, and the learning efficiency and generalization ability of the model are improved.

The invention provides an oil depot tank field inspection robot task allocation method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. Constructing a multi-target collaborative scheduling model, extracting features by adopting a space-time attention mechanism, establishing a task priority evaluation and path planning sub-model, and generating an optimal task allocation strategy; and an inspection task instruction is issued, the execution condition is monitored in real time, and when abnormity is detected, the emergency module is started for optimization adjustment. According to the invention, intelligent and collaborative routing inspection task distribution is realized, and the routing inspection efficiency and safety are improved.

The invention provides an intelligent task alarm rule self-learning method and system based on support priority, and relates to the technical field of intellectualization, and the method comprises the steps: constructing a bidirectional feature extraction network to collect task operation data, and combining historical alarm data to generate a task exception association model; and carrying out migration mapping on the model and a business scene knowledge base by utilizing a migration learning algorithm, generating a task priority quantitative index, and constructing a self-adaptive priority scoring system. And training an alarm rule generator based on the model and a scoring system, generating a multi-level alarm strategy tree, and realizing differential alarm. And the incremental learning engine monitors a task state in real time, dynamically adjusts an alarm response strategy and feeds back data to optimize a rule. A federal learning mechanism is adopted to cooperatively train a model, and the generalization ability and adaptability of alarm rules are improved, so that more accurate and efficient task alarm is realized, the false alarm rate is reduced, and the operation and maintenance efficiency is improved.

The invention discloses a method for constructing a knowledge graph based on a large language model and a vector library, and relates to the technical field of knowledge graphs, and the method comprises the following steps: carrying out word segmentation, part-of-speech tagging and semantic analysis processing on a collected natural language text by using a pre-trained RoBERTa model to obtain a semantic representation vector of the natural language text; through a Seq2Seq framework and an attention mechanism, obtaining association between the semantic representation vector and the corresponding named entity, and obtaining the named entity through the obtained association; constructing a knowledge graph; fusing the constructed knowledge graph with a relational database of a preset field to generate a fused knowledge graph; named entities and corresponding attributes are obtained through dependency syntax analysis, the entity relation of the named entities is obtained, and a structured triple fusing the named entities, the attributes and the entity relation of the knowledge graph is constructed. In order to solve the problem of high entity linking difficulty in the prior art, the knowledge graph is converted into low-dimensional dense vector representation, and an inverted index database and the like are constructed, so that the difficulty is reduced.

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

The invention provides a cross-domain network security policy automatic generation and protection policy collaboration method and system, and relates to the technical field of network security, and the method comprises the steps: collecting multi-domain security event data; constructing a security threat map; generating a cross-domain security risk score by using a deep learning algorithm; determining an inter-domain access control rule set and an intra-domain protection strategy according to the score; forming a cross-domain collaborative protection strategy; deploying a strategy on the multi-domain security device; establishing an inter-domain information sharing channel; monitoring the strategy effect; optimizing the strategy by using a reinforcement learning algorithm; and updating to each domain security device. According to the method, the effectiveness of cross-domain network security protection is improved.

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The embodiment of the invention provides a network flow anomaly monitoring method and device based on a BiLSTM-Att network, and the method comprises the steps: obtaining network flow time sequence data, system resource use data and service call topological data, constructing a multi-modal time sequence feature matrix through employing a sliding time window method, calculating the importance score of each feature in the traffic feature subset, the resource feature subset and the service feature subset through an information gain algorithm, and screening the most discriminative feature based on a score threshold to construct a fusion feature subset; inputting the fusion feature subset into a set bidirectional long short-term memory-attention network, performing multi-level analysis on target features, performing comprehensive evaluation on a detection result by adopting a confidence-based multi-view decision fusion method, and generating a detection report containing an anomaly level, an anomaly type and an anomaly reason; according to the invention, the efficiency and accuracy of network flow anomaly monitoring based on the BiLSTM-Att network can be effectively improved.

The invention provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

The invention discloses a multi-modal perception humanoid robot action adaptive control method and system, and belongs to the technical field of computers, and the method comprises the steps: obtaining first multi-modal data; the first multi-modal data comprises first image data, first point cloud data and first force sense data; performing data fusion on the first image data and the first point cloud data, and constructing a three-dimensional model of the scene in real time; generating an optimal path according to the real-time environment data of the three-dimensional model, converting the optimal path into a joint instruction of the humanoid robot, and generating an action control strategy; according to the action control strategy, arm movement of the robot is adjusted through visual servo control, and first operation data are obtained; the path and action of the robot are adjusted in a closed-loop control mode, and second operation data are obtained; and reinforcement learning and regular evaluation are carried out through a DDPG algorithm, and an action control strategy is continuously optimized. According to the invention, multi-modal sensing robot motion adaptive control with strong adaptive capability, high operation precision and good robustness can be realized.

The invention provides a space field game behavior dynamic evolution and strategy deduction optimization method and system, and relates to the technical field of space field game behavior optimization, and the method comprises the steps: analyzing historical behavior data through a deep neural network, constructing a dynamic relation graph of participants, inputting a multi-agent reinforcement learning model, and outputting a game behavior model. Then, a multi-layer game scene evolution tree is constructed, the instant income and the long-term risk are calculated in combination with a dual neural network, dynamic updating is performed by using a search algorithm of a hierarchical attention mechanism, and candidate evolution paths are generated; and finally, evaluating the candidate paths by using a strategy evaluation model constructed by a space-time diagram neural network, selecting an optimal strategy path, and generating a strategy deployment scheme implemented by stages in combination with a knowledge inference engine. According to the method, the evolution trend of the game behavior in the space field can be effectively predicted, a feasible strategy deployment scheme is provided, and the scientificity and effectiveness of strategy decision are improved.

The invention, which relates to the technical field of medicine inventory management, discloses an intelligent medicine inventory management and optimization system comprising a demand prediction module, an inventory monitoring module, a purchase decision module and a medicine supply module. The demand prediction module is used for performing drug demand prediction according to historical sales data, seasonal factors and market trends; and analyzing and predicting by adopting a machine learning algorithm. According to the intelligent medicine inventory management and optimization system, based on a multi-objective optimization algorithm, in combination with the inventory turnover rate, the purchase cost and the medicine validity period, a scientific and reasonable purchase replenishment strategy is generated, including purchase time, quantity and supplier selection, and the scientificity and the accuracy of a purchase decision are improved; by integrating historical sales data, seasonal factors and market trends, demand prediction is performed by using a machine learning algorithm, and an accurate drug future demand trend prediction result can be generated.

The invention provides a solenoid valve precise control method based on flow dynamic adjustment, which comprises the following steps: acquiring working parameters in real time through a sensor, obtaining real-time working condition data of a solenoid valve, and carrying out noise reduction and normalization preprocessing on the data to generate a comprehensive working condition feature vector; and on the basis of the feature vectors and the target output parameters, flow, temperature and outlet pressure values of future working conditions are calculated through a prediction algorithm. And in combination with the calculated future working condition and real-time working condition data, an optimized control signal is generated and used for dynamically adjusting the opening degree of the electromagnetic valve, and initial dynamic control over the flow and the pressure is achieved. Outlet pressure and flow are monitored in real time through high-frequency sampling, the deviation between actual output and a target value is calculated, and a control signal is corrected based on the deviation. The electromagnetic valve is suitable for high-precision flow and pressure regulation under complex working conditions, has the characteristics of quick response and high regulation precision, and can be widely applied to the fields of environmental protection, industrial automation and fluid control.

The invention provides a robot alarm processing method and system based on a target detection algorithm and a cloud platform, and relates to the technical field of robot alarm processing, and the method comprises the steps: carrying out the detection of an abnormal target based on a convolutional neural network target detection model of the cloud platform in combination with a residual structure, a channel attention mechanism and an improved anchor-frame-free target detection algorithm, performing feature fusion through a feature fusion network guided by depth information to obtain accurate three-dimensional representation data, constructing a dynamic anomaly association graph by using an improved time sequence graph neural network, and generating a multi-level decision sequence in combination with an improved hierarchical reinforcement learning algorithm, and finally, collaborative task planning is carried out through a task decomposition network to obtain a task allocation scheme, and the robot carries out exception handling according to the optimal motion track, obstacle avoidance track data, real-time pose data, key operation sequence data and contact force feedback data.

The invention provides a machine room energy consumption optimization method and system based on temperature monitoring, and relates to the technical field of energy consumption optimization, and the method comprises the steps: collecting the temperature data of a machine room in real time, continuously monitoring the temperature gradient of each region, carrying out the fusion processing, and constructing a three-dimensional dynamic temperature distribution model; a hot spot area, an airflow dead angle and a cooling capacity waste area are identified through a deep learning algorithm, a flowing track and a mixing process of cold and hot air in a machine room are predicted through a track regression neural network in combination with machine room layout and equipment distribution, and a machine room temperature control strategy set is generated through adaptive neural fuzzy reasoning, correction and updating of a prediction result; and by taking minimum energy consumption and maximum refrigeration efficiency as optimization objectives and taking a safe operation temperature range of equipment in the machine room as a constraint condition, a plurality of parameters are jointly adjusted through a temperature control optimization algorithm to obtain an optimal temperature control curve, and the optimal temperature control curve is converted into a refrigeration system control instruction, so that collaborative optimization of the temperature and the energy consumption of the machine room is realized.

The invention discloses an intelligent visualization and text association method for a multi-modal knowledge graph, and relates to the technical field of information processing. According to the intelligent visualization and text association method for the multi-modal knowledge graph, multi-modal data in an internal database of an enterprise is obtained, and a multi-modal feature vector is generated by using a data preprocessing and feature extraction technology; the feature vectors are mapped to a unified vector space through a cross-modal embedding algorithm, semantic alignment, association and fusion among multi-modal data are realized, and a complete multi-modal knowledge graph is constructed. Through bidirectional association and multi-modal joint query, the information acquisition efficiency is improved, and visual display of the knowledge graph is realized by using the graph neural network. Meanwhile, the knowledge graph is dynamically updated through a graph reasoning algorithm, and the timeliness and accuracy of the knowledge graph are ensured. A more efficient solution is provided for an enterprise in the aspects of management and application of multi-modal data, and the intelligent process of knowledge management is promoted.

The invention provides an unstructured text data calculation method and system, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the multi-level processing of input unstructured text data, dynamically adjusting the word segmentation granularity according to the word frequency distribution, constructing a part-of-speech co-occurrence matrix in combination with context semantic information, and extracting entity information. And fusing the part-of-speech co-occurrence matrix and the entity information to generate a hierarchical semantic tag sequence. Feature extraction units with different convolution kernel sizes are adopted to extract feature representation, and cosine similarity between different semantic hierarchies is calculated to establish an association weight matrix. A semantic enhancement vector is constructed based on entity information, and adversarial training is carried out to obtain a multi-modal semantic feature matrix. And calculating the semantic similarity between the fusion feature vectors for clustering, setting difficulty weight sorting according to the complexity, consistency and ambiguity of class clusters, then inputting the class clusters into a classifier, and iteratively optimizing a classification result to obtain a calculation result.

The invention discloses a highway construction safety monitoring system and method, and belongs to the technical field of highway construction. The method comprises the following steps of 1, collecting construction site data in real time, wherein the construction site data comprises environment data, video data and construction management data; step 2, generating global situation awareness data; 3, evaluating the safety risk probability and severity in real time, and predicting a risk propagation path and a cascade effect; step 4, generating a plurality of risk disposal schemes for sorting and screening, and selecting an optimal disposal suggestion; 5, precise early warning and differentiated management are achieved, the false alarm rate is reduced, and high-risk events are found in time; and step 6, displaying the construction site through three-dimensional reconstruction, superposing construction site data and a risk assessment result, and providing remote monitoring and emergency command of a mobile terminal.

The invention relates to the field of natural language processing, and discloses an SQL statement generation method, system and device based on a large model. Respectively carrying out vectorization processing and document processing on the metadata and the metadata description text information from the database, and storing the metadata and the metadata description text information into a vector database and a full-text retrieval library; in response to the query instruction, respectively performing query matching on the vector database and the full-text retrieval library, screening matching results which are ranked in the front and are preset ranks according to the similarity from high to low, and determining an optimal SQL statement list and optimal metadata; on the basis of the current language of the query instruction, generating a cue word of the current language in combination with the optimal SQL statement list and the optimal metadata; the large model of the preset type is determined according to the language type of the cue word, the preset large model is called to conduct reasoning on the cue word, and the SQL statement is generated. Based on the large model and the generative AI technology, the accuracy of the generated SQL statement is improved, the use threshold of non-technical personnel is lowered, and the query efficiency of the SQL statement is also improved.

The invention relates to the technical field of intelligent prevention and control of typical coal mine disasters, and discloses a coal mine fire and gas disaster multi-modal knowledge base construction and optimization method, which comprises the following steps: constructing a unified ontology structure based on typical coal mine fire and gas disasters; based on the unified ontology structure, extracting entities, attributes and relationships among the entities from the multi-modal data source; integrating the extracted entities, attributes and relationships by using a graph neural network model to form a structured knowledge graph; based on the structured knowledge graph, identifying and linking the multi-modal data entities, and establishing a multi-modal knowledge base; historical data and real-time monitoring data of a to-be-assessed area are acquired, and dynamic risk assessment is performed by using the constructed risk assessment hybrid model based on entities and relationships in the multi-modal knowledge base; wherein the risk assessment hybrid model fuses GNN, DBN and LSTM; and according to the real-time monitoring data and the risk assessment result, updating the attribute values and relationships of the entities in the multi-modal knowledge base.

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The invention provides a plasticizing product recommendation method and system based on user demands, and relates to the technical field of plasticizing product recommendation, and the method comprises the steps: S1, constructing a plasticizing industry knowledge graph; s2, acquiring user historical data and current user requirements, including historical search data, historical browsing data, historical interaction data and historical transaction data; s3, preprocessing the historical data of the user, analyzing the preprocessed historical data of the user, and extracting historical demand features; s4, performing keyword extraction on the current user demand by utilizing the semantic understanding model, and generating an optimized query condition in combination with the chemical and plastic industry knowledge graph; s5, searching in a plasticizing product database by utilizing the optimized query condition to obtain a first list; and S6, carrying out personalized sorting on the first list by utilizing the chemical and plastic industry knowledge graph, generating a recommendation list from the first k lists, and presenting the recommendation list to the user. According to the invention, the relevance and accuracy of recommendation can be improved, so that the user experience is improved.

The invention discloses a power distribution network power transmission line intelligent control and measurement system and method based on an AI algorithm, and the system comprises a data collection module, a data preprocessing module, a multi-modal data fusion module, an AI fault diagnosis module, a control execution module and a communication module. Comprehensive monitoring of the running state of the power transmission line is achieved, historical and current data are analyzed in real time based on an improved dynamic multi-mode adaptive optimization model DMAOM, and the self-learning capability is achieved. A flexible adaptive control strategy is introduced, and a control scheme is dynamically adjusted according to real-time data. Under the abnormal condition, the system can rapidly execute current limiting, load transferring or circuit breaking operation, and through a feedback mechanism, the system can continuously optimize monitoring and response strategies after each fault processing. According to the method, the safety, stability and operation efficiency of the power transmission line of the power distribution network are remarkably improved, and the method has important practical application value and wide market prospects.

The invention provides a building construction potential safety hazard management method and system, and relates to the technical field of construction safety management, and the method comprises the steps: dividing a plurality of sub-regions, collecting data, classifying known potential safety hazard events, and obtaining a first classification; through comparative analysis of the reference features and the contrast features, corresponding data features and fusion features are selected, and risk assessment models of the first classification are established respectively; obtaining a first risk probability and a comprehensive risk index of each sub-region according to real-time monitoring data and the risk assessment model; according to the first risk probability, the comprehensive risk index and / or the risk change, performing early warning on the sub-region; according to an early warning processing result and field feedback; adjusting an early warning threshold value and / or optimizing the risk assessment model; real-time monitoring, early warning and management of potential safety hazards of the construction site are achieved, the efficiency and accuracy of potential safety hazard management can be improved, the labor cost can be reduced, and the safety level of the construction site can be improved.

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

The invention relates to the technical field of data processing, and discloses a road infrastructure health monitoring and evaluation method and device, equipment and a medium. The method comprises the following steps: performing multi-mode acquisition and multi-path transmission processing on data acquired by an intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data through a multi-scale time sequence analysis algorithm to obtain health state evaluation data of the road infrastructure; based on the health state evaluation data, performing dynamic threshold calculation and intelligent diagnosis on the road infrastructure to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion and an optimization scheme. The road infrastructure health monitoring and evaluation efficiency and accuracy are improved.

The invention relates to the field of knowledge maps and intelligent questioning and answering, in particular to a domain intelligent questioning and answering system and method based on a knowledge map library and a text vector library. Firstly, a user stores unstructured texts and structured knowledge which are collected and arranged in a certain domain into the text vector library and a knowledge map respectively; the system adopts a customized knowledge retrieval algorithm to perform parallel retrieval on the text vector library and the knowledge graph so as to identify and extract related knowledge points. And based on the retrieved knowledge points, the system generates a question-answer reply of a natural language by using a large language model, so that a knowledge-driven question-answer process is realized. The domain questions and answers based on the knowledge graph library and the text vector library improve the accuracy and integrity of system answers, the illusion of a large language model is avoided, the answers of the system are traceable, and the credibility of the system is realized.

The invention provides a network attack risk mapping assessment method and system, and relates to the technical field of network attack risks, and the method comprises the steps: collecting a network flow data packet to extract a multi-dimensional attack feature vector, employing a deep neural network to carry out feature learning to obtain an attack type recognition result and an attack intensity assessment value, and constructing an attack feature standard model; based on the attack type identification result and the attack strength evaluation value, combining an asset weight coefficient, a vulnerability hazard level and a vulnerability utilization difficulty coefficient to construct an asset vulnerability evaluation model; and performing association analysis on the real-time attack feature data and the asset vulnerability assessment score, constructing a risk mapping matrix, calculating a comprehensive risk score, and generating risk early warning information. According to the invention, accurate identification, dynamic assessment and timely early warning of the network attack risk are realized, and the accuracy and real-time performance of risk assessment are improved.