659 results about "Service Interface for Real Time Information" patented technology

The invention discloses a multi-source data fusion method and system based on cloud computing, and particularly relates to the technical field of multi-source data fusion, and the method comprises the steps: obtaining the real-time information of multi-source data, generating a health score and a label through a health evaluation model, and marking a data state and an abnormality type; performing format conversion and semantic enhancement processing on the health data to generate structured data; based on a knowledge graph reasoning technology, extracting a logical relationship to generate a fusion rule, and calculating a rule weight and confidence in combination with a health label, a data source confidence and a semantic feature; a fusion result of conflict data is judged through a semantic consistency index, if the fusion result does not conform to expectation, historical data and rules are loaded according to a verification model, a verification score is calculated, an optimization process is triggered after the verification score is compared with a threshold value, health assessment model parameters, a semantic embedding model and fusion rule logic are optimized, and fusion accuracy and reliability are improved.

The invention discloses a multi-agent cooperation system and method based on spatial calculation and multi-modal AI fusion, and the method comprises the steps: collecting all real-time data of a construction site through a multi-modal data collection and fusion unit, and carrying out the processing and integration of all collected data through a multi-modal AI algorithm, and generating a unified semantic association model; a three-dimensional virtual environment of a construction site is constructed through a space calculation unit, real-time synchronous mapping is formed for the construction site, and dynamic changes in the construction process are simulated and predicted; task allocation and behavior planning are carried out on multiple agents through an agent behavior management unit; and the intelligent agents are configured to perform real-time information sharing and task cooperation through a communication protocol through the multi-intelligent-agent cooperation unit. According to the scheme, the problems of difficulty in multi-source data integration, low cross-department cooperation efficiency, insufficient construction dynamic adjustment and the like in intelligent construction in the building industry can be solved.

The invention discloses a supply chain intelligent management system based on dynamic collaborative optimization, and relates to the technical field of hotel supply chain management, and the system comprises a supply chain intelligent management platform which is in communication connection with the following modules: a multi-modal data sensing module, a multi-modal data processing module and a multi-modal data processing module. The multi-modal data acquisition module is used for acquiring multi-modal data in a hotel through a LoRaWAN + BLE hybrid sensor network, accessing an external data source and acquiring real-time information through an API (Application Program Interface); and the dynamic demand prediction module captures a time sequence trend through bidirectional LSTM based on an ASTGNN model. According to the method, external dynamic data such as social media public opinions and weather are fused, the multi-modal data analysis technology is combined, the accuracy of demand prediction is remarkably improved, the ASTGNN model is utilized, the time sequence trend and the demand association between the branches are analyzed in combination with bidirectional LSTM and GCN, and the feature weight is dynamically adjusted, so that the demand prediction error rate is greatly reduced, and the demand prediction efficiency is improved. A more reliable demand prediction basis is provided for enterprises, and optimization of inventory management and production plans is facilitated.

The invention relates to a man-machine hybrid autonomous navigation system in an unknown dynamic environment, which comprises an environment sensing module for acquiring environment information by using sensors such as a laser radar, a camera and an inertial measurement unit and performing data preprocessing; the local target point selection module selects a local target point set meeting requirements in the environment according to the real-time information provided by the environment sensing module; the decision planning module is used for generating an optimal path and a navigation strategy based on mixed training of a deep reinforcement learning algorithm and artificial experience; and the learning optimization module improves the generalization ability of the algorithm through online learning and transfer learning technologies, and realizes effective introduction and strategy adjustment of human experience in combination with the man-machine interaction module. The man-machine interaction module can work in cooperation with the local target point selection module so as to dynamically adjust target point selection according to external input and optimize path planning. According to the method, high decision stability can be kept in an unseen environment, the training cost is reduced, and the method is more suitable for autonomous navigation application in the real world.

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

The invention provides an end-to-end automatic driving track evaluation method and system based on a world model, and the method comprises the steps: generating bird's-eye view state real-time information according to multi-mode sensor data obtained in a current automatic driving scene; inputting the aerial view state real-time information into a strategy network to obtain a plurality of real-time track anchor points output by the strategy network; according to the bird's-eye view state real-time information and the real-time track anchor point, constructing a real-time state-action pair, and inputting the real-time state-action pair into a bird's-eye view world model to obtain a predicted state-action pair output by the bird's-eye view world model; according to the real-time state-action pair and the predicted state-action pair, the predicted driving track corresponding to the predicted state-action pair is evaluated, and an evaluation result of the predicted driving track is obtained. According to the method, the predicted driving track can be evaluated more accurately, and the safety and reliability of the automatic driving system are improved.

Systems and methods for dynamic knowledge integration in LLM systems including receiving multimodal input data comprising text, image, audio, video, and / or code data, extracting information by processing the multimodal input data through a document processor, storing the extracted information in a dynamic knowledge base, receiving a user query at a query processor, identifying knowledge domains related to the user query using domain-specific agents, retrieving real-time information from the dynamic knowledge base responsive to the identified knowledge domains, integrating the real-time information into the processing of an LLM by a dynamic knowledge integrator, and generating a response using the LLM with the real-time information.

Described herein are systems, devices, methods, and media for an emergency response data exchange system. The system provides an operations center emergency management application with a textual and / or graphical notification of emergency communications initiated on a premises managed by the operations center. The system exchanges operations center data (e.g., floorplans, video feeds, point of contact information, building access controls) with an emergency communications center (ECC) and / or with a first responder device to facilitate reducing emergency response times. The system selectively establishes communications (e.g., messaging windows) between the operations center, ECC, and / or first responder device to facilitate real-time information sharing between relevant parties of an emergency.

The invention relates to the technical field of navigation, in particular to a bicycle refined intelligent navigation method, system and device and a storage medium, and the method comprises the following steps: obtaining information such as gradient and length of a ridable route; a physical exhaustion result is obtained through a physical exhaustion calculation model in combination with user weight, bicycle type and the like; calculating a time consumption result according to the riding ability of the user and evaluating a route difficulty level; acquiring real-time information of weather and road conditions, and generating detailed prompt information according to the real-time information; and sorting routes are screened according to user preferences, recommended and displayed. According to the method, refinement and intellectualization of bicycle navigation can be improved, physical power and time consumption are accurately calculated by integrating multiple factors, comprehensive and detailed route prompt and personalized recommendation are provided for users, different user requirements are met, and riding experience and efficiency are improved.

The invention relates to the technical field of Internet of Things security, and discloses an Internet of Things control system and a security early warning method, and the method comprises the steps: firstly collecting equipment basis and real-time information in an Internet of Things system, then evaluating equipment risk, network security and data encryption conditions, setting an early warning threshold according to multi-aspect evaluation results and historical security data, and finally, carrying out early warning. And finally, real-time monitoring is carried out, and early warning is triggered when the risk index exceeds a threshold value. The method has obvious advantages, equipment aging and safety risks can be accurately evaluated, equipment maintenance is planned in advance, and the maintenance cost is reduced; network attacks can be effectively identified, and network security is guaranteed; the data encryption security is enhanced, and the data leakage risk is reduced; early warning is given out in time, and the system response speed is increased; and the equipment safety state can be dynamically updated, the system safety management is continuously optimized, the safety and reliability of the Internet of Things system are comprehensively improved, and the stable operation of the Internet of Things system is promoted.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention provides an information resource matching recommendation method and system based on context awareness, and the method comprises the steps: firstly obtaining a context data set which is generated by real-time interaction of a target user and comprises user operation behavior data and scene awareness parameter data, and then carrying out the demand feature extraction of the context data set, the method comprises the following steps: acquiring a dynamic demand feature and a context association feature, matching the dynamic demand feature and the context association feature with a pre-stored information resource library to generate a real-time information resource matching result set, and optimizing a matching strategy of a target user matching model in real time according to a dynamic feedback parameter of the real-time information resource matching result set. According to the method, a resource matching strategy is optimized to obtain an optimized resource matching strategy, and finally, a target information resource set related to the dynamic demand characteristics is pushed to a target user terminal device based on the optimized resource matching strategy, so that more accurate and personalized information resource recommendation can be realized, and the recommendation quality and the efficiency of obtaining effective information by a user are improved.

The invention provides a data physical node isolation scheduling method and system, and belongs to the technical field of distributed storage and cloud computing. The method comprises the steps of collecting attribute information of tenants and establishing a tenant demand model; monitoring in real time and predicting the resource load of the physical node by using machine learning, and generating a resource evaluation report; formulating differentiated isolation strategies according to the data security levels of the tenants, adopting physical isolation for the tenants with high security levels, and distributing independent physical nodes for the tenants; the scheduling priority is allocated based on the business emergency degree and the resource demand of the tenant, the node load condition is combined, the high-priority tenant is preferentially scheduled to the node with the load lower than the preset threshold value, and meanwhile, the strategy is dynamically adjusted according to real-time information. The corresponding system comprises a tenant management module, a resource monitoring module, an isolation control module and a scheduling decision module. Unification of data security isolation and efficient and dynamic resource scheduling in a multi-tenant environment is achieved, data security and service quality are effectively guaranteed, and the resource utilization rate is increased.

The invention provides an electronic acupuncture closed-loop control method and system based on multi-modal information feedback. The method comprises the following steps: acquiring long-term, short-term and real-time representation information of a testee under an initial stimulation scheme; the long-term and short-term characterization information is input into a collaborative generative network model for learning, a target stimulation scheme is output, the collaborative generative network model comprises traditional Chinese and western medicine agents, the traditional Chinese and western medicine agents learn the long-term and short-term characterization information, and a corresponding traditional Chinese medicine treatment scheme and an electrical stimulation scheme are output. The collaborative generation network model carries out collaborative training on traditional Chinese and western medicine agents; calculating an index deviation between the real-time characterization information and the standard characterization information to generate a real-time information deviation matrix; inputting the target electrical stimulation scheme and the real-time information deviation matrix into a parameter deviation prediction agent for learning and prediction; and performing parameter adjustment on the target stimulation scheme based on the predicted stimulation parameter deviation matrix to realize long / short time and real-time double-layer closed-loop control of the electronic acupuncture.

The invention relates to the technical field of supply chain management, and discloses an Internet of Things platform supply chain data processing method and system based on artificial intelligence, and the method comprises the steps: constructing an Internet of Things information sensing network of a supply chain through the deployment of a sensor unit and an intelligent terminal, and carrying out the digital feedback simulation; a supply chain model is constructed, model parameters are adjusted through real-time information collection, supply chain process link actual conditions and article operation track data are obtained and substituted into the model, supply chain project features are deeply analyzed by using a pre-trained artificial intelligence algorithm, and analysis features of all analysis projects are extracted and transmitted to an application service layer; and adjusting execution parameters to output a supply chain optimization decision, realizing intelligent decision through real-time data perception, analysis and optimization of a supply chain process and deep analysis based on an AI model, reducing human errors and delay, optimizing each link of the supply chain, and solving the problem of difficulty in effective processing and analysis of supply chain data of the Internet of Things in the prior art. And the intelligent decision-making problem is supported.

The invention provides a vehicle enterprise APP intelligent assistant navigation method and system based on a large model. The method relates to collection of user navigation requirements and identification of key entities and user intentions by using a large language model. And when the navigation intention is identified, generating a navigation route through map data and a path planning algorithm in combination with the current position and the target position. And when the vehicle control intention is identified, remotely controlling the vehicle through a vehicle control interface. And when the intention is identified as the information query intention, relevant information is retrieved and displayed from the vehicle information base. And collecting user feedback, an optimization model and a navigation strategy. The navigation demand information comprises text and voice information. The navigation requirement understanding of the user is improved through the large language model, tedious operation is avoided, and the access efficiency and the user experience are improved. Personalized and real-time recommendation is realized, real-time information is provided according to requirements and environments, and travel route planning is optimized. Dialogue management supports multiple rounds of dialogues and deep interaction, complex requirements are effectively processed, and the ability to deal with complex scenes is improved.

The invention provides a heterogeneous resource dynamic sensing and self-adaptive scheduling method, which comprises the following steps of: dynamically acquiring basic attributes such as resource types, core configuration, memory capacity and bandwidth and real-time information such as load state and temperature through a heterogeneous resource feature extraction module by utilizing a hardware interface and software probe technology, and storing data in a feature table after processing, accurate scheduling decision basis is ensured; a self-adaptive scheduling algorithm is designed, matching with resource characteristics is carried out based on task calculation, memory and parallelism requirements, proper resources are preferentially allocated, competition is avoided by checking a load state, and the algorithm supports dynamic adjustment so as to optimize allocation efficiency; and finally, through a real-time monitoring and dynamic adjustment mechanism, regularly collecting resource and task state information, detecting a competition or low-efficiency condition, executing operations such as task migration, priority adjustment or resource reservation and the like, and ensuring efficient operation of the system. The heterogeneous resource scheduling efficiency and stability are improved, and the method is suitable for a modern computing environment.

The invention discloses a bucket wheel anti-collision system and an anti-collision method, and relates to the technical field of bucket wheel anti-collision. The system comprises a data acquisition module used for acquiring operation data, environment data and historical collision data of the bucket wheel machine, the operation data comprises the position, the speed, the angle and the operation mode of the bucket wheel machine, and the environment data comprises a coal yard three-dimensional map, obstacle coordinates and meteorological data; the historical collision data comprises accident records and false alarm analysis. The state and environment of the bucket wheel machine are mastered in real time through high-precision positioning and multi-mode sensing, collision is effectively prevented in combination with intelligent graded alarm, and safety is remarkably improved. The risk is predicted through big data analysis and machine learning, the operation path is dynamically adjusted, operation is optimized, the maintenance cost is reduced, and the service life of equipment is prolonged. Meanwhile, the cloud monitoring and the mobile terminal APP support remote operation and real-time information pushing, the convenience and applicability of the system are enhanced, and the management efficiency is improved.

The invention discloses a train-ballast track-roadbed system simulation method based on MBD-DEM-FDM coupling, and the method comprises the steps: building a train model based on a multi-body dynamics MBD method: building a dynamic model of a train system, simplifying a suspension system of a train through a spring and a damper, and considering the interaction between the train and a track; establishing a three-dimensional ballast track bed model based on a discrete element method DEM; establishing a roadbed finite difference model based on a finite difference method FDM, including: regarding the roadbed as a continuous medium, and simulating mechanical behaviors and deformation characteristics of the roadbed under the action of a train load based on the finite difference method FDM; setting a coupling medium so as to couple the MBD-DEM-FDM, and carrying out real-time information exchange between the MBD and the DEM and FDM systems; and establishing a coupling model based on MBD-DEM-FDM coupling, and verifying and applying the coupling model. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium, and the comprehensive influence of the micromechanical characteristics of particles in the ballast bed and the train power effect on the track and the roadbed is considered.

A computer-implemented method for performing searches in a document database is disclosed. The method comprises automatically detecting a line of business associated with a user, receiving a text query from the user, and generating a query embedding from the text query. The method further comprises scoring entries in a reverse index using a hybrid scoring function. The reverse index comprises titles, title embeddings, sentences, sentence embeddings, and entity tags corresponding to documents in the document database. The hybrid scoring function is used to generate a score based both on a keyword match score between the text query and the reverse index and on a cosine similarity score calculated from embeddings in the text query and in the reverse index. The method also comprises ranking scores for sentences in the document database, and displaying a sentence associated with a top score to the user.

The invention discloses a multi-channel coal quality online detection system and method based on signal enhancement, belongs to the technical field of coal quality detection, and solves the problem that the accuracy and stability of a detection result are affected as a fixed threshold rule adopted by wavelet threshold denoising in the existing method cannot adapt to noise characteristic changes in different temperature intervals. The method comprises the steps of performing signal enhancement preprocessing on a real-time information set, performing initial feature selection on a signal enhancement set based on an index identification model, and performing index collaborative correction based on a drift risk amount of an initial index set and the initial index set; according to the method, signal enhancement preprocessing is carried out on coal quality multi-channel spectral information and associated environment information which are acquired in real time, interference of environmental factors on spectral signals is considered during signal enhancement preprocessing, and interference of the environmental factors on the signals can be effectively compensated through coupling of a field three-dimensional temperature field and a self-adaptive time period; therefore, the stability and the accuracy of the signal are improved.

The invention relates to the technical field of artificial intelligence and education, and discloses an AI Agent technology-based college entrance examination voluntary reporting intelligent consultation system, which comprises a multi-Agent cooperation module, a domain knowledge graph module, a natural language processing module and a multi-source database module. According to the college entrance examination voluntary reporting intelligent consultation system based on the AI Agent technology, a college entrance examination consultation domain knowledge graph is constructed based on a Neo4j graph database through the domain knowledge graph module, the relationship between entities such as schools and majors and admission batches is stored, multi-dimensional association is efficiently processed through an entity relationship network, university and Major Recovery Agent of the multi-Agent cooperation module is combined with the knowledge graph to screen the schools and the majors, and the college entrance examination voluntary reporting intelligent consultation system based on the AI Agent technology is obtained. The Check Agent verifies data, the Web Search Agent supplements real-time information, and the problems that a traditional database is low in complex relation processing efficiency and cannot recommend in real time are solved.

An autonomous advertising cart with display panels and speakers integrated into its navigation, movement and location tracking system. The cart displays advertising containing text, picture and video images, and speakers play sounds, music or other recordings. The cart selectively provides advertising communications depending on the items on the cart, where the cart is in the building, the time of day, the presence of people, etc., as the autonomous cart travels through its work environment. The advertisements are stored on-board the cart or received real-time via wireless communication. The cart preferably has microphones and customer observation cameras to collect real-time information about the working environment and customers to enhance the advertising experience and provide feedback about the advertising experience.

The invention discloses a multiplexing method and system for improving 5G mobile communication spectrum efficiency, and belongs to the technical field of dynamic spectrum multiplexing communication. The method comprises the steps of collecting real-time information, predicting spectrum availability and inter-band interference by utilizing a neural network model, generating a dynamic spectrum multiplexing strategy, issuing the strategy, adjusting parameters and continuously monitoring and optimizing. According to the method, an intelligent prediction model fusing MLP and LSTM networks is adopted, spectrum availability and inter-band interference prediction are provided, and prediction confidence information is generated; a self-adaptive multi-target intelligent optimization algorithm is applied, and the algorithm can dynamically adjust and optimize the weight according to a real-time network; local optimality judgment is carried out by combining local real-time information through the intelligent reconstruction capability of the 5G base station; and continuous monitoring and closed-loop optimization are realized by using an online learning mechanism. According to the invention, dynamic intelligent management of 5G network spectrum resources can be realized, and the 5G mobile communication spectrum efficiency is significantly improved.

Various embodiments described herein disclose an endpoint modeling and grouping management system that can collect data from endpoint computer devices in a network. In some embodiments, agents installed on the endpoints can collect real-time information at the kernel level providing the system with deep visibility. In some embodiments, the endpoint modeling and grouping management system can identify similarities in behavior in response to assessing the data collected by the agents. In some embodiments, the endpoint modeling and grouping management system can dynamically model groups such as logical groups, and cluster endpoints based on the similarities and / or differences in behavior of the endpoints. In some embodiments, the endpoint modeling and grouping management system transmits the behavioral models to the agents to allow the agents to identify anomalies and / or security threats autonomously.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to the technical field of data processing, and discloses an operation and maintenance task processing intelligent distribution management system and method based on data analysis, and the system comprises a data collection module; a life cycle analysis module; a task plan making module; an operation and maintenance personnel task distribution module; and a path planning and dynamic adjusting module. Based on data analysis, in combination with the equipment operation state, the skill of the operation and maintenance personnel and the load condition, the execution sequence and route of the operation and maintenance tasks are optimized through intelligent allocation and route planning, task allocation and route can be dynamically adjusted according to the health condition of the equipment, the task priority and the real-time information of the operation and maintenance personnel, and the operation and maintenance efficiency is improved. The operation and maintenance task is efficiently and accurately completed, and the overall working efficiency and the resource utilization rate are improved.

The invention provides an unmanned aerial vehicle commanding and dispatching system and an unmanned aerial vehicle combined inspection method and device. The system comprises a monitoring module, a situation overview module, a commanding and dispatching module, a combined flying module, a task module and an operation analysis module. The monitoring module is used for monitoring the frame outside the nest of the unmanned aerial vehicle and the frame of the unmanned aerial vehicle; the situation overview module is used for browsing a base station list, an unmanned aerial vehicle list, an inspection statistics list, an inspection record list and an operation analysis list to query corresponding information; the commanding and dispatching module is used for switching the base stations, acquiring real-time information corresponding to the base stations, and commanding and controlling the unmanned aerial vehicle based on the real-time information and a predetermined commanding strategy; the combined flying module is used for the plurality of base stations to perform combined inspection through a combined inspection route; the task module is used for displaying an inspection record list and an inspection plan list; and the job analysis module is used for displaying the task list and the result list. According to the mode, display and route planning of multi-base-station unmanned aerial vehicle combined inspection are realized.

The invention discloses an under-actuated unmanned surface vehicle safe formation tracking control method based on robust obstacle control. The method comprises the following steps: establishing a multi-unmanned ship group and performing real-time communication, and enabling each follower to follow a leader according to a formation configuration and to avoid an obstacle; a safe formation tracking control framework of the underactuated unmanned surface vehicle is established, during formation tracking, followers input state information into the control framework, processing is carried out according to a reference trajectory of a leader and real-time information of a current follower and an obstacle, and forward and steering control torque instructions are output to control the current follower. Obstacles are avoided and formation configuration is restored to continue following the leader. According to the method, the underactuation characteristic problem of the unmanned surface vehicle is solved, the worst case error generated when the neural network approaches the unknown dynamic state can be effectively compensated, it is ensured that the unmanned surface vehicle can strictly avoid collision with the obstacle in the complex environment, and meanwhile the formation tracking performance is optimally kept.