103results about How to "Strong scalability" patented technology

The invention relates to a quantum key management method and system for satellite internet biological characteristics, and the method comprises the following steps: carrying out the registration of biological characteristics and the initialization of communication protocol parameters for a user terminal which is accessed for the first time; for the registered user terminal, before initiating communication each time, binding the real-time biological characteristics with the quantum key, and carrying out encrypted transmission of the session key by using the bound quantum key, so as to carry out encrypted transmission of satellite internet transmission data by using the session key; and in the encryption transmission process of the satellite internet transmission data, according to a preset period, performing satellite link security level adjustment and quantum key dynamic updating. According to the invention, identity authentication and terminal authentication in a high-delay and narrow-bandwidth application scene can be realized, the security problems of identity counterfeiting, key leakage and the like are solved, and the method can be applied to satellite internet scenes such as emergency communication, field operation and the like, and has the advantages of strong compatibility, low deployment cost and the like.

The invention discloses a test method for a high-load heating tube for a mold, and belongs to the technical field of single-head electric heating tube detection. The method specifically comprises the following steps: S1, historical data acquisition: acquiring cold-state electrical performance data of single-head electric heating tubes with different specifications and under different working conditions; s2, historical data preprocessing: performing preprocessing including data cleaning, data normalization and data standardization on historical data to obtain a standardized data set; the test process design covers a full-period closed loop of cold state detection, hot state leakage current test, reliability test, secondary hot state leakage current test and secondary cold state detection, the electrical safety (leakage current control) under a high-load working condition is verified through the hot state test, and the reliability of an actual use scene is simulated through a rapid on-off test. Meanwhile, the appearance, temperature, gas release and other states of the heating pipe are monitored in real time, abnormal testing is terminated in time, safety risks are avoided, and it is ensured that the detection result can comprehensively reflect the actual use performance of the heating pipe.

The invention discloses a communication monitoring method and a communication system. The system comprises a monitoring module, a feature extraction module, a dynamic identification module, a behavior analysis module, an adaptive optimization module and a visual interface module. The monitoring module collects communication data flow through a mirror image port, the feature extraction module extracts packet length, time interval, direction sequence and encryption features by using a multi-dimensional feature fusion algorithm, and the dynamic recognition module realizes multi-protocol type recognition based on an improved self-attention convolutional neural network. The behavior analysis module constructs a communication relation graph and performs abnormal communication tracing; and the adaptive optimization module realizes model self-learning and parameter optimization through reinforcement learning. According to the method, through sliding window sampling, self-correlation analysis, entropy detection and GNN map modeling, accurate identification and behavior restoration of implicit traffic in a complex network environment are realized. According to the method, high-precision identification can be kept under the conditions of multi-protocol mixing and encrypted communication, and the characteristics of intelligence, self-adaption and expandability are achieved.

The invention relates to the technical field of Internet of Things communication, discloses an Internet of Things multi-Android client state synchronization method, and solves the problems of poor state consistency, high back-end dependence, large delay and insufficient compatibility in the prior art. According to the scheme, the method comprises the steps that after a client completes initialization, MQTT connection with a rear end is established, an MQTT state message pushed by the rear end is monitored, after the message is received, equipment state information is obtained through analysis, local state cache and a UI interface are updated, meanwhile, the client serves as a distribution node, a UDP synchronization message containing the equipment state and the IP of the distribution node is established, and the UDP synchronization message is sent to the client. The UDP synchronization message is broadcasted to the local area network, other clients in the local area network receive the UDP synchronization message and then verify whether the distribution node IP is the client in the local area network, if the verification is passed, the message is analyzed, the state updating timestamp and the local cache timestamp are compared, and the local state cache and the UI interface are updated only when the state updating timestamp is larger.

The invention provides a parameterization calculation method for a top cover modal and a pitch diameter of a water turbine, and belongs to the technical field of hydraulic mechanical structure dynamics and simulation automation. The method comprises the steps of parameter input, parametric modeling, automatic simulation control, pitch diameter identification and analysis and result output: after a parameter set is input, constructing a parametric modeling module based on Python and ANSYS Spaceclaim, and automatically generating a three-dimensional geometric model containing a basic panel, a rib plate and a connecting hole; an automatic simulation control module is constructed based on Python and ANSYS Workbench, and full-process automatic simulation is completed to obtain a modal analysis result; pitch diameter characteristic parameters are automatically recognized through a least square discrete Fourier transform algorithm of non-equiangular sampling and confidence degree judgment; and outputting results to form a standardized database. According to the method, automation and quantification of the whole process are achieved, modeling and simulation efficiency is improved, manual misjudgment is avoided, the method is suitable for different types of water turbine top covers, and reliable data support is provided for structural optimization and resonance risk assessment.

This invention relates to the field of automated warehousing technology and discloses an automated storage and retrieval system (AS / RS) based on a heavy-duty stacking robot. A material storage area is located on one side of the material entry / exit area, and a preparation and charging area is located on the other side. An import / export docking area is located inside the material entry / exit area, and a lifting and transferring machine is also included within the import / export docking area. An automated storage and retrieval system with a three-dimensional racking mechanism is located inside the material storage area. This AS / RS based on a heavy-duty stacking robot, through its modular architecture and intelligent collaborative design, effectively solves the problems of low space utilization, high cost, and poor flexibility in traditional AS / RS. It achieves precise and efficient handling of heavy-duty goods, eliminates the need for dedicated tracks, reduces modification costs, and significantly improves the overall automation level, space utilization, and operational stability of warehousing operations. It caters to both new construction and renovation needs, providing enterprises with a low-cost, highly adaptable heavy-duty warehousing solution.

The application discloses a shield tunnel excavation disturbance data intelligent identification method based on dynamic time sequence prediction, which comprises the following steps: in the case of excavation disturbance, collecting various time sequence data of shield tunnel monitoring in real time, carrying out denoising, interpolation and normalization processing on original data, and inputting time window samples into a DFCM algorithm; calculating a membership matrix based on the distance between samples and cluster centers, and updating the membership in real time, and constructing an LSTM model of the membership vector output by the DFCM clustering algorithm; predicting the membership of the next time step, outputting a predicted difference vector, and determining a difference dynamic threshold value by using an adaptive threshold method, and when the predicted difference exceeds the threshold value, it is judged that the data is disturbed. The method can realize real-time identification and uncertainty quantification of disturbed data in a tunnel structure monitoring environment with strong interference and high noise, improve the accuracy and stability of underground structure health monitoring, and is suitable for urban shield tunnel operation monitoring and maintenance.

The application discloses an intelligent internet unmanned aerial vehicle (UAV) box and an automatic docking and locking control system thereof, and belongs to the technical field of UAV cargo hoisting. The system comprises a suspension connection unit, an intelligent cargo box unit, a positioning unit, a mechanical lock unit and an intelligent control unit. When the suspension unit and the positioning unit are magnetically connected, the intelligent control unit determines the locking or releasing mode through the number of signal contact connections; in the locking mode, a power-on timing control module supplies a first direction current to an electromagnetic adsorption module, sequentially drives a lock mechanism to close, and realizes mechanical locking; in the releasing mode, a reverse current is supplied, the lock mechanism is sequentially driven to open, and automatic unloading is realized. The system realizes full-process automation of UAV hoisting, has a high-reliability double-locking mechanism, effectively prevents aerial misrelease, is integrated into a smart internet network, and significantly improves the safety, efficiency and informatization level of hoisting operation.

This invention belongs to the field of copper alloy material design and preparation technology, and discloses a new process for efficiently designing high-performance copper-iron alloys. Based on the 5% Fe content vertical cross-sectional characteristics of the Cu-Fe-Si ternary phase diagram, the Si content is designed in stages (0-4%) to achieve targeted performance optimization. Combined with a three-stage heat treatment process of "homogenization-basic aging-gradient aging", a closed-loop system of "phase diagram design-preparation-detection-optimization" is constructed. Through precise composition control by EPMA and real-time feedback of mechanical properties, the problems of blind composition design and insufficient precipitation of strengthening phases in traditional processes are solved. The resulting alloy has excellent tensile strength, yield strength, and total elongation at break, with synergistic improvement in mechanical and magnetic properties, and a design efficiency improvement of more than 40%, making it suitable for electronic, mechanical and other fields.

The invention discloses a blood pressure and heart function parameter monitoring method, device and equipment and a readable storage medium, and relates to the technical field of blood pressure and heart function parameter monitoring. Comprising the steps of collecting physiological signals of a target user according to a preset collection frequency; extracting a feature parameter set of the target user from the physiological signal; and finally, inputting the characteristic parameter set into a pre-trained blood pressure and cardiac function parameter calculation model, and outputting the blood pressure and cardiac function parameters of the target user. According to the method, the limitations that the measurement precision of a traditional single signal is easily interfered, instantaneous fluctuation cannot be captured by intermittent measurement, and the cardiac function cannot be synchronously evaluated are overcome, and the accuracy, continuity and comprehensiveness of monitoring are remarkably improved. Meanwhile, a calculation model based on multi-dimensional data driving has high adaptability and expansion potential, and a reliable technical basis is provided for developing a personalized and wearable cardiovascular health monitoring system.

The invention discloses a three-dimensional packaging chip system with a built-in interconnection test function and a detection method thereof, and the system comprises a packaging semiconductor main body which is internally provided with a plurality of wiring layers, a testability design (DFT) circuit which is integrated in a chip, and an internal multi-channel selection switch network which is controlled by the DFT circuit and is arranged on each wiring layer. And at least two detection pins / pads which are arranged outside the package and have a small number and increased pin spacing. According to the method, the DFT circuit is used for controlling switching of the switching network, different internal interconnection paths are multiplexed to the detection point in a time division mode for electrical measurement, and accurate positioning of fault nodes is achieved by analyzing multiple sets of measurement results. According to the method, the problem that the needle card is difficult to test due to warping of high-density packaging is effectively solved, hierarchical and nodal accurate diagnosis of internal interconnection faults of three-dimensional packaging is realized for the first time, the test cost and complexity are remarkably reduced, and a hardware basis is provided for online function repair.

The invention discloses an API path aggregation identification method and device and a storage medium, and relates to the technical field of electronic digital data processing, and the method comprises the steps: carrying out the preliminary aggregation identification of API path data based on the path structure features of the API path data; if the preliminary aggregation identification is not successful, performing deep aggregation identification on the API path data based on word segmentation features and / or statistical features of the API path data; and generating an aggregation identification result of the API path data. According to the method and the device, the API path aggregation identification accuracy is improved.

This invention relates to the field of solid-state lithium metal battery technology, specifically to a lithium deposition redirection hybrid conductor layer, its preparation method, and a lithium metal battery. The hybrid conductor layer is disposed between a sulfide solid electrolyte layer and a negative electrode current collector, comprising an oxidized carbon-based conductive framework and an alloyed deposition acceptor component formed in situ by coordination and anchoring of oxygen-containing functional groups on the surface of the oxidized carbon-based conductive framework. The hybrid conductor layer forms an electron-damping lithium-ion exchange region near the sulfide solid electrolyte layer and an alloyed deposition acceptor region near the negative electrode current collector, allowing lithium metal to preferentially deposit on the side near the negative electrode current collector during charging. This reduces the risk of side dendrite growth, micro-short circuits, and physically irreversible lithium generation in the sulfide solid electrolyte, thereby improving the coulombic efficiency and cycle stability of the solid-state lithium metal battery.

This invention discloses a short-window gamma-gamma turbulence parameter prediction method for space-to-ground laser communication, belonging to the technical field of space-to-ground laser communication and atmospheric turbulence channel parameter prediction. This method addresses the problems of traditional methods, such as strong dependence on long observation windows, significant degradation in prediction accuracy under short-window scenarios, and insufficient robustness under low signal-to-noise ratio conditions. It establishes a short-window observation model for the space-to-ground optical link and a gamma-gamma channel statistical model, constructs time-dependent short-window training data, and designs a short-window gamma-gamma network. Temporal features are extracted through a convolutional backbone, and a scintillation exponential physical regularization auxiliary head is used to achieve joint parameter prediction under physical constraints, outputting predicted gamma-gamma distributed parameters. This method can achieve high accuracy and good stability in parameter prediction under short observation windows and low signal-to-noise ratio conditions, and can be used for turbulence channel state characterization at the space-to-ground laser communication receiver.

The application discloses a kind of general task scheduling framework systems and methods supporting dynamic arrangement, belong to computer software system architecture technical field, including framework engine core, field adaptation layer and functional plug-in;The framework engine core is the general scheduling execution engine irrelevant to specific business field, including resource abstract module, task abstract module, dynamic arrangement and execution engine and plug-in warehouse and manager;The resource abstract module defines uniform resource description interface, and converts original resource data into resource image.The application can dynamically retrieve, load plug-in and assemble into exclusive plug-in workflow according to the unique needs of individual task from plug-in warehouse during scheduling operation by dynamic arrangement mechanism, realize the "thousand people thousand faces" and "one task one strategy" of scheduling strategy, so that scheduling strategy can dynamically adapt to task demand, completely solve the defect that static strategy cannot match individualized needs of task.

The invention relates to the technical field of wind power generation, in particular to an anomaly monitoring method and system for a wind power generation yaw system based on deep learning. The method comprises the steps that multi-mode operation data of a yaw system of the wind driven generator are acquired and preprocessed, and an initial data set is obtained; extracting features for effectively distinguishing the normal state and the abnormal state of the yaw system from the initial data set, and comparing the features to obtain a target data set; dividing the target data set into a training set, a verification set and a test set; the training set is input into a pre-trained Transform model for model training, and a yaw system anomaly monitoring model is obtained; and inputting the verification set and the test set into a yaw system anomaly monitoring model for model evaluation, and obtaining a yaw system anomaly monitoring result. According to the anomaly monitoring method, the multi-mode operation data are fused and input into the yaw system anomaly monitoring model, the anomaly condition of the yaw system can be accurately captured, and the model has high generalization ability and expandability.

This application relates to a joint estimation method for wireless communication delay and frequency offset based on hill-climbing fine search. The method includes: setting a time offset search range, a frequency offset search range, a coarse search step size, and a fine search step size based on prior information; generating a two-dimensional grid point set within the search range according to the coarse search step size; calculating the cost value of each grid point based on a cost function; taking the time offset and frequency offset corresponding to the maximum value as the coarse estimate; using the coarse estimate as the initial point, performing fine search iterations using hill-climbing; calculating the cost value of multiple candidate points in the neighborhood in each iteration, comparing and determining the update direction, and updating the current time offset and / or frequency offset; repeating the iteration until a preset exit condition is met, and outputting the final estimate. This application effectively solves the parameter coupling problem of traditional step-by-step estimation in NTN scenarios, while balancing search range and computational complexity, facilitating engineering implementation, and being applicable to various NTN systems and terminal forms.

The invention discloses a low-altitude flight collision detection method based on conical ray detection. Acquiring environmental space data and position and attitude information of the unmanned aerial vehicle in a three-dimensional space; the flight direction of the unmanned aerial vehicle is obtained according to attitude information processing of the unmanned aerial vehicle in the three-dimensional space, the position of the unmanned aerial vehicle serves as a conical vertex, the flight direction serves as a central axis, and a conical detection area is constructed in combination with the field angle of the unmanned aerial vehicle and a preset detection distance; discretizing the conical detection area to generate a plurality of sub-rays covering the conical detection area; detecting a spatial relationship between each sub-ray and a spatial obstacle according to the environmental spatial data to obtain spatial relationship information of each sub-ray; and adjusting the attitude information of the unmanned aerial vehicle according to the spatial relationship information of each sub-ray. A conical ray detection model is introduced, and compared with traditional single-straight-line ray detection, missing detection and false detection are reduced.

This invention relates to the field of brine purification technology in the salt production industry, and discloses a silicon carbide flat-sheet membrane brine purification system and process for the salt production industry. The invention uses a modular silicon carbide flat-sheet membrane filtration unit as its core, combined with a minimalist pretreatment feeding unit. Through the modular collaborative design of an open-top membrane tank shell at atmospheric pressure, a detachable stackable membrane tower, a negative pressure water production component, an array-type aeration component, and a refined brine backwashing component, it achieves negative pressure suction filtration with the membrane tower completely submerged in the crude brine, directly treating crude brine containing calcium and magnesium precipitates without the need for pre-filtering and circulation equipment. The modular design of each component in this system allows for easy assembly and disassembly, high filtration accuracy, low energy consumption, strong anti-fouling properties, and stable production of calcium and magnesium precipitates. 2+ +Mg 2+ The refined brine with ≤1ppm and SS≤1ppm has significant advantages over traditional ceramic membrane systems in terms of equipment investment, operating costs, membrane life and industrial adaptability. It can be widely used in brine refining in the salt industry and brine refining in chlor-alkali systems.

The application discloses a terminal consumption behavior verification method and system based on vacancy state recognition and adaptive verification, acquires a verification request submitted by a terminal user; the verification request comprises a package image and a package unique identification code; the state of the package is judged by using the package image; the state of the package comprises a vacancy state of content exhaustion and a non-vacancy state of content non-exhaustion; if the state of the package is the vacancy state, terminal consumption behavior data records responding to the package unique identification code are generated. The application improves the reliability and confidence of consumption verification.

The utility model relates to a gesture recognition light control system, which comprises a high-definition camera, an embedded artificial intelligence experiment box and a lamp panel suite, the high-definition camera and the lamp panel suite are respectively connected with the embedded artificial intelligence experiment box; the embedded artificial intelligence experiment box comprises a power supply, an MIPS processor, a neural network acceleration rod, a radiator, a mouse, a keyboard and a display, wherein the MIPS processor is electrically connected with the power supply, the neural network acceleration rod, the radiator, the mouse, the keyboard and the display. Gesture images are collected through a high-definition camera, gesture recognition is carried out through a trained neural network model in an embedded artificial intelligence experiment box, gestures can be accurately recognized, the lamp panel suite is controlled to be turned on, and real-time control is achieved. According to the utility model, image acquisition and image identification are completed through local equipment, calculation can be carried out without the help of a network or a cloud server, the privacy of a user can be better protected, and the intelligent use experience of the user is greatly improved.

The invention relates to the technical field of farmland improvement, in particular to a farmland improvement zoning method and system, and the method comprises the following steps: 1, zoning factor selection and weight calculation; 2, processing partition basic data; 3, partition factor calculation and statistics; 4, performing partition data standardization calculation; and 5, constructing an accurate zoning model for farmland improvement. According to the method, farmland improvement suitability zoning can be accurately carried out, and a scientific and reasonable basis is provided for farmland improvement project planning and design.

This invention discloses a trusted auditing method for the operation and maintenance of an points mall based on a blockchain distributed ledger, comprising the following steps: Step 1: Monitoring the operation event flow in the points mall, collecting points operation behavior data, and parsing the event types; Step 2: Using an improved SHA-256 hash algorithm to form a points operation behavior evidence record; Step 3: Constructing an operation behavior graph based on the points operation behavior evidence record; Step 4: Performing numerical processing to generate behavior representation vectors; Step 5: Performing feature analysis, extracting explicit risk factors, and forming a risk score; Step 6: Generating interpretable audit records for operation paths exceeding a preset risk threshold, and performing digest compression; Step 7: Writing the structured audit evidence data into the blockchain ledger and performing manual review. This invention integrates on-chain evidence storage and behavior scoring mechanisms to achieve trusted auditing and risk control supervision throughout the entire points operation process.

The present application relates to the technical field of Internet of Vehicles communication and calculation, in particular to a remote area Internet of Vehicles task offloading method and device based on high altitude platform assistance, which comprises the following steps: constructing a vehicle connection judgment model based on a high altitude platform and an edge server, obtaining the position of a vehicle, judging whether the vehicle is connected to the edge server or the high altitude platform or whether the vehicle is not connected to the edge server or the high altitude platform; constructing a communication model, a time delay model and an energy consumption model, calculating the offloading transmission rate, the total time delay of the task and the total energy consumption of the task according to the communication model, the time delay model and the energy consumption model and the connection judgment result; constructing an optimization objective function according to the time delay model and the energy consumption model; modeling the task offloading problem as a Markov decision process and training it by using a MAPPO algorithm to output a task offloading strategy, thereby providing a solution to the problem of limited task offloading and high time delay of the remote area Internet of Vehicles.

The invention belongs to the technical field of gas separation membranes, and particularly relates to a molecular simulation-based screening method of a coordination regulation mixed matrix membrane. The method is combined with a molecular simulation technology and is used for quantitatively evaluating the interface compatibility of the mixed matrix membrane and reasonably screening and designing a coordination active additive so as to synergistically optimize the interface structure and the gas separation performance. According to the method disclosed by the invention, a new method for accurate prediction and rational design from a microscopic molecular structure to macroscopic membrane performance is realized, and the method can be used for guiding efficient development of high-performance MMMs.

The application relates to a lithium niobate / lithium tantalate thin film polarization method for accurate control of a period domain reversal duty cycle, which comprises the following steps: S1, providing a lithium niobate / lithium tantalate thin film as a polarization substrate; S2, defining an asymmetric interdigital electrode pattern on the surface of the polarization substrate; S3, forming a metal layer on the polarization substrate after step S2 through metal deposition, and then forming an asymmetric interdigital electrode through a stripping process; and S4, applying a high-voltage pulse sequence on the asymmetric interdigital electrode to drive the ferroelectric domains in the lithium niobate / lithium tantalate thin film to be periodically reversed. According to the method, the electric field distribution is geometrically preset controlled through the asymmetric interdigital electrode structure, the ferroelectric domain reversal region is mainly defined by the electrode structure, and thus the polarization result is less dependent on the accurate adjustment of the voltage amplitude and pulse parameters.

The invention discloses a shield tunnel excavation disturbance data intelligent identification method based on dynamic time sequence prediction, and the method comprises the steps: collecting various time sequence data of shield tunnel monitoring in real time under the condition of excavation disturbance, carrying out the denoising, interpolation and normalization processing of original data, and inputting a time window sample into a DFCM algorithm; calculating a membership matrix based on the distance between the sample and the cluster center, updating the membership in real time, and constructing an LSTM model of a membership vector output based on a DFCM clustering algorithm; and predicting the membership degree of the next time step, outputting a prediction difference vector, determining a difference dynamic threshold value by adopting an adaptive threshold value method, and when the prediction difference value exceeds the threshold value, judging the disturbance data as the disturbance data. According to the method, real-time identification and uncertainty quantification of disturbance data can be realized in a strong-interference and high-noise tunnel structure monitoring environment, the accuracy and stability of underground structure health monitoring are improved, and the method is suitable for operation monitoring and maintenance of urban shield tunnels.

The invention discloses a power transmission line alarm prediction method based on remote sensing image interpretation and power business, and the method comprises the steps: firstly carrying out the unsupervised classification of all types of ground features in a remote sensing image through a remote sensing image interpretation algorithm in combination with sample information, extracting edge information, and standardizing the edge information into a JSON file; combining tower coordinate information in a power transmission service ledger by using a drop point statistical algorithm, and resolving an actual distance between the classified ground feature and the tower; and finally, in combination with scene perception data and a distance early warning guide rule, judging a ground feature threat level and generating early warning push. According to remote sensing image classification, a K-means algorithm optimized by an elbow algorithm is adopted, the distance measurement precision is improved through precise coordinate calculation and drop point matching, and integration with a monitoring platform is supported to achieve multi-scene thematic early warning. According to the invention, the limitation of traditional manual inspection is broken through, the large-range, dynamic and non-contact intelligent monitoring of the power transmission line and channel environment is realized, and the threat identification accuracy and the emergency response efficiency are remarkably improved.

This invention discloses a method for predicting the maneuvering motions of hydrogen fuel cell-powered ships based on multi-source data fusion, relating to the field of intelligent ship navigation. The method collects geodetic coordinate system position and heading data from the BeiDou Navigation Satellite System and ship hull coordinate system acceleration data from the Inertial Measurement Unit (IMU). After differential and integral preprocessing, a neural network model is built. A fusion-type loss function is constructed based on the transformation relationship between the ship hull coordinate system and the geodetic coordinate system. The model is trained using a backpropagation algorithm and can also be extended to fuse data from multiple sensors such as GPS. This invention fully leverages the complementary advantages of multiple sensor sources, effectively compensating for the inherent deficiencies of single-sensor data, significantly improving the accuracy and robustness of ship maneuvering motion prediction, and providing reliable technical support for intelligent navigation decision-making and safety assurance of hydrogen fuel cell-powered ships.