861 results about "Control network" patented technology

The invention provides a remote office network security protection method and system based on big data, and the method comprises the steps: firstly obtaining network activity data, including traffic, equipment states and user behavior data, of a plurality of terminal equipment in a remote office scene, and then carrying out the multi-dimensional security feature analysis of the network activity data, after a dynamic security feature set containing traffic, device fingerprints and behavior pattern features is generated, abnormal behaviors are identified through a multi-layer anomaly detection model, a detection result with an anomaly type and confidence is output, a target protection strategy is matched from a preset library according to the result, the weight is dynamically adjusted in combination with the anomaly confidence, and a real-time protection strategy instruction is generated. And finally, executing a protection strategy instruction to synchronously regulate and control network connection, equipment permission and a user access path, and feeding back regulated and controlled network state data to optimize abnormal identification logic, thereby realizing comprehensive and intelligent remote office network security protection.

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention discloses a security risk autonomous sensing system and method based on a digital twin industrial control network. The method comprises the following steps: performing four-dimensional multi-modal modeling on an industrial control network, and constructing a twin model integrating entities, rules, services and behaviors; constructing a high-fidelity simulation network by utilizing a containerization technology based on the model; deploying a gateway at an actual network boundary, collecting traffic characteristics in real time and identifying suspicious behaviors; performing protocol restoration and behavior reconstruction on the suspicious traffic to generate a behavior sequence; performing anomaly recognition and attack classification on the behavior sequence through a deep learning model; evaluating a risk level; and matching and verifying a defense strategy according to an evaluation result, and automatically synchronizing the defense strategy to a master control network to realize a detection-response-protection closed loop. According to the invention, autonomous identification of suspicious behaviors in the industrial control network and intelligent decision-making of security policies are realized, and the method has the advantages of high simulation, low invasion, closed-loop controllability and the like.

The invention relates to the technical field of temperature equalization control, and discloses a temperature equalization control method and device for a liquid cooling over-charging system. The method comprises the following steps: carrying out grid division on the liquid cooling overcharge system, inputting thermophysical parameters, and obtaining a temperature field calculation matrix through finite difference time domain analysis; establishing a two-layer distributed control network of a station-level control layer and a pile-level control layer based on the temperature field calculation matrix; acquiring real-time temperature data of the two layers of distributed control networks, and performing temperature field mapping to obtain temperature field distribution data; performing linear prediction and nonlinear prediction calculation on the temperature field distribution data to obtain a temperature change trend and a temperature extreme point; the target temperature is calculated according to the temperature change trend and the temperature extreme point, the cascade PID controller is adopted to adjust the rotating speed of the cooling liquid pump, and the temperature balance control parameters are obtained, station-level and pile-level cooperative control is achieved, cooling resources among the multiple charging piles are reasonably distributed, and the overall cooling efficiency of the system is improved.

The invention relates to the technical field of computer networks, in particular to a monitoring method and system based on industrial computer network fault data, and the method comprises the steps: collecting the heterogeneous fault data of each layer of equipment in an industrial control network in real time through distributed probe nodes; performing multi-modal normalization processing on the original fault data; constructing a fault knowledge graph, and dynamically associating an equipment topological relation, a historical fault mode and a current production task context; fault root cause analysis is carried out by adopting a hybrid inference engine, and a potential fault propagation path is predicted in combination with a rule-based expert system and an LSTM-GNN joint model; generating a grading alarm strategy, triggering a self-adaptive fault-tolerant mechanism, and dynamically adjusting network bandwidth allocation or starting redundant equipment switching according to the fault grade; according to the invention, by constructing the industrial knowledge graph and the adaptive fault-tolerant mechanism, efficient, accurate and interpretable fault diagnosis and prediction are realized, and the reliability and operation and maintenance efficiency of an industrial network are improved.

The invention discloses a spatial-temporal joint modeling system and method for watershed water quality prediction. The system comprises a data acquisition module, a feature extraction module, a feature fusion module, a model training module and a prediction output module. In the watershed water quality prediction process, the combined influence of time and space is considered, a space-time position coding combined embedded layer is designed, an attention mechanism guided by hydrological characteristics is combined, a door control network dynamically fused with space-time characteristics is constructed, and the space-time coupling influence of a watershed topological structure on pollutant diffusion is considered; the attention weight is dynamically adjusted by quantifying the topological importance of the monitoring points in the network, a feature channel which is most effective for a current prediction task is highlighted, and noise or redundant information is suppressed; in the model training process, a simplified gating mechanism is adopted, the gradient dispersion problem is reduced, the nonlinearity of a layer is kept, convergence is accelerated, a dynamic graph learning device is used, physical rules are respected, data changes are self-adapted, and more accurate water quality modeling is achieved.

The invention provides an intelligent driving simulation test method and system for an internal combustion locomotive, and relates to the technical field of intelligent driving, and the method comprises the steps: collecting the driving state information and scene complexity data of the internal combustion locomotive through constructing a virtual simulation environment; acquiring multi-modal information of the obstacle by using a virtual sensor and performing fusion processing; and constructing a deep reinforcement learning network comprising a prediction branch network and a dual-channel control network, dynamically adjusting safety, comfort and efficiency index weights based on scene complexity, and generating driving control parameters. On the premise of guaranteeing safety, dynamic balance of high efficiency and comfort of intelligent driving of the diesel locomotive can be achieved, and adaptability and reliability of an intelligent driving system are improved.

The invention relates to an injection mold temperature control method and device and computer equipment. The method comprises the following steps: acquiring temperature data of a plurality of temperature measuring points of the injection mold to obtain a temperature data matrix; establishing a heating and cooling input matrix and a temperature response matrix, and solving to obtain a heat transfer path coefficient matrix; taking the temperature data matrix as state input, constructing a depth deterministic strategy gradient control network comprising a gating circulation unit and a full connection layer, and performing linear mapping to obtain a hybrid action space; inputting the heat transfer path coefficient matrix and the mixed action space into a training environment, performing iterative training on a depth deterministic strategy gradient control network based on an experience playback mechanism, and updating network parameters by calculating a reward function to obtain a temperature control model; and deploying the temperature control model to a target control system, and calculating a real-time control parameter set. According to the invention, coordinated control of the heating stage and the stabilizing stage is realized, the injection molding period is shortened, and the energy consumption is reduced.

The invention discloses a video portrait consistency editing method based on optical flow guidance and text driving, and the method comprises the steps: firstly, employing a face key point extraction method and a depth estimation method to extract a face key point and a depth map of each frame of an input video, and guaranteeing the consistency of the facial expression of an edited portrait and a source image; a diffusion control network is used to edit and generate the face depth map, the key points and the user input text description as conditions, and the consistency of multi-view editing is realized through the potential code alignment of an attention mechanism and the guidance of optical flow change; thirdly, constructing a diffusion model with consistent time by utilizing the inter-frame optical flow change and the depth map change; through the above steps, the problems of multi-view and time consistency in video editing are solved, and high-quality video portrait editing is realized.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

The invention discloses a power monitoring system anomaly detection method based on a multi-model voting mechanism, and the method mainly comprises the following steps: firstly, collecting network flow data and equipment behavior data in a power monitoring system, and converting the network flow data and the equipment behavior data into multi-dimensional feature vectors; secondly, multiple types of anomaly detection models are constructed, and each model independently judges the anomaly of the input data; performing fusion processing on judgment results of the models through a majority voting or weighted voting mechanism, and outputting a comprehensive anomaly judgment result; and finally, dynamically adjusting the weight and detection parameters of each model based on a feedback mechanism, outputting alarm information when a fusion result exceeds a set threshold value, and linking a system response module. The power monitoring system anomaly detection method provided by the invention for the first time has the advantages of high detection accuracy, low false alarm and missing report rate, dynamic adjustability, high adaptability, flexible deployment and the like, and is particularly suitable for industrial control network security protection scenes in key infrastructures.

The invention provides a deep learning-based ship motion trajectory optimization control method, which relates to the technical field of ship control, and comprises the following steps: constructing a ship motion simulation environment, and establishing a dynamic simulation system containing marine environment parameters; acquiring ship operation data; a dual-network structure model is constructed, the dual-network structure model comprises a macroscopic strategy network and a microscopic control network, the macroscopic strategy network generates a course planning strategy at a long time interval, and the microscopic control network generates a specific control instruction at a short time interval; a hierarchical training strategy is adopted to train the dual-network structure model in a ship motion simulation environment, firstly, a macroscopic strategy network is trained to generate an overall course strategy, and then a microscopic control network is trained to realize accurate control; and deploying the trained dual-network structure model to a ship control system, generating an optimal control instruction, and realizing real-time optimization control of the ship motion trail. According to the invention, on the premise of ensuring the control precision, the real-time optimization control of the ship motion trail is realized.

The invention discloses a dynamic Gaussian scene reconstruction method based on depth regularization. The method comprises the following steps: step (1), initializing a group of 3D Gaussian sets by using an SfM point cloud, wherein each Gaussian body is described by parameters such as a central position and a covariance matrix; (2) introducing a control network decoupling motion and a geometric structure, performing offset calculation by using a time variable and a Gaussian center position, and controlling dynamic transformation of a Gaussian body; (3) introducing a loss function of scale-independent depth alignment based on local depth normalization and Pearson correlation; and (4) a new loss function is obtained through calculation according to the rendered image, and the Gaussian field and the control network are optimized through reverse gradient return. Experiments on dynamic scene data sets such as D-NeRF and NeRF-DS show that the method is superior to an existing method in the aspects of reconstruction quality, geometric consistency and new view rendering effect, the stability and detail fidelity of the dynamic scene are improved, and the method has important research value and application prospect.

The invention provides a drilling formation identification and adaptive parameter adjustment method and system, and the method comprises the steps: converting multi-source sensing data into structured formation feature representation through constructing a formation response mode library, and achieving the precise identification of formation interface distribution; a parameter response area distribution analysis and missing information reconstruction technology is adopted, recessive stratum characteristics are deeply excavated, and the internal law of stratum changes is revealed; an adjusting point optimization selection and enhanced drilling path planning mechanism is introduced, and an efficient drilling control network is constructed; potential field distribution is constructed through parameter change features, and coupling correlation between the parameter features and spatial positions is established; intelligent configuration and self-adaptive adjustment of drilling parameters are realized by combining harmonic force analysis and execution scheme generation; and finally, a complete drilling stratum identification and parameter control system is formed, an accurate and efficient technical solution can be provided for drilling operation in a complex stratum environment, and the drilling efficiency and the operation quality are improved.

The invention belongs to the technical field of reinforcement learning and aircraft attitude control, and particularly relates to an aircraft attitude control method based on explainable reinforcement learning, which comprises the following steps: firstly, designing an aircraft attitude controller with strong robustness, introducing a multi-channel control network and combining a reinforcement learning algorithm to control the attitude of an aircraft; the controller can effectively cope with challenges brought by external interference and system nonlinearity, and the adaptability and robustness of an aircraft control system are remarkably improved. Secondly, the invention provides an interpretability method combined with visual analysis. The t-SNE algorithm is used for carrying out dimensionality reduction visualization on the input characteristics of the aircraft control system, the SHAP value is combined for carrying out attribution analysis on the input characteristics, key factors in the model decision making process can be revealed, and understanding how the aircraft control system makes decisions according to different input characteristics is helped. According to the invention, high adaptability and transparency of the aircraft control system in a complex environment are realized.

The invention provides an urban low-altitude AI collaborative management and control method and system based on a Beidou grid code and a block chain, and the method comprises the steps: obtaining the information of an unmanned plane, and searching a corresponding evidence storage record in the block chain; the unmanned aerial vehicle information is obtained through a low-altitude intelligent flight control box compatible with various unmanned aerial vehicle protocols; verifying the qualification of the unmanned aerial vehicle through the evidence storage record, and bringing the unmanned aerial vehicle into a distributed collaborative management and control network for unified scheduling when the unmanned aerial vehicle passes the qualification authentication; setting flight authority according to airspace classification of the airspace map and generating a corresponding geofence to limit the flight range of each unmanned aerial vehicle; the airspace map is divided into space-time grids with specified resolution based on Beidou grid codes, state information of each grid unit in the space-time grids is analyzed based on an AI reinforcement learning engine, and flight paths of the unmanned aerial vehicles are dynamically optimized. Flight proxy of the unmanned aerial vehicles is realized through the low-altitude intelligent flight control box, unified scheduling of the multi-source heterogeneous unmanned aerial vehicles is realized, and the airspace use efficiency is improved.

The invention discloses an industrial control network anomaly detection method considering priori knowledge, and the method comprises the steps: enabling a spatial feature extraction module to consider the function attributes, communication specifications and topological structure features of equipment in an industrial control network, introducing equipment role perception coding and topological perception position coding, improving the self-attention through protocol constraint, and achieving the detection of the anomaly of the industrial control network. The spatial dependency relationship of an industrial control scene can be better understood, and the recognition capability of the model on key spatial features is improved. Meanwhile, a periodic sensing door is arranged in the time sequence feature extraction module, historical state information is aggregated to obtain scene context vectors, so that an attention mechanism is updated, weighted features are obtained, features of different time scales are subjected to weighted fusion in combination with a multi-scale time window, and finally, the three features are spliced together, so that a scene is obtained. And final time feature representation is formed. According to the method, the prior knowledge can be effectively utilized, the interaction mode between devices is captured, the time sequence dependency relationship is analyzed, and the anomaly detection effect is improved.

Systems and methods of generating fraud detection models and controlling network permissions of one or more systems within a network environment are disclosed. A network activity dataset comprising data representative of network activity within a network environment is received and at least one co-controlled system in the network activity dataset is identified by implementing a trained fraud detection model configured to receive the network activity dataset and output a fraud determination for each system having at least a first role in the network activity data. The fraud determination represents a likelihood of a system having the first role engaging in a co-controlled network activity. In response to identifying the at least one co-controlled system, one or more permissions of the at least one co-controlled system for operating within the network environment are modified.

The invention is suitable for the field of industrial safety monitoring, and provides an industrial control safety monitoring method and system, and the method comprises the steps: collecting the network flow data of an industrial control network in real time based on an industrial protocol analysis engine; synchronously acquiring operation state parameters of the key equipment by adopting an edge computing node; constructing a coupling relation quantitative model of the network flow and the equipment operation state, and calculating a dynamic correlation coefficient between the network flow and the equipment operation state through the coupling relation quantitative model; constructing reference correlation coefficient distribution in a normal production state based on historical data, and dynamically updating a safety threshold value; when the dynamic correlation coefficient exceeds a safety threshold value, triggering a multi-stage early warning mechanism; according to the method and the system, deep fusion monitoring of the industrial control network and the equipment operation state is realized, and the limitation of traditional split type monitoring is broken through. Through dynamic quantification of a coupling relationship, adaptive threshold adjustment and multi-level early warning, the timeliness and accuracy of abnormal risk identification are significantly improved, and network attacks and equipment fault hazards are effectively reduced.

The invention relates to the technical field of air conditioning system control, and discloses an air conditioning system intelligent flexible regulation and control method based on an AI algorithm, and the method comprises the steps: obtaining an original environment parameter flow containing temperature, humidity and energy consumption signals, inputting the original environment parameter flow into a pre-trained intelligent flexible regulation and control network to generate a regulation and control strategy matching result set, and determining each parameter regulation and control instruction set according to the regulation and control result set. The network is trained by historical operation data, and the data comprises historical regulation and control features (fusing time sequences and periodic features), operation mode labels and weight coefficients extracted from continuous air conditioner operation parameters. Periodic characteristics are extracted through periodic decomposition, an operation mode label is determined based on a decision algorithm, and network parameters are dynamically updated according to a weight coefficient. The system is provided with a multi-modal fusion module, a time sequence prediction module, a multi-target optimization module and a hierarchical regulation mechanism, influence factors can be dynamically distributed, multi-target self-adaptive regulation and control are achieved, and the regulation and control precision, the energy efficiency and the environmental adaptability of the air conditioning system are improved.

The invention discloses a dynamic identification system and method for fault nodes of a heat pump measurement and control network, and aims to solve the problems of one-sided weight distribution, lack of directional modeling and insufficient dynamic adaptability in the prior art. The system comprises an energy transfer topology network model building module which is used for respectively quantifying the physical connection strength and the directional energy transfer path of the heat pump system by building an undirected graph and directed graph bimodal model; the multi-dimensional weight distribution module adopts a subjective and objective fusion strategy and combines a complex network theory, an entropy weight method and an analytic hierarchy process to distribute comprehensive weights for nodes and explicit modeling directivity dependence; the dynamic robustness analysis module is used for simulating and positioning structural hub nodes through static attacks, simulating and tracking cascade failure and topology reconstruction processes through dynamic attacks, and comprehensively evaluating the robustness of the system; and the fault simulation verification module verifies weight rationality and method effectiveness from four dimensions of objectivity, center matching degree, interpretability and robustness based on a fuzzy comprehensive evaluation framework. According to the method, through fusion topology modeling, dynamic weight distribution and dynamic and static combination robustness analysis, key fault nodes are accurately recognized, the system vulnerability is revealed, a data driving basis is provided for redundancy design, intelligent operation and maintenance and reliability improvement of the heat pump system, and the operation and maintenance cost is remarkably reduced.

The invention relates to the technical field of thermal equipment operation state dynamic test and compensation control, in particular to an AI-based boiler air pre-heater temperature uniformity regulation and control system, which comprises a collaborative closed-loop control network of a data acquisition module, an AI prediction module, a heat storage system control module, a temperature compensation control module and a thermal stress analysis module. The data acquisition module acquires flue gas temperature, coal quality parameters and load instructions in real time, the flue gas temperature, the coal quality parameters and the load instructions are transmitted to the AI prediction module through an industrial bus, XGBoost, LightGBM and a deep neural network model are used for carrying out spatial-temporal feature extraction on the evolution trend of a temperature field, and the heat storage system control module dynamically switches heat storage and release modes according to the load state. The heat distribution proportion among the multiple units is adjusted by combining the three-dimensional stress field distribution data of the thermal stress analysis module; the bottleneck of lack of thermal inertia regulation of an existing system is broken through, dynamic balance of a temperature field under the condition that thermal stress of a heat transfer element is controllable is achieved, and the heat exchange efficiency and operation stability of the air pre-heater are improved.

The invention discloses an industrial control network data monitoring system based on a cloud platform, and particularly relates to the technical field of industrial control network communication, comprising a data acquisition module for acquiring communication protocol data between an industrial control network and the cloud platform in real time to form a data set; the data analysis module analyzes the data set, calculates indexes such as network timeliness, throughput capacity and transmission efficiency, and generates a performance evaluation data set; the fuzzy reasoning module is used for deducing communication protocol performance based on the data set and dividing the communication protocol performance into high-class, medium-class and low-class; the feedback module sends corresponding signals according to protocol performance classification, and the adjusting module adjusts the size of a data monitoring window according to feedback signals; the method can be suitable for various industrial communication protocols, provides sufficient basis for whether the communication protocols need to be improved or not through a unified analysis mechanism, adapts to dynamic changes of an industrial network, achieves self-adaptive adjustment, and improves the stability and efficiency of the industrial control network.

The invention provides a flow anomaly analysis and detection method, system and device based on an industrial control network, and a medium, and the method comprises the steps: carrying out the collection and analysis, data preprocessing, data calculation and analysis, and persistent storage of the flow data of a process business network of an industrial control system; a network flow behavior baseline model is automatically generated according to a process service communication condition, a flow threshold sensitivity dynamic adjustment mechanism can be utilized, learning and self-optimization can be performed from new flow data, and the network flow behavior baseline model becomes more intelligent and accurate as time goes on, and can better adapt to continuous evolution of network threats; the network traffic behavior baseline model is used for carrying out real-time network traffic dynamic detection, the traffic abnormal condition of each device in the network is found in time, an alarm is given, and the problems that in the prior art, a statistical analysis means is not established for a specific process service communication link, threshold setting is lack of accuracy, and threshold adjustment is difficult are solved.

The invention relates to the technical field of high-pressure hydrogenation reactor control, and discloses a multi-stage pressure optimization control method and system for a high-pressure hydrogenation reactor, and the method comprises the steps: obtaining temperature, pressure and flow monitoring data, generating original parameter data, and deploying a distributed control network; collecting and analyzing multi-stage pressure sensor data, and generating a pressure change influence factor; sensing a valve state, and generating a valve change influence factor; collecting data of a flow sensor, and dividing a control area after calibration; and calculating a pressure optimization index based on the control area data, integrating multiple factors to generate comprehensive control data, and further generating a pressure control signal and outputting an instruction. The system comprises a control network deployment module, a pressure analysis module, a valve analysis module, a control area analysis module and a pressure control signal generation module. Through multi-dimensional data fusion, a distributed control network and dynamic optimization calculation, the accuracy and stability of pressure control of the high-pressure hydrogenation reactor are improved, and the method is suitable for efficient and safe production in the field of petrochemical engineering.

The invention relates to a segmentation, block and carrying stage construction method for a ship draft sign, which comprises the following steps of: manufacturing a segment where the bottom of a middle water gauge sign is located, and drawing and mounting the bottom of the middle water gauge sign; a block where the top of the middle water gauge and the freeboard mark are located is manufactured, and the top of the middle water gauge mark and the freeboard mark are worked out and installed; manufacturing a section where the bottom of the head water gauge mark is located, and performing drawing and mounting on the bottom of the head water gauge mark; a block where the head water gauge mark is located is manufactured, and the top of the head water gauge mark is worked out and installed; building a dock and slipway three-dimensional measurement and control network, and carrying out rapid and refined large-closure carrying work of each subsection and block; according to the base line of the segmented block where the middle water gauge mark and the freeboard mark are located, the whole ship body base line is determined, and finally water gauge mark positioning scribing is conducted on the tail area according to the whole ship body base line. According to the method, the draft water gauge mark and the freeboard mark are marked and installed in the segment or block stage, the production procedure is effectively moved forwards, cross construction of the segments and the blocks in the large closure carrying stage is reduced, the later production construction difficulty is lowered, and the overall integrity is improved.

Disclosed in the present application are an aircraft final assembly and integration testing platform and testing method. On the basis of a rational platform architecture, structured process data preparation, model-based operation guideline document preparation, and dispatch assistance decision-making are realized by means of an application layer, such that uniform data sources in the field of final assembly can be achieved. Data uploading and issuing between an industrial control network and an internal network, and data interaction control between layers are implemented by means of the mutually collaborative operation of the application layer, a storage layer, an edge layer and an execution layer, thereby improving the information acquisition efficiency and the operating efficiency. The edge layer and the execution layer collaboratively operate, so as to realize the interactive collaboration between the assembly guidance work for operators and automatic testing, thereby improving the efficiency of data information acquisition, operation instruction and fault handling during a final assembly and testing process of an aircraft.

The invention belongs to the technical field of image processing and deep-sea mining, and discloses a deep-sea polymetallic nodule single-view three-dimensional reconstruction method and system. Aiming at the problem that the real three-dimensional data of the underwater polymetallic nodule is difficult to acquire, a multi-view generation model is adopted as 2D prior, 3D Gaussian is optimized through fractional distillation sampling loss, any three-dimensional data training is not used, the problem of three-dimensional data acquisition is solved, and three-dimensional reconstruction of the polymetallic nodule is realized. In addition, the invention provides a method only through image fine tuning, function decoupling of a noise prediction module (Unet) and a condition control network (multi-view model) is realized based on DreamBooth, and multi-view generation stability can still be maintained through pre-training view angle parameters under empty condition input; and the problem of parameter acquisition of the underwater camera is avoided through a conditional blanking mechanism, so that the model can be finely adjusted only by depending on a single-view image, and the data acquisition cost is reduced.

The invention provides an intelligent layout method, system and device for a land survey control network and a medium, and the method comprises the steps: building a space-time dynamic terrain model based on the obtained measurement data of a construction region; generating a candidate control point set in the construction area according to a preset layout density requirement; based on the space-time dynamic terrain model, performing stability analysis on each candidate control point in the candidate control point set to obtain a stability index of each candidate control point; based on the space-time dynamic terrain model, any two candidate control points in the candidate control point set are used as candidate control point pairs for time sequence intervisibility analysis, and time sequence intervisibility indexes of the candidate control point pairs are obtained; establishing a multi-objective optimization model according to the stability index and the time sequence intervisibility index; and solving the multi-objective optimization model by adopting a multi-objective evolutionary algorithm, and outputting a control network layout scheme set. By adopting the method, the point distribution quality of the control network can be improved.

The invention provides a scanning method and system for hidden asset identification in an electric power industrial control network, and aims to solve the problem that silent, non-registered or illegal access equipment is difficult to discover by the existing asset surveying and mapping means. According to the method, multiple protocol induced detection messages are injected into a target network, equipment response data are collected, feature vectors are extracted, and abnormal equipment behaviors are identified by using an unsupervised clustering and anomaly detection algorithm; and meanwhile, in combination with a graph neural network and a hidden Markov model, modeling is performed on a network communication chain structure and a historical behavior sequence, and potential hidden asset nodes are deduced. According to the method, active discovery and risk reasoning of hidden assets can be realized on the premise of not influencing industrial control services, and the method is suitable for industrial control scenes such as transformer substations and dispatching centers in the power industry and has high safety, intelligence and feasibility.