545results about How to "Achieve balance" patented technology

The invention provides an intranet service quality optimization method and system based on deep reinforcement learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional performance index data, carrying out the preprocessing, constructing an analytic hierarchy model, outputting the performance score of each layer, integrating the performance score of each layer, and outputting an intranet service quality score. Constructing a simulation environment, adjusting hyper-parameters of the adaptive deep reinforcement learning model, continuously learning in an actual intranet environment, determining multi-dimensional rewards, performing multi-target optimization, and generating a multi-target optimization result; and setting a hierarchical performance index threshold, if a hierarchical optimization strategy is triggered, adjusting a network topology structure, adjusting resource allocation and service configuration, adjusting weights of different indexes in the service quality evaluation model, updating a reward function, solving an optimal service decision, generating an optimal service decision sequence, and executing the optimal service decision sequence.

The invention discloses a coal mine heat energy monitoring and cooling regulation and control method based on the Internet of Things, and relates to the technical field of coal mine heat energy monitoring and cooling regulation and control. The collected temperature data are transmitted to a central monitoring system in real time through an Internet of Things gateway; redundant temperature sensors are arranged at key positions, and data of the temperature sensors in the same area are regularly compared and verified. According to the invention, the monitoring robustness is improved through a redundant temperature sensor and a data fusion algorithm, a monitoring blind area caused by a single-point fault is avoided, and the accident risk is reduced; a self-adaptive learning algorithm is adopted to dynamically optimize a detection threshold value and a compensation strategy, so that the anomaly detection accuracy is improved, and false alarms and missing alarms are reduced; the temperature trend is pre-judged through a predictive control algorithm, cooling is conducted in advance, balance between safety and energy efficiency is achieved, overheating accidents are prevented, and energy consumption is reduced.

The invention relates to the technical field of data packet transmission, and discloses a data packet transmission control method and device for a low-power-consumption dual-mode Bluetooth chip, and the method comprises the steps: obtaining a signal strength parameter, a power consumption parameter, a transmission rate parameter and an environment parameter of the dual-mode Bluetooth chip, inputting the parameters into a deep time convolution network, and carrying out the time sequence feature extraction, obtaining a transmission mode switching judgment result; calculating transmission state evaluation data; the chip transmitting power is adjusted in a grading mode, standby power consumption is controlled through a nanoamp LDO circuit, and a dynamic power control parameter is obtained; performing compression coding on the data packet to obtain a dual-mode transmission protocol configuration parameter; executing time sequence sampling, and constructing a dual-mode state feature sequence; the data packet delivery success rate, the end-to-end transmission time delay and the total power consumption of the system are calculated, transmission control parameter optimization is carried out through double-layer deep reinforcement learning, target transmission control parameters are obtained, and the optimal balance of the transmission performance and the power consumption of the dual-mode Bluetooth chip is achieved.

The invention discloses a tobacco enterprise composite scheduling method based on multi-agent collaborative optimization, and the method comprises the steps: obtaining the system data of a tobacco enterprise, and obtaining a plurality of corresponding decision-making schemes based on a sales prediction agent, a production plan agent, a purchase stock agent, and an inventory management agent; performing iterative optimization on the decision scheme based on a Nash equilibrium game and a Lupinstein bargaining model, and calculating a system utility value to obtain an optimal decision scheme; according to the optimal decision-making scheme, based on an adaptive mechanism, adaptive optimization is carried out, and an adjusted decision-making scheme of each agent is obtained, so that tobacco enterprise composite scheduling of multi-agent collaborative optimization is realized; the sales prediction agent, the production plan agent, the purchase stock agent and the inventory management agent perform information interaction through an attention mechanism. Interactive cooperation among multiple agents is realized by introducing an attention communication mechanism and a game theory cooperation mechanism, and overall optimization of tobacco enterprise scheduling is promoted.

The invention discloses an intelligent data information rapid checking and question-answering system, which relates to the technical field of intelligent data information and comprises a document import module, a vectorization analysis module, an NLP processing module, a question-answering matching module and a result output module. The document import module is used for receiving and importing documents including but not limited to PDF, DOC, TXT and PPT, analyzing the documents in different formats by adopting corresponding libraries and tools, identifying and classifying the files by identifying title formats and question number formats when processing the files in test questions and test paper formats, and performing preliminary analysis on the contents of the documents by utilizing a machine learning algorithm; according to the invention, the system not only can realize the rapid checking and question-answering functions of documents, but also can be applied to the field of educational training, realizes the online editing, management and test paper formation of test questions, reduces the manpower consumption in basic training business, and integrally improves the working quality and efficiency; meanwhile, the system can help enterprises to carry out internal training and examination, and improves the practicability and application range of the system.

The invention relates to a multi-modal emotion recognition method based on zero-knowledge machine learning, and belongs to the crossing field of artificial intelligence and information security. Key features of local face images, voice signals and posture data multi-modal data are extracted through an attention module, then the key features are transmitted to a server after homomorphic encryption, training of a convolutional neural network (CNN) model is carried out in an encryption domain, and finally a result is sent back to the local for decryption and emotion recognition. The method has the advantages that by using the homomorphic encryption technology, in the whole data processing and transmission process, privacy data of a user is always kept in an encrypted state, and due to fusion of multi-modal features, the emotion recognition accuracy is improved, the robustness of the system in a complex environment is enhanced, the emotion recognition result is more stable and reliable, and the user experience is improved. The method can be widely applied to multiple fields of intelligent monitoring systems, user sentiment analysis, human-computer interaction, mental health monitoring and the like.

The invention discloses a reservoir group scheduling interval multi-target optimization method under an uncertain condition, and belongs to the technical field of reservoir optimization scheduling. The method comprises the following steps: firstly, establishing an innovative cascade reservoir group interval multi-target scheduling model; secondly, providing a double-population dynamic coordination interval optimization method for solving the interval multi-target scheduling model, and further obtaining an optimal scheduling scheme of the reservoir group; and finally, outputting an optimal scheduling scheme set of the reservoir group. According to the optimization method, through organic combination of a chaos-order mapping initialization strategy, a dynamic adaptive mutual promotion updating strategy and an elite guidance optimization strategy, optimization is rapidly and efficiently carried out to obtain a group of reliable and accurate scheduling scheme sets considering runoff uncertainty; an effective decision-making technical means for solving the cascade reservoir group optimization multi-target complex engineering problem is provided for a scheduling decision maker, the intelligent level and comprehensive benefits of water resource management are improved, and the capacity of a water resource system for coping with complex environments and uncertain factors is further enhanced.

The invention provides a 5G intelligent networking method and system based on edge computing. The method comprises the following steps: deploying edge computing nodes, and designing a hierarchical network architecture and a dynamic routing mechanism; deploying an edge model on the edge computing node, and carrying out real-time processing and anomaly detection; dynamically distributing network resources, and carrying out network switching and load balancing by utilizing an edge model; decomposing the complex task into a plurality of sub-tasks, and performing distribution and parallel processing of the sub-tasks; updating and optimizing the edge model, and dynamically adjusting a networking strategy; and data security protection is realized, and network data and user feedback are monitored. The system comprises an edge deployment module, a data analysis module, a networking strategy module, a collaborative computing module, a collaborative optimization module and a security protection module. According to the invention, network resources can be efficiently utilized, intelligent networking and network switching are carried out, load balancing is realized, the response speed, the processing capability and the adaptive capability of the network are greatly improved, and the experience feeling of a user is improved.

The invention discloses a data privacy protection method under a federated learning framework and a medical service system. The method comprises the steps that a gradient optimization direction classification model is trained, and the gradient optimization classification model takes an update gradient of a client local model as input of data and outputs optimization target features corresponding to preset classification; the server sends the aggregated global model and a pre-trained gradient optimization classification model to the client; and after each round of model training is completed, each client encrypts model parameters by using homomorphic encryption, uploads the updated model to a server and the like. According to the method, the gradient optimization direction classification model and the homomorphic encryption technology are combined, so that the privacy of the client is protected, and personalized model training and updating of the client are realized. The method not only considers privacy protection, but also considers optimization of model performance, and is a comprehensive solution.

The invention provides a multi-stage structure topological optimization method based on a potential diffusion model, and relates to the technical field of engineering design and structure design optimization. Creating a structure topology optimization image data set by using a traditional SIMP method and performing preprocessing; mapping the structure topological optimization image to a low-dimensional potential space for representation so as to reduce the data dimension and improve the calculation efficiency; a forward diffusion process and a reverse diffusion process are carried out on the initial potential variable in the low-dimensional potential pair, a denoising network is added in the reverse diffusion process, and a cross attention mechanism and a condition mechanism are introduced, so that a topological structure is adjusted and designed according to physical and functional requirements, and the structural fidelity is ensured; after training is completed, potential variables of a low-dimensional space are mapped back to a high-dimensional space, a topological structure is generated, few-step SIMP direct optimization is carried out on the topological structure, the structure is rapidly refined, physical information is directly injected into the design so as to adapt to given boundary conditions, and generation of the optimal topological structure can be completed through few-step iteration.

The invention discloses a lightweight unmanned aerial vehicle target tracking method and system based on dual cross-correlation fusion. The method comprises the following steps: acquiring a video stream of a target area; adding an initial target frame to a first frame image of the video stream to calibrate the initial position of a target, taking the first frame image as a template image, and taking other frame images of the video stream as search images; preprocessing the template image and the search image; inputting the preprocessed template image and the search image into a pre-trained target tracking model to obtain a predicted position of the target in a certain frame of image of the video stream, thereby realizing target tracking; a twin network is adopted as an overall framework, a backbone network is a lightweight network, the real-time performance of an algorithm is improved while the network parameter quantity and the calculation quantity are reduced, a dual cross-correlation fusion module is adopted, the nonlinearity of fusion features is enhanced, target feature information is highlighted, an anchor-free position prediction head is used, and the tracking speed is improved. And the network parameter quantity is reduced.

The invention belongs to the technical field of building energy conservation and carbon reduction, discloses a building energy conservation and carbon reduction method, device and equipment and a storage medium, and constructs a high-precision building BIM model. A digital twinborn model of a Xian mansion is constructed by applying a BIM technology, and the model not only depicts the physical structure of the building in detail, but also simulates key operation details such as personnel flow and equipment operation. By strategically simplifying the model, it is ensured that the model is efficient and accurate. The comparative analysis of the corrected model and actually measured data shows that the prediction error of the model is only 0.10%, the extremely high consistency of the prediction result and the actual situation is highlighted, the high accuracy of model correction is verified, and the high standard requirement of in-depth research is met; according to the invention, balance optimization of carbon emission minimization and comfort maximization is realized. By intelligently adjusting the operation strategy of the air conditioning equipment, the method obviously reduces the carbon emission of the building while ensuring the living comfort.

The invention relates to a robot intelligent sorting path planning method and a robot intelligent sorting path planning device for stacked parts in a charging basket, an RRT * algorithm is combined with an artificial potential field algorithm, random search of the RRT * algorithm in a barrier-free free space can be reduced by utilizing an artificial potential field, and meanwhile, in a complex barrier environment close to a part pile, the robot intelligent sorting path planning method and the robot intelligent sorting path planning device have the advantages that the random search of the RRT * algorithm in the barrier-free free space can be reduced; and an RRT * algorithm is used for accurate collision-free path planning, so that the problems of low path planning precision and local minimum of an artificial potential field algorithm are avoided. On the basis, the RRT * algorithm is improved, the problems of many redundant nodes, low planning efficiency and poor path quality existing in path planning of the RRT * algorithm are solved, meanwhile, the improved RRT * algorithm is combined with the artificial potential field algorithm, the planning scale is further reduced, and random search of the RRT * algorithm in a free space is reduced. According to the method and device provided by the invention, an efficient and accurate collision-free picking path planning scheme can be provided for industrial application of mechanical arm material frame sorting.

The invention discloses a management method and system of a smart park based on digital twinning, and relates to the technical field of digital modeling, when the system operates, a real-time park operation model is established by using the digital twinning technology, and comprehensive dynamic monitoring and optimization of the park operation state are realized. Through feature extraction and preprocessing, the system can accurately obtain use and demand features of resources, and a more efficient resource scheduling index OSP is generated. And generating and testing a production plan factor PPG according to the optimized resource scheduling strategy, thereby adjusting a production plan and realizing balance and optimization of production tasks. The real-time monitoring and feedback module ensures the real-time monitoring of the execution state, generates a comprehensive optimization index COF through a feedback vector, further adjusts the resource scheduling and production plan, effectively reduces the operation cost, solves the large consumption of human resources, reduces the dependence on the maneuverability of personnel, and improves the production efficiency. And the operation flexibility and the economic benefit of the park are obviously improved.

The invention discloses a federated learning privacy protection method based on a generative adversarial network and knowledge distillation, and relates to the technical field of privacy protection. Comprising the following steps: S1, local model training; s2, generative adversarial network training; s3, communication in an initialization stage; s4, carrying out distillation training; s5, communication at a distillation stage; and S6, updating the client. According to the method, the generative adversarial network and the knowledge distillation technology are creatively combined, the generative adversarial network model is locally trained at the client, the synthetic data conforming to private data distribution is generated, dependence on a public data set is avoided, knowledge distillation is performed by using the generated synthetic data, and the knowledge distillation efficiency is improved. Knowledge migration without selecting a public data set is realized, and direct exposure of private data distribution is effectively avoided, so that the risk of privacy disclosure is remarkably reduced.

The invention relates to the technical field of flue gas purification and discloses a flue gas low-temperature adsorption, regeneration and purification system for realizing return balance of nitrogen oxides, the flue gas low-temperature adsorption, regeneration and purification system comprises a boiler, an adsorption and regeneration device and a regeneration rich gas circulating pipe, the boiler comprises a boiler body and a flue, and a hearth is arranged in the boiler body; the flue is communicated with the hearth to discharge smoke in the hearth, and the hearth is provided with a first combustion area with the temperature larger than or equal to 700 DEG C. The adsorption regeneration equipment is connected with the flue of the boiler. The inlet end of the regeneration rich gas circulating pipe is connected with the flue gas adsorption regeneration equipment, and the outlet end of the regeneration rich gas circulating pipe is communicated with the first combustion area of the hearth, so that the regeneration rich gas is conveyed into the first combustion area of the hearth. According to the flue gas low-temperature adsorption, regeneration and purification system for realizing the return balance of the nitrogen oxides, disclosed by the embodiment of the invention, the content of NOx in the system cannot be continuously increased, the NOx balance is realized, the regeneration rich gas treatment process is simple, the cost is low, and the boiler runs safely.

The invention discloses a federated undersampling learning method for unbalanced network intrusion detection, which comprises a federated learning server and a client, and communication connection is established between the federated learning server and the client; all models for network intrusion detection are configured in the server; the server is used for maintaining a global model and deploying the global model to the client for training and learning, and the client adopts a local data set to train a local model downloaded from the server and uploads the local model after training and learning to the server; the server aggregates the local models uploaded by the client to obtain a global model with updated parameters; and when the client adopts the local data set to train the server, undersampling processing is performed on the local data set to obtain a balanced data set to perform learning training on the client. When federated learning is carried out on an intrusion detection network model, the influence of training data imbalance in a client on the model detection performance is solved through an undersampling method.

The embodiment of the invention relates to the field of trajectory tracking, and discloses a target detection tracking method, system and device and a storage medium, and the method comprises the steps: obtaining a plurality of image frames in a video or image sequence; performing detection by using a target detection model to obtain feature detection information of each image frame; inputting the feature detection information into a trajectory tracking model for analysis and processing, and establishing and tracking a motion trajectory of the target object; wherein the method specifically comprises the following steps of: associating the target object by utilizing the frame similarity among the plurality of bounding boxes; and establishing and tracking a motion track of the target object based on an association result. According to the method disclosed by the invention, the technical problems that the tracking object is difficult to accurately detect and the calculation time is relatively long and relatively large computing power resources need to be occupied are solved, the computing power can be reasonably distributed, the balance of high-precision detection and real-time monitoring is realized, the target object is quickly sensed in a complex shielding environment, and the target object detection and tracking effect is improved.

The invention relates to the technical field of intelligent manufacturing, in particular to a distributed flexible job shop scheduling method and system based on double deep reinforcement learning and a multi-layer agent. A global complex problem is decomposed into a plurality of local problems by introducing a multi-level agent scheduling framework and through cooperative work and hierarchical decision making of a top layer agent, a middle layer agent and a bottom layer agent; besides, different scheduling tasks of the distributed flexible job shop are executed dispersedly through the intelligent agent of each level, each decision does not need to be managed by a central coordinator, and when the environment is locally changed, each level can quickly adapt and make an optimal decision according to the specific context of the environment, so that the scheduling efficiency of the distributed flexible job shop is improved. And meanwhile, the overall goal of the whole system is still served. The objective of the invention is to solve the problem of how to improve the scheduling precision of the distributed flexible job shop.

The invention relates to the technical field of nuclear power plant safety, in particular to a nuclear power plant fault diagnosis method based on data balancing, which establishes a data set balancing technology for deep learning through SA-TimeGAN, can effectively solve the problem of data imbalance caused by large normal data volume and small fault data volume in a nuclear power plant, and improves the fault diagnosis accuracy of the nuclear power plant. According to the method, firstly, small sample fault data are preprocessed, then, SA-TimeGAN is used for conducting adversarial training on the multiple types of small sample fault data, generated fault samples are added into an original training data set to achieve balance of sample data, and finally, the balanced data set is input into a CNN-BiLSTM diagnosis model for training, so that fault diagnosis is achieved. According to the method, the recognition capability of the model on minority classes of fault samples is effectively enhanced, and real data distribution is approached through adversarial training, so that high-quality synthetic data is generated.

The invention relates to the technical field of power grid operation, and discloses a topological power grid operation-based multi-level vulnerability quantitative index construction method, which comprises the following steps of: obtaining topological structure data of a power grid dispatching system to perform hierarchical division, analyzing node data to form a hierarchical matrix, calculating a shortest path length matrix and analyzing betweenness centrality. According to the method, a power grid is divided into a high-voltage level, a medium-voltage level and a low-voltage level according to voltage levels, so that the topological structure of each level is independently analyzed, the modularity increment change of each node moving to an adjacent node is calculated based on a Louvain algorithm, subgroup junctions are gradually optimized by maximizing the modularity increment, and therefore the topological structure of each node is optimized. The hierarchical modularity is combined with the node vulnerability index of the hierarchy to form a more comprehensive vulnerability evaluation model, and the inter-hierarchy coupling problem can be more effectively identified and solved in the power grid optimization process through the comprehensive analysis of the inter-hierarchy and intra-hierarchy vulnerability indexes.

The invention provides a spinal endoscopic image deblurring method and system, and the method comprises the steps: carrying out the multi-scale convolution operation of a spinal endoscopic image, obtaining the difference of image contrast among different scales, segmenting the spinal endoscopic image into a plurality of local structure images, and obtaining a plurality of local structure images; determining the brightness uniformity of each local structure image through the distribution gradient of the pixels in each local structure image and the difference quantity of the contrast; determining the dependency relationship of the textures between the adjacent local structure images through the texture difference between the adjacent local structure images on the spine tissue structure; performing contrast enhancement on the brightness of the local structure image according to the dependency relationship and the brightness uniformity to obtain a local enhanced image; and fusing and reconstructing the fuzzy region through all the local enhanced images to obtain a deblurred spinal endoscope image. By adopting the scheme provided by the invention, local adaptive equalization enhancement of the image brightness of the spinal endoscope can be realized, so that detail blurring caused by non-uniform illumination is reduced.

The invention discloses a hydrogen fuel cell hybrid power system grid-connected control method, which comprises the following steps of: analyzing and establishing a plurality of models of a system, comprehensively considering a plurality of cost weights on the basis of a hydrogen consumption model of the system, and constructing a comprehensive cost function; establishing a system operation state model; the speed, the acceleration and the system operation state serve as input of a self-adaptive Elman neural network together, a state transition matrix real-time correction factor of the system in the state is obtained, and a basic state transition matrix is adjusted through the real-time correction factor to obtain a real-time state transition matrix after the influence of the current driving condition on system state transition is considered; and according to the real-time state transition matrix, predicting the system demand power within a period of time, solving the comprehensive cost to enable the cost to be minimum, and completing the power distribution between the multiple stacks of fuel cells and storage batteries. According to the method, the fuel economy of the system is ensured, the internal balance of the multi-stack fuel cell system is improved, the service life of the system is prolonged, and the real-time prediction capability of the system is improved.

The invention belongs to the technical field of computer vision, and discloses a small target detection method based on improved RT-DETR, and the method comprises the steps: employing public data / self-built data to make a data set according to a small target task; expanding the data set by adopting a data enhancement method, and dividing the data set into a training set, a test set and a verification set; a small target recognition model based on the improved RT-DETR is constructed, deformable convolution is adopted, an attention mechanism is introduced, the relation of all positions of a target is fully considered, a target area with important information is focused on, and other irrelevant feature information is inhibited; training the constructed model by using the training set and the verification set to obtain a trained detection model; and detecting a small target to be detected by using the trained detection model. According to the invention, the network is reset according to the specific features of the small target, the precision of small target detection is improved, and the balance between the detection processing speed and precision under a complex background is realized.

The invention provides a quasi-zero stiffness vibration damping device based on a tensegrity, which comprises a first tensegrity vibration damping device, a second tensegrity vibration damping device and a cam group buffer device, and utilizes the nonlinear quasi-zero stiffness characteristics of the self-balance, low dynamic stiffness and high static stiffness characteristics of the tensegrity structure. Through cooperative work of the first tensioning overall vibration reduction device and the second tensioning overall vibration reduction device, longitudinal stress is converted into deformation of an elastic element of a quasi-zero-stiffness mechanism, and therefore vibration reduction is effectively achieved. According to the damping device, the whole tensioning structure and the cam set structure are combined, the damping work is achieved through the combination of the damping performance of the quasi-zero stiffness structure and the damping performance of the tensioning mechanism, the device is high in impact resistance and remarkable in self-balancing and self-stabilizing performance, vibration can be effectively reduced, and the safety of the damping device is improved.

The invention discloses a heterogeneous computing architecture testing method of an ISP module, and belongs to the technical field of heterogeneous computing. According to the technical scheme, a high-level synthesis tool is used for generating and verifying an IP to be tested; a target algorithm IP application system is constructed by using a microprocessor IP hardcore and an AXI bus connector of a heterogeneous computing platform, a target algorithm IP core is operated by using software to carry out an application test, and actually measured data is stored in a nonvolatile storage medium; transplanting an operation system and an OpenCV library to the heterogeneous computing platform for the development board, and storing a target algorithm software operation result to a data file after a test program is operated; and evaluating the function correctness and the performance index of the target algorithm IP by using the actually measured data and the software operation result. The method has the advantages that effective testing and optimization of the heterogeneous computing architecture of the ISP module are achieved through the software and hardware co-simulation and IP core verification technology, middle-level optimization, a comprehensive testing framework and other methods, and powerful support is provided for technology development, application and popularization in related fields.

The invention discloses a bad video classification method and system based on multi-modal alignment and category balance, and the method comprises the steps: inputting a data set into a bad video classification model, training the bad video classification model, and enabling the bad video classification model to be called a model for short; when the total loss function value of the model does not decrease any more, a trained bad video classification model is obtained; in the training process, the model firstly extracts visual mixed features and audio mixed features from a video; the total loss function of the model comprises the weighted summation result of a first sub-loss function, a second sub-loss function and a third sub-loss function, and the first sub-loss function refers to the difference between the Gaussian distribution of the visual mixed features and the Gaussian distribution of the audio mixed features; the second sub-loss function refers to the difference between the visual mixing feature and the audio mixing feature; and the third sub-loss function is used for increasing the minimum distance between a model decision boundary and a sample point and reducing the influence of class imbalance on the training process.

The invention provides a four-redundancy travel switch self-power-on method and structure, and the method employs four travel switch contacts which are connected to two ends of a power-on control signal of a satellite power supply controller in a mode that every two contacts are connected in parallel and then are connected in series. The satellite is provided with two satellite and rocket separation pressing points, each pressing point is provided with two travel switches, and travel switch contacts of the pressing points are connected in series. According to the invention, the release of any satellite-rocket separation pressing point can be ensured, the power supply controller can be triggered to be powered on autonomously, and the reliable power-on of the satellite can still be ensured when any travel switch fails.

The invention belongs to but is not limited to the technical field of electrical control, and particularly relates to a DC-DC converter control method and system based on adaptive droop control, and the method comprises the steps: deducing a corresponding mathematical model before designing a controller of an interleaved multi-level boost converter; the original nonlinear model can be converted into a regular linear form by using the new state variable; the converted reduced-order model is equivalent to the differential homomorphism of the reduced-order model, a control target can be changed, and the total storage energy gradually tracks a reference value of the reduced-order model; aiming at a state variable and a disturbance quantity in an established system model, designing a self-adaptive sliding-mode observer which can compensate a disturbance influence in a feedforward mode; controlling a state variable by using a sliding mode controller based on a disturbance observer; a phase current balance compensator is designed, wherein simple and effective phase current balance compensation is adopted in a closed-loop interleaving multi-level boost converter system; a typical distributed control strategy, namely droop control, is added in the system.

The utility model discloses a vertical electric heating faucet with a backwater stopping structure. The vertical electric heating faucet comprises a shell, an inner container, a heating assembly and a valve element assembly. The inner container is arranged in the shell; the heating assembly is arranged in a heating cavity in the inner container, a water inlet is formed in the lower end of the inner container, and a water outlet is formed in the upper end of the inner container. The inner container is provided with a water inlet channel, and a valve element of the valve element assembly is communicated with the water inlet through the water inlet channel. A backwater stopping structure is arranged in the heating cavity in the inner container and can prevent water in the heating cavity from flowing back to the water inlet channel through the valve element; the backwater stopping structure comprises a first vertical channel and a second vertical channel which are parallel to each other, the lower end of the first vertical channel is communicated with the valve element, the lower end of the second vertical channel is communicated with the heating cavity, the upper end of the first vertical channel is communicated with the upper end of the second vertical channel, and the communication point is higher than the standing water level of the heating cavity in the water leakage state. The water is prevented from flowing back from the heating cavity, it is guaranteed that the heating assembly is covered with the water all the time in the heating cavity, and the situation of accidental empty burning is avoided.