1689results about How to "Improve utilization" patented technology

The invention discloses a distributed multi-tenant data security isolation system and method, and relates to the technical field of data isolation, and the system comprises a data isolation module which is responsible for distributing independent container instances and hardware resources for each tenant, and adjusting the data access authority according to an access control strategy; the dynamic access control module is responsible for generating an access control strategy according to the user identity, the authority, the tenant strategy and the access behavior; the distributed data management module is responsible for storing data fragments on different storage nodes; the resource scheduling and optimizing module is responsible for collecting resource demand data of each tenant, calling an intelligent scheduling algorithm, performing dynamic prediction and analysis on resource demands, and then automatically adjusting a resource allocation strategy according to a prediction result; and the security auditing and monitoring module is responsible for collecting operation logs and event records of each module and carrying out comprehensive analysis. The invention aims to solve the problems of data security and resource management in a multi-tenant environment.

The invention relates to the technical field of intelligent scheduling and prediction of a power system, and discloses a power grid load prediction system and method based on machine learning, and the system comprises a multi-modal data processing and fusion module which is used for collecting multi-modal data from power grid load data, meteorological data, economic data and social events, cleaning and feature extraction processing are carried out on the multi-modal data, weighted fusion is carried out on different data features through a self-attention mechanism, and standardized feature input is provided for the hybrid prediction model module. Through a multi-modal data processing and fusion module, deep fusion is carried out on multi-modal data of power grid load data, meteorological data, economic data and social events, and a self-attention mechanism is introduced to carry out weighting processing on different data features, so that dynamic weighting is realized, key features are screened, and the accuracy of data processing is improved. The accuracy of load prediction and the comprehensive utilization capability of multi-source information are effectively improved, so that the sensitivity of the prediction model to complex environmental factors is remarkably enhanced.

The invention discloses a satellite-ground collaborative edge network resource allocation method based on depth deterministic strategy gradient, belongs to the field of network resource optimization in satellite internet, and adopts a DRL method to optimize allocation of bandwidth resources and computing resources. Firstly, a satellite-ground collaborative edge network resource allocation model containing a physical space and a digital twin space is designed. Secondly, establishing an allocation problem of communication resources and computing resources to balance the time delay and energy consumption of the system, and modeling a joint target optimization problem in the satellite-ground collaborative edge network as a Markov decision process (MDP); then, an algorithm based on a depth deterministic policy gradient (DDPG) is provided to intelligently decide allocation of bandwidth resources and calculate the allocation proportion of the resources, and system time delay and energy consumption are reduced to the maximum extent under the condition that constraints are met.

The invention discloses a cloud game resource dynamic allocation and management system, and relates to the technical field of resource scheduling, and the system comprises a first prediction module which is used for obtaining user behavior data of a cloud game in a first time period, inputting the user behavior data into a preset resource prediction model, and obtaining a predicted resource utilization rate in a second time period; the second prediction module is used for acquiring the resource utilization rate and inputting the resource utilization rate into the demand value prediction model to obtain a current resource demand value; the environment analysis module is used for acquiring environment parameter data of the cloud game, inputting the environment parameter data into a pre-trained random forest model as input data so as to acquire error adjustment data, and correcting the resource demand value based on the error adjustment data; the resource allocation module is used for generating a resource allocation strategy of the cloud game according to the user behavior data, the resource demand value and the environment parameter data; a distribution execution module; executing a resource allocation plan according to the resource allocation strategy; and the allocation evaluation module is used for evaluating the resource allocation effect.

The invention provides a multi-modal intelligent medical scene dynamic reasoning method and device based on medical knowledge, and the method comprises the steps: obtaining multi-modal data of a user, and extracting the multi-modal data as multi-modal features; inputting the text modal features into a graph neural network preset with a medical knowledge graph for feature enhancement to obtain medical knowledge enhanced text features, and inputting the text modal features into a memory network stored in a user personalized health file for feature enhancement to obtain file enhanced text features, splicing the medical knowledge enhanced text features and the file enhanced text features to obtain context enhanced text features; inputting the context enhanced text features and the multi-modal features into a pre-trained multi-modal fusion model to output fusion features; and inputting the fusion features into a dynamic reasoning tree constructed by a plurality of reasoning nodes in a graph structure to determine an optimal reasoning node, and reasoning the fusion features by using the optimal reasoning node to realize dynamic reasoning adaptive to the medical scene.

The invention discloses an automatic driving platform for a long-distance pipe network heat supply system based on a large model and a knowledge graph, and the platform comprises an infrastructure layer which is used for connecting an actual heat source, a long-distance pipe network and a multi-stage pump station heat supply facility of the long-distance pipe network heat supply system into the platform; the data processing layer comprises a data acquisition unit and a multi-source data fusion unit; the digital twinborn layer is used for establishing a digital twinborn model of the long-distance pipe network heat supply system; the large language model-knowledge graph layer comprises a graph construction unit which is used for constructing each business knowledge graph; the system also comprises a large language model construction unit which is used for constructing and training large language models of all businesses and processing automatic driving related businesses of the long-distance pipe network heat supply system. The business application decision-making layer comprises a business application unit which is used for providing a business application module of the long-distance pipe network heat supply system; and the intelligent decision-making unit is used for carrying out simulation analysis according to the digital twin model and calling each business large model to output a corresponding business decision-making result.

The invention discloses a metal surface defect detection method. The method comprises the following steps: establishing a metal surface defect image data set; the metal surface defect image is preprocessed, and it is guaranteed that multiple defect types exist in the same image; a YOLOv7 network model is improved; carrying out training evaluation on the metal surface defect data set by using the improved YOLOv7 model, and selecting proper hyper-parameters in the training process; data obtained after model training are analyzed and judged, and the model is adjusted and optimized; and keeping the optimal model weight, and carrying out experimental testing on the trained model. The method comprises the following steps: establishing a multi-type metal surface defect data set; a polarization self-attention mechanism and an SPDConv module are introduced into the neck network, and important information is captured in a self-adaptive mode while image information is reserved; and an NWD loss function is introduced, and the positioning capability of the network on small-size defects is improved. The method can meet the requirements of metal surface defect detection under different working conditions, and has good robustness.

The invention provides a method and system for virtual power plant resource allocation optimization and computing equipment, and is applied to the technical field of new energy prediction. The method comprises the following steps: collecting data of a distributed energy production and elimination main body through a centralized control center; establishing prediction data by using the prediction model; forming an internal scheduling plan by using a master-slave game model so as to determine the output and income of each distributed energy production and elimination main body; the operation state of the distributed energy production and elimination main body is monitored in real time through a sensor, intelligent metering equipment and a communication network, and the internal scheduling plan is dynamically adjusted according to the operation state, so that the stability and economy of the system are ensured. By using the method and the system of the invention, flexibility and economy of internal and external resource allocation of the virtual power plant can be improved.

The invention discloses a plastic part injection molding excess material recycling mechanism, and belongs to the technical field of injection molding excess material recycling. The plastic part injection molding excess material recycling mechanism comprises a shell, a partition plate is vertically arranged in the shell, a discharging plate is horizontally connected between the shell and the partition plate, and a feeding groove plate is arranged on one side wall of the shell; a discharging frame and a movable door are arranged on the front side wall and the rear side wall of the shell correspondingly, the discharging frame corresponds to the discharging plate, a cleaning assembly located below the discharging plate is arranged in the shell, the cleaning assembly corresponds to the feeding groove plate, a transferring assembly is arranged on the side, away from the cleaning assembly, of the partition plate, and a smashing assembly located above the discharging plate is arranged on the inner side of the shell. And a screening assembly corresponding to the movable door is movably arranged between the crushing assembly and the discharging plate, a drying assembly matched with the transferring assembly and the screening assembly is arranged on the shell, and the device has the advantages of comprehensive cleaning, stable transferring, rapid crushing, reciprocating screening, double drying, reliable structure and easy and convenient operation.

The invention relates to the technical field of data processing, and discloses a CNN-BiLSTM-MATT time sequence prediction method, which comprises a plurality of feature value inputs and a feature value output. Carrying out data normalization processing; performing normalization processing on the input data, mapping a normalized data set between [-1, 1], and performing reverse normalization processing on the output data after prediction; the hyper-parameters in CNN-BiLSTM-MATT are optimized, the hyper-parameters in CNN-BiLSTM are initialized according to an IHO algorithm, the hyper-parameters comprise the learning rate, the L2 regularization coefficient and the hidden layer number, and the optimal hyper-parameter value is obtained in the iteration process; performing feature extraction: performing convolutional feature extraction by using a convolutional neural network CNN, and taking obtained data features as input of subsequent BiLSTM; and carrying out time sequence analysis. The method overcomes the defects of a current time sequence prediction method. The method can be widely applied to the parameter optimization problem, the convergence degree is improved, and the optimal solution is efficiently found.

The invention discloses an indoor point cloud semantic segmentation method based on self-adaption and multi-scale feature fusion. The indoor point cloud semantic segmentation method comprises the steps of indoor point cloud preprocessing, construction of an indoor point cloud semantic segmentation network, training of the indoor point cloud semantic segmentation network and testing of the indoor point cloud semantic segmentation network. A self-adaptive local neighborhood search module is adopted to construct a graph structure of indoor point cloud, and the problem that local details are lost due to the fixed neighborhood radius in an existing method is solved; a context perception attention convolution module and a graph space attention convolution module are adopted to obtain key context semantic features and complex space geometric features between the points respectively; indoor point cloud spatial pyramid pooling is introduced into a multi-scale feature fusion module based on double attention, multi-level features obtained by different receptive fields are combined, and the ability of the whole network to extract and express features of different scales is enhanced. Comparison experiments show that the method can accurately identify semantic categories of a few samples and easily-confused building structures in a complex indoor environment.

The invention relates to the technical field of data processing, in particular to a multi-modal recommendation method based on an information bottleneck principle. The method comprises the following steps: constructing a data set # imgabs0 # according to a user-article historical interaction record in an Amazon data set; constructing a user-article graph for each mode according to the data set, and performing multi-layer graph convolution calculation on the embedding of the user and the article to obtain the final expression of the user and the fusion embedding of the article; an article-article similarity graph is constructed for each mode of the articles, neighbors of the first k articles with the highest similarity are reserved, article semantic signals are grouped according to the contribution degree of the article modes to downstream recommendation tasks, and final expressions of the articles are obtained; and performing optimization through denoising, loss calculation and an Adam optimizer. According to the method, key information can be extracted from multi-modal data, redundancy and noise can be removed, and the contribution degree of article characterization to downstream recommendation tasks in different modals can be calculated.

The invention provides velvet antler mushroom sporocarp water extraction residue polysaccharide as well as an extraction method and application thereof, and belongs to the technical field of edible mushroom polysaccharide preparation. The invention provides an extraction method of water extraction residue polysaccharide of a mushroom fruiting body, which comprises the following steps: sequentially carrying out solid state fermentation, post-treatment and hot water extraction on the water extraction residue of the mushroom fruiting body to obtain an extraction product containing the water extraction residue polysaccharide of the mushroom fruiting body. According to the method disclosed by the invention, the water extraction residues of the pilose antler mushroom sporocarp are used as raw materials, so that high-value utilization of the water extraction residues of the pilose antler mushroom sporocarp can be realized. Moreover, the method can improve the yield and content of polysaccharide, and has the advantages of no chemical residue, ecological environmental protection, high production efficiency and the like. Besides, the polysaccharide prepared by the extraction method disclosed by the invention is stable under a digestion condition, and the prepared polysaccharide can improve the content of short-chain fatty acids in intestinal tracts, so that a basis is provided for application of the polysaccharide of the water-extracted residues of the fruiting bodies of the velvet antler mushrooms in new potential prebiotics.

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The invention discloses a Carnot battery system coupled with an uninterrupted low-temperature heat source and an integrated optimization method of the Carnot battery system. The Carnot battery system comprises a high-temperature heat pump system, a heat storage system and a power circulation system, in the charging process, the high-temperature heat pump system operates, and waste heat of the uninterrupted low-temperature heat source module is improved into high-grade heat energy to be stored in the heat storage system through renewable energy source electric power; in the discharging process, the power circulation system operates, gradient utilization is conducted on heat energy stored in the heat storage system and an uninterrupted low-temperature heat source in the discharging stage, and generated mechanical energy is converted into electric energy through a generator and output to a power grid. The system configuration does not depend on the working medium type, and the comprehensive utilization rate of the uninterrupted low-temperature heat source and the back-and-forth efficiency of the Carnot cell are improved; according to the integrated optimization method, the working medium is coded to become a continuous variable capable of being synchronously optimized, and the optimal charge-discharge cycle working medium combination and system operation parameters suitable for different operation conditions can be obtained only through one-time optimization.

The invention relates to an intelligent photovoltaic power generation control device which integrates a photovoltaic power generation panel, a series connection base, a supporting assembly, a water collection groove, a rainwater collection assembly, a cleaning assembly and a control assembly. A series base and a series interface are arranged below the photovoltaic panel, so that modular installation is facilitated; the supporting assembly comprises two supporting rods, the light following function is achieved, and the photovoltaic panel is automatically adjusted to face the sun. A water collecting groove above the photovoltaic panel and a rainwater collecting assembly at the bottom of the photovoltaic panel can collect and store rainwater; the cleaning assembly comprises a spraying unit and a cleaning brush and is used for automatically cleaning the surface of the photovoltaic panel; the control assembly monitors operation of the system and adjusts the posture of the photovoltaic panel and work of the cleaning assembly. Redundant electricity generated by the photovoltaic power generation panel in sunny days is stored for the system to use, and the light following function is not started in cloudy days, so that electric energy is saved; through the design, the efficiency and sustainability of photovoltaic power generation are improved; the energy conversion efficiency can be improved, the reliability and automation level of the system are enhanced, and the utilization and environmental friendliness of rainwater are improved.

The invention discloses an LDI target high-precision positioning method based on deep learning, and relates to the field of image processing, and the method comprises the steps: carrying out the edge detection of a to-be-positioned LDI target image through an edge detection model; and further carrying out gray gradient analysis on candidate edge pixel points in the obtained detection result image, and screening out target edge pixel points to carry out circle fitting so as to realize high-precision positioning. A pooling module and a parallel jump connection module in the edge detection model are designed by introducing wavelet transform into the edge detection model, so that the problem of sampling high-frequency detail loss under the edge detection model can be avoided, and the multi-level feature extraction and utilization capability of the edge detection model is improved; in combination with a method of first coarse positioning and then fine positioning, high accuracy and robustness of edge positioning can be ensured, so that the method can stably detect the edge under complex background and changeable image conditions, and the detection effect and practicability are greatly improved.

The invention provides a multi-data source plug-in computing resource scheduling method and system supporting algorithm iteration, relates to the technical field of data sources, and comprises the steps of extracting multi-dimensional features by constructing a data source feature analysis engine, generating a feature fingerprint identifier, designing an intelligent matching mechanism based on the feature fingerprint identifier, and optimizing algorithm plug-in selection. The method has the beneficial effects that the utilization efficiency of computing resources is improved, resource allocation is adjusted in real time, stable system performance is ensured, and closed-loop optimization is formed.

The invention provides a coupling micro-grid optimization scheduling method and system considering electric and thermal load demand collaboration, and relates to the technical field of micro-grid optimization scheduling, and the method comprises the steps: building a hydrogen-containing micro-grid operation model, considering the peak-valley complementary characteristics of an original electric load and a thermal load in the system in time distribution, building an electric and thermal load demand collaboration response model, and carrying out the optimization scheduling of the hydrogen-containing micro-grid operation model; the electric load is described by a time-of-use electricity price demand response method, and flexible adjustment is carried out based on different electricity prices by considering the heat transfer inertia of the heat load and the fuzziness of user temperature perception; based on a hydrogen-containing micro-grid operation model and an electric heating load demand collaborative response model, an objective function is constructed by taking the minimum sum of the total operation cost, the renewable energy wind and light abandoning cost and the demand response compensation cost as an objective, and an optimal dispatching model of the electricity-hydrogen coupling micro-grid is established according to constraint conditions. And solving the hydrogen-electricity coupling micro-grid optimization scheduling model by adopting mixed integer linear programming, and applying the solved equipment optimal operation scheduling strategy to micro-grid execution.

The invention discloses a multi-agent experience exploration cooperation method based on a curiosity mechanism. The method comprises the following steps: constructing a multi-agent decision framework; a curiosity mechanism suitable for a multi-agent decision-making framework is constructed; constructing an experience exploration component suitable for the multi-agent decision-making architecture; constructing an experience playback pool and a state playback pool; a multi-agent cooperative confrontation environment is adapted; training the obtained deep neural network formed by the multi-agent experience exploration decision architecture based on the curiosity mechanism to obtain a multi-agent experience exploration collaborative strategy model; and in the multi-agent experience exploration collaborative strategy model, solving the multi-agent collaborative confrontation strategy in the current confrontation scene. Through the above mode, the method can balance the exploration and utilization process of a multi-agent algorithm during training, alleviates the influence caused by reward sparsity in a multi-agent confrontation environment, helps the multi-agent to carry out experiential exploration, searches an optimal strategy for confrontation, and improves the learning efficiency at the same time.

The invention relates to the technical field of electricity transaction, in particular to an hour-level green electricity transaction matching method and system considering a green certificate transaction blockchain, and the method comprises the following steps: S1, data collection and preprocessing: collecting the power generation operation data of a green electricity supplier and the power utilization behavior data of a user, preprocessing the collected power generation operation data and power consumption behavior data; s2, constructing a green certificate block chain: constructing a green certificate transaction platform based on a block chain technology, and recording green certificate transaction information; s3, establishing a matching model: constructing a multi-objective optimization model, and integrating multiple factors to carry out transaction matching; s4, transaction execution monitoring: after successful matching, executing the transaction and performing real-time monitoring; s5, transaction settlement evaluation: performing settlement and evaluation after the transaction is completed; according to the invention, the efficiency of green electricity transaction and the satisfaction degree of users and suppliers are effectively improved, and efficient utilization of green electricity resources and vitality improvement of the energy market are realized.

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 an efficient intelligent data compression and feature extraction method and system. The method comprises the following steps: S1, data preprocessing: performing data cleaning, denoising and standardization processing on to-be-processed data; s2, data compression and storage: determining a compression algorithm according to the type of the data, and uploading a compressed data file to a database; s3, feature extraction: deploying a machine learning model, and extracting representative features from the compressed data; and S4, data post-processing: further processing and optimizing the extracted features. According to the method, the data compression technology and the feature extraction technology are combined, the most representative information is extracted from the original data, meanwhile, the data storage and transmission cost is reduced, key features in the data are automatically recognized and reserved through an intelligent algorithm, and therefore efficient data compression and feature extraction are achieved.

The invention relates to the technical field of isoflavone biotransformation, in particular to a soybean isoflavone aglycone preparation method based on enzymolysis. Comprising the following steps: inoculating Kluyveromyces marxianus into a basic culture medium for shake cultivation; after culturing, centrifuging, washing and resuspending to obtain a bacterial suspension as a leavening agent, and inoculating the bacterial suspension to a bean dreg composite culture medium for shaking table fermentation for 48-96 hours; centrifuging the fermentation liquor at 0-4 DEG C, and collecting supernate to obtain crude beta-glucosidase; the crude enzyme liquid is sequentially subjected to ammonium sulfate salting-out, ion exchange chromatography and twice sephadex chromatography, and purified beta-glucosidase liquid is obtained; and carrying out enzymatic reaction on soy isoflavone by using the purified beta-glucosidase liquid to obtain the soy isoflavone aglycone. The method has the advantages that the bean dreg culture medium is utilized for fermentation to produce beta-glucosidase, and the combined soy isoflavone is subjected to enzymolysis, so that the utilization of the byproduct bean dregs is improved, the biotransformation of isoflavone is realized, and the obtained product has better activity.

The invention relates to a bimetallic porphyrin / GO / TiO2 nanotube composite photocatalytic material and application thereof. The bimetallic porphyrin / GO / TiO2 nanotube composite photocatalytic material is prepared by the following preparation method: preparing Ni-CoTCPP; the preparation method comprises the following steps: preparing a bimetallic porphyrin solution, immersing TiO2NTs into the porphyrin solution, stirring, taking out a titanium foil, and drying at room temperature to prepare Ni-CoTCPP / TiO2NTs; and soaking the Ni-CoTCPP / TiO2NTs into the suspension of GO, stirring, taking out the titanium foil, and drying at room temperature to obtain the Ni-CoTCPP / GO / TiO2NTs. The bimetallic porphyrin / GO / TiO2NTs ternary composite material is constructed through the action of porphyrin in photosynthesis, various targets in the field of photocatalytic CO2 reduction are achieved, and selectivity of products can be controlled while photocatalytic activity is improved.

The invention discloses a preparation method of a gas-liquid fluctuation resistant backflow electrolyzed water microchannel, and relates to the technical field of preparation of hydrogen by electrolyzed water, the preparation method comprises the following steps: S1, selecting a polar plate of an unprocessed flow channel, and designing and processing a Tesla valve array structure comprising a Tesla island; s2, selecting a processed polar plate, and processing a micro-column array at a position close to the side wall of the Tesla structure channel; s3, respectively coating the anode side and the cathode side of the proton exchange membrane with iridium oxide and platinum carbon to prepare a catalyst layer of the membrane electrode; s4, preparing a diffusion layer, and hot-pressing the proton exchange membrane loaded with the catalyst layer and the diffusion layer together to form a membrane electrode with catalytic activity; and S5, assembling the polar plate and the membrane electrode to form the proton exchange membrane water electrolysis device with the Tesla structure and the local wettability channel. According to the invention, the phenomenon of gas-liquid backflow of the electrolytic water flow channel under large current can be effectively inhibited, and the loss of active sites caused by blockage of the flow channel by the backflow electrolytic gas is avoided.

The invention provides a sound source localization method based on an improved particle swarm algorithm. Comprising the following steps: selecting a sound source positioning scene, acquiring a measurement data set by using a microphone array, and establishing a mathematical model; initializing the positions and speeds of the particles, and calculating the fitness value of each particle to evaluate the distance between the current position of the particle and the target solution; setting an inertia weight to control the influence of the current speed of the particles on the new speed, and setting an individual learning factor and a social learning factor to control and coordinate the behaviors of the particles in the search space; setting an iteration control condition, combining with a hybrid optimization algorithm, and utilizing a boundary processing strategy to carry out iteration updating of particles; and after iteration updating is completed, error analysis is performed on a sound source positioning result, and the particle swarm optimization algorithm is continuously optimized by using an optimization strategy according to an analysis result. The method can effectively improve the sound source positioning precision and algorithm performance, is suitable for a complex acoustic environment, and provides an efficient and feasible solution for the application in the acoustic field.

The invention discloses a driving permanent magnet synchronous motor with a heat dissipation mechanism, and relates to the technical field of 999. The outer side face of the rear end cover is fixedly connected with the inner side face of a base, a copper bar is embedded in the side, away from a shell, of the rear end cover, the inner side face of the rear end cover is fixedly connected with the surface of the copper bar, the side, away from the rear end cover, of the copper bar makes contact with the base, and the base is grounded; the air cooling assembly is fixedly connected with the end, close to the rear end cover, of the rotating shaft, the natural cooling assembly surrounds the outer portion of the shell, the inner side face of the natural cooling assembly is fixedly connected with the outer side face of the shell, the natural cooling assembly can increase the contact area with the outside, and the heat dissipation effect is enhanced. The rear end cover sucks air in the gap of the natural cooling assembly into the rear end cover to form flowing airflow, so that a better cooling effect is achieved, and the cooling effect of the air cooling assembly is radiated to the outside of the shell.

The invention belongs to the technical field of sewage treatment, and discloses an anaerobic ammonia oxidation enhanced denitrification device and method based on an electron mediator type biological carrier and iron oxide. The anaerobic ammonia oxidation enhanced denitrification device comprises a reactor main body, a heat preservation circulating water device, a stirring device, a water inlet pipeline and a water outlet pipeline, anaerobic ammonium oxidation bacteria are contained in the reactor main body, a certain proportion of electron mediator type biological carriers are filled in the reactor, and a proper amount of iron oxide is added; wherein the added high-valence iron oxide can be used as an extracellular electron acceptor of anaerobic ammonium oxidation bacteria, the electron mediator type carrier can be used as a redox mediator, and nitrogen removal of the anaerobic ammonium oxidation system is promoted by strengthening the extracellular electron transfer process of the anaerobic ammonium oxidation bacteria.

The invention provides a neurosteroid derivative solid dispersion, a preparation method and application thereof, and a medicine containing a hot-melt extrusion solid dispersion, and belongs to the technical field of pharmacy. The preparation method comprises the following steps: carrying out melt extrusion on a raw material mixture at 125-160 DEG C to obtain the neurosteroid derivative solid dispersion, the mixed material comprises an active component, a plasticizer and a carrier material in a mass ratio of 10: (1-15): (10-90); the active component comprises at least one of a neurosteroid derivative (with a structure as shown in a formula 1), an isomer, a solvate and a pharmaceutically acceptable salt thereof; the plasticizer is selected from one or more of polyethylene glycol, poloxamer and Tween, and the average molecular weight of the polyethylene glycol is 1t; 6000, 6000; active components in the neurosteroid derivative solid dispersion exist in an amorphous state. The neurosteroid derivative in the neurosteroid derivative solid dispersion disclosed by the invention has relatively high oral bioavailability. # imgabs0 #