327results about How to "Guaranteed adaptability" patented technology

The invention provides a textile defect positioning method, which comprises the following steps: according to an extracted defect feature map, fusing feature maps of different scales, extracting multi-scale defect features, and comprehensively depicting space distribution and combination information of defects; aiming at the condition that multiple flaws exist at the same time, a multi-task learning model is constructed, flaw detection, classification and positioning tasks are carried out at the same time, and a back propagation algorithm is adopted to optimize the multi-task learning model; positioning the defect area by adopting an image segmentation algorithm, obtaining the contour of the defect area according to the positioning result of the defect area and by adopting a Canny edge detection algorithm, and calculating the area and position information of the defect; and based on an evaluation result, a dynamic threshold adjustment algorithm is adopted, a defect judgment threshold is adjusted according to a production batch and a product type, and a threshold parameter is dynamically updated by analyzing historical data and a current production condition.

The invention relates to the technical field of clothing data processing, in particular to a digital design method of three-dimensional prototype clothing. The method comprises the following steps: obtaining human body three-dimensional scanning data and carrying out feature point labeling, reconstructing a three-dimensional human body model as basic data of costume design, carrying out contour looseness calculation on the human body model, generating a looseness data set, carrying out sample plate plane analysis to obtain a plane sample plate data set, and after collecting costume fabric data, carrying out data processing on the plane sample plate data set. The method comprises the following steps: performing physical characteristic mapping on a human body model and a planar template, reconstructing a three-dimensional garment model conforming to fabric characteristics, performing spatial positioning on the template, performing virtual sewing simulation, generating a shape data set of the garment, performing dynamic motion analysis on the shape data of the garment according to the human body three-dimensional model, and evaluating the comfort level of the garment. The garment form is locally adjusted to reconstruct a planar template. According to the invention, a more comprehensive and more reliable digital design method of the three-dimensional prototype clothing is realized.

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.

The invention relates to the technical field of computer television image communication, in particular to a computer television all-in-one machine control method, system and device. The method comprises the following steps: through multi-view image acquisition and processing, generating a three-dimensional scene grid and carrying out scene element analysis to form a scene element set, then chasing limbs of a user, generating a user limb image set, restoring an environment basic tone, obtaining display content of the all-in-one machine and carrying out content semantic analysis, and finally obtaining a user limb image set. Calculating interaction intensity to obtain content interaction intensity data, speculating a user body language and performing user intention prediction in combination with the interaction intensity data, adjusting display content of the all-in-one machine based on the predicted user operation intention, generating simulation adjustment content, performing composition optimization through a scene element set, and generating updated display content. The method for controlling the computer and television all-in-one machine is more accurate and more efficient.

The invention relates to the technical field of production task decision making, in particular to a furniture production task automatic matching method and system based on intelligent manufacturing. And extracting personalized demand information corresponding to the production task, converting the personalized demand information into standard design parameters, and generating a customized task. The production task is disassembled into a plurality of sub-tasks, and each sub-task has independent attributes including process requirements, resource requirements and time requirements; and performing one-to-one mapping on the customized tasks and the subtasks to generate an execution plan. Performing resource matching by using a constraint algorithm based on the execution plan and the existing production resource information to obtain a production plan; and obtaining an optimal scheduling plan by using a constraint algorithm according to the production plan. And based on the real-time production data and the burst information, adjusting the optimal scheduling plan through the data analysis model to obtain a final scheduling plan. The method can cope with emergencies or real-time data changes, and manual decisions are reduced.

The invention relates to the technical field of chip stacking, and discloses an intelligent temperature control method and device of a multi-chip stacking structure and computer equipment. The method comprises the following steps: acquiring target temperature data of each layer of chip in a multi-chip stacking structure, and establishing a nonlinear temperature response model; constructing a hierarchical control framework, and inputting the target temperature data into an agent auxiliary temperature control algorithm for processing to obtain temperature distribution characteristic data; performing optimal sampling analysis to obtain local temperature regulation and control parameters; constructing a temperature-flow multi-stage coordination control model, and generating a cooling liquid flow distribution scheme and a flow direction control instruction; the flow and temperature of cooling liquid are controlled, gradient cooling treatment is conducted on the temperature abnormal area, and temperature adjusting process data are obtained; and carrying out multistable distribution analysis to generate a temperature control optimization strategy. According to the invention, the problem of temperature balance in a multi-chip stacking structure is effectively solved.

The invention discloses a distributed energy dynamic load adjusting method and system based on a virtual power plant, and relates to the technical field, and the method comprises the following steps: obtaining first data of the virtual power plant, and building a topological structure of the virtual power plant; constructing a distributed energy output prediction model based on an LSTM deep learning algorithm; combining the output of the distributed energy output prediction model and the topological structure to generate an energy scheduling scheme in real time; monitoring real-time data of the distributed energy, comparing the real-time data with the energy scheduling scheme, and performing feedback updating; according to the invention, a virtual power plant topological structure is constructed, a prediction model is constructed based on an LSTM algorithm, an energy scheduling scheme is generated in combination with model output and the topological structure, and a feedback updating technology is carried out. The problems that existing regulation and control based on a static model cannot adapt to dynamic changes of energy production and load requirements in real time, and due to the fact that an effective cooperation mechanism is lacked between distributed energy sources, energy waste is caused, and the regulation efficiency is low are solved.

The invention relates to the technical field of operation and maintenance of complete switch equipment, and discloses a DMC material-based operation and maintenance method for complete switch equipment, which comprises the following steps of: determining key performance parameters such as insulating strength, heat conductivity coefficient and arc resistance of a DMC material through an experiment, and constructing an aging characteristic model; calculating a material health index based on running state data collected in real time, and dynamically generating a degradation trend and residual life in combination with a time sequence prediction algorithm; and generating a maintenance plan through an optimization model, and introducing a risk weight coefficient to realize dynamic balance between maintenance cost and equipment reliability. According to the method, through material performance modeling and multi-source data dynamic fusion, the accuracy and response speed of equipment state monitoring are remarkably improved; through multi-dimensional cooperation of trend prediction and maintenance optimization strategies, full-life-cycle closed-loop management is formed, and intelligent upgrading of operation maintenance under complex working conditions is realized.

The invention provides an adaptive CFAR detection method based on scene classification, and the method comprises the steps: carrying out the scene classification of an input SAR image through employing a K-means clustering algorithm, carrying out the target detection of a pure sea area obtained through classification through employing VI-CFAR, and carrying out the CFAR detection of a coastal area through employing a significance model. According to the method, the problems that a traditional CFAR algorithm is high in false alarm rate and unstable in detection performance in port and land complex backgrounds are effectively solved, detection parameters are optimized according to the complexity of different scene backgrounds, and therefore it is ensured that the CFAR algorithm can still maintain the constant false alarm rate in the sea clutter background, and the robustness of target detection is remarkably improved. The ocean area and the land area in the image are effectively distinguished, and the land area is shielded. According to the technical scheme, the problem that a traditional CFAR detection algorithm is interfered by a complex land background when detecting ships on the sea surface is solved.

The invention relates to a method, a device and equipment for estimating the state of a power battery of a sweeper. The method comprises the following steps: performing impedance characteristic acquisition on a power battery of the sweeper under various working conditions, and performing random low-frequency modulation multi-sine excitation signal measurement to obtain battery working condition characteristic data; performing fitting operation through the equivalent circuit model to obtain a battery health factor model; performing charging process feature extraction analysis to obtain a battery charging curve reconstruction model; carrying out extended Kalman filtering operation to obtain a battery state-of-charge value; performing segment processing and Gaussian process regression calculation on voltage and current data in the charging process to obtain a battery health state value; and updating the polynomial mapping coefficient of the battery health factor model and the weight parameter of the battery charging curve reconstruction model to obtain a target health factor model and a target charging curve reconstruction model. According to the invention, the adaptability and accuracy of the state estimation method in the whole life cycle of the battery are ensured.

The invention provides a transformer substation secondary circuit intelligent identification method based on multi-scale large-kernel attention YOLOV8, aiming at the requirements of the transformer substation secondary circuit intelligent identification field on identification accuracy and model lightweight in a small sample environment, and the transformer substation secondary circuit intelligent identification method comprises the following steps: carrying out data enhancement and label labeling on original drawing data; a YOLOV8 based on transfer learning is adopted as a backbone network of a model, pre-training and fine tuning are carried out on a large-scale data set, MAB is introduced, an MLKA module is designed based on a large kernel structure and a multi-scale gating mechanism, the gating mechanism and space attention are combined to propose a GSAU, a training set is input into an MLKA-YOLO network model for training learning, and an MLKA-YOLO network model is constructed. After training is completed, a test set is input to carry out secondary circuit terminal strip identification, based on a Paddle OCR framework, line name identification of the terminal strip is realized by using a cyclic convolutional neural network, and a line information comparison library is constructed by using the identified line content.

The invention relates to the technical field of hydraulic engineering management, in particular to a hydraulic engineering management method and system based on digital twinning. The method comprises the following steps of obtaining hydraulic engineering design data, hydraulic engineering operation data and hydraulic engineering environment data, and performing digital twin space modeling on the corresponding hydraulic engineering to obtain a hydraulic engineering digital twin space model; performing operation condition virtual simulation analysis and water flow distribution change analysis on the hydraulic engineering digital twin space model, and performing engineering structure stress load analysis and engineering operation risk assessment to obtain hydraulic engineering simulation operation process risk data under different operation and environment change conditions; and performing risk decision management processing on the corresponding water conservancy project to generate a water conservancy project operation process risk management decision so as to execute corresponding water conservancy project operation risk management adjustment work. According to the invention, the management effect and response capability of the hydraulic engineering can be greatly improved.

The invention discloses a flue gas emission continuous monitoring device and a monitoring method, and relates to the technical field of flue gas emission, the device realizes gas inlet detection and multidirectional gas inlet and gas exhaust of cleaning pipelines and parts through a U-shaped gas inlet pipe, a preheating pipe, a first gas return pipe, a first gas inlet pipe, a gas collection chamber, a condensation pipe, a liquid collection chamber and a second gas return pipe; preheating and heat preservation are achieved through air inlet and air outlet, heat preservation measurement is achieved in the heat preservation shell, pipelines and parts, interference of temperature, oil and particulate matter is effectively reduced, and the monitoring precision is improved; the laser scattering sensor is used for measuring the concentration and the particle size of particulate matters, the optical fiber measuring unit is used for monitoring other components in flue gas, and real-time monitoring and emission component analysis of the flue gas are realized by collecting released liquid and measuring substances dissolved in the liquid; the problems that smoke monitoring is easy to interfere, detection data are easy to fluctuate, and a multi-operation-mode switching technology of a detection device is lacked, so that the smoke monitoring precision is low are solved.

The invention belongs to the technical field of ship and ocean engineering, and discloses a ship seakeeping intelligent forecasting method and system based on a physical information neural network. According to the method, a CFD-RAO method is combined with PINNs, and rapid and high-precision prediction of ship seakeeping is realized by constructing a coupling model of a physical equation and a neural network loss function. According to the invention, rapid and high-precision prediction of ship seakeeping is realized. The method not only can effectively cope with multi-degree-of-freedom coupling motion under complex sea conditions, but also has high real-time performance and adaptability, and provides more scientific and reliable technical support for ship design and navigation safety.

The invention discloses an air drying system for seasoning packaging containers, comprising a conveying mechanism which is horizontally supported by a bracket; the automatic stacking mechanism is arranged above the feeding end of the conveying mechanism and is used for uniformly stacking the packaging containers on the conveying mechanism for conveying; the air shower mechanism is arranged at the bottom of the conveying mechanism and inputs positive pressure air to the conveying surface of the conveying mechanism; the conveying face of the conveying mechanism keeps ventilated, and the automatic stacking mechanism buckles the open ends of the packaging containers to the conveying face of the conveying mechanism. The air-drying system can be used for rapidly air-drying the cleaned packaging containers, the packaging containers can be automatically stacked through the automatic stacking mechanism arranged at the front end of the conveying mechanism according to the width of the conveying mechanism, and then the opening ends of the packaging containers are buckled to the conveying face of the conveying mechanism, so that drainage is facilitated; and the air shower mechanism is combined, so that the packaging container can be conveniently air-dried, and the packaging container can be conveniently filled with materials after being sterilized subsequently.

The invention discloses an electric power engineering technical-economic review method based on deep learning. The method comprises the following steps: S1, automatically capturing project data such as engineering quantity, an equipment list and material cost, and carrying out preliminary detection; s2, analyzing data by using a deep learning model, and analyzing and classifying text information through natural language processing; s3, establishing a charging benchmark rule base, and automatically checking the cost data in the technical and economic files; s4, adopting a self-adaptive material semantic matching algorithm to examine equipment and material data, and carrying out market information price matching; s5, constructing a mapping relation between the project amount and the typical cost, and generating a deviation report; s6, establishing a data center to support multi-dimensional statistical analysis and a real-time cost report; and S7, combining historical and real-time data, performing early warning through a deep learning algorithm, and generating an intelligent review report. According to the invention, automation and intellectualization of electric power engineering technical-economic review are realized, and data processing efficiency and review accuracy are optimized.

The invention relates to the technical field of power industrial control network asset management, in particular to a power industrial control network security-oriented asset dynamic digital fingerprint construction system, which comprises a data acquisition module, a first determination module, a second determination module, an identification module, an adjustment module and a construction module. Through accurate multi-level data acquisition, behavior analysis and threshold adjustment, the security of an electric power industrial control network is remarkably improved, the system can acquire key behavior data of equipment in real time, such as the number of bytes of a data packet, the memory utilization rate and the number of login failures, and normal equipment and abnormal equipment are accurately identified through multi-stage screening, so that the security of the electric power industrial control network is improved. The system flexibly deals with the dynamic change of the equipment behavior by adjusting the byte number fluctuation threshold value, ensures the high efficiency and accuracy of the safety detection, and effectively solves the problems that the system cannot adapt to the network asset change and cannot identify the abnormal equipment in real time due to the dependence on a static fingerprint database and a fixed similarity threshold value.

The invention discloses an intelligent meat duck weight estimation method combining deep learning and multi-modal data, and the method comprises the steps: S1, obtaining the visual modal data of a meat duck based on a multi-view camera, S2, collecting the non-visual modal data of the meat duck, and S3, fusing the visual modal data and the non-visual modal data based on a deep learning network, s4, performing meat duck key point detection based on the fusion data, and correcting the posture and shielding error; S5, estimating the weight of the meat duck by using the corrected key point data; according to the intelligent meat duck weight estimation method combining deep learning and multi-modal data, real-time weight estimation of mass meat ducks is realized, the manual operation time is saved, the production management efficiency is remarkably improved, different postures of the meat ducks and complex breeding scenes in which the meat ducks are mutually shielded can be dealt with, and the meat duck weight estimation efficiency is improved. The adaptability and robustness of the weight estimation system in a real breeding environment are enhanced, and the stability performance under various conditions is ensured.

The invention relates to the technical field of equipment fault detection, and discloses an equipment fault prediction and diagnosis method which is applied to an ATE system, and the method comprises the steps: collecting the real-time operation data of equipment through a plurality of sensors, and dynamically scheduling the sensors through an adaptive algorithm, so as to adjust the data collection frequency and range according to the state of the equipment; the collected data is preprocessed; training a plurality of machine learning models based on the historical fault data and the operation data, and deploying the models to an ATE system for real-time analysis; predicting an equipment fault according to the model, generating alarm information when a potential fault is detected, and sending the alarm information to an operator through a user interface or a notification system; generating a fault diagnosis report according to an analysis result and providing a repair suggestion; recording a fault instance and data, and updating and retraining the machine learning model to improve prediction and diagnosis accuracy; and generating a digital twin model of the equipment based on real-time and historical data, simulating the running state of the equipment, predicting future faults, and evaluating the health condition of the equipment.

The invention provides a multi-head milling machine feeding control system based on workpiece characteristics, and relates to the technical field of milling machining. A workpiece transfer module; the feature recognition module is used for performing workpiece feature recognition to obtain a workpiece structure feature set; the scheme matching module is used for carrying out feeding scheme matching to obtain an initial feeding scheme set; the fitting relation establishing module is used for establishing a function fitting relation between the machining evaluation parameters and the feeding control parameters; the feed parameter optimization module is used for performing feed parameter optimization on the initial feed scheme set to obtain a target feed scheme; and the feeding control module is used for carrying out feeding control on the milling assembly set according to the target feeding scheme. The technical problems that a traditional multi-head milling machine feeding control system usually conducts control according to fixed parameters, accurate analysis on machining parameters is lacked, adaptability is lacked, and the optimal machining effect cannot be achieved are solved.

The invention relates to the technical field of multi-agent control, in particular to a second-order multi-agent system control method based on dynamic event triggering, and the method comprises the steps: building a kinetic model of a leader-follower second-order multi-agent system considering unknown external interference, and designing a distributed fixed time observer on the basis, so as to achieve the control of the leader-follower second-order multi-agent system. State information of the leader is estimated in real time; introducing a fixed time extended state observer to estimate external disturbance; proposing a sliding mode surface based on the observation error; a distributed fixed-time sliding mode consistency tracking control protocol is designed, so that tracking errors are accurately converged within fixed time, an ideal control effect can be achieved even if external interference exists, the communication burden is effectively reduced through introduction of a dynamic event triggering mechanism, and the sesame phenomenon is avoided; tracking consistency control of the multi-agent system can be realized in a fixed time, the robustness and reliability of the system are improved, and communication resources are saved.

The invention provides a multi-source signal assisted positioning vehicle-mounted tracking and pointing system and method, and belongs to the field of photoelectric tracking. The system comprises a laser range finder, a vehicle-mounted pan-tilt camera, a first encoder, an interface communication module, a comprehensive control computer, a display, a data collector, a servo controller, a second encoder, a servo pan-tilt and a laser aiming confirmation device. According to the invention, a vehicle-mounted pan-tilt camera is used for identifying and capturing a target, laser ranging is used for assisting in positioning after the tracking target is confirmed, and a servo controller receives a positioning resolving signal after positioning resolving by combining encoder data on the vehicle-mounted pan-tilt camera, so that the center of a servo pan-tilt aiming optical axis is aligned to the center of the target. The rotary table is provided with a laser aiming confirmation device, the matching of a laser spot and a target is confirmed through a vehicle-mounted pan-tilt camera, and a confirmation signal is fed back to carry out a next target task. The feedback mechanism ensures the self-adaptive capability of the whole system, so that the system can be continuously adjusted in a dynamic environment to realize accurate aiming.

The invention discloses a customer support and feedback analysis processing method based on a large language model, and the method comprises the following specific steps: obtaining customer support and feedback data, and judging whether the customer support and feedback data are of a voice type or not; preprocessing the client support and feedback data to obtain input data of a large language model; based on a plurality of independent large language models, performing sentiment analysis, theme recognition and question classification on the input data at the same time to obtain corresponding analysis results; scoring the analysis results and selecting the analysis result with the highest score as an initial optimal solution; circularly feeding back the initial optimal solution to a plurality of independent large language models for further analysis, and obtaining a target optimal solution through convergence evaluation; and matching the target optimal solution with the knowledge base. By combining the large language model and the knowledge base, client support requests and feedback data are processed in real time, the response speed and accuracy are improved, deep feedback analysis is provided, and service decisions are supported.

The invention relates to the technical field of image processing, and discloses an image denoising method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the preliminary denoising of a noise-containing image based on a diffusion coefficient function model, and obtaining a preliminary denoised image; and performing second denoising processing on the preliminarily denoised image by using a denoising convolutional neural network, and outputting a denoised image. And according to the feature information of the image, adjusting the threshold parameter and the number of iterations of the diffusion coefficient function so as to adapt to the noise characteristics of different images, and finally outputting an optimized target de-noised image. According to the image denoising method, noise in the image can be effectively removed, and detail and edge information of the image can be accurately reserved. The optimal balance between noise removal and detail protection is realized by adaptively adjusting the threshold parameter and the number of iterations, and the denoising effect is remarkably improved. And the finally generated image is higher in quality, richer in details and clearer in edge.

The invention provides a circuit board test method and device based on a flying probe tester. According to the method, a test point set is determined by obtaining a design file of a circuit board, establishing an electrical connection model and identifying components and pins on the circuit board. And generating test combination set data by analyzing the conduction relationship between the test points, and further generating test scheme data. And based on the test scheme data, a test path is generated by using a path optimization algorithm, finally, the flying probe tester is used for testing, and a test report is generated according to a test result. The flying probe tester adopts the combination of an annular probe and a sharp needle probe, the annular probe is used for a pin connector to ensure accurate contact, and the sharp needle probe is used for other types of components to improve the test precision and adaptability. Compared with a traditional test method, the test scheme can be generated more efficiently, the test path can be optimized more efficiently, the test time is remarkably shortened, and the reliability and accuracy of circuit board testing are improved.

The invention discloses a prestressed pipeline cleaning method and device.The method comprises the steps that S100, the device is placed into a to-be-cleaned pipeline, and a base is adjusted to stretch automatically till supporting wheels and driving wheels are attached to the inner wall of the pipeline; s200, the telescopic rod extends till the cleaning brush is attached to the pipeline; s300, a telescopic rod is driven to rotate, and a dust collection unit is started to provide negative pressure for a dust collection bin; s400, the adjusting base is rotated, so that an angle is formed between the supporting wheel and the driving wheel and the advancing direction of the device, and the driving unit drives the driving wheel to rotate to drive the rotating wheel to advance; s500, the telescopic rod continuously rotates, so that the cleaning brush continuously rubs with the inner wall of the pipeline, sundries on the cleaning brush are swept, and the swept sundries are sucked into a dust collection bin by a dust collection head; and S600, when all impurities in the pipeline are cleaned, or the electric quantity of the device is about to be exhausted, the telescopic rod is shrunk, the dust collection unit is closed, and the driving wheel rotates reversely to exit from the pipeline. The cleaning efficiency is improved, the adaptability to different pipe diameters is guaranteed, and the cleaning effect is optimized.

The invention provides a test evaluation method and device in agile iteration and a storage medium. The method mainly comprises the steps of data acquisition, index analysis, dynamic early warning, visual display and feedback optimization. The device comprises a data acquisition module, an index analysis module, a dynamic early warning module, a visualization module and a feedback optimization module. The storage medium implements the test evaluation method when the executable instruction is executed by the processor. By the adoption of the technical scheme, the test index early warning system is designed according to the agile development characteristics, through a series of real-time data evaluation indexes, team conditions and service conditions are dynamically combined, test activities can be accurately tracked, contribution and efficiency of testers can be quantified in real time, and the test efficiency can be improved. Intelligent index early warning can be provided to help to optimize task allocation, iteration strategies and the like, and project progress, team efficiency and product quality are improved; and meanwhile, a closed-loop feedback mechanism is also constructed, and the early warning rule is continuously optimized.

The invention relates to the technical field of electric vehicle charging and discharging regulation and control, in particular to a large-scale electric vehicle charging and discharging rate multi-level optimization regulation and control method. Establishing a daily driving mileage prediction model by analyzing historical driving and charging data of the electric vehicle, and calculating a charging demand of the daily driving mileage according to the daily driving mileage prediction model; designing an optimization model based on linear programming based on the determined charging demand, and obtaining reasonable charging power of each electric vehicle in different time periods; on the basis of the determined charging demand, constructing a multi-level optimization model; under the guidance of the constructed multi-objective optimization model, a specific charging and discharging strategy of each electric vehicle is refined by adopting a heuristic algorithm; the charging power is dynamically adjusted by combining real-time data of the electric vehicle and the power grid; and based on the dynamic adjustment result, the charging power of different levels is coordinated, and multi-level cooperative control is realized. According to the large-scale electric vehicle charging and discharging rate multi-level optimization regulation and control method, efficient and stable operation of a charging system is achieved.

The invention provides nickel-carbon conductive shielding foam. The nickel-carbon conductive shielding foam sequentially comprises a conductive layer, foam, a bonding layer and an isolating layer from top to bottom, the foam comprises 25 to 40 parts of nickel-carbon nanotube-cerium oxide composite powder, 1 to 5 parts of an ionic liquid dispersant, 10 to 25 parts of organic silicone oil, 20 to 40 parts of silicone rubber, 5 to 10 parts of polyfuran, 5 to 10 parts of maleimide, 0.5 to 3 parts of a silane coupling agent, 0.01 to 0.1 part of a platinum catalyst and 0.2 to 1.5 parts of a cross-linking agent. The conductive foam can effectively restrain high-frequency electromagnetic interference generated in modern electronic equipment and ensure normal work of the equipment. A self-repairing system is constructed by adding polyfuran and maleimide. When the foam generates cracks under the action of mechanical stress or thermal cycle in the using process, the self-repairing material can automatically repair a conductive network through a reverse Diels-Alder reaction, and the shielding performance and the conductivity of the material are recovered.

The invention relates to a neural network optimization-based intelligent prediction method for carbon emission in a building operation stage. The method comprises the following steps of S1, acquiring multi-dimensional data generated in a building operation process; s2, the obtained multi-dimensional data is preprocessed; s3, constructing a preliminary carbon emission prediction model, and capturing a complex nonlinear relationship in the data by adopting a Swin Transform; s4, optimizing the model by combining an adaptive differential evolution algorithm and a quantum behavior particle swarm optimization algorithm; s5, verifying the optimized model by adopting K-fold cross validation; s6, performing real-time carbon emission prediction by using the optimized model, and outputting a predicted value; and S7, generating a carbon emission analysis report according to a prediction result. According to the method, the enhanced neural network optimization algorithm and real-time dynamic data are combined, high-precision carbon emission prediction and energy consumption management optimization are realized, the prediction accuracy and adaptability are improved, and building energy conservation and emission reduction management is effectively supported.