1332 results about "Visual interface" patented technology

The invention discloses a BIM-based electromechanical installation project progress planning and resource scheduling method and system, and belongs to the technical field of intelligent construction. The method comprises the following steps: 1) collecting multi-dimensional parameters (real-time construction parameters, prediction model parameters and external constraint parameters) in construction in a classified manner, and constructing a dynamic knowledge graph; 2) detecting progress and resource deviation based on a preset threshold value of the BIM model, and dynamically correcting a resource demand curve through the model; 3) in combination with constraint conditions such as policies and weather, optimizing an equipment scheduling path by adopting an algorithm, and screening compliance candidate schemes; 4) performing multi-objective optimization (minimizing progress deviation, maximizing resource utilization rate and controlling supply chain risk) on the scheme by using a genetic algorithm, and verifying through simulation iteration; and 5) outputting the optimal scheme and synchronizing the optimal scheme to a visual interface. According to the invention, real-time closed loop of data is realized through hardware-algorithm cooperation, and an efficient, dynamic and extensible intelligent management scheme is provided for electromechanical engineering.

The invention relates to the technical field of building data interaction, in particular to a building engineering interaction method and system based on a BIM model and a medium. The method comprises the following steps: collecting construction site environment parameters and structure response data by using a distributed sensor network; preprocessing the data by an edge computing node; generating standardized environment data and key structure indexes; comparing environment threshold values to identify abnormal events; the method comprises the following steps of: displaying spatial positioning and risk levels through a visual interface, receiving a regulation and control instruction input by a user, dynamically adjusting construction parameters of a BIM model, generating an optimized construction progress scheme, comparing the optimized scheme with an original model to identify a construction conflict area, and generating a conflict resolution report. Through the advanced monitoring technology and construction management technology, the response speed and decision-making efficiency of the building engineering project are improved, and the safety guarantee and resource utilization efficiency in the construction process are improved.

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention relates to a regional pollution process real-time monitoring, regulation and control system and method based on artificial intelligence. The system is composed of a data acquisition module, a communication module, an artificial intelligence analysis module, an intelligent regulation and control module, an execution terminal and a user interaction module. The data acquisition module acquires multi-source data such as pollutant concentration and meteorological parameters by means of a multi-modal sensor, the data are processed and transmitted by the data communication module, and the artificial intelligence analysis module realizes pollution source tracing and accurate prediction by using technologies such as a space-time diagram convolutional network and an LSTM-Transform hybrid neural network. The intelligent regulation and control module generates a regulation and control strategy based on an NSGA-II algorithm, and the execution terminal is responsible for implementation. The user interaction module provides a visual interface and a manual intervention channel. The method comprises the steps of data acquisition and processing, model construction and prediction, regulation and control strategy formulation and execution and feedback optimization closed-loop operation. According to the invention, comprehensive real-time monitoring and accurate regulation and control of regional pollution are realized, the prediction accuracy is improved, the environmental, economic and social benefits are balanced, and an efficient technical means is provided for regional pollution treatment.

The invention provides a soybean planting fertilization management system, and relates to the technical field of management systems.Soil physicochemical properties, a meteorological microenvironment, a plant growth state and agricultural machinery operation data are collected in real time through a field sensor network and an unmanned aerial vehicle multispectral imaging technology, and a farmland dynamic portrait is constructed; the intelligent decision-making unit uses a deep learning model and a GLCM texture analysis technology to dynamically identify plant nutrient deficiency pathological characteristics, determines a nutrient deficiency type in combination with an element concentration threshold and triggers early warning; the scheme decision module dynamically optimizes the nitrogen-phosphorus-potassium ratio through a reinforcement learning algorithm, generates a field-level fertilization prescription map by using Kriging interpolation, recommends the optimal fertilization opportunity and dosage, and the precise execution module is linked with a variable fertilizer applicator to realize integrated precise application of water and fertilizer. The user interaction module supports remote monitoring and intervention of farmers through a GIS visual interface and a mobile terminal APP, can dynamically adapt to environmental changes and crop requirements, and significantly improves the nutrient utilization efficiency.

The invention discloses an unmanned aerial vehicle trajectory tracking system based on multi-sensor fusion, which relates to the technical field of unmanned system autonomous navigation and control, and comprises an unmanned aerial vehicle data acquisition module, an unmanned aerial vehicle feature extraction module, a trajectory prediction deviation scoring module, a trajectory dynamic correction module and a trajectory monitoring module. The unmanned aerial vehicle data acquisition module acquires and preprocesses unmanned aerial vehicle and GPS data, the unmanned aerial vehicle feature extraction module constructs a trajectory tracking feature sequence table, the trajectory prediction deviation scoring module outputs three-dimensional trajectory prediction coordinates and deviation scores, and the trajectory dynamic correction module generates correction parameters. Based on the judgment logic, the judgment logic is converted into an unmanned aerial vehicle correction instruction and attitude feedback quantity, and the track monitoring module constructs a visual interface. According to the method, the trajectory tracking precision of the unmanned aerial vehicle in a complex environment is remarkably improved, real-time detection and dynamic correction of abnormal deviation are realized, the state monitoring capability is enhanced by a three-dimensional visual interface, the trajectory tracking error is reduced, the misjudgment rate is reduced, and the flight safety and the control efficiency are guaranteed.

The invention provides a workflow condition node management method based on a dynamic rule engine, and the method comprises the steps: obtaining a business circulation demand, configuring a task node attribute, and carrying out the adaptation of the business circulation demand according to the task node attribute, and obtaining a process template model; setting rule conditions, binding the rule conditions with the task nodes, and processing the flow template model; generating a process instance according to the updated process template model, analyzing rule conditions, initializing task allocation logic, and pushing a to-do task to a user; the user executes a to-do task, triggers the rule engine to carry out condition judgment, and updates a process instance state to obtain an updated process instance state; and according to the updated process instance state, generating a visual interface to display the process progress, and carrying out real-time early warning on an overtime or abnormal task to obtain a monitoring report and the process instance state after abnormal processing. Tasks can be decomposed, so that complex tasks are easier to manage, and the execution difficulty is reduced.

The invention discloses a railway intelligent construction site safety penetration type management messenger platform which comprises a multi-modal data fusion processing module, an edge computing node cluster module, a three-dimensional visual penetration type management interface module, an intelligent early warning and emergency response module, a self-adaptive network transmission module and the like. Real-time cleaning, alignment and correlation analysis are realized by using a dynamic data calibration algorithm, and a data island is broken; the edge computing node cluster carries out localization preprocessing and the like on data in a key area, so that the load of a central server is reduced; the three-dimensional visual interface is based on a digital twinborn construction model, supports drilling type viewing and realizes three-dimensional monitoring; the intelligent early warning system adopts a reinforcement learning model to automatically trigger multi-channel early warning; and the adaptive network transmission module dynamically switches communication modes to ensure low-delay transmission of key data. The platform realizes real-time acquisition and integration of construction site data and reduces manual intervention.

The invention discloses a metadata real-time self-adaptive standardization system for multi-field data sharing. The metadata real-time self-adaptive standardization system comprises a metadata information layering module, an intelligent information acquisition module, a real-time self-adaptive standardization module and a visual management module, the metadata information layering module identifies sources of various metadata, and sequentially performs cleaning, classification and layering preprocessing on the metadata; the intelligent information acquisition module uniformly acquires metadata information and converts the acquired data into standardized intermediate representation; the real-time adaptive standardization module is used for realizing rule dynamic adaptation and real-time optimization in a metadata standardization process, is internally provided with a dynamic rule library, is used for storing dynamically generated metadata mapping rules and supports automatic loading, updating and semantic extension of the rules; and the visual management module realizes management, analysis and dynamic adjustment of metadata through a visual interface. The method is high in accuracy of processing unknown data, can adapt to different data scenes in real time, and is high in semantic interoperation capability.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention discloses a power equipment data acquisition and processing system, which comprises a multi-protocol compatible acquisition module, an intelligent network detection module, a system service performance monitoring module and a high-concurrency processing module, the multi-protocol compatible acquisition module is responsible for adaptation and data acquisition of different power equipment protocols and normalization processing of the acquired data; the intelligent network detection module monitors a network link state, diagnoses a network fault reason and pushes fault information in real time; the system service performance monitoring module collects key performance indexes of a server, displays performance data through a visual interface, and triggers an alarm when the indexes are abnormal; the high-concurrency processing module compresses the collected data, realizes efficient pushing and priority processing of the data through a message queue, and guarantees the data processing efficiency in a high-concurrency scene; comprehensive monitoring of power equipment communication and system performance is realized, and data acquisition efficiency and system reliability are improved.

The invention relates to the field of physiological status real-time monitoring, provides a virtual patient physiological status real-time monitoring method and system based on digital twinning, and is used for solving the problems of non-linear combination deviation and multi-source data synchronization lag of virtual patient physiological status monitoring caused by dependence on a static model and one-way feedback. The method comprises the steps that a valve three-dimensional model is built based on aorta CT data, and a simulation model containing motion conditions is built in combination with a fluid mechanics equation; the blood flow pressure difference and vortex parameters of valve opening and closing are calculated in real time, an optical fiber virtual sensor is embedded in the topological surface of the valve to simulate blood flow deformation, and a valve state mechanical signal is output; parameters and signals are input into a digital twin engine, particle trajectories and refractive indexes are adjusted to generate a visual interface, blood flow velocity-pressure gradient spatio-temporal data are extracted, model boundary conditions are reversely corrected, and valve movement is synchronized with the physiological state of a patient. According to the technical scheme, heart valve movement is simulated in real time, and the real heartbeat rhythm of the patient is automatically matched.

The invention discloses intelligent knowledge service electronic equipment for information retrieval, and the equipment comprises a data management layer which is responsible for providing original data for a cognitive calculation layer, and carrying out the closed-loop optimization through continuously monitoring the feedback of a man-machine interaction layer; the cognitive calculation layer is responsible for converting original data into a semantic tag system and a dynamic knowledge graph, applying BERT + GNN hybrid analysis, BERT processing text context and GNN analysis entity relationship in the knowledge graph, and dynamically adjusting a reasoning path according to user field preference by combining an SWRL rule engine and reinforcement learning; the service arrangement layer is responsible for extracting data from the multi-modal data pool, solidifying and arranging a complex query stream to perform logic decoupling and efficient multiplexing, and providing a visual interface to support non-technical personnel to configure a new scene; and the man-machine interaction layer is used for constructing a natural interaction bridge through the structured answers and the visual maps output by the service arrangement layer, carrying out personalized interface adaptation based on historical dialogues, and establishing a user feedback closed loop.

The invention discloses a multifunctional subway tunnel disease intelligent inspection rail car and a detection method, and belongs to the technical field of rail detection. Aiming at the problems of low manual detection efficiency and single equipment function in the prior art, the invention provides an intelligent inspection vehicle integrating laser scanning, ground penetrating radar and artificial intelligence. The inspection vehicle is provided with a support telescopic system, and camera height self-adaptive adjustment is achieved through a laser distance measuring sensor and an XGBoost algorithm. The disease image acquisition system and the three-dimensional laser scanner cooperate to obtain surface disease data; the internal disease data acquisition system detects internal cracks of the lining through a ground penetrating radar; the disease analysis system deploys an improved YOLOv11 model to realize intelligent identification; and the global control system is integrated with a multi-source data visual interface. The detection method comprises the steps of adaptive adjustment, multi-source data acquisition, preprocessing, intelligent identification and positioning feedback. The system constructs an Internet of Things architecture, significantly improves the detection efficiency and precision, and is suitable for health monitoring of the subway tunnel structure.

The invention discloses an ultrasonic vibrator group intelligent prediction maintenance system and method, and relates to the technical field of industrial equipment fault diagnosis and predictive maintenance, the ultrasonic vibrator group intelligent prediction maintenance system is composed of a data acquisition module, a data transmission module, a data processing module, a model analysis module, an application module, a feedback optimization module and the like, and vibration, current, voltage, resistance, temperature, power and other data are acquired in real time through a multi-source sensor; the sampling frequency is dynamically adjusted under the abnormal working condition; multi-dimensional feature fusion and fault prediction are carried out by utilizing a pre-trained Transform model, mechanical, electrical, thermal and other fault types are automatically identified, maintenance suggestions are output, a user can obtain fault early warning, residual service life and maintenance guidance through a visual interface, and an actual maintenance result is transmitted back to a system to continuously optimize the model, and compared with a traditional mode, the method has the advantages that the operation is simple, and the efficiency is high. According to the invention, accidental shutdown can be effectively reduced, the maintenance cost is reduced, the service life of equipment is prolonged, the real-time monitoring and accurate prediction of the operation state of the equipment are realized, and the production efficiency is improved.

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 discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The invention relates to the technical field of dynamic monitoring, in particular to an AI-based coal mine underground and roadway three-dimensional earthquake dynamic monitoring system, which comprises an underground edge computing node cluster module, an underground edge computing node cluster module and an underground edge computing node cluster module, the multi-source heterogeneous data fusion processing module is used for receiving the original signal data set, performing space-time alignment and feature decoupling processing, and generating a standardized seismic-electric feature matrix; the three-dimensional geological dynamic modeling module is used for generating a three-dimensional geological dynamic model updated in real time through a dynamic voxel compensation algorithm based on the standardized seismoelectric characteristic matrix; the abnormal characteristic evolution analysis module is used for receiving time sequence data of the three-dimensional geological dynamic model and constructing a space-time two-dimensional abnormal degree evolution graph; and the visual early warning decision module is used for generating graded early warning signals and a three-dimensional visual interface. According to the invention, the six-level progressive processing architecture is innovatively designed, so that the performance of the three-dimensional earthquake dynamic monitoring system for the underground coal mine and the roadway is remarkably improved.

The invention discloses an integrated verification and test system and method for complex rules of a substation monitoring system, and the system comprises a rule editing module which provides a visual interface to support a user to edit the complex rules containing various types of variables and time sequence constraints; the analysis and verification module is used for performing grammar, variable conflict and business compliance multi-level verification on the rules; the intelligent test case generation module is used for automatically generating test cases covering normal, boundary and abnormal conditions based on the rule tree structure; the simulation test interface module converts the use case into a standard test script, submits the standard test script to a simulation tool for execution, and outputs the standard test script to a monitoring system through protocol conversion; the result analysis module is used for comparing an execution result with an expected result and generating a structured test report; according to the invention, full-process automation of rule verification and testing is realized, the verification efficiency is remarkably improved, and the test requirements of various transformer substation business scenes are met.

The invention discloses a visual precise plastic mold surface milling system and method, and the method comprises the steps: obtaining and analyzing the surface topography data of a milling region and a tool displacement track in real time through an optical sensor, and extracting wear-related micro-deformation features through multi-band filtering processing; generating a deviation vector matrix through three-dimensional mapping of the deviation vector matrix and a reference curved surface, and realizing dynamic calculation and path correction of the cutter offset; the high efficiency and stability of the compensation cutting process are ensured by combining the self-adaptive adjustment of the rotating speed and the feeding rate of the main shaft; meanwhile, the compensation effect is projected to a visual interface in real time to form a comparison layer, hardware acceleration is started to maintain the system response speed when the frame rate fluctuates, and finally the material removal volume is evaluated according to the deviation vector matrix to intelligently control the machining cycle termination time. Therefore, the problem of microtopography deviation caused by tool abrasion in the milling process of the complex curved surface mold is effectively solved, and the machining precision and efficiency as well as the robustness and the automation level of the system are remarkably improved.

The invention discloses a building quality inspection management system, and the system comprises a distributed intelligent sensor network which comprises a multi-type sensor array disposed at a key node of a building structure, a mobile detection terminal, and an unmanned plane inspection unit; the multi-modal data fusion module is used for performing space-time alignment and feature extraction on the sensor data, the image data and the BIM model data; the digital twinborn visual platform comprises a quality evaluation engine, a defect traceability module and a three-dimensional visual interface; the defect detection rate in a dark light environment is obviously improved, full-coverage detection of floors can be realized in a short time, field sensor data and a BIM model are synchronized in real time through a 5G network, and when a quality problem is found, the system analyzes 12 types of influence factors such as materials, processes and environments through a Bayesian network, so that the quality problem is solved. The system can trace back to specific construction teams, equipment numbers and even meteorological data, and can reduce the requirements of personnel through intelligent detection, thereby reducing the working cost.

The invention relates to the technical field of metering pumps, in particular to an external meshing gear metering pump flow calculation method based on linear regression and curve fitting, which comprises the following steps: generating an integrated data set by monitoring the flow rate, pressure, temperature and viscosity in real time, setting flow rate, pressure and temperature thresholds based on the data set, judging the flow state and recording abnormity, and calculating the flow rate of the external meshing gear metering pump. Flow velocity and viscosity are selected for flow calculation, a corrected flow value is generated, whether flow is normal or not is analyzed and determined, a flow stability evaluation result is generated, and a visual interface is created to display parameters and a calculation result. According to the method, the multiple sensors are installed at the inlet and the outlet of the metering pump, the flow speed, the pressure, the temperature and the viscosity are monitored in real time, an integrated data set provides a precise basis for flow calculation, a threshold value is set, data are monitored, it is ensured that parameters exceeding the normal range can be recorded and analyzed in time, and the process improves the accuracy of flow calculation and improves the working efficiency. And flow evaluation is optimized, and the operation safety and efficiency are improved.

The invention discloses a transformer multi-dimensional state sensing and diagnosing system, which comprises a multi-dimensional state sensing module for acquiring transformer multi-dimensional data in real time through various sensors; the data acquisition and processing module performs processing such as synchronous sampling and filtering on the data and extracts features; the state evaluation and fault diagnosis module evaluates a state and diagnoses a fault by using a three-dimensional evaluation model and a CNN-LSTM fusion model; the self-adaptive early warning module dynamically adjusts a threshold value according to an evaluation result and sends out multi-level early warning; the communication and interaction module realizes data encryption transmission, has a visual interface, and supports interaction diagnosis and remote configuration updating. The system can comprehensively collect data, accurately diagnose faults and efficiently early warn, is convenient to operate and maintain, and improves the operation reliability and the operation and maintenance efficiency of the transformer.

The invention relates to the technical field of port ship scheduling, and discloses a ship scheduling carrying method based on space-time constraint scheduling simulation and conflict optimization. Obtaining ship scheduling tasks and port space-time resource data, and generating structured data in a classified manner; constructing a multi-dimensional space-time constraint model, and determining an initial constraint condition; scheduling simulation is carried out on the basis, and the robustness of the scheme is improved by introducing real-time data; detecting conflicts and carrying out iterative optimization by adopting a multi-objective optimization algorithm; and synchronously correcting model parameters with real-time data, generating a scheduling instruction set, and pushing and executing the scheduling instruction set. And meanwhile, path planning is performed by using a deep reinforcement learning algorithm, and a visual interface is constructed to assist scheduling. The method can effectively integrate data, optimize a scheduling scheme, improve the utilization rate of port resources, enhance the ability to deal with emergencies, and realize efficient and intelligent management of port ship scheduling.

The invention discloses a software project development method based on a low-code platform, which belongs to the technical field of software development, and specifically comprises the following steps: receiving original demand description through an embedded natural language parser, and establishing a semantic incidence matrix to extract demand elements; encoding the demand elements into three-dimensional structured demand vectors; mapping the demand vector to a three-dimensional orthogonal feature space, calculating the coordinates of a functional module, and generating an initial topological structure based on a Delou inner triangulation algorithm; rendering an interactive three-dimensional network map on a visual interface; monitoring user operation, and recalculating the adjacency relation of the affected module when the displacement of the module exceeds a threshold value or the connection edge is reconstructed; compared with a historical version, when a key service path is interrupted or a data flow ring is broken, a compensatory connection bridge is activated; a deployable architecture with a version traceability identifier is output, and a traceability code is built in the module to reversely map an original demand vector.

A method for measuring the size of a slide block of a guide rail with a complex shape based on intelligent vision belongs to the field of machine vision, and comprises the following steps: DXF file data acquisition: reading by using Python; image acquisition and preprocessing: acquiring image data in real time by using an industrial camera, and performing image preprocessing; hole detection and measurement: adopting a Hough transform algorithm to realize sliding block hole detection, introducing a sub-pixel counting method to improve the algorithm, and realizing aperture high-precision measurement; edge line detection and measurement: extracting edge and line segment features of the sliding block image by adopting Canny edge detection and LSD line segment detection technologies, automatically removing invalid line segments, determining an optimal fitting line segment, and realizing accurate detection and measurement of a target edge line; and detection and measurement result visualization: displaying a sliding block size measurement result in an image, comparing the sliding block size measurement result with a standard parameter of a DXF file, judging the qualification of the sliding block, constructing a visual interface by adopting Pyside2 to record the measurement result, and supporting a plurality of input source detection functions.

The invention provides a visual early warning method and system for a network security event in the cross technical field of network security and machine learning, and the method comprises the steps: S1, creating a security event recognition model, and setting a loss function of the security event recognition model; s2, acquiring historical monitoring data to construct a data set; s3, the data set is divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are respectively utilized to train, verify and test the security event identification model; s4, performing knowledge distillation and deployment on the security event recognition model passing the test; s5, collecting real-time monitoring data, and inputting the data into the security event recognition model to obtain a security event recognition report; and S6, displaying the security event identification report and statistical data of the security event identification report and the real-time monitoring data in real time through a visual interface. The method has the advantage that the accuracy and the timeliness of early warning of the network security event are greatly improved.

The invention discloses an intelligent facial skin care monitoring management system which is composed of a hardware layer, a data layer, an algorithm layer and an application layer, the hardware layer collects facial skin images and external environment data through an RGB camera, a UV light sensor, a depth sensor and an environment sensor, and the data layer achieves encryption transmission and cloud storage of the collected data. The algorithm layer performs skin state detection, classification and trend prediction based on multi-modal data fusion, a deep convolutional neural network and a time sequence model, and the application layer provides skin care scheme suggestions and virtual trial functions through a personalized recommendation algorithm. The system can detect the multi-dimensional features of the skin at high precision, generate a dynamic skin care scheme, predict the change trend of the skin state, and improve the user experience through a visual interface and an interaction function. The method has the advantages of being high in detection precision, scientific in recommendation scheme, accurate in trend prediction, safe in user privacy and the like, and can be widely applied to skin care product research and development, personalized skin care management and beauty industries.

The invention discloses a high-voltage cable on-line safety monitoring and fault positioning system, and relates to the technical field of high-voltage cable monitoring, and the system comprises a data processing module which is responsible for the centralized processing and analysis of original data; the data acquisition module is responsible for acquiring cable operation data and surrounding environment data; the safety monitoring module is responsible for monitoring the operation state of the cable in real time and evaluating the safety risk of cable operation; the fault early warning module is responsible for receiving data generated by the safety monitoring module; the fault positioning module is used for positioning the occurrence position of a cable operation fault; the operation and maintenance management module is responsible for coordinating maintenance resources and plans; and the client provides a visual interface. According to the invention, the cable operation fault can be predicted in advance, and the accuracy of fault positioning can be improved by generating multi-stage early warning information and optimizing the original signal through wavelet change.

The invention discloses a building facade optimization method and system based on sustainable analysis, and relates to the technical field of data processing. According to the method, parametric modeling is carried out on the external facade of a target building, and a design space is defined; training a machine learning model integrating energy consumption, carbon emission, cost and lighting prediction functions by using historical data; building an optimization problem by taking external facade parameters as decision variables and taking minimization of energy consumption, carbon emission and cost and maximization of lighting as targets, and automatically generating an optimal scheme set by adopting a multi-target optimization algorithm; a decision maker is assisted to select a final scheme through a visual interface, and a corresponding BIM model is output; the method effectively solves the problems that a traditional design method is difficult to coordinate multi-target conflicts, depends on experience and is low in efficiency, and can automatically and intelligently generate an optimal design scheme which is energy-saving, low-carbon, low-cost and good in lighting performance.