12928 results about "Data input" patented technology

The invention discloses a dynamic visual content collaborative generation method based on a cognitive logic chain, and belongs to the technical field of visual content generation, and the method comprises the following steps: S1, user intention analysis and data input; s2, dynamically constructing a cognitive logic chain; s3, intelligent scheduling of the multi-modal generation module; s4, cross-modal content collaborative generation is carried out; s5, collaborative editing and real-time feedback are carried out; s6, iterative optimization of logic chain driving; s7, multi-dimensional quality evaluation: constructing an evaluation matrix containing semantic consistency, visual attraction and user participation degree, predicting a content propagation effect in combination with a deep learning model, and generating a quantitative improvement suggestion report; and S8, updating the self-adaptive knowledge reversely marking the cognitive logic chain according to the finally adopted content version, extracting a new association rule, and injecting the new association rule into the rule base. Through deep semantic analysis and dynamic logic chain construction, the system accurately captures a core creation target of a user and converts the core creation target into an executable visual strategy.

The invention relates to the field of frequency converter fault detection, and discloses a frequency converter fault prediction method and system based on machine learning, and the method comprises the steps: obtaining multi-dimensional real-time data in the operation process of a frequency converter; constructing a dynamic mapping relation to obtain a basic feature set; generating a time sequence feature vector capable of reflecting the state change of the equipment based on the basic feature set; comparing, analyzing and judging whether the equipment state deviates from a normal operation interval or not based on the historical operation data and the time sequence feature vector, and outputting a state deviation index; performing abnormal fluctuation judgment on the time sequence feature vector; extracting fluctuation amplitude and frequency characteristics of the key indexes to obtain quantitative description data of abnormal fluctuation; inputting the quantitative description data of the abnormal fluctuation into an abnormal prediction model; and generating a coping strategy and a triggering condition of the coping strategy based on the risk prediction result. The method has the advantages that the abnormal state of the frequency converter is recognized in time, and potential risks are predicted.

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

An interface circuit includes a reference voltage generation circuit to generate a reference voltage, a differential voltage signal generation circuit to convert send data input in sending data into a pair of differential voltage signals and output the pair of differential voltage signals based on the reference voltage generated by the reference voltage generation circuit, a receiver to convert a pair of differential voltage signals input in receiving data and output received data, and a receiver test circuit to perform a sensitivity test of the receiver, the receiver test circuit having a resistance circuit to generate a pair of differential voltage signals having a potential difference being necessary for the sensitivity test of the receiver.

In a described embodiment, a multi-agent system for processing information is provided including a data processing agent configured to ingest and normalize raw data inputs to produce standardized data and a standards integration agent configured to apply reporting standards into the standardized data thereby generating integrated reporting standards. The system further includes a performance alignment agent configured to align performance indicators based on the standardized data and the integrated reporting standards and an information synthesis agent configured to process narrative information from the standardized data and the integrated reporting standards. An orchestration framework configured to manage operations of the data processing agent, the standards integration agent, and the performance alignment agent to produce a regulatory repot compliant with regulatory requirements is further provided. The orchestration framework is further executable by a large language model.

The invention discloses a composite board ultrasonic defect detection method which comprises the following steps: S1, fixing a carbon fiber circumferential winding composite pressure container on a bracket, and establishing a coordinate system with the axis of the container as a z axis; s2, a 40 MHz dry coupling phased array ultrasonic probe is attached to the scanning starting point, and the contact angle of the probe is recorded; s3, moving the probe along the spiral track of the outer surface of the container at the speed of 20mm / s, and collecting A-scanning echo signals of all array elements; s4, performing pulse compression and time domain deconvolution processing on the acquired signal to obtain a time domain echo sequence; s5, delay time is calculated according to the shell curvature, the array element signals are compensated, and focusing B-scanning data are generated; s6, splicing the B-scanning data at the step length of 0.5 mm * 0.5 mm, and reconstructing C-scanning data corresponding to the space coordinates; and S7, inputting the C-scanning data into the trained Transform network, and outputting defect types, sizes and three-dimensional coordinates. The method realizes high-frequency ultrasonic composite board defect accurate detection, remarkably improves the microcrack recognition rate, and is widely applied to safety evaluation scenes of high-pressure hydrogen storage tanks and pressure vessels.

The invention discloses a multi-modal data driven general report generation method and system based on a large model, and belongs to the technical field of intelligent report generation. Firstly, texts, images and sensor data related to a report theme are obtained and subjected to standardized preprocessing; analyzing the report generation instruction, and matching and querying a task modal mapping library matching modal configuration scheme according to a task demand; quantitatively evaluating the data quality of each modal, dynamically calculating the final decision weight of each modal in combination with the basic weight, and distributing the final decision weight to a corresponding processing path to form dominant, supplementary and reference data; inputting the dominant data and the supplementary data into a multi-modal model for analysis to obtain a preliminary conclusion with confidence score, performing consistency judgment, if no conflict exists, performing fusion to form a comprehensive conclusion, and if the conflict exists, combining a quality evaluation result and an arbitration rule to complete conflict judgment; and finally, inputting the comprehensive conclusion and the reference data into a large language model to generate a report text, and outputting a complete report after typesetting and proofreading.

The invention relates to the technical field of automobile part manufacturing, and discloses an automobile injection molding part production process control system and method, and the system comprises the following modules: a data collection module which is used for collecting technological parameters, molds, raw materials and equipment operation original data, attaching timestamps, and storing the data in a database; the process parameter prediction module is used for reading original data to construct a time sequence data set, inputting the time sequence data set into a TFT model to obtain a pre-training model, and predicting a short-term process parameter fluctuation range in combination with current production working condition parameters; and the quality risk index acquisition module is used for inputting the process parameter data and the mold data into a quality risk index calculation formula to obtain a quality risk index. Through the system, data-driven comprehensive production optimization is realized, the process control accuracy and adaptability are improved, the quality control scientificity and reliability are enhanced, the intelligent level of the production process is improved, the production efficiency is effectively improved, the defective rate is reduced, and the product quality is stabilized. The problem that process control lacks system intelligence is solved.

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention discloses an automatic soft soil foundation settlement monitoring system based on multi-source data fusion, and relates to the technical field of soft soil foundation monitoring, the system comprises an information acquisition module, a fusion processing module, a settlement prediction module and an intelligent monitoring module; the information acquisition module is used for acquiring foundation settlement sensing data and inputting the acquired data into the fusion processing module; the fusion processing module is used for preprocessing and integrating the collected data; the settlement prediction module is used for soft soil foundation settlement prediction; the intelligent monitoring module comprises a self-adaptive processing module and an interaction alarm module, the self-adaptive processing module is used for generating an optimization strategy, and the interaction alarm module is used for carrying out user interaction and multi-mode abnormal alarm reminding. An early warning response window is provided for engineering personnel, and the occurrence rate of sudden settlement accidents is reduced.

The invention provides an automatic energy-saving control method and device for a ring cooling fan based on load feedback, relates to the field of ring cooling fans, and solves the technical problem of delay of regulation and control in an energy-saving working state. The method comprises the steps that working condition data are input into a preset powder box model, and a feed-forward air volume instruction is obtained through calculation; and inputting the working condition data into a state observer to obtain an optimal estimated temperature value. And the deviation between the optimal estimated temperature value and a preset temperature set value is calculated, and a feedback air volume compensation instruction is obtained through calculation of a feedback controller according to the deviation. And fusing the feed-forward air volume instruction and the feedback air volume compensation instruction to obtain a final air volume control instruction, and issuing the final air volume control instruction to a fan frequency converter for execution. And continuously monitoring the numerical value and the change trend of the feedback air volume compensation instruction, taking the feedback air volume compensation instruction as a prediction error signal of the powder box model, and adaptively adjusting key thermal parameters in the powder box model. The method is used in the control process of the ring cooling fan.

The invention provides a tunnel grouting dynamic adaptive simulation method and system based on multi-physics field coupling, and belongs to the technical field of grouting simulation, and the method comprises the steps: obtaining original geological information, constructing a three-dimensional geological geometric model and a fracture network, constructing a multi-physics field coupling mechanism, and carrying out discrete solution; further predicting slurry diffusion and crack filling to obtain an isobaric envelope diagram and an early warning area, and simulating a grouting process; acquiring real-time sensor data, inputting the isobaric envelope diagram, the early warning area and the real-time sensor data into an adaptive parameter prediction model, dynamically updating the weight of the adaptive parameter prediction model according to the change of the real-time sensor data by adopting rolling training to obtain a prediction index, and constructing a feedback control chain based on the prediction index. Determining an optimal grouting parameter combination by adopting a particle swarm optimization algorithm; and the optimal grouting parameters are fed back to the simulated grouting process for automatic parameter adjustment, and the optimized grouting strategy is executed. The intelligent numerical values of the grouting parameters can be dynamically and adaptively adjusted.

The invention discloses a three-dimensional human body posture estimation method based on hybrid architecture space-time modeling, and the method comprises the steps: obtaining historical human body posture data, and generating a three-dimensional human body posture estimation network training set; constructing a three-dimensional human body posture estimation network model, wherein the network model comprises a space-time position embedding module, a space-time Mama block, a space-time self-attention block, a full connection layer and a regression layer; based on the estimation network training set and a loss function training network model, learning a mapping relation from a two-dimensional attitude joint point sequence to a three-dimensional attitude joint point sequence; and inputting two-dimensional attitude joint point sequence data to be estimated into the trained network model, and outputting a three-dimensional attitude joint point sequence. According to the method, the global spatio-temporal features are rapidly extracted by using Mama, then the global spatio-temporal feature information is further supplemented by using Transform, and the spatio-temporal features are fused and complemented more effectively by using a hybrid architecture.

A drifting buoy trajectory prediction method based on a hybrid neural network prediction model, includes: S1, obtaining marine environmental data and historical trajectory data of a drifting buoy; S2, performing preprocessing on the marine environmental data and the historical trajectory data to obtain input data configured to predict northward and an eastward velocities of the drifting buoy; S3, inputting the input data into the hybrid neural network prediction model to obtain predicted values of the northward and eastward velocities; S4, calculating latitude and longitude coordinates of a trajectory point of the drifting buoy based on the predicted values of the eastward and northward velocities; and S5, predicting, by repeating the step S1-S4, latitude and longitude coordinates of trajectory points of the drifting buoy at multiple time points to obtain a sequence of trajectory point coordinates to thereby achieve trajectory prediction of the drifting buoy over a target future period.

The invention discloses an intelligent detection method for fault detection of an outdoor power line, and the method comprises the following steps: 1, carrying out the collection and preprocessing of multi-modal data, and carrying out the collection and preprocessing of the multi-modal data through an unmanned plane cluster, a distributed optical fiber sensor, a laser radar and meteorological monitoring equipment; visible light image data, infrared image data, laser point cloud data, vibration waveforms, temperature distribution and environmental parameters of the power line are synchronously obtained, and multi-source image data are processed, namely the visible light image data, the infrared image data and the laser point cloud data are processed; and 2, intelligent fault diagnosis: inputting the data acquired in the step 1 into a multi-task neural network model, and outputting a fault positioning and type identification result. According to the novel detection method based on multi-modal data fusion, an intelligent algorithm and closed-loop optimization, the fault identification precision, the dynamic decision-making capability and the comprehensive protection efficiency are improved, and the intelligent operation and maintenance requirements of a modern power grid are met.

The invention provides an electromagnetic field prediction method and device and electronic equipment, and relates to the technical field of electromagnetic field solving. The method comprises the following steps: acquiring historical electromagnetic original data in a field-line coupling scene, preprocessing the historical electromagnetic original data, inputting the preprocessed historical electromagnetic original data into an LSTM-PINN model, and outputting a physical field quantity mapping result; wherein the physical field quantity comprises an electric field component and a magnetic field component; setting a weighted loss function, and performing optimization training on the LSTM-PINN model based on a physical field quantity mapping result and an error of a real physical field quantity corresponding to historical electromagnetic original data to obtain a trained electromagnetic field prediction model; wherein the weighted loss function comprises a data loss function, a physical residual loss function, an initial condition loss function and a boundary condition loss function; and inputting real-time electromagnetic original data into the trained electromagnetic field prediction model to obtain a spatio-temporal distribution electromagnetic field prediction result. The method can effectively extract the spatial distribution features and the time sequence features at the same time, and is suitable for a complex field-line coupling problem.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention discloses a multi-source-domain multi-teacher knowledge distillation method and system based on reinforcement learning, and the method comprises the steps: obtaining target domain sample data, inputting the data into N pre-trained teacher models, and generating the output features of all teacher models; inputting the target domain sample and all teacher model outputs into a reinforcement learning strategy network, generating a dynamic weight of each teacher model, and calculating a knowledge distillation loss function based on the dynamic weights; constructing a total loss function according to the knowledge distillation loss function and the cross entropy loss output by the student model; student model parameters are updated through gradient descent; and calculating a reward value according to student model performance change, and updating reinforcement learning strategy network parameters. According to the method, the reward function based on student model performance improvement is constructed, the strategy network is continuously updated in a strategy gradient optimization mode, the distillation efficiency is effectively improved, knowledge conflicts among teachers are relieved, and the robustness and generalization performance of the student model in a multi-source complex environment are remarkably improved.

The invention discloses an SF6 gas leakage detection method and system based on a photoacoustic spectrum analyzer, and the method comprises the steps: arranging a sampling end to collect an SF6 gas sample, and obtaining stable gas input through constant-current sampling and steady-state pretreatment; steady-state gas is guided into the photoacoustic spectrum analyzer, and resonance frequency stabilization and signal amplification output are achieved through the self-tuning unit; executing double-microphone differential detection and digital filtering processing, and outputting a stable SF6 detection signal with a high signal-to-noise ratio; performing time calibration, abnormity elimination and consistency processing on the detection signal to generate standardized detection data; inputting the standardized data to a Transform model, and performing inversion to generate an SF6 leakage source position and a diffusion path; and displaying an inversion result on a monitoring interface, triggering a sound-light alarm when the inversion result exceeds a limit, and uploading the inversion result to a cloud monitoring platform. According to the invention, the intelligent photoacoustic spectrum system combining photoacoustic-fluid steady-state control and self-tuning detection is constructed, so that high-sensitivity detection and accurate positioning of SF6 gas leakage are realized.

The invention relates to the technical field of large model intention understanding, and discloses an APP dialogue type service reaching method and system based on large model intention understanding. The method comprises the steps of receiving a natural language text input by a user on an APP dialogue interface and performing large model semantic analysis to obtain service semantic data; inputting the business semantic data into an intention recognition model for business intention understanding to obtain business intention data; performing multi-agent cooperative process planning to obtain execution process data; carrying out interactive confirmation on the service parameters to obtain service execution parameters; transmitting the service execution parameters to corresponding service tool interfaces for calling execution to obtain a service tool calling result, and performing intelligent analysis and card rendering on the service tool calling result to obtain display card data. According to the method, the real business intention of the user is accurately understood, and the problems of execution efficiency and stability of a traditional single interface calling mode in a complex business scene are solved.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention discloses a cross-domain heterogeneous data query system and method based on a large model and a knowledge graph, belongs to the technical field of information retrieval, and aims to solve the technical problem of complex relation reasoning in cross-domain heterogeneous data query. Comprising a data input and preprocessing module used for collecting multi-modal data to obtain feature vectors; the knowledge graph construction and management module is used for constructing a knowledge graph and providing query service based on the knowledge graph; the bidirectional enhancement module is used for writing the reasoning result of the large language model into a knowledge graph and carrying out version management; the domain adaptation layer is used for carrying out model training on the large language model based on a lightweight adapter and a domain adaptation mechanism; the real-time query and reasoning module verifies and supplements the candidate answers based on a knowledge graph to generate an initial answer, and explains a reasoning path based on a causal reasoning network to generate a final answer; and the interpretability and transparency module is used for displaying knowledge in the knowledge graph through a visual interface and providing auditing service based on the operation day.

The invention relates to a big data-based passenger-roll transport demand prediction and ship intelligent scheduling method and system. The method comprises the steps of obtaining multi-source shipping data for a target area; inputting the multi-source shipping data into the spatial-temporal feature mining model, and predicting passenger rolling transportation demand information of the target area; acquiring ship real-time position, passenger carrying capacity, energy consumption data and port real-time operation state in the target area, and dynamically generating an optimal scheduling scheme by adopting a shipping scheduling model in combination with the predicted passenger transport demand information; the shipping scheduling model is obtained by interacting a decision scheduling model with an intelligent agent corresponding to the passenger roller transportation system and performing iterative training by adopting a reinforcement learning algorithm; and converting the optimal scheduling scheme into visual information, pushing the visual information to operation terminals of the ship and port workers so as to start corresponding shipping scheduling operation, and monitoring the execution effect of the shipping scheduling operation in real time.

The invention discloses a method and system for predicting leakage of a water supply pipe network, and the method comprises the steps: modeling nodes and pipe sections of the pipe network into a graph topological structure, and endowing the nodes and the pipe sections with static attributes; collecting operation data of the water supply network, and constructing time-varying graph data corresponding to the graph topology; combining the time-varying graph data with the static attributes to form space-time input features; constructing a graph time sequence prediction model based on a deep learning framework, performing graph structure feature extraction on node graph features and pipe section graph features of each time step to obtain node space features and pipe section space features, and outputting a node and pipe section space-time representation set; evaluating and analyzing the leakage level of each DMA or pressure partition; generating a pipe section leakage risk space distribution set; constructing a joint loss function, and training and updating the graph time sequence prediction model; and inputting operation data acquired in real time into the trained graph time sequence prediction model, and generating a leakage rate prediction value of each partition and a leakage risk index of each pipe section on line for leakage prediction and operation and maintenance decision.

The present invention relates to the technical field of power systems and operations research. Disclosed are a method and system for coordinated source-grid-load-storage dispatch optimization during post-disaster restoration of a power distribution system. The method comprises: acquiring network topology constraint data for a power distribution system, wherein network topology constraints for the power distribution system comprise a virtual network topology constraint for the power distribution system, a geometric network topology constraint for the power distribution system, and an electrical network topology constraint for the power distribution system; on the basis of fault situations of a power source device, a load device and an energy storage device that are caused by a disaster, generating operational characteristic constraint data for devices in the power distribution system, wherein the operational characteristic constraint data for the devices in the power distribution system comprises operational characteristic constraints for the power source device, the load device and the energy storage device; and inputting the network topology constraint data for the power distribution system and the operational characteristic constraint data for the devices in the power distribution system into a pre-established coordinated source-grid-load-storage dispatch optimization model for power distribution systems in a post-disaster restoration scenario, such that a coordinated dispatch optimization result is output.

The invention discloses a computer network security threat real-time monitoring method and system, and relates to the technical field of network security, and the method comprises the steps: collecting and preprocessing multi-source network data, organizing the multi-source network data into a behavior sequence according to a time sequence, and forming time sequence behavior data for analysis; constructing an attack atlas based on the preprocessed multi-source network data, and in the atlas construction process, forming dynamic representation of the attack atlas in combination with time attributes of behavior events and inter-entity contexts; time sequence behavior data are input into an RCLNet architecture for analysis, the RCLNet extracts spatial features through CNN, the LSTM captures time features, key behavior features are concerned by using an adaptive attention mechanism, and a high-dimensional behavior embedding vector is generated. High-precision and real-time detection and response to network threats are realized, and the intelligence and actual combat adaptability of the system are greatly improved.

The invention discloses a multi-sensor monitoring landslide early warning method, device and equipment and a medium, and the method comprises the steps: collecting three-dimensional displacement data and multi-source environmental factor data of a to-be-monitored slope region through a plurality of environmental sensors, and carrying out the preprocessing of the collected data; constructing an initial landslide probability prediction model, and training based on a preset algorithm to obtain a target landslide probability prediction model; inputting the current monitoring data into the trained prediction model, and outputting a landslide probability prediction result; a preset feature contribution interpretation mechanism is introduced to quantify the influence of each environment feature in the prediction of the landslide probability; and dynamically adjusting a landslide probability index based on the predicted landslide probability and the feature contribution degree, and setting a multi-stage early warning threshold to realize graded early warning of the landslide risk. According to the invention, a landslide early warning technical framework with adaptive updating and interpretable analysis capabilities is constructed, and the accuracy, stability and engineering adaptability of landslide early warning are improved.