2338results about How to "Enhanced interaction" patented technology

The invention provides an auricle anaphora segmentation method and system, and the method comprises the steps: inputting a to-be-segmented human ear image and text description into an auricle anaphora segmentation model which comprises a text coding module, a text-guided visual coding module and visual decoding module, and an angle transformation module; the text coding module obtains a text feature # imgabs0 #; a visual coding module realizes fusion of text features and image features through a structure organized into four stages, a visual encoder in each stage generates visual features # imgabs1 #, a cross-modal sensing module aligns # imgabs2 # and # imgabs3 # to obtain multi-modal features # imgabs4 # and # imgabs5 #, each element in the multi-modal features # imgabs4 # and # imgabs5 # is weighted by an attention gating module to obtain weighted multi-modal features # imgabs6 #, and the weighted multi-modal features # imgabs6 # and # imgabs5 # are weighted by an attention gating module. Adding the elements and # imgabs7 # to generate an enhanced visual feature # imgabs8 #, inputting # imgabs9 # into a visual decoding module to gradually recover the spatial resolution of the image, and further fusing the text and the visual feature to output a multi-scale feature; the angle transformation module carries out angle transformation on the multi-scale features and outputs a segmentation mask of a text description related region. According to the invention, the auricle can be subjected to anaphora segmentation.

The invention belongs to the technical field of perovskite solar cells, and particularly discloses a carbazolyl conjugate enhanced self-assembled monomolecular layer and a preparation method and application thereof, and the self-assembled monomolecular layer takes carbazole as a substrate and an aromatic ring as a conjugate linking group. The highly conjugated molecular system can induce molecular orbital overlap, increase dipole moment, promote formation of a compact self-assembled monomolecular film, reduce interface non-radiative recombination and enhance hole extraction and transmission performance. Meanwhile, phosphoric acid is used as an anchoring group, so that firm self-assembly of SAM molecules on the surface of the ITO substrate can be realized. The SAM material is simple in preparation method, high in yield, low in preparation cost and good in stability. When the compound is applied to a trans-perovskite solar cell as a hole transport layer, photoelectric conversion efficiency exceeding 24.6% can be obtained, and the compound is an organic semiconductor material with a very good prospect.

The invention belongs to the technical field of nano oil displacement, and particularly relates to rambutan-shaped polyacrylamide nanospheres as well as a preparation method and application thereof. The microspheres prepared by taking PAMAM as a matrix to load the copper quantum dots to initiate acrylamide polymerization have smaller particle sizes. The spherical particle characteristic of the metal copper quantum dots leads to non-uniform polymerization rate, formation of irregular morphology of the nanospheres can be promoted, and finally the rambutan-shaped polyacrylamide nanospheres are prepared. The nanosphere has good hydration expansion performance, the swept volume of injected water is increased, and the effect of deep profile control is achieved. Moreover, the surface of the polyacrylamide nanosphere presents the shape of rambutan, so that the expansion and fracture phenomenon after water absorption can be avoided, the interaction force between the nanospheres is enhanced, and the plugging effect is better. Meanwhile, as the nanospheres absorb water and swell, the swelled antennas play a role in crosslinking the nanospheres, and a better plugging effect can be achieved.

The invention belongs to the technical field of information recommendation, and discloses a financial information recommendation method and system based on multi-channel distribution, and the method comprises the steps: collecting behavior data of multiple channels of a user, carrying out the cross-platform data integration, and forming a complete user behavior data set; constructing a multi-dimensional user portrait, establishing a financial information database, and marking a keyword tag for each piece of financial information; according to the keyword matching degree of the user portrait and the information content, using content recommendation and financial information conforming to user preferences, and based on historical behavior data of the user and behavior habits of similar users, using collaborative filtering to recommend financial information in which the user is interested to obtain a financial information recommendation list; according to the use habits of the user at different terminals, cross-platform information distribution is performed on the financial information recommendation list through multiple channels, so that the accurate recommendation capability of financial information is improved, and the user experience is improved.

The invention relates to the technical field of power grid coordination control, and provides a coordination control method for a smart power grid, which comprises the following steps: S1, collecting voltage, current, frequency and load data in real time by deploying sensors and monitoring equipment, S2, processing the data by using a big data analysis technology, extracting load trend and fault early warning information, and carrying out early warning on the load trend and the fault early warning information. Real-time data collection is realized by deploying sensors and monitoring equipment, data analysis and load prediction are performed by using big data and a machine learning technology, an optimized objective function is constructed, fine coordination and a global optimal solution are realized by using a multi-agent system and a distributed optimization algorithm, and meanwhile, energy privacy is protected. The reliability of the power grid is improved through intelligent fault detection and a quick response mechanism; according to the technical scheme, interaction between the power grid and users is enhanced, demand side management is achieved, an efficient safety monitoring system is established, multi-level protection measures are taken, safe and stable operation of the power grid is ensured, and the problem that optimization and coordination are not comprehensive in the prior art is solved.

The invention belongs to the technical field of power systems, and discloses a method for maintaining electric energy metering, which comprises the following steps of: acquiring electric energy metering related data; preprocessing the electric energy metering related data to obtain an electric energy metering comprehensive characteristic data set; according to the electric energy metering comprehensive feature data set, training to obtain an electric energy metering error prediction model, and based on the electric energy metering error prediction model, predicting to obtain an electric energy metering error; judging whether error correction is needed or not according to the predicted electric energy metering error; if error correction needs to be carried out, early warning information is generated, and electric energy metering error data is automatically collected; training to obtain an error type identification model according to the electric energy metering error data, and predicting to obtain electric energy metering error type data based on the error type identification model; based on the electric energy metering error type data, utilizing a distribution calibration algorithm to correct the electric energy metering error; the requirement for manual intervention is reduced, and the working efficiency of the system is improved.

The invention relates to a multi-focus image fusion method based on multi-layer semantics and multi-scale self-attention, and the method is characterized in that the method comprises the steps: obtaining a source image, inputting the source image into an MSI-Dtrans model, carrying out the feature extraction of the source image through Haar wavelet transform, obtaining the feature information of different scales, and carrying out the fusion of the multi-focus image through the MSI-Dtrans model. Inputting the feature information of different scales into a dynamic Transform for processing, and obtaining an initial focus image; wherein the network architecture of the MSI-Dtrans model adopts a multi-stage up-down sampling structure of a U-shaped network and is obtained through training of a training set, and the training set comprises multiple image pairs generated based on blurred images of different degrees and training labels; and fusing the initial focus image with the source image to obtain a final fused image. According to the invention, image distortion is effectively avoided and a good visual effect is achieved.

The invention provides a VR-based primary and secondary school student multi-person safety education training method and system, and relates to the technical field of VR education, and the method comprises the steps: obtaining the behavior data of each student in a historical safety education training process, and carrying out the evaluation of the safety literacy capability of each student according to a preset safety literacy evaluation model, the safety literacy level of each student is obtained, a safety education training project recommendation sorting result for each student is obtained, and the students are allocated to corresponding safety education training projects according to the recommendation sorting result; real-time synchronous rendering is carried out on virtual avatars of the multiple students in the target VR system through the head-mounted display device, and cooperative training of the multiple students is realized; and after each student completes the safety education training project, scoring the completion condition of the safety education training task of each student by using a preset training task evaluation model, and generating a corresponding personalized evaluation report.

The invention relates to a load regulation and control method and system based on reinforcement learning, and the method comprises the steps: 1, decomposing a multi-time scale optimization problem into a plurality of layers in a source network load storage management and control scene through employing a layered optimization frame, enabling each layer to correspond to a different time scale, and carrying out the optimization; a model prediction control algorithm is adopted in the short-term optimization layer, a mixed integer linear programming algorithm is adopted in the medium-term optimization layer, and a reinforcement learning method is adopted in the long-term optimization layer to solve a multi-objective optimization problem; and 2, long-term load regulation and control decision making: controlling the power grid to make different load regulation and control decisions through an actually adjustable reward mechanism based on reinforcement learning, so that the intelligent power grid load regulation and control system adapts to continuously changing environments and targets, and the flexibility and adaptability of the system are improved. According to the invention, the reliability and stability of power supply can be ensured, fine management and regulation of power loads are realized, and the operation efficiency, reliability and safety of a power system are improved.

The invention relates to a composite phase-change film based on liquid metal and PBO fiber and a preparation method thereof, and belongs to the technical field of phase-change material preparation, and the preparation method comprises the following steps: the composite phase-change film comprises a poly (p-phenylbenzobisoxazole) fiber (PBO) base material, a phase-change material and a heat-conducting filler, the phase-change material is composed of phase-change microcapsules (PM) which are prepared by taking polyurethane (PU) as a wall material and organic solid-liquid phase-change materials (OPCMs) as a core material through a Pickering emulsion polymerization method. The prepared PM has a core-shell structure, and leakage of OPCMs is effectively prevented. The PM and the PBO fiber network are combined, so that the dual packaging of the OPCMs is realized, and the leakage of the OPCMs is further prevented. Meanwhile, dispersion and interaction of the OPCMs in a PBO matrix are optimized, and formation of a network structure with a limited domain is promoted. The composite phase change film prepared by the invention has relatively high mechanical properties, remarkable heat conductivity, excellent and stable flexibility and excellent heat storage capacity.

The invention provides a coal gangue multi-scale detection method and system based on a YOLOv8 network, and the method comprises the steps: enabling a CBAM attention mechanism to be embedded in a whole network core module C2f, and improving the small target detection capability; secondly, a fast spatial pyramid pooling module is adopted in the backbone network, more front and back features are fused, interaction of feature information is enhanced, and the multi-scale detection capability is further improved; then, through a bidirectional feature fusion module with more jump connections, the multi-scale detection capability is increased, and small target features are prevented from being lost due to network depth; and finally, convolution blocks used for feature fusion in the detection head are combined for weight reduction, regression loss is set as EIoULoss, and errors caused by the fact that the length-width ratio of a prediction frame is consistent with that of a real frame are avoided. By means of the target network model, coal gangue with the size fraction ranging from 6 mm to 50 mm can be effectively detected, cross-size-fraction detection is achieved, and the target network model can be effectively deployed in coal gangue separation equipment.

The invention provides an intelligent optimization advertisement putting method, which belongs to the technical field of advertisement putting channel strategies, and is characterized in that interaction between users and advertisements is enhanced through multi-dimensional user feature extraction, double-tower model matching and a multi-head self-attention mechanism, advertisement matching scores are accurately calculated, and contribution of the advertisements to GMV is evaluated in combination with a multi-contact attribution model; the advertisement conversion rate is predicted through a logistic regression dichotomy model, the payment capacity of a user is evaluated in combination with a user lifetime value model, and an ROI prediction model is constructed to optimize advertisement budget allocation; the advertisement effect and the ROI are monitored in real time, the putting strategy is dynamically adjusted, and high efficiency and sustainability of advertisement putting are ensured. According to the method, accurate user matching, efficient cost control and global optimization are realized, the return on investment of advertisers and the long-term value of the users are improved, the flexibility and adaptability of advertisement putting are ensured, and a transparent and explainable putting solution is provided for the advertisers.

The invention discloses a multi-modal feature fusion model construction method and a multi-step wind power prediction method, and belongs to the field of wind power prediction.The multi-modal feature fusion model construction method comprises the steps that firstly, two data conversion channels are added on the basis of a Grubrum angle field to construct a multi-channel Grubrum angle field, and wind power data are converted into RGB color images with color information; secondly, the time text features are embedded into the data features and the image features respectively, and the time sequence expression ability of the data features and the image features is improved; fusing the data features and the image features by using a cross-modal fusion method, and adjusting dynamic weights of the data features and the image features during fusion by using an adaptive decision mode to obtain fusion features containing different modal information; and finally, extracting association information between the fusion features and the wind power by using an encoder of the Transform, and decoding the input data into the wind power to be predicted by a decoder according to the association information and the prior data. A simulation experiment shows that the method effectively improves the prediction precision of the wind power.

The invention relates to a composite multi-effect scale and corrosion inhibitor and a preparation method thereof, and belongs to the technical field of water treatment agents.The composite multi-effect scale and corrosion inhibitor is prepared from carboxylic acid monomers, sulfonate monomers, organophosphorus compounds, modified chitosan, modified nanometer titania, oxidation type initiators, reduction type initiators, azole derivatives and the like, the modified chitosan is formed by coating the surface of chitosan with cerium chloride; the modified nano titanium dioxide is formed by coating the surface of nano titanium dioxide with sodium polyaspartate. The composite multi-effect scale and corrosion inhibitor is prepared in a mode of compounding while synthesizing, the scale inhibition effect is excellent, the comprehensive descaling capacity can reach 97% or above, and the composite multi-effect scale and corrosion inhibitor can be used for a high-pH, high-alkalinity, high-temperature and high-hardness circulating cooling water system under the high-concentration-multiple operation condition; the method is suitable for circulating cooling water treatment requirements under harsh conditions in industries such as chemical industry, smelting and light industry.

The invention discloses a remote sensing target detection method based on a target feature enhanced multi-scale fusion network, and the method comprises the steps: constructing a multi-layer feature aggregation module as a main feature extraction module, and better extracting the target features of each category; a multi-channel feature fusion module is constructed, the network layer parameter quantity is reduced, a cross-stage hierarchical structure is adopted to design an MCFCSP module, richer gradient combination and feature expression are realized, and the network feature fusion capability is enhanced; by adopting a top-down and bottom-up feature fusion strategy, information interaction between different levels of features is promoted; fractional Fourier transform convolution is constructed, and parameters are adjusted according to the characteristics of small targets and the change of scenes to adapt to small targets of different types, different sizes and different complexities. The method provided by the invention has the characteristics of high detection precision, low calculation complexity, strong feature representation capability and the like, can effectively improve the detection precision of the small target, and further improves the detection performance of the small target of the remote sensing image.

The invention relates to an antibacterial and anti-fouling ceramic product glaze and a preparation method, and belongs to the technical field of ceramic product glaze preparation, the ceramic product glaze comprises the following raw materials: a component A: a base material, a water glass solution, a sodium bicarbonate solution, a gadolinium nitrate solution, a europium nitrate solution, a silver nitrate solution and sodium cocoate; and the component B comprises the following components: dimethoxy methyl (3, 3, 3-trifluoropropyl) silicon, polysiloxane, dodecenyl succinic anhydride, gamma-methacryloxy propyl trimethoxy silane and white gasoline. Silver nitrate serves as a main material, europium nitrate serves as an auxiliary material, gadolinium nitrate excites the activity of silver and europium ions, and the silver nitrate, the europium nitrate and the gadolinium nitrate cooperate, so that the prepared ceramic product glaze has excellent antibacterial performance; sodium cocoate is added to form a steric hindrance effect, so that agglomeration among system particles is inhibited, and the antibacterial property of the glaze is enhanced; according to the anti-fouling glaze disclosed by the invention, the dimethoxymethyl (3, 3, 3-trifluoropropyl) silicon and the polysiloxane are added, so that the prepared glaze has a lasting anti-fouling effect.

The invention relates to the technical field of advertisement data processing, and discloses a method and device for achieving creativity generation by means of AI, and the method comprises the steps: obtaining user generation content from each social media platform in real time, and collecting hot topics of a current market; performing semantic analysis and sentiment analysis based on the hot topics to generate hot portraits; performing matching degree calculation on the hot portraits and target audience portraits of the product, and screening the hot portraits with the highest matching degree as the hot portraits of the target audience portraits; according to the hotspot portraits of the target audience portraits, performing two-dimensional screening in combination with the popularity of the hotspots to obtain target hotspot portraits; and inputting the target hotspot portrait and the associated product information into a preset deep learning model based on semantic matching, and outputting originality. The method has the advantages that the timeliness of the creativity is guaranteed, and the click rate and the conversion rate of the creativity are improved.

The invention discloses an integrated construction method and system for human settings, images, sounds and life tracks of a digital human, and belongs to the technical field of the Internet. The method comprises the following steps: generating a virtual digital human; constructing a life track, an image and voice of the virtual digital human; and performing behavior analysis on the virtual digital person, and updating the life track, image and voice of the virtual digital person. According to the invention, the defects of too long time and low efficiency of traditional digital human creation are overcome; the method solves the problems that the personality of the digital person in the traditional digital person creation depends on a model too singly, so that the characteristics of the digital person are too single, and the digital person cannot automatically learn and grow.

The invention discloses a small sample image classification method and system based on multi-scale cross-modal prompt enhancement, and relates to the technical field of small sample image classification, and the method comprises the steps: obtaining a query image and a support image small sample data set with a labeled category; inputting the acquired data into a trained small sample image classification model, extracting visual features of a query image and enhanced visual features of each support image through a feature extractor based on hierarchical multi-modal prompt enhancement in the model, and performing global average pooling on all the extracted enhanced visual features to obtain an enhanced visual feature of each support image; obtaining an enhanced visual feature prototype of each category, calculating similarity scores of the categories according to the visual features of the query image and the enhanced visual feature prototype of each category, determining the category with the highest score as a classification result of the query image, and outputting the classification result; wherein the small sample image classification model is obtained by training a global-local self-guiding supervision mechanism. According to the invention, the precision and robustness of small sample image classification can be significantly improved.

The invention belongs to the technical field of spectral analysis, and discloses a spectral analysis and image recognition combined intelligent stoma evaluation system and method.A three-dimensional scanning module captures the three-dimensional form of a stoma and the three-dimensional form of surrounding tissue of the stoma through the high-precision laser or structured light scanning technology; the spectral analysis module is used for accurately analyzing the color change of the stoma; the tiny color difference of the stoma area is detected to help to identify potential tissue abnormality; the image recognition and processing module is used for analyzing the collected stoma image by using a deep learning algorithm and automatically recognizing morphological characteristics of the stoma; the data analysis and feedback system is used for automatically generating a stoma health assessment report according to the collected multi-dimensional data; through a set standard reference value, an actual measurement value is compared, and a corresponding suggestion is given, for example, whether a nursing scheme needs to be adjusted or further medical intervention needs to be carried out. According to the method, the real-time state of the stoma is accurately evaluated through non-contact scanning and spectrum detection.

The invention discloses an energy-saving ground source heat pump control method and system, and the method comprises the steps: building a ground source heat pump system dynamic model which comprises an indoor heat balance model, a ground source heat pump unit model and a soil heat exchange model, obtaining a topological network through coupling, designing a distributed control and fault-tolerant operation strategy according to the topological network structure, and carrying out the control of the ground source heat pump system. Each node is locally controlled according to the position and adjacent node information, a state space model is constructed, an optimal control action sequence is solved through quadratic programming to balance energy consumption and an indoor and outdoor temperature deviation objective function, the optimal operation state is obtained, and the mapping relation between the optimal operation state and environment parameters is learned through a neural network; a ground source circulation flow control algorithm is designed, and circulation pump flow distribution and unit working parameter automatic adjustment are achieved. According to the method, the control precision and stability of the ground source heat pump system can be effectively improved, reliable operation of the ground source heat pump system under complex working conditions is ensured, the energy utilization efficiency is remarkably improved, energy conservation and consumption reduction are achieved, and meanwhile good interpretability is achieved.

The invention discloses a driver eye movement prediction method based on multi-scale attention, which mainly solves the problems that multi-level features cannot be effectively fused and the prediction of driver eye movement points is inaccurate in the prior art, and the scheme is as follows: constructing a driver eye movement data set, and dividing a training set and a test set; constructing an encoder to obtain driving image sequence multi-level features; constructing a space-time attention feature fusion module to obtain multi-scale space-time attention features; constructing a driver subjective consciousness guiding module to obtain multi-scale spatial features of driver subjective consciousness guiding; constructing a decoder to obtain an eye movement fixation point diagram of the driver; an encoder, a space-time attention feature fusion module, a driver subjective consciousness guiding module and a decoder are cascaded to form a driver eye movement prediction model; training the model by using a training set; and inputting the test set into the trained model to obtain a predicted eye movement image of the driver. The method can effectively predict the eye movement fixation point of the driver, is high in accuracy and robustness, and can be used for an intelligent driving assistance system.

The invention discloses a bonding composition for a battery diaphragm, slurry, a diaphragm and a lithium ion battery. The bonding composition for the battery diaphragm comprises polymer microspheres and a modifier, and the modifier is a microsphere surface macromolecule free volume modifier; the polymeric microspheres comprise an affinity monomer. The surface-modified polymer microsphere particle dispersion liquid, namely the bonding composition, which is uniform in particle size is prepared through affinity of the surface polymer free volume modifier by virtue of the affinity monomer in the polymer microsphere, so that the bonding force of a particle coating film and a pole piece in a dry state and a wet state is further improved; the technical problems that in the prior art, strong bonding force cannot be achieved in a dry state and a wet state at the same time, and a rolled and coated film is prone to adhesion and powder falling are solved.

The invention discloses an image super-resolution reconstruction method and device based on high-frequency feature enhancement, and relates to the technical field of image super-resolution reconstruction. In the training stage of a high-frequency feature enhanced image super-resolution reconstruction model, a multi-scale sampling network is adopted to extract high-frequency features; a feature mask module is introduced into the multi-scale sampling network to improve the accuracy of feature fusion, and meanwhile, a lightweight high-frequency attention enhancement mechanism is introduced and fused with the multi-scale sampling network to construct a high-frequency feature self-adjusting module; in the process, high-frequency features of different scales and levels are effectively fused into the network through learnable parameters, so that the feature extraction capability is greatly improved, and then a mapping relation between high-frequency features of different layers in a plurality of high-frequency feature self-adjustment modules is established through a mask matrix produced by a feature adjustment layer. The interaction between the features is greatly enhanced, so that the utilization rate of the features is greatly enhanced, and the quality of the reconstructed image is greatly improved.

The invention relates to the technical field of cutter manufacturing, in particular to a high-strength and high-toughness ceramic cutter and a preparation method thereof. The metal ions are loaded on the boron nitride nanosheets, the agglomeration phenomenon of the boron nitride nanosheets can be improved through loading of the metal ions, the interaction force between the boron nitride nanosheets is enhanced, and therefore the mechanical strength of boron nitride is improved. According to the preparation method disclosed by the invention, a complex with adhesion is formed on a boron nitride nanosheet by utilizing the chelation of tannic acid and iron ions, and boron nitride loaded with the iron ions and modified hydrotalcite are adhered together by utilizing the adhesion of the complex. The prepared modified soybean protein has a large number of amino groups, the boron nitride-hydrotalcite compound with the amino groups has better compatibility with a modified soybean protein binder, the dispersity of the boron nitride-hydrotalcite compound in a matrix is improved, and the strength and toughness of the ceramic cutting tool are improved.

A rigid-flexible coupling type turnout damping fastener system comprises an iron base plate, an under-plate elastic cushion layer, a heightening base plate, an elastic rail brace, eccentric bushings and an under-rail elastic base plate, the eccentric bushings are inserted into oblong holes in the two ends of the iron base plate and the two ends of the under-plate elastic cushion layer, and turnout tie bolts penetrate through the eccentric bushings and then are fixed to a turnout tie; the under-plate elastic cushion layer is formed by compounding low-rigidity and high-rigidity materials, the elastic rail brace is in elastic contact with a steel rail, the track gauge is adjusted by rotating the eccentric sleeve by 180 degrees, and the transverse rigidity of the fastener system is increased through the elastic rail brace; the fastener system has small vertical rigidity and large transverse rigidity, it can be ensured that the fastener system can provide enough vibration reduction elasticity for the steel rail in the vertical direction, meanwhile, the vertical displacement can be controlled within the design range, and therefore the stability of the steel rail and the running smoothness of a train are improved; and on the other hand, the mutually assembled parts of the fastener system are designed according to the rigid-flexible coupling contact relation of metal and nonmetal, so that the impact vibration of a train on the steel rail can be buffered.

The invention discloses a recommendation method and device based on user interest points, and relates to the technical field of interest point recommendation, and the method comprises the steps: S1, obtaining behavior data, time information and attributes of interactive items of a user on a platform, and carrying out the preprocessing of the obtained data; and S2, performing short-term preference analysis and long-term preference analysis based on the processed user data, modeling interest points of the user, generating a user portrait and the like. According to the method, short-term preference analysis and long-term preference analysis are respectively carried out by utilizing the user data, and a unified user portrait is formed by combining short-term and long-term preference characteristics, so that more accurate personalized recommendation is provided for the user, the user satisfaction and the scene matching degree are improved, and the user experience is improved through a long-term and short-term preference fusion strategy. Therefore, the recommendation system can utilize the existing resources more efficiently and obtain more comprehensive and abundant preference representation of the user, so that more efficient and accurate interest point recommendation is realized.

The invention belongs to the technical field of chip processing, and discloses a wafer laser cutting device based on chip processing, which comprises a vibration mechanism, the vibration mechanism comprises four sliding blocks, the interiors of the sliding blocks are fixedly connected with first motors, and the first motors are rotatably connected with cams; through cooperation of the cam, the clamping plates and other structures, the laser cutting effect of the wafer is improved, the first motor is started to enable the wafer clamped among the four clamping plates to generate slight vibration in the vertical direction, the slight vibration promotes micro movement of a material in the laser cutting process, better interaction between a laser beam and the material is facilitated, and the laser cutting efficiency is improved. A more uniform and smoother cutting face is achieved, the vibration effect can accelerate the melting or crushing process of materials, the cutting time is shortened, the cutting efficiency is improved, meanwhile, vibration can accelerate airflow on the surface of a wafer, a certain heat dissipation effect is achieved, local overheating of the materials is reduced, expansion of a heat affected zone is avoided, and the flatness of the cutting edge is improved.

The invention belongs to the field of power system cascade fault prediction, and relates to a graph neural network power grid cascade fault prediction method based on physical information guidance, and the method comprises the steps: obtaining the operation data of a cascade fault, inputting the operation data of the cascade fault into a trained power grid cascade fault prediction model, and obtaining a prediction result; the power grid cascade fault prediction model comprises a common embedded graph neural network and a structure coding module; according to the invention, data enhancement is carried out by using a multi-step random walk matrix considering power grid line attributes and structure coding represented by node degrees, so that the capability of capturing topological information is improved; according to the invention, the interaction between the power grid nodes and the power grid lines is promoted by using the co-embedded graph neural network, and the accuracy of power grid cascade fault prediction is improved; according to the method, the node-edge interaction aggregation function of the co-embedded graph neural network is changed through the power transmission distribution factor of the power grid, the description capability of the actual complex interaction relation of the power grid is improved, and the accuracy of power grid cascade fault prediction is improved.

The invention discloses a PPy / NiCo-LDH / NF composite material and a preparation method and application thereof, and belongs to the technical field of electrocatalyst preparation. The PPy / NiCo-LDH / NF composite material provided by the invention comprises a foamed nickel substrate as well as NiCo layered double hydroxides and polypyrrole which are compounded on the foamed nickel substrate, the polypyrrole is compounded on the surface of the NiCo layered double-metal hydroxide. The surface of NiCo layered double hydroxides is induced and modified by polypyrrole, so that the layered double hydroxides are arranged more regularly and densely, efficient mass transfer of electrons is facilitated, an electron transfer interface can be induced, the composite material exposes more catalytic active sites, the electron interaction is enhanced, and the catalytic activity of the composite material is improved. According to the present invention, with the preparation method, the excellent electrocatalytic activity is provided for the HER and the OER in the alkaline medium, and the excellent cycle stability is provided for the HER and the OER in the alkaline medium after the 1000 times of cycle, such that the wide application prospect in the electrocatalytic reaction is provided.