448results about How to "Respond effectively" patented technology

The invention provides a vehicle collision risk early warning method, system and device and a medium, and belongs to the technical field of vehicle safety control. The method comprises the steps that surrounding environment information of a vehicle is collected in real time through a vehicle built-in sensor and a GPS module; a multi-sensor fusion device technology is used to carry out comprehensive processing on surrounding environment information of a vehicle, and an environment perception model is generated after data integration; based on the environment perception model, target identification is carried out by using a computer vision technology based on a convolutional neural network so as to identify travelers, vehicles, traffic signs and obstacles; performing behavior prediction based on a deep learning model to predict a behavior track of a front vehicle or pedestrian; evaluating a potential collision risk in the current scene in combination with the target recognition result and the behavior prediction result; when a potential collision risk is detected, an early warning signal is sent to a driver, and when the driver does not take corresponding measures, active safety measures are automatically triggered to avoid collision or reduce accident damage.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention provides a digital project management method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. Constructing a multi-level resource constraint matrix; generating a resource collaborative optimization configuration model by using a deep neural network; calculating a real-time scheduling weight coefficient; constructing a resource scheduling objective function; solving an optimal resource scheduling scheme by adopting an improved ant colony algorithm; inputting the optimal scheme into an intelligent cooperative controller for dynamic adjustment; and intelligent cooperative scheduling of power grid engineering construction resources is realized. According to the invention, the resource scheduling efficiency and accuracy are improved.

The invention discloses an efficient skin disease segmentation method based on multiple scales and a mixed attention mechanism, and the method comprises the steps: firstly carrying out the preprocessing of a skin mirror image through data enhancement, and then inputting the skin mirror image into a convolutional neural network for feature extraction, thereby obtaining a skin lesion segmentation region prediction map. According to the method, the convolutional neural network is applied, multi-scale features of local information and global information can be extracted from a skin mirror image, and the feature representation capability is enhanced through feature fusion. The objective of the invention is to solve the problems of changeable lesion areas, irregular shapes and the like of skin disease images, and improve the accuracy and robustness of skin disease image segmentation.

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention discloses a ship dynamic path adjusting method combining visual navigation and radar data. The ship dynamic path adjusting method comprises the following steps: S1, collecting data; s2, carrying out pretreatment; s3, performing data fusion to generate an environment perception model; s4, combining the generative adversarial network and the space-time diagram neural network to construct an environment prediction model; s5, performing initial path planning by using a heuristic search algorithm to generate an initial path; s6, refining and optimizing the path by using a fast extension random tree algorithm to generate an optimal path; s7, generating future possible environment change according to the environment prediction model, and calculating a coexistence reserve path for each prediction scene in combination with the environment perception model; s8, when the current path is predicted to have risks in the future, switching to a preset standby path in advance; and S9, automatically generating a navigation control instruction, and transmitting the instruction to a ship automatic driving system. According to the invention, intelligent optimization of the ship path is realized through a multi-sensor fusion and environment prediction method.

The invention discloses a power grid fault early warning system based on a topology change scene, and relates to the technical field of power grid fault early warning. The topological graph construction module constructs a network topological graph; for each element in the network topological graph, the abnormity positioning module determines whether the element is abnormal or not according to the contrast parameter and the actual parameter; the topology reconstruction module is used for obtaining a setting topological graph according to the element abnormity, and substituting the setting topological graph into the Bayesian compressed sensing model to obtain a target network topological graph; the fault probability calculation module is used for calculating the fault probability of each node; and the setting value determining module is used for determining the setting value of each node according to the fault probability of each node. Network topology reconstruction is carried out through the Bayesian compressed sensing model, the adaptability and flexibility of the early warning system are improved, the efficiency and reliability of power grid fault early warning are improved by calculating the fault probability of each node and quantifying the fault risk of each node in a power grid, and safe and stable operation of a power system is guaranteed.

The invention discloses a water and soil loss telemetry data analysis method and system based on deep learning, and relates to the technical field of data analysis, and the method comprises the steps: extracting soil loss data, surface runoff data and topographic data; fusing the soil loss data, the surface runoff data and the topographic data, and obtaining water and soil loss risk index data and soil erosion rate data through a feature extraction technology; calculating weight values of the water and soil loss risk index data and the soil erosion rate data by using a feature evaluation technology; and based on the water and soil loss risk index data, the soil erosion rate data and the weight value, constructing a water and soil loss prediction model by using a deep learning framework and a space-time analysis algorithm. According to the invention, through telemetry data collection and feature extraction, key water and soil loss information can be rapidly obtained, the data processing flow is simplified, and the accuracy and reliability of data are ensured.

The invention provides a distribution line fault waveform denoising analysis method based on an improved wavelet threshold function and computer equipment, and relates to the technical field of power system fault detection and signal processing. The method comprises the following steps: S1, obtaining a fault waveform of a distribution line through actual measurement or analogue simulation, and converting the fault waveform into a fault signal; s2, through denoising effect comparison, selecting an applicable wavelet basis function and a decomposition layer number, and carrying out wavelet transformation to decompose a fault signal; s3, carrying out wavelet decomposition coefficient threshold processing on the fault signal by adopting a self-adaptive adjustment mechanism of a threshold and an improved threshold function; and S4, performing discrete wavelet inverse transformation on the wavelet decomposition coefficient subjected to threshold processing to reconstruct a signal so as to obtain a denoised fault signal. According to the method, through the improved threshold function and the multi-scale analysis technology, the fault waveform denoising effect can be remarkably improved, the accuracy and the real-time performance of fault detection are ensured, and therefore the reliability and the safety of a distribution line are improved.

The invention discloses an online public opinion research, judgment, analysis, classification and grading early warning method and system, and the method comprises the steps: constructing a malicious public opinion data detection and judgment model, and carrying out the iterative training of a pre-training neural network through large-scale online public opinion historical data, so as to achieve the fine adjustment and optimization of the model; after malicious public opinion data are detected, fine-grained classification of malicious public opinions is realized through deep semantic comprehension and multi-dimensional text structure analysis by adopting BERT and ERNIE deep learning models and combining semantic role labeling and dependency syntactic analysis technologies; a malicious public opinion data grading and distinguishing model is constructed, deep correlation between public opinion events is captured through multi-layer convolution operation, and the recognition capacity for public opinion sensitivity and urgency degree is improved. According to the invention, malicious public opinion data related to a specific industry field can be efficiently and accurately identified and classified, and the malicious public opinion data is subjected to severity grading, so that the accuracy and the real-time performance of network public opinion management are improved.

The invention discloses a network threat knowledge graph construction method based on SecBABC. The method can be applied to construction of a network threat knowledge graph. The method comprises the following steps: collecting existing network information, and constructing a network threat domain ontology CTDOM according to an STIX network threat information standard; the method comprises the following steps: constructing a SecBABC knowledge extraction model, capturing a complex semantic relationship between network threat intelligence through a pre-training model SecBert, constructing an adversarial training sample by using PGD, optimizing semantic understanding errors and inaccuracy conditions, updating model parameters through BiLSTM, and finally decoding entities and relationships of the network threat intelligence through CRF; constructing an entity alignment algorithm EAABNA, and performing knowledge fusion from two aspects of entity names and entity attributes; and constructing a network threat knowledge graph by using the fused network threat knowledge. By applying the knowledge extraction model constructed by the method, network threat intelligence can be effectively identified and extracted, and the efficiency and accuracy of knowledge graph construction are improved.

The invention discloses an accurate electric power theft detection method based on multi-modal Mamba. The method comprises the following steps: S1, carrying out adaptive multi-scale data preprocessing, and carrying out denoising and normalization processing on input power time sequence data by adopting an adaptive score standardization method and a local abnormal factor technology so as to improve data quality; s2, a multi-mode Mama feature extraction layer is adopted; s3, a federated learning training layer: constructing a secure federated learning framework, realizing cooperative training among a plurality of power supply areas, and introducing a differential privacy mechanism at the same time to protect privacy of model parameters and training data; s4, an intelligent confrontation defense layer; s5, an adaptive classification decision system; s6, a lightweight optimization strategy; efficient and safe cooperative detection is realized through innovative fusion of a multi-modal Mamba model and a federated learning framework; through an intelligent confrontation system and lightweight optimization, the robustness and practicability of the model are improved; according to the method, the detection accuracy, the calculation efficiency, the privacy protection and the like are remarkably improved.

The invention relates to the field of voltage regulation and control, in particular to a real-time voltage regulation method for a new energy automobile. Initializing a power distribution network voltage measurement module, and collecting voltage data of each node of the power distribution network; setting a voltage deviation threshold value, and judging whether the voltage is in a normal range or not; collected voltage is compared with a set voltage deviation threshold value, a result obtained after preliminary voltage regulation of an on-load voltage regulation transformer is compared with the set voltage deviation threshold value, and a result obtained after cooperative voltage regulation of an electric vehicle charging pile is compared with the set voltage deviation threshold value. According to a comparison result, tap adjustment of an on-load voltage regulation transformer, cooperative voltage regulation of an electric vehicle charging pile and voltage regulation participated by a distributed photovoltaic system are carried out in sequence; and formulating an optimization strategy based on historical data and results of three times of voltage regulation. By means of cooperative voltage regulation, optimal control, intelligent management and the like, the voltage stability is effectively improved, and the adaptability and flexibility of the system are enhanced.

The embodiment of the invention discloses a multi-lane shunting management and control method, equipment and a medium, is applied to the technical field of intelligent traffic, and is used for solving the problem that an existing shunting mode is low in efficiency and low in accuracy. The method comprises the following steps: dividing monitoring sub-regions based on road network data, and determining topological connection data of each sub-region by using historical vehicle driving data; and identifying target traffic characteristics of each sub-region through clustering analysis, and determining a corresponding shunting traffic region according to the target traffic characteristics. And identifying the current congested road section and the congested direction according to the real-time vehicle driving data, and formulating a shunting strategy in combination with the condition of the shunting traffic area. And inputting the passing speed of the current congested road section, the driving data of the shunting traffic area and the shunting strategy into a preset flow prediction model, and outputting and adjusting the shunting strategy according to the model to realize dynamic shunting management and control.

The invention discloses an industrial control ring network monitoring system and a monitoring method thereof. The system comprises a data acquisition module which is used for acquiring an industrial control ring network data packet in an industrial control network; the data processing module is used for carrying out data cleaning and data arrangement on the collected data and carrying out protocol matching on a data packet so as to identify an industrial protocol in the data packet; the data identification module is used for carrying out protocol matching on data in the network data packets and identifying communication data of each network packet; through organic cooperation of a plurality of units, the system realizes omnibearing monitoring, analysis and dynamic adjustment of the network, so that the system architecture provides a real-time, intelligent and self-adaptive network security solution, and compared with a traditional intrusion detection system, the system pays more attention to real-time optimization of network performance, and the network security is improved. Under the condition that the bandwidth occupancy rate is relatively high, unnecessary interference to other servers is reduced, so that the problems of lengthy and tedious protocol identification are solved.

The invention discloses an abnormity monitoring method of Internet of Things equipment, a storage medium and a computer program product, and relates to the technical field of Internet of Things, and the method comprises the steps: obtaining the current equipment state data of the Internet of Things equipment at the current moment; determining a data value range based on the current equipment state data, and detecting whether the current equipment state data is within the data value range; if the current equipment state data is not in the data value range, determining that the Internet of Things equipment is in an abnormal operation state at the current moment; and determining the current abnormal degree of the Internet of Things equipment at the current moment, determining a target strategy corresponding to the current abnormal degree from preset response strategies corresponding to different abnormal degrees, and controlling the Internet of Things equipment to operate according to the target strategy. According to the invention, the anomaly detection accuracy of the Internet of Things equipment and the overall reliability and efficiency of the system are improved.

The invention relates to the technical field of building shock insulation, and discloses a building shock insulation rubber support which comprises a bottom connecting plate and a top connecting plate, a damping rubber body is arranged between the bottom connecting plate and the top connecting plate, and a first through hole is formed in the middle of the damping rubber body; a plurality of fixing grooves are formed in the damping rubber body in the axial direction, and metal plates are arranged in the fixing grooves. By arranging the bottom connecting plate with the adjusting rod, one part of the adjusting rod is inserted into the damping rubber body, and one end of the adjusting rod is movably connected with the bottom connecting plate, after vibration is finished, if the damping rubber body cannot restore to the original shape, force is applied to one end of the adjusting rod through a hydraulic mechanism in a pushing or pulling mode, and the damping rubber body cannot restore to the original shape. Under pushing of the adjusting rod, resetting of the damping rubber body can be rapidly completed, and operation is convenient and rapid.

The invention discloses an unmanned aerial vehicle image splicing method based on local distortion and seam guidance, and relates to the technical field of computer image splicing. The technical problem that an image splicing result generates artifacts due to the fact that parallax exists between input images in image splicing and warping characteristics of non-overlapping areas of the images are not fully considered is solved. According to the method, image warping is guided by using a grid, labels are created among pixels in an overlapping region, and a logic function is designed to optimize the minimization of a label energy function; meanwhile, searching an optimal seam in the overlapping region by adopting a seam cutting method; the method focuses on an unmanned aerial vehicle remote sensing image, and solves artifacts caused by parallax from the image warping characteristic; according to the method, the target image is firstly subjected to grid warping processing, then the optimal splicing seam is found by using the seam cutting method and is fused with the reference image, and finally accurate alignment is realized, so that the quality of the spliced image is effectively improved, and a better scheme is provided for unmanned aerial vehicle image splicing.

The invention discloses a signal optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an initial signal, and dividing an available frequency band of the initial signal into a plurality of sub-frequency bands; according to an interference evaluation model, obtaining interference degrees between different sub-frequency bands, and obtaining an interference evaluation result; dynamically adjusting a frequency range corresponding to each sub-band according to the interference evaluation result; wherein the interference evaluation model is constructed based on a support vector machine and / or a random forest. Therefore, a new signal optimization strategy can be provided, and the signals can be dynamically allocated to adapt to a dynamically changing signal environment.

The invention discloses an unmanned aerial vehicle safety scheduling optimization algorithm, and relates to the technical field of storage scheduling, and the algorithm comprises the steps: carrying out the modeling of a storage environment based on the position, shape and size information of goods shelves and mechanical equipment in a warehouse, and building a preliminary environment model; and scanning the environment by using a laser radar carried by the unmanned aerial vehicle to obtain environment data in the warehouse, and updating the environment model in real time in combination with the preliminary environment model. According to the unmanned aerial vehicle safety scheduling optimization algorithm, the accuracy and efficiency of a flight path are remarkably improved through an integrated path planning technology, the optimal flight path is customized for the unmanned aerial vehicle in combination with actual conditions of topography and landform of a storage area and obstacle distribution, redundancy and waste in the flight process are reduced, and meanwhile, the unmanned aerial vehicle safety scheduling optimization algorithm has good application prospects. And the path can be dynamically adjusted according to the real-time environment change to effectively deal with emergencies, so that the flight efficiency and safety are further improved.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project digital management method based on a BIM model. The method comprises the following steps: acquiring real-time monitoring data; carrying out high-precision topographic data acquisition on a water conservancy project area, and carrying out data preprocessing to obtain preprocessed data; according to the preprocessed data, hydrological information integration modeling is carried out by utilizing a BIM platform, and a hydrological information integration model is obtained; performing multi-dimensional terrain dynamic updating on the hydrological information integration model by using the real-time monitoring data to obtain digital terrain data; performing flood simulation calculation based on the digital topographic data to obtain a flood simulation calculation result; and performing flood scene simulation according to the flood simulation calculation result to obtain flood simulation data. Through real-time monitoring, accurate prediction and timely early warning of the risk of the water conservancy project, a more effective technical means is provided for flood control and disaster reduction and safety management of the water conservancy project.

The invention provides an SQL (Structured Query Language) attack false alarm elimination method, which comprises the following steps of: logging in network security equipment to extract alarm information, screening alarms related to WEB service SQL injection attacks from the alarm information, and forming an SQL injection alarm list S; and for each alarm in the SQL injection alarm list S, downloading a data packet related to the alarm, and extracting feature information: based on the extracted feature information, carrying out false alarm analysis and judgment on each alarm to obtain a comprehensive judgment score, calculating a final score according to the comprehensive judgment score and the output of the machine learning model, and sending the final score to the server. And comparing the final score with a dynamic threshold value Threshold, and judging whether a false alarm exists or not. Through the SQL attack false alarm elimination method provided by the invention, the problem caused by SQL attack false alarm in related technologies is solved, the elimination method is faster, the accuracy is higher, and the SQL attack false alarm problem can be more effectively solved.

The invention belongs to the field of base station resource management, and provides a base station resource management method and system, and the method comprises the steps: collecting the service demands and network load information of user equipment; constructing an optimization objective function of base station resource allocation; introducing a Lagrange multiplier, and constructing a Lagrange function; calculating a preliminary resource allocation scheme of each user, wherein the preliminary resource allocation scheme comprises spectrum resource allocation and power resource allocation of the users; each user adjusts the power strategy to maximize the utility function according to the preliminarily distributed frequency spectrum and power; the base station updates a resource allocation strategy of the user through feedback interaction with the user; after multiple times of adjustment, the system reaches Nash equilibrium, and finally a resource allocation scheme of each user is determined.

The invention relates to a three-dimensional instance segmentation method in a paddy field high-density shielding scene in combination with a multi-modal fusion network. The method comprises the steps of collecting a multi-view RGB image and three-dimensional point cloud data of a paddy field for preprocessing; inputting the multi-view RGB image in the multi-source data set into the image segmentation model to generate an image instance segmentation result; matching the image instance segmentation result with the three-dimensional point cloud data in the multi-source data set; and inputting the point cloud data containing the multi-view RGB image information into the improved three-dimensional instance segmentation network model, and outputting a three-dimensional instance segmentation result. According to the method, accurate segmentation in a high-density shielding scene is realized, and the segmentation accuracy is remarkably improved; effective fusion of multi-modal data is realized, the robustness and precision of instance segmentation are effectively improved, and the method is especially suitable for a fine-grained segmentation task in a dense vegetation scene. The calculation complexity is reduced, the processing efficiency is improved, and the long-distance dependency relationship in the point cloud can be captured while the efficient calculation performance is kept.

The invention discloses a remote data control system and method for a laminating machine based on PLC control, and belongs to the technical field of industrial automation control. The system comprises a data acquisition and preprocessing module, a simulation model construction and data analysis module, a fault diagnosis and anomaly detection module and an abnormal change trend analysis and feedback module. The data acquisition and preprocessing module is used for acquiring historical sensor data and laminating machine fault logs and marking historical fault event data segments; the simulation model construction and data analysis module is used for constructing a simulation model of the laminating machine by utilizing simulation software, generating simulation data, enabling the simulation data to correspond to historical fault event data segments, and carrying out trend analysis; the fault diagnosis and anomaly detection module analyzes a corresponding relation between a historical fault event data segment and simulation data so as to obtain a fault event trend anomaly point; and the abnormal change trend analysis and feedback module performs trend analysis according to the real-time sensor data and outputs corresponding prompt information.

The invention discloses a dependent task computing unloading method meeting random preference of a user, and belongs to the technical field of computing unloading of a mobile communication technology. In order to solve the problem that in the prior art, when a satellite depends on task calculation unloading, the requirement for random change of a user optimization target is often ignored, the application of a ground mobile terminal is divided into a workflow composed of a plurality of tasks, and the workflow is represented by a DAG; determining a scheduling sequence of the dependent tasks according to the DAG; constructing a random preference dependent task calculation unloading optimization model and function; converting a calculation unloading problem into an MDP decision process according to the created model, and solving an unloading decision by adopting multi-target reinforcement learning; and comprehensively considering the dynamic preference demand of the optimization target, and designing a composite reward function to obtain an optimal unloading decision. According to the method, the optimal unloading decision model meeting different preference requirements is provided, the solving efficiency and flexibility are improved, and the completion time and energy consumption of the dependent task are reduced.

The invention discloses a macro-micro integrated microscopic three-dimensional simulation method and system for a composite traffic network, and relates to the technical field of traffic simulation. The method comprises the following steps: constructing a real-time linkage global dynamic traffic scene; carrying out multi-source traffic agent parameter fusion calibration; a front-end and back-end bidirectional integrated communication rendering module; and development of a composite traffic network scene reproduction analysis response dynamic visualization demonstration interaction and evaluation system and full-view simulation video stream archiving return visit analysis. On the basis of macro-micro integration of a composite traffic network, a micro three-dimensional simulation system is constructed through an improved centralized learning-distributed execution depth multi-source agent algorithm, micro simulation model parameter multi-dimensional calibration, a micro traffic flow individual interaction model and a micro traffic evaluation index calculation system; and a reliable and practical means is provided for three-dimensional simulation of urban traffic from a microscopic angle.

The invention discloses a coal mine dust concentration real-time monitoring method based on big data analysis, and relates to the technical field of coal mine dust concentration monitoring, and the method comprises the following steps: obtaining various parameter information generated in the data fusion process of a big data system in real time, including transient response information and time synchronization information, and analyzing and processing the obtained transient response information and the time synchronization information. According to the method, the transient deviation index and the phase error index are introduced, the data fusion process is intelligently evaluated in combination with the machine learning model, the error problem caused by the sensor response time difference is solved, and multi-level intelligent sensing is achieved. The system accurately judges the risk level, reduces false alarm and missing alarm, takes maintenance, adjustment or emergency intervention measures corresponding to different risks through a grading early warning mechanism, remarkably improves the coal mine monitoring safety, and reduces the accident risk.

The invention discloses an underwater robot self-adaptive control system and method based on data analysis, and relates to the technical field of automatic control. Environment data and underwater robot data in the operation process of an underwater robot are collected, an operation data set S is constructed, feature extraction is performed, and the operation data set S is obtained; the method comprises the following steps: acquiring an acceleration fluctuation coefficient # imgabs0 # and an attitude angular rate fluctuation coefficient # imgabs1 #, summarizing and calculating to obtain a stability score WD, evaluating the operation state of the underwater robot, predicting water flow data at a future time point t + k when the operation state of the underwater robot is abnormal, acquiring an underwater robot adjustment thrust # imgabs2 # in combination with a robot dynamic model, and using a genetic algorithm to calculate the operation state of the underwater robot. According to the method, the optimal thrust distribution scheme of the underwater robot is obtained, the underwater robot is controlled to execute the operation task, strong turbulence and water flow reversing can be effectively handled, the operation failure and damage risk of the underwater robot are reduced, and the operation stability and safety of the underwater robot are improved.

The invention discloses a three-section self-assembly composite polypeptide nanoparticle for targeted repair of endothelial barrier and regulation and control of immune recruitment cascade and a preparation method and application thereof, and the preparation method comprises the following steps: (1) preparation of a hydrophilic section head multifunctional peptide solution, (2) preparation of a middle section connector ROS response thioketal solution, and (3) preparation of a three-section self-assembly composite polypeptide nanoparticle for targeted repair of endothelial barrier and regulation and control of immune recruitment cascade. (5) preparing a three-section type compound of a hydrophilic section, the hydrophobic section and the middle section, and (6) mixing the connection compound solutions of the three types of polypeptide of the hydrophobic section, the middle section and the hydrophilic section, and dialyzing to remove DMSO (Dimethylsulfoxide), so that the compound solution is changed from an organic phase to a water phase, and the three-section type compound of the hydrophobic section, the middle section and the hydrophilic section is obtained. And promoting amphiphilic self-assembly of the compound and co-assembly of the three polypeptide compounds, and freeze-drying to obtain the three-section type self-assembly composite polypeptide nanoparticles. According to the invention, inflammatory endothelial cells are targeted, local high ROS microenvironment is responded, endothelial barrier repair peptide is released to repair endothelial barrier, complement receptor antagonistic peptide is released to regulate complement cascade reaction so as to recover immune microenvironment, and body inflammation and immune disorder are cooperatively regulated.