5630 results about "SMARAD" patented technology

The invention relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention belongs to the technical field of coupling alignment, and particularly relates to an intelligent auxiliary system for coupling alignment based on dual-laser displacement and an MEMS gyroscope, which adopts a mode of cooperative work of an active stable attitude measurement module and a space reference beacon module to establish a dynamic stable measurement basis which is not influenced by barring operation and environmental disturbance. The micro attitude change of the measuring device can be sensed and actively compensated in real time in the measuring process, it is ensured that laser displacement data at all angles are collected in the same absolute space coordinate system, and the high consistency and accuracy of multi-point measurement data are ensured. And then fusing the attitude feedback signal and the attitude reference signal by using an intelligent resolving module to generate an attitude control instruction, and processing displacement measurement data acquired by a double-laser displacement sensor after the attitude is stable to resolve a centering deviation parameter, thereby being beneficial to improving the efficiency, precision and automation level of coupling alignment work.

The invention particularly relates to a leakage current testing method, device and system for an intelligent circuit breaker, and relates to the technical field of electrical safety detection. A composite signal generation and injection module; a broadband signal acquisition and conditioning module; and a line fault positioning and impedance analysis module. According to the invention, the composite signal generation module realizes the non-interference isolation injection of a leakage current signal and a high-frequency positioning signal, and guarantees the high fidelity of data through adaptive filtering, multi-channel synchronous sampling and high-precision timing. The line positioning module integrates TDR and a frequency domain impedance method, and through multi-frequency point deviation correction and topology-GIS association, the fault type is accurately identified, and the test and troubleshooting period is significantly shortened.

The invention discloses a multi-modal attack identification method fusing BMama and difference to guide trans-attention, which comprises the following steps: simulating a false data injection attack, a denial of service attack, an address resolution protocol spoofing attack and a domain name system spoofing attack, collecting physical layer sensor data and network layer flow data, and preprocessing multi-modal data; bMama is constructed to perform dynamic time modeling on multi-modal data, a graph neural network is combined to adversariate a variational auto-encoder, features of a power grid system topology and a communication topology structure are fused, and robustness of potential representation is enhanced through adversarial training; the method comprises the following steps of: guiding feature complementary fusion by using modal difference through a difference guide iteration cross-attention fusion mechanism, improving the capability of distinguishing complex attacks, finally carrying out attack detection and classification on fused modals, and executing end-to-end optimization according to a weighted combination of loss of each part. The method can effectively detect and classify the multi-modal attack in the smart power grid, and enhances the safety and reliability of a complex system.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

The invention relates to the technical field of data compression, in particular to an intelligent data compression method and system based on multi-protocol heterogeneous equipment interconnection. The method comprises the following steps: when it is detected that a new heterogeneous device applies to join a network, carrying out communication protocol real-time analysis and protocol conversion adaptation to obtain a heterogeneous device adaptation protocol; carrying out multi-device data stream receiving according to a heterogeneous device adaptation protocol, and generating data streams of a plurality of time windows; adaptive coding and multi-level compression are carried out on the data streams of the multiple time windows, and a multi-device adaptive compression mechanism is constructed; identifying compression load characteristics of each heterogeneous device, and performing dynamic task allocation to obtain an intelligent task allocation strategy; and driving real-time data intelligent compression operation based on an intelligent task allocation strategy and a multi-device adaptive compression mechanism. According to the invention, efficient and adaptive data compression of different devices is realized, and the data transmission efficiency and quality are improved.

The invention discloses an open pit coal mine slope monitoring and early warning method and system based on multi-modal satellite fusion AI, and belongs to the technical field of geological disaster monitoring. The method comprises the following steps: S1, collecting multi-source data; s2, transmitting data in real time; s3, data preprocessing and space-time registration; s4, multi-band InSAR phase reconstruction and deformation field extraction are carried out; s5, deeply fusing the multi-source data; and S6, AI intelligent prediction and grading early warning. According to the open pit coal mine slope monitoring and early warning method and system based on multi-modal satellite fusion AI, slope global millimeter-level precision continuous deformation field monitoring is achieved, vegetation terrain shielding interference is reduced, the monitoring range is expanded, meanwhile, monitoring blind areas are eliminated, data processing efficiency is improved, a high-reliability deformation field is constructed, and the early warning error and missing report rate is reduced.

The invention relates to the technical field of high temperature sensing and data fusion, in particular to a method for array thermocouple multi-mode compensation and DIC stress field space-time coupling fusion, which comprises the following steps: step 1, in a thermotechnical signal intelligent processing and compensation unit, completing hardware and algorithm collaborative design of an electronic cold junction compensation module; 2, constructing a multi-algorithm fusion compensation module, integrating nonlinear correction, drift compensation and interference suppression functions, and accurately coping with various interference signals through a dynamic weighting strategy; step 3, adopting a sub-pixel-level matching algorithm and a homography matrix calibration technology to realize high-precision space alignment of the temperature and stress measurement units; and establishing a nonlinear incidence relation between the temperature and the stress based on an improved Gaussian process regression model. According to the invention, based on collaborative design of the thermotechnical signal intelligent processing and compensation unit and the DIC vision and temperature data conjoint analysis module, the core precision problem of temperature and stress detection in a high-temperature environment is solved through hardware optimization and algorithm innovation.

The present disclosure relates to a communication scheme and a system for combining an IoT technology with a 5G communication system for supporting a higher data transfer rate than a 4G system. The present disclosure may be applied to intelligent services (e.g., smart homes, smart buildings, smart cities, smart cars or connected cars, health care, digital education, retail business, and security- and safety-related services) on the basis of a 5G communication technology and an IoT-related technology. The present disclosure proposes a method and an apparatus for beam failure recovery. An embodiment of the present disclosure provides a method of a terminal in a wireless communication system. The method of the terminal comprises the steps of: obtaining information on at least one reference signal for beam failure detection; identifying whether a beam failure is detected for each of a first reference signal set and a second reference signal set included in the at least one reference signal; and if a beam failure is detected for at least one of the first reference signal set and the second reference signal set, performing a beam failure recovery procedure for the reference signal set in which the beam failure is detected, wherein the first reference signal set is related to a first control resource set (CORESET) pool, and the second reference signal set is related to a second CORESET pool.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

The invention belongs to the technical field of intelligent engineering management, and discloses an automatic monitoring and control method of an engineering management system based on the Internet of Things. According to the method, a four-layer Internet of Things architecture comprising a sensing layer, a network layer, a platform layer and an application layer is constructed, and the four-layer Internet of Things architecture is uploaded to a cloud platform through a 5G / narrowband Internet of Things dual-mode transmission channel. A digital twinborn technology is innovatively adopted to construct a three-dimensional visual engineering model, intelligent identification and prediction of abnormal working conditions are realized through a machine learning algorithm, and a multi-stage linkage control mechanism is established. And when construction deviation or equipment failure is detected, the system automatically generates an optimization control strategy and issues the optimization control strategy to the execution terminal, so that accurate regulation and control of the construction machinery and intelligent pushing of early warning information are realized. According to the method, a manual inspection mode of traditional engineering management is broken through, full-process automatic supervision is realized through data fusion analysis and closed-loop control, the construction quality supervision precision is effectively improved by more than 30%, the safety accident rate is reduced by 50%, and the engineering management efficiency is remarkably improved.

The invention discloses a submarine cable insulation performance online monitoring and fault positioning method, a medium and equipment. The method comprises the following steps: S1, synchronously acquiring multi-source sensing data in real time; s2, multi-modal noise cooperative suppression and feature extraction; s3, insulation state degradation evaluation based on multi-feature fusion; s4, double-end traveling wave accurate fault positioning is carried out; and S5, carrying out data visualization and alarm linkage. According to the invention, marine complex environmental noise interference can be effectively suppressed, key parameters representing the insulation state of the submarine cable are monitored in real time in a multi-dimensional manner, and early warning of insulation degradation and preliminary judgment of a fault mode are realized through an intelligent fusion evaluation model. And the improved traveling wave accurate capture and wave velocity dynamic correction technology is utilized to realize accurate positioning of a fault point, so that the accuracy and sensitivity of submarine cable state monitoring and the fault positioning precision can be remarkably improved, and powerful technical support is provided for safe and economical operation of submarine cables.

The invention relates to the technical field of task scheduling management, in particular to an intelligent scheduling management method for water conservancy and hydropower engineering detection tasks, which comprises the following steps of: acquiring active power data of a unit, smoothing, differentially generating a rate and an acceleration sequence to construct a load characteristic matrix, comparing the load characteristic matrix with a steady-state threshold to generate a quasi-steady-state interval, and calculating a quasi-steady-state interval; calculating a load instruction deviation to generate a steady-state confirmation identifier, calculating a predicted steady-state window based on the identifier, if the window is longer than the minimum sampling duration, generating a trigger acquisition signal, starting vibration acquisition to generate an original waveform, and uploading the original waveform. According to the method, the load characteristic matrix is constructed through power data differential operation, load instruction deviation dual verification is combined to recognize the steady-state time period, the steady-state window length is pre-judged, and collection is triggered when the requirement is met, so that noise interference introduced by working condition fluctuation is effectively avoided, and it is ensured that original vibration waveform data originates from a stable working condition; and the data sample purity and the fault analysis value are obviously improved.

The invention discloses a deep and far sea wind power foundation platform and load integrated system under extreme environment conditions, and belongs to the technical field of offshore wind power platforms. The system comprises a basic platform body, a multi-dimensional monitoring subsystem, an environment-load coupling analysis subsystem, an intelligent regulation and control execution subsystem, a safety early warning decision subsystem and a shore-based collaborative management subsystem. Through synchronous acquisition of extreme marine environment, basic platform structure load and operation state data, distributed multi-type sensor layout and high-frequency sampling, data acquisition comprehensiveness and timeliness are realized, monitoring data are fused, a coupling model is constructed, 24-hour load change is predicted, and the real-time performance of the system is improved. According to the method, the attitude of the platform is dynamically adjusted, bearing configuration is optimized, emergency protection is started, the limitation of traditional separation analysis and fixed regulation and control is broken through, and the risk disposal and operation and maintenance efficiency is improved through risk quantification and grading early warning, remote monitoring and intelligent scheduling of a shore-based collaborative management subsystem and four-level early warning accurate matching coping strategies.

The invention discloses an optical cable micro-disturbance identification method based on physical simulation and self-supervised time sequence decoupling, and relates to the technical field of optical cable identification, and the method comprises the steps: constructing a physical digital twin simulator, and generating a high-fidelity training set; constructing a deep learning model, wherein the deep learning model adopts a lightweight time sequence decoupling network; training the model by adopting a staged training strategy, and sequentially carrying out self-supervised noise distribution pre-training, simulation supervised training and spectral domain physical consistency fine tuning operation; inputting DAS time sequence data collected in real time into the trained model, and outputting the data as an optical cable identity ID and a physical position; the lightweight time sequence decoupling network comprises a physical guide preprocessing module, a lightweight U-Net separation module, a sparse gating module and an intelligent parallel decoding module. Through the technical means of simulation-driven data generation, staged training strategies and the like, the defects of the prior art in the aspects of reducing the data cost, improving the detection capability in a low SNR environment, realizing multi-source blind source separation and the like are overcome.

The invention discloses a Beidou time service synchronous multi-source data fusion monitoring device, system and method, and aims to solve the core problems of low meteorological factor-ground surface settlement causal correlation analysis precision and poor early warning reliability caused by multi-source data time asynchronization due to sensor clock independence. The device directly drives each sensor in the multi-source data acquisition module to perform physical-level synchronous acquisition through a hardware synchronous trigger pulse generated by the Beidou time service module, and ensures that settlement, weather and positioning data have the same timestamp at the source; and time axis calibration and dynamic weight fusion based on real-time data quality are carried out through the preprocessing module. The system integrates the devices, a cloud analysis model and an early warning engine. According to the invention, a closed loop from hardware-level synchronous acquisition, space-time alignment to intelligent analysis is realized, a multi-source data synchronization error is reduced from a second level to a millisecond level, and the accuracy and timeliness of water and soil conservation monitoring are remarkably improved.

The invention discloses a safe operation system and a safe operation method of a space-ground integrated rescue vehicle, which are used for the rescue vehicle working in a complex disaster environment and ensure stable and reliable two-way communication connection between the rescue vehicle and a command center by establishing an intelligent switching mechanism of a low-orbit satellite main link and a ground network standby link. Real-time transmission of key instructions and field information is guaranteed, and a foundation is laid for safe operation. Advanced risk early warning is achieved through a space-ground cooperation mechanism, dynamic decision making and path planning are carried out by integrating satellite information and local sensor data, and the global adaptive capacity of the rescue vehicle to the complex disaster environment is improved. Satellite pre-sensing information is combined with a local decision, and a pre-control instruction is generated to act on a drive-by-wire chassis, so that the rescue vehicle can smoothly deal with hidden or developmental risks in advance, and the driving safety and stability are improved.

The invention discloses a new energy broadband test analysis management and control method and system, and relates to the technical field of new energy detection and intelligent management and control, and the method comprises the following steps: obtaining a multi-window splicing frequency spectrum of a load switching edge, superposing phase lock consistency verification in a splicing frequency spectrum result, generating a frequency spectrum leakage suspicion chart, and carrying out the boundary constraint for energy track extraction; under the constraint of a spectrum leakage suspicion chart, an energy center track, a group delay curve and a phase derivative sequence are extracted, and a cross-frequency migration fingerprint is constructed and is used as a basic evidence for misjudgment traceability. By means of spectrum suspicion identification, feature fusion modeling, misjudgment traceability, label correction, energy reverse intervention and the like, accurate identification and closed-loop regulation and control of false operation caused by spectrum leakage are achieved, high identification accuracy, low misjudgment rate, high anti-interference performance and quick response capacity are achieved, and the operation stability and safety of a new energy field station are remarkably improved.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention relates to the technical field of low-altitude equipment, in particular to a low-altitude resource intelligent scheduling method and system based on deep learning, and the method comprises the steps: collecting the real-time state and network load data of a low-altitude flight equipment group, and constructing a dynamic operation data set; generating an operation mode feature set through multi-dimensional airspace situation awareness and analysis, and performing sparse clustering division based on the feature set to form a network resource demand priority mapping table; traversing the mapping table to dynamically calculate the resource demand, determining a multi-dimensional weight coefficient, and performing high-dimensional feature dimension reduction and optimization through a mixed integer nonlinear programming solver to obtain a resource demand feature vector; constructing a resource scheduling strategy optimization model by adopting a deep reinforcement learning algorithm based on the vector; inputting real-time data into the model to execute a resource scheduling decision, and outputting a dynamic allocation strategy; simulation deduction and compliance verification are carried out on the strategy in the digital twin simulation platform, and cooperative intelligent scheduling of communication, calculation and spectrum resources is achieved.

The invention discloses an intelligent path planning method for an intelligent medicine delivery robot based on UWB and laser radar, and the method comprises the steps: carrying out the tight coupling fusion of UWB absolute coordinates, laser radar scanning matching data and odometer / IMU track plotting data through a filter, and obtaining a real-time high-precision pose without an accumulative error; and then constructing and maintaining a semantic grid map aligned with the UWB coordinate system. In the path planning stage, a hierarchical strategy is adopted, a task target is analyzed based on a semantic map to plan a static optimal path, and a TEB algorithm is adopted to carry out space-time joint optimization to realize dynamic obstacle avoidance; and performing global re-planning when the path is seriously deviated or the prediction is continuously blocked. According to the invention, the absolute positioning robustness of UWB and the fine environment perception capability of the laser radar are fused, and the hierarchical planning architecture of global guidance and local optimization is combined, so that the drug delivery robot can efficiently, reliably and safely complete autonomous navigation and drug delivery tasks in a complex and dynamic indoor environment.

The invention belongs to the technical field of hydropower station monitoring and early warning, and particularly relates to a hydropower station intelligent inspection and fault early warning linkage method, which comprises the following steps: arranging a redundant sensor network at key equipment parts, synchronously acquiring pressure, flow, vibration and water turbidity data, establishing a flow-pressure loss reference relationship, and calculating real-time pressure-flow ratio deviation; performing spectral analysis on the vibration signals, extracting characteristic frequency band energy, performing collaborative study and judgment in combination with a turbidity change trend, and ensuring data credibility through data cross validation of main and auxiliary sensors; performing time sequence difference operation on the pressure-flow ratio deviation to obtain a change rate, fusing vibration-turbidity abnormal performance, and comprehensively diagnosing water diversion blockage or foreign matter invasion; early warning levels are divided according to the deviation and the change rate, corresponding response measures are triggered, response actions are executed according to a preset risk priority sequence, and linkage control of the whole process from state sensing, anomaly recognition, fault diagnosis to intelligent response is achieved.

The invention discloses a heat supply pipe network operation fault diagnosis method based on digital twinning, and the method comprises the following steps: S1, constructing a digital twinning architecture of a heat supply system, and constructing a complete digital twinning system based on a physical entity, a virtual model, a data synchronization layer and a decision control layer; s2, multi-source data fusion and state mapping; s3, a fault prediction and diagnosis mechanism is adopted, an SVM-LSTM joint model is adopted to carry out time sequence anomaly detection and fault prediction, an XGBoost classifier is optimized in combination with an improved sparrow optimization algorithm, and a classification result is output; and S4, performing self-healing control and cooperative regulation and control. According to the invention, a complete digital twin architecture is constructed, through a five-layer system of a physical entity, a sensing and execution network, a data synchronization layer, a virtual model layer and a decision control layer, a full-link closed loop from data acquisition, model driving to intelligent decision is realized, and systematic support is provided for fault diagnosis and self-healing control.

The invention relates to an intelligent scheduling method and device for communication network resources, equipment and a medium. The method comprises the following steps: performing IP packet header analysis on a service data packet to obtain an initial service priority identifier, and performing adjustment in combination with a preset priority rule, an equipment alarm state and a network congestion rate to generate a dynamic service label; based on the dynamic service label, the real-time link state and the energy consumption coefficient, generating a resource allocation strategy and a path scoring weight parameter through a preset deep reinforcement learning network; and calculating a path score based on the resource allocation strategy and the path score weight parameter, generating a path mapping table, generating a port suppression instruction based on the dynamic service label and the resource allocation strategy, and extracting a base station power regulation instruction. According to the method, the dynamic adaptability, the resource utilization rate and the energy consumption management and control capability of communication network resource scheduling are improved by means of dynamically adjusting the service priority, comprehensively modeling a resource allocation strategy and the like, and the transmission guarantee level of high-priority services is enhanced.

The invention belongs to the technical field of intelligent logistics and storage automation, and particularly relates to a seeding wall scheduling method and system.The closed-loop intelligent scheduling of perceptual prediction decision correction is achieved by constructing a real-time synchronous three-dimensional digital twinning environment, and the method comprises the steps that a wave task is decoupled into an atomic unit; dynamically and preferentially allocating the AGVs based on a multi-factor cost function fusing the driving distance, the network load and the order emergency degree; advanced simulation is carried out in a digital twin environment, time overlapping conflicts of path nodes and queuing conflicts of seeding wall entrances are accurately predicted, and a differentiation strategy is adopted for resolution; meanwhile, on the basis of real-time communication, track and order abnormity detection results, an AGV path is corrected in real time. According to the method, the transformation from passive response to active prediction and from local optimization to global coordination is realized, and the AGV cluster operation efficiency, the response speed and the scheduling robustness are remarkably improved.

The invention relates to a computing power resource dynamic scheduling method, device and equipment based on deep reinforcement learning and a medium thereof, and the method comprises the steps: constructing a joint state vector through real-time fusion of a network layer channel state and computing layer node load data, and driving a strategy network to generate transmission parameters and resource allocation actions of cooperative control; the code modulation parameters of the wireless transmission module and the computing resource proportion of the target node are synchronously configured in the execution layer, and dynamic task scheduling in the channel decay environment is achieved; a multi-target reward mechanism is designed to couple transmission bit error rate penalty, resource utilization efficiency and task timeliness evaluation indexes, and a reinforcement learning agent is guided to balance communication stability and computing power demand conflicts; according to the method, strategy network parameters are optimized through time difference errors, closed-loop feedback is formed in combination with channel state prediction and node load updating, the problems of network and calculation layer splitting decision, insufficient dynamic adaptability and multi-target optimization imbalance in the prior art are effectively solved, and the task scheduling success rate in the time-varying wireless environment is improved.

The invention discloses a scraper conveyor middle trough production line operation state monitoring method based on digital twinning, and belongs to the technical field of industrial intelligent manufacturing. The method comprises the following steps: constructing digital twin bodies in one-to-one correspondence with physical production line elements; establishing a real-time data driving channel between the digital twin and the physical elements; based on the channel, driving the digital twin to synchronously map the real-time operation parameters, and generating a virtual operation state of the production line; based on the virtual operation state, performing time sequence deduction on the processing flow in the digital twin according to the processing technology logic and the equipment performance parameters to obtain a production line pre-estimation state of a future time point; and comparing the parameters in the pre-estimated state with a preset threshold range to generate prediction information. According to the invention, real-time data driving and bidirectional interaction of the physical production line and the virtual model are realized, the problems of data isolation and feedback lag of a traditional monitoring mode are overcome, the prediction capability is provided, and the operation reliability and intelligent management of the production line are effectively improved.

The invention discloses an open architecture system for an underwater acoustic signal processor, and belongs to the technical field of underwater acoustic signal processors. The system comprises a hardware resource pool, a standardized algorithm container group, a visual processing link construction module, a containerized deployment and scheduling platform and a data processing and communication plane. According to the system, special hardware resources are pooled, an underwater sound processing algorithm is packaged into a standardized container, a user is allowed to arrange a processing flow in a graphical mode, an intelligent scheduling platform dynamically deploys the algorithm container to matched hardware for execution according to real-time resource conditions, and meanwhile, an efficient and reliable data communication channel is provided. According to the method, the problems that a traditional system is closed, poor in expansibility and low in resource utilization rate are solved, elastic sharing of hardware resources, agile deployment of an algorithm and flexible construction of a flow are achieved, and openness, flexibility and overall efficiency of an underwater acoustic signal processing system are remarkably improved.