209results about How to "Achieve co-optimization" patented technology

The invention belongs to the technical field of the Internet of Things and energy management, and particularly relates to an energy consumption data collector based on the Internet of Things and a collection method, the collector comprises a data processing module, a network state evaluation module, a data value evaluation module and a communication and control module, real-time protocol analysis and data conversion are carried out on original data, and the energy consumption data is obtained. Generating a standard energy consumption data packet with a unified timestamp and a device identifier, storing the standard energy consumption data packet according to a receiving sequence, and eliminating a time reference deviation caused by an instrument clock difference; communication signal strength and round-trip time with a remote Internet of Things platform are collected in real time, through multiple times of detection and normalization processing, a maximum value is selected as a network state factor, a dynamic change reference and a self-adaptive fluctuation interval are calculated based on a historical power data sequence, an instantaneous deviation rate and a continuous deviation duration are determined, and the network state factor is determined. A data value factor is generated through processing, and the network state factor and the data value factor are matched with a preset period adjustment mode to realize collaborative optimization.

The invention discloses a wireless communication dynamic anti-interference optimization method and platform based on edge computing, and relates to the technical field of wireless communication anti-interference, and the method comprises the steps: collecting wireless channel state information and external interference source features in real time at an edge node, and constructing a real-time interference feature vector according to a collected data set; generating an interference prediction matrix; constructing a dynamic interference topological graph among network nodes, taking the dynamic interference topological graph as environment input, and utilizing reinforcement learning to generate an adaptive anti-interference strategy; and executing self-adaptive anti-interference strategy compensation, issuing a compensation result to the edge node, and executing anti-interference control. The technical problems that in the prior art, wireless communication anti-interference real-time response lags behind, global adaptability is insufficient, and local immediate response and overall reliable anti-interference requirements are difficult to consider are solved, and edge real-time anti-interference and cloud global collaborative optimization are achieved. And the technical effects of real-time performance, reliability and adaptability of wireless communication anti-interference are improved.

The invention discloses an AI marking closed-loop method based on active learning and difficult case mining, and the method comprises the steps: carrying out the parallel calculation of uncertainty, representativeness and diversity three-dimensional value indexes after training an initial model, and carrying out the dynamic weighting screening of a high-value sample through a closed-loop feedback controller; difficult cases are recognized, DBSCAN clustering is adopted, similar sample expansion is retrieved, and the samples are stored in a dynamic pool; annotations are distributed, intelligent judgment is triggered for dispute samples after consistency verification, and labels are determined by fusing node reputation, model confidence and feature similarity; a mixed loss function is adopted for incremental training, and if the performance does not reach the standard, sampling, difficult examples and training parameters are adjusted in a linkage mode and then retry is conducted; the effectiveness of the difficult cases is evaluated after each round of iteration, the invalid difficult cases are eliminated, redundant clusters are combined, and the pool capacity is adaptively maintained; and the performance of the monitoring model is iteratively optimized until the standard is reached. According to the method, the labeling cost is reduced, and the boundary sample and long tail category recognition capability is improved.

The application provides an amorphous alloy magnetocaloric performance prediction method and system based on interpretable machine learning, relates to the technical field of material science and engineering, and comprises the following steps: step 1, constructing an amorphous alloy magnetocaloric performance data set, wherein the data set comprises alloy composition data, physical and chemical descriptors, external conditions and structural characteristics of a plurality of amorphous alloy samples and corresponding experimental values of maximum magnetic entropy change and Curie temperature; step 2, screening and optimizing the features in the data set, eliminating the collinearity between the features and performing recursive feature elimination to determine the final feature subsets for predicting the maximum magnetic entropy change and the Curie temperature respectively; and step 3, establishing machine learning models for predicting the maximum magnetic entropy change and the Curie temperature respectively through the final feature subsets, and training and optimizing the models to obtain the trained prediction models. The application realizes a closed loop of high-precision performance prediction, physical mechanism analysis and directional composition design.

The application relates to the technical field of hydrological prediction and deep learning, and provides a conceptual hydrological neural network hydrological prediction method and system considering space-time dynamic parameters, the method comprising the following steps: acquiring digital elevation model data of a target region, extracting a river network structure, and dividing the target region into a plurality of sub-basin units; constructing a sub-basin feature set and a river channel feature set based on underlying surface information data of the target region; constructing an original sample set based on historical hydrological and meteorological data of the target region; fusing the sub-basin feature set and the original sample set to obtain a standardized time series sample set; training a conceptual hydrological neural network model based on the standardized time series sample set, the original sample set, the sub-basin feature set and the river channel feature set to obtain a trained conceptual hydrological neural network model; and inputting real-time hydrological and meteorological data into the trained conceptual hydrological neural network model to obtain hydrological prediction information of the target region. Thus, the hydrological prediction accuracy is effectively improved.

The invention discloses a martensite reinforced self-lubricating copper-based alloy coating and a laser cladding preparation method thereof, the martensite reinforced self-lubricating copper-based alloy coating comprises the following components in percentage by mass: 66 to 84 weight percent of Cu, 10 to 20 weight percent of Al, 3 to 10 weight percent of Ni, 3 to 10 weight percent of Mn, 0 to 2 weight percent of Sn and 0 to 2 weight percent of Pb. In the preparation process, the laser input energy density is controlled within the range of 8.96-13.27 J / mm, and the double strengthening effects of grain refinement and martensite content increase can be synchronously achieved. The main phase structure of the obtained coating is twin-crystal martensite, meanwhile, the coating contains a small amount of alpha-Cu lubricating phase, gamma phase and Fe-rich hard phase, and the dry friction coefficient of the coating is 1t; 0.35, and the solidification hardness gt; and the voltage is 350HV. Compared with the existing self-lubricating tin-based babbitt metal and copper-tin alloy with similar friction coefficients, the coating disclosed by the invention realizes synergistic improvement of high hardness and low friction performance, and is particularly suitable for dynamic friction and wear working conditions with higher impact resistance requirements, such as robot motion joints, precision mechanical transmission parts and the like.

The application discloses a kind of integrated energy system low carbon operation method, belong to power grid technical field.The method includes based on baseline method allocation system free carbon emission quota, calculate actual net carbon emissions;Set integrated energy system objective function;Adopt two-stage robust optimization method, handle renewable energy and load uncertainty, first based on predicted worst-case scenario information obtains pre-dispatch optimization result, subsequently based on pre-dispatch information seeks worst-case scenario, ensure stable operation of system by iterative solution;Build and include integrated energy system, integrated energy system and photovoltaic production and sales between and photovoltaic production and sales between multilayer transaction framework, adopt Nash negotiation model and multistage robust method, realize the energy distribution between multiple stakeholders;By solving integrated optimization model, obtain the optimal strategy of integrated energy system low carbon operation.Thereby realized the coordination optimization of carbon emission right and renewable energy consumption, further improve the low carbon operation effect of system.

The application discloses a kind of federal positioning method based on individualized patch and dynamic fusion, belong to indoor positioning and federal learning technical field.When applying federal learning to indoor Wi-Fi fingerprint positioning of Internet of Things, global model is often difficult to capture local data feature preference, local training cost is high and poor adaptability alone, the present application is by setting individualized patch and dynamic parameter fusion mechanism, reduce the influence of above-mentioned problem on positioning performance.The algorithm configures private position decoder as individualized patch for each local client, extracts local data features in training and uploads to server, server calculates the similarity of data distribution between clients to generate combination weight, for local clients with similar data distribution, individualized patch can selectively linearly combine with the decoder of the local model with higher relevance, avoid local model overfitting, improve the positioning performance of global model in local scene.

This invention discloses a graphic calibration system and method for CNC mold machining. The system includes: a data acquisition module configured to receive CNC machining tasks and acquire graphic data and measurement data of the mold to be machined based on the machining tasks; a data calibration module configured to perform geometric feature calibration on the graphic data based on preset calibration data, generate calibrated calibrated graphic data, and spatially register the measurement data and calibrated graphic data to obtain deviation data; a data processing module configured to map the deviation data to a machining parameter library in the machining task, calculate the correction values ​​of each machining parameter to obtain calibration parameters; and a data feedback module configured to feed back the calibrated graphic data and / or calibration parameters to the system, and perform the remaining graphic data input and machining parameter input for the machining task. This invention significantly improves mold machining accuracy and production efficiency, providing a high-confidence graphic input benchmark for CNC mold machining.

The application discloses a Brillouin sensing device based on optical parallel differential pulse pairs and a working method, and belongs to the technical field of optical fiber sensing. In view of the problem that the prior art is difficult to consider high resolution, high precision and fast measurement, the device used by the application comprises: an optical source module, which is used to generate an optical carrier; a first optical path module, which is connected with the optical source module and is used to receive the optical carrier and generate optical parallel differential pulse pairs containing anti-Stokes and Stokes components and having different pulse lengths as pump light through double-frequency double-sideband pulse modulation; a second optical path module, which is connected with the optical source module and is used to generate double-sideband probe light; and a sensing optical fiber, which is connected with the first and second optical path modules respectively and is used to receive the pump light and the probe light and directly complete signal differentiation in the optical domain through stimulated Brillouin scattering. Thus, high resolution, high precision and fast measurement are synergistically optimized, the gain / loss mechanism can be flexibly switched, and the scene adaptability is enhanced.

The invention relates to the technical field of lithium ion batteries, and discloses a disulfide bond dynamic network binder for a silicon-based negative electrode plate, and a preparation method and application thereof, and the preparation method comprises the following steps: carrying out ring opening polymerization on lipoic acid and derivatives thereof, and reacting with a functional monomer containing carbon-carbon double bonds capable of free radical polymerization to obtain a disulfide bond dynamic network binder; the binder containing the disulfide bond dynamic network structure is obtained; the functional monomer is a cross-linking / film-forming monomer or a mixture of the cross-linking / film-forming monomer and a monomer carrying a functional side group; according to the binder disclosed by the invention, reversible network reconstruction is realized through disulfide bond exchange, high binding power and pulverization resistance are considered, continuity of a conductive / ion channel can be maintained, surface resistance of a pole piece is reduced, and cycling stability and rate capability are improved. No additional solvent is added in the whole process, the process is simple, cost is low, and the method is suitable for continuous large-scale production.

The invention provides an operation path planning method and system integrating environmental perception and occupational health, and the method specifically comprises the steps: obtaining a three-dimensional map of a farm, and building a harmful gas prediction model related to an exhaust state; taking a personnel starting and ending point as a root node, performing bias sampling on a low-concentration region according to the model, and respectively constructing a forward and backward random search tree, a path cost comprehensive geometric distance and a predicted average concentration; performing reconnection optimization on the double trees, and updating father nodes of lower-cost adjacent nodes; connecting the optimized dual trees to generate an initial path; accumulated harmful gas exposure of the path is calculated, the gas exposure is the integral of the product of the retention time and the concentration, and target operation parameters of all exhaust equipment along the line are determined accordingly; and updating the prediction model by using the new parameters, repeatedly executing path search and exhaust parameter optimization until the total cost change of adjacent iteration paths is smaller than a convergence threshold value, and outputting an operation path and exhaust cooperation scheme.

The embodiment of the invention provides a method and equipment for determining a power system configuration scheme and a medium. The method comprises the following steps: determining the initial inertia of a system; if the initial inertia of the system is smaller than the inertia demand value, the reactive response capability of each new energy node is calculated, and a first node is determined; if the first node is different from the previous configuration scheme, taking a new energy node of which the short-circuit ratio is smaller than a short-circuit ratio threshold value as a second node after simulating to carry out network construction type transformation on the first node; determining a current configuration scheme and a corresponding configuration cost; after the second node is simulated to be subjected to synchronous phase modifier configuration, the initial inertia of the system is determined again, and the step of judging whether the initial inertia of the system is larger than or equal to the inertia demand value or not is executed again; and if the first node is the same as the previous configuration scheme, selecting the scheme with the lowest configuration cost. According to the scheme, various configuration schemes of the new energy power system can be determined, the scheme with the lowest cost is selected as the target configuration scheme, and the economical efficiency of system transformation is guaranteed.

The application discloses a preparation method of a large-bore air-cooled diesel engine piston and specifically comprises the following steps: S1, modeling and dividing regions; S2, gradient printing; and S3, coating addition; and relates to the technical field of diesel engine pistons. The preparation method of the large-bore air-cooled diesel engine piston realizes the synergistic optimization of the overall performance of the piston through gradient printing of the piston, realizes the lightweight of the piston in cooperation with the honeycomb lattice structure, reduces the abrasion amount by using silicon carbide, improves the wear resistance of the top region, forms zirconium tri-aluminum nano precipitated phases by zirconium, refines the grains, forms a composite precipitated phase by using scandium, zirconium and aluminum, improves the high-temperature strength, constructs an efficient heat conduction network by using graphene nanosheets, improves the heat conductivity of the middle region, forms a three-dimensional network by using carbon nanotubes, improves the toughness of the skirt region, and has high interfacial bonding strength by spraying a wear-resistant and corrosion-resistant coating on the surface of the piston, reduces the friction, improves the service life of the piston and effectively reduces the maintenance cost.

The invention discloses a waveform feature extraction method and system for irregular waveforms of a hyperspectral laser radar, and aims at solving the problem that asymmetric waveforms, weak echoes and overlapped echoes in multi-channel full-waveform laser radar signals are difficult to accurately decompose, modeling full-waveform signals collected by multiple spectral channels into superposition of echo components and background noise. A multi-channel waveform is preprocessed through noise threshold and pulse width combined constraint, the waveform position is determined by adopting a gravity center discrimination method, and time synchronization calibration of each channel signal is realized in combination with a multi-echo quality index. The method further utilizes a cubic B-spline function to carry out modeling on transmitted waveforms of all spectrum channels, carries out weighted accumulation on multi-channel echo signals to enhance weak echo signals, and adopts a multi-channel interconnection decomposition optimization strategy to realize accurate extraction of irregular waveform features under the constraint condition of keeping time parameters of sub-echoes of all the channels consistent.

This utility model discloses a residual current operated protector with special wave function, relating to the field of residual current protection. It includes: a power supply unit for acquiring signals using a metering chip, detecting voltage information, smoothing the voltage, and outputting low-voltage DC power to achieve power management; a main control unit for acquiring current signals, identifying and analyzing the current signals, obtaining status detection results, and outputting an operation protection command based on the detection results; a trip coil control unit for executing a power-off protection operation according to the operation protection command output by the main control unit; and an LCD display unit for displaying the status information of the operated protector. This utility model achieves accurate detection and protection against special waves through high-frequency sampling, harmonic analysis, and a fast tripping mechanism, realizing the synergistic optimization of high-frequency signal processing and the metering chip, and overcoming the limitations of traditional protectors in harmonic scenarios.

Embodiments of the present disclosure provide a data processing method, a distributed system, an electronic device and a storage medium. The method is applied to a distributed system comprising a master node and a plurality of slave nodes, and comprises: the master node determining task information allocated to each slave node according to a hierarchical attribute of a target model and a computing power state of the plurality of slave nodes, wherein the task information is used to specify a model layer processed by the corresponding slave node; each slave node performing weight quantization processing of the corresponding model layer based on the allocated task information to obtain complete quantized weights of the target model. The method realizes full-link collaborative optimization from computing, memory to storage, significantly improves the execution efficiency of weight quantization tasks and the utilization rate of hardware resources, and provides technical support for efficient deployment of large-scale models.

The invention relates to the technical field of solid lasers, and particularly provides a narrow-linewidth tunable dual-wavelength laser output system based on data driving, which comprises a pumping source, a resonant cavity, a data acquisition unit and a data receiving and driving device, a single gain medium, two F-P etalon and an F-P angle regulator are arranged in the resonant cavity, the inclination angles of the two F-P etalon are adjusted through an F-P angle adjuster so as to perform longitudinal mode selection and wavelength tuning on the output laser of the resonant cavity; a neural network screening model is arranged in the data receiving and driving device, the neural network screening model is used for learning the mapping relation between intracavity parameters and output laser optical parameters, and dual-wavelength simultaneous locking and line width prediction are achieved through dual-etalon collaborative mode selection and data driving of the neural network screening model. According to the invention, the complexity of the system is greatly simplified, and dual-wavelength, narrow-linewidth, high-stability, repeatable and automatic laser output regulation and control which cannot be achieved by a traditional architecture are realized functionally.

The application discloses a manufacturing method, structure and use method of a greenhouse enclosure, and the manufacturing method comprises the following steps: providing a frame of a transparent hollow structure, the frame comprises oppositely arranged first and second substrates, and an air layer is formed between the first and second substrates; PAM hydrogel is arranged on the opposite inner sides of the first substrate, and PAM-LiCl hydrogel is arranged on the opposite inner sides of the second substrate to form a heat conduction unit; and a selective light-heat coating is coated on the opposite outer sides of the second substrate. Passive day and night two-way temperature regulation can be realized, and sustainable cyclic operation is realized.

The invention provides an electric vehicle power controller parameter adaptive setting method and system, and relates to the technical field of electric vehicle control, and the method comprises the steps: obtaining a motor torque feedback signal, carrying out the wavelet decomposition, extracting a working condition identifier, determining a phase margin and an amplitude margin offset based on Fourier transform, and obtaining a motor torque feedback signal; parameters are adjusted in combination with a gradient projection algorithm and a primal-dual interior point algorithm, and an optimal parameter vector is obtained through a multi-target particle swarm optimization algorithm. According to the invention, on-line adaptive adjustment of the control parameters is realized, and the power performance and the energy efficiency performance of the electric vehicle are improved.

This application discloses a method and system for access control of a hospital information system based on multi-factor authentication. The method includes: obtaining a user's access request to the hospital information system and determining the medical data type corresponding to the access request; constructing a set of candidate multi-factor authentication factors; obtaining the privacy intrusion attribute and security contribution attribute of each authentication factor; under the premise of conflict constraints between the privacy intrusion attribute and the security contribution attribute, constructing a partial order relationship between the authentication factors and eliminating authentication factors that are simultaneously dominated by other authentication factors in both the privacy intrusion attribute and the security contribution attribute, to generate a set of non-dominated authentication factors; selecting authentication factors from different attribute types from the set of non-dominated authentication factors to form an authentication factor combination; authenticating the access request based on the authentication factor combination, and controlling access permissions to the hospital information system according to the authentication result. This application achieves access control that balances privacy protection and system security.

The invention discloses a cool regenerated colored polyamide fiber and a preparation method thereof, and belongs to the technical field of chemical fiber preparation. The preparation method comprises the following steps: firstly, melting and spinning cool master batch, regenerated chinlon chips and other raw materials to prepare cool regenerated chinlon POY (Polyester Pre-Oriented Yarn) fibers; meanwhile, melt spinning is conducted on the color master batch, the regenerated chinlon slices and other raw materials, and stock solution coloring regenerated chinlon POY fibers are prepared; and finally, stranding the two functional POY fibers through a false twist texturing process to prepare the cool regenerated colored chinlon DTY fiber. By means of the step-by-step spinning and plying elasticizing integrated technology, the cool feeling, coloring and regeneration environment-friendly characteristics are combined in the fiber manufacturing stage, and the final product has the comprehensive advantages of being excellent in cool feeling durability, high in color fastness, simplified in production technology and environmentally friendly.

The application relates to the technical field of underwater robot vision processing, and discloses a GAN-based underwater robot image real-time repairing and target identification method. The method acquires an original image sequence obtained by an optical sensor of an underwater robot, extracts degradation features of each frame of image, including scattering noise distribution, color distortion parameters and motion blur intensity. The original image sequence is input into a network, and based on the degradation features, a repaired image is generated frame by frame, and local structure consistency features and global semantic coherence features of the repaired image are extracted. A cascaded classifier is used for multi-scale target detection of the repaired image, local structure features and global semantic features are combined, and a target boundary box and a category label are output. According to the spatial distribution density of the target boundary box, the priority of the repaired area is dynamically adjusted, and the synchronous output of the repaired result and the target identification result is realized. The method can effectively improve underwater image quality and target identification precision.

The application discloses a user-side source network load storage collaborative regulation method and system, takes net load, real-time electricity price and storage state of charge as input, realizes power distribution of distributed power supply, storage and power grid through four preset collaborative modes: when real-time electricity price is higher than high price threshold, storage discharges to cut peak; when real-time electricity price is lower than low price threshold, storage charges from power grid; when distributed power supply has surplus power, the surplus power is preferentially charged into storage; after storage is fully charged, surplus power is fed into power grid or abandoned. The application takes demand constraint as a priority, takes electricity meter electricity charge optimization as a secondary target, does not need load prediction, does not need to depend on external peak-valley time period table, and can be operated in a user-side local closed loop only by relying on real-time data, and simultaneously reduces user basic electricity charge and electricity meter electricity charge.

This invention provides a federated learning method based on prototype alignment and knowledge distillation, belonging to the field of distributed machine learning technology. It includes: a server initializing global model parameters and a global prototype set; each client performing local model training based on its own private dataset, extracting sample feature representations, and constructing local prototypes for each category of local samples; each client performing hierarchical prototype alignment during local training; each client calculating distribution offset based on the degree of offset between the local feature distribution and the global feature distribution; after completing local training, each client uploading the updated local model parameters and local prototypes to the server; the server aggregating the local model parameters uploaded by each client and aggregating the local prototypes uploaded by each client by category; and the server proceeding to the next round of federated training. This method can improve the training stability, classification accuracy, and communication efficiency of federated learning models in heterogeneous scenarios.

The application provides a storage management method and a vehicle, the method is applied to the field of data processing, and the method comprises the following steps: when it is detected that a remaining quota of a first application meets quota borrowing conditions, initiating a quota borrowing request to at least one second application to obtain a temporary quota for data writing; detecting whether a frequency at which the first application triggers quota limitation meets storage IO resource adjustment conditions; and when it is determined that the frequency meets the storage IO resource adjustment conditions, adjusting a storage IO resource configuration item corresponding to the first application. The application comprehensively borrows quotas flexibly and adjusts IO resources dynamically, realizes the coordinated optimization of storage resource allocation and IO performance, and guarantees the stability of the car machine device and the use experience of each application.

The application discloses a high-speed wire head and tail ear accurate control method and system, belongs to the metal material rolling technical field, and the method comprises the following steps: collecting the rolling piece head and tail position, real-time temperature and dynamic carbon equivalent, and constructing a process fingerprint vector; taking the vector as input, using the rolling piece size prediction model, adopting the model predictive control algorithm to roll to solve the optimal tension adjustment amount and the roll gap compensation amount; the optimization result is executed, and the dynamic air pressure of the air cooling line fan is calculated based on the dynamic carbon equivalent and the temperature; the model parameters are adjusted online according to the measured size deviation, and the successful process data is stored in the process block chain. The application realizes the high-precision collaborative control of the head and tail ear, improves the yield, energy efficiency and equipment life, and has the self-adaptive learning and data traceability ability.

A traffic control method and network system for service data backhaul, applied to a network system comprising N terminals and a gateway, wherein at least two terminals use different communication modes to perform service data backhaul operation with a server through the gateway, characterized in that the method obtains decision information of each time slot in a future preset period by time slot by time slot, and controls the data transmission operation of the terminal and the gateway based on the decision information, so that the weighted sum of the average transmission delay in the service data transmission process of each terminal and the average transmission delay in the service data forwarding process of the gateway is minimized in the entire preset period, effectively controlling the data queuing delay on the terminal side and the gateway side, and realizing efficient backhaul of data.

The application relates to an active cache routing method and system of a satellite content distribution network, the satellite content distribution network comprising at least one satellite node. The method comprises: extracting first semantic information features of distributed content and second semantic information features of historical popular content by using a deep learning model; calculating semantic similarity between the first semantic information features and the second semantic information features; calculating a satellite semantic cache priority value of a satellite node corresponding to the distributed content according to the semantic similarity; and planning an active cache routing path of the distributed content according to the satellite semantic cache priority value of each satellite node. The application can enable the satellite to automatically make a cache decision of the distributed content in the content distribution process, and improve the efficiency of active cache by combining content distribution and cache deployment.

The invention relates to the technical field of vehicle control, in particular to a torque self-adaptive control method, device and system based on the slip rate and an electric vehicle, the torque value at the end moment of a first mode is used as an integral term of closed-loop control in a second mode, smooth transition from the first mode to a rotating speed control mode is achieved, and the torque self-adaptive control method, device and system based on the slip rate are achieved. Torque step and whole vehicle jerking at the moment of mode switching are avoided; meanwhile, in the second mode, the target rotating speed enabling the slip rate to approach zero is determined according to the current vehicle speed, the motor output torque is adjusted based on the difference value between the target rotating speed and the actual rotating speed, tire slip in the starting stage is actively restrained, tire abrasion is effectively reduced, and the power battery energy utilization rate is increased; according to the method, driving smoothness and power responsiveness are both considered, and collaborative optimization of vehicle control and motor control is achieved.