116results about How to "Verify validity" patented technology

The invention discloses a short-term traffic flow prediction method based on a dynamic multi-dimensional global perception graph convolutional network, and belongs to the field of traffic flow prediction. The method comprises the following steps: acquiring an input dynamic graph feature sequence, and generating space-time element parameters; carrying out feature enhancement on the dynamic graph feature sequence, fusing the dynamic graph feature sequence with the space-time element parameters, and then respectively carrying out space-time feature coding; fusing the spatio-temporal characteristics obtained by coding the spatio-temporal characteristics; and carrying out space-time trend alignment decoding on the fused space-time features. According to the method, the defects of limited modeling precision, insufficient dynamic adaptability, incomplete spatial-temporal feature capture and the like in short-term traffic flow prediction are overcome, and the accuracy and the applicability of short-term traffic flow prediction are improved.

The invention relates to a BD-RIS-based auxiliary satellite RSMA system safety performance improvement method. The method comprises the following steps of: decomposing information to be transmitted into a public stream and a private stream by using an RSMA technology according to a cascade channel coefficient from a satellite to a legal user and an illegal node, and calculating a receiving signal-to-noise ratio of the two parties so as to obtain a public stream communication rate and a private stream communication rate of the legal user and an eavesdropping rate of an eavesdropper. And constructing a target function according to the rates, and establishing a BD-RIS reflection coefficient and satellite emission beamforming vector optimization model in combination with a preset loss function. And solving by adopting a double-circulation PDD algorithm, splitting the problem through a BCD method, processing non-convexity through SCA, firstly relaxing symmetric unitary constraint of a reflection coefficient matrix, and then projecting to obtain an optimized vector and matrix. By adopting the method to calculate the system security transmission rate, the security performance is enhanced.

The embodiment of the invention provides a simulation method and device for automatic deceleration of a spoiler of a medium-sized propeller-driven aircraft, computer equipment and a medium, and the method comprises the following steps: determining a use scene in which the spoiler of the aircraft needs to be automatically decelerated, according to the use scene of automatic deceleration, flight data needing to be introduced into the simulation system is determined, and the flight data comprises a ground signal, a speed signal, an engine power signal and a spoiler normal working signal; according to the flight data, judging whether an entry condition for starting to use an automatic deceleration function is met, and if yes, unfolding the spoilers on the two sides to the maximum through a simulation system to automatically decelerate the aircraft; and continuously monitoring the flight data until the quit condition of quitting the automatic deceleration function is met, quitting the automatic deceleration, and resetting the double-side spoilers through the simulation system. According to the scheme, a method for automatically decelerating the ground through the spoiler is designed, and the effectiveness of the scheme is verified through simulation.

The application discloses a method for designing and optimizing a VLCP system assisted by light reflection RIS under low signal-to-noise ratio, wherein the VLCP system comprises an intelligent wall adopting a smart surface RIS with mirror reflection, a transmitter and a user terminal equipped with a receiver with a liquid crystal smart surface, communication and positioning signals are integrated into the transmitter, the transmitter comprises an LED, and the method comprises the following steps: under low signal-to-noise ratio, taking asymptotic capacity as a communication metric constraint condition, taking a Cramer-Rao lower bound of positioning error as a positioning metric constraint condition, combining a positioning constraint satisfaction process based on a two-stage greedy algorithm and a communication performance maximization process based on a greedy strategy convex relaxation algorithm, optimizing elements of an RIS allocation matrix to maximize the asymptotic capacity, obtaining an optimal RIS allocation matrix, and completing the design and optimization of the VLCP system. In a low signal-to-noise ratio scene, the VLCP system of the application can significantly improve system performance by applying RIS.

This invention belongs to the field of solid oxide fuel cell control and optimization technology, and particularly to a dual-input model predictive control method for solid oxide fuel cells with carbon deposition constraints. It includes the following steps: S1: Constructing a DIR-SOFC controlled object model with 11 state variables; S2: Solving for the optimal control increment through sequential quadratic programming; S3: Constructing a constraint system; S4: Introducing an adaptive setpoint for the output voltage that adjusts linearly with the load current density. The model dimension of this invention is expanded from the existing 3 states to 11 states, fully covering six gas phase partial pressures, bilinear coupling of carbon accumulation and catalyst activity, and three-zone thermal dynamics. The ODE white-box prediction engine accurately reflects MSR nonlinearity and positive feedback of carbon deposition. Compared with ARMAX black-box prediction, the error is reduced from approximately 3% to less than 0.8%, with an average single-step calculation time of 133ms, meeting the real-time constraint of a 1s sampling time.

The application provides a large-diameter slurry shield safety stability multi-objective online optimization method and system based on GAT-LSTM-MOMPA, and belongs to the technical field of shield tunnel construction parameter multi-objective online optimization. The method comprises the following steps: obtaining monitoring data of shield construction parameters and preprocessing, and establishing an original sample set; adopting a method combining knowledge driving and correlation analysis, input parameters and output targets of a prediction model are determined; a space-time deep learning network framework GAT-LSTM is built, the mapping relationship between the input and the output is determined, and accurate prediction of the future time step optimization value is realized; through connection with MOMPA and TOPSIS, the historical value of the model is constantly updated, and accurate and effective real-time online optimization is achieved. The GAT-LSTM-MOMPA multi-objective online optimization model established by the application effectively improves the safety and stability of the shield machine operation.

The application discloses a kind of based on flow pattern recognition's deep sea mining multiphase flow particle delivery prediction and regulation method, the method includes: by acquisition equipment periodic acquisition pressure in pipeline, flow, bubble or droplet volume fraction, solid particle volume fraction in different positions;According to pipe diameter and the gas-liquid ratio obtained by calculation, mixed flow velocity, call pre-built flow pattern division algorithm, distinguish bubble flow, slug flow, vortex, annular flow;Solve the gas-liquid discrete unit volume fraction of any diameter in different flow patterns;According to the gas-liquid discrete phase volume fraction, obtain the delivery speed of solid particles in multiphase flow;Whether the solid particle delivery speed obtained is judged to reach the best interval, according to the result control multiphase flow in pipeline flow pattern or gas-liquid ratio, to adjust the delivery speed of solid particles.The present application can improve the efficiency of seabed mineral exploitation, and avoid the dangerous flow pattern formed by unreasonable gas-liquid distribution ratio, efficiency and safety are taken into account.

The invention discloses a full-direct-current offshore wind power system zero-voltage ride-through method, equipment and a medium, and relates to the field of full-direct-current offshore wind power. The objective of the invention is to solve the problem that a receiving-end converter station cannot transmit power when a land AC power grid breaks down. When the high-voltage direct-current voltage of the offshore direct-current transformer exceeds the first preset threshold value, the medium-voltage direct-current voltage control of the offshore direct-current transformer is switched to the high-voltage direct-current voltage control, so that the offshore direct-current transformer absorbs power from a high-voltage direct-current link; when the medium-voltage direct-current voltage of the fan direct-current transformer exceeds a second preset threshold value, the fan direct-current transformer is switched from low-voltage direct-current voltage control to medium-voltage direct-current voltage control, so that the fan direct-current transformer can absorb power from the low-voltage direct-current voltage side through a fan grid-side direct-current transformer; and when the low-voltage direct-current voltage of the offshore direct-current transformer exceeds a third preset threshold value, the brake resistor is activated to dissipate surplus power until the low-voltage direct-current voltage is lower than a fourth preset threshold value.

This invention relates to the field of multimodal sentiment analysis technology, and in particular to a method and system for multimodal sentiment analysis based on sequential translation under uncertain and missing modalities. The method includes: acquiring multimodal sentiment data, including visual, textual, and auditory modalities; constructing a multimodal sentiment analysis model based on sequential translation, firstly by extracting self-attention encoding features of each modality using a Transformer encoder; and then designing a bidirectional sequential translation architecture centered on the textual modality. This invention improves the utilization of textual modal features in the feature fusion process, thereby enhancing the quality of multimodal fusion features and providing strong support for subsequent complex sentiment analysis.

The invention discloses a multi-frequency-point interference suppression method of a low-cost array gyroscope. The multi-frequency-point interference suppression method comprises the following steps: establishing an interference noise model; carrying out multi-frequency-point interference suppression by adopting an interference suppression algorithm; establishing a constraint of the interference suppression algorithm; the input of the interference suppression algorithm is a signal with an interference noise frequency point, and the output of the interference suppression algorithm is mean value fused data which passes through the interference suppression algorithm of a plurality of frequency points and temperature compensation. Under the condition that the low-frequency interference noise is effectively suppressed, the expected performance improvement of the MEMS IMU array is realized, and the effectiveness of the interference suppression algorithm in the high-density MEMS IMU array is verified.

This invention belongs to the field of computer vision and image processing technology, and particularly relates to a query key-guided asymmetric feature enhancement system and method. The system includes: Q-ASFE modules embedded in the backbone network, specifically in the link where features are transmitted from the C3K2 module to the CBS module. The P2 and P3 layer feature maps output by the backbone network are processed by the neck network to output three feature fusion maps at different scales. These three feature fusion maps are then processed by a detection head to obtain the final detection result. This invention achieves efficient enhancement of image features through the organic integration of spatial detail awareness and global semantic guidance.

This invention relates to the field of low-altitude emergency response technology and discloses a method for joint scheduling optimization of heterogeneous equipment for low-altitude emergency response tasks. The method includes: obtaining a task set and a set of heterogeneous equipment for the low-altitude emergency response task; constructing a dual-objective joint scheduling model for the low-altitude emergency response task based on the task set and the heterogeneous equipment set; performing non-dominated sorting optimization on the dual-objective joint scheduling model to generate a Pareto optimal solution set for the low-altitude emergency response task; executing the scheduling scheme in the Pareto optimal solution set; and monitoring the heterogeneous equipment of the low-altitude emergency response task in real time based on a preset dynamic rescheduling mechanism to obtain triggering events for the heterogeneous equipment; dynamically rescheduling the scheduling scheme based on the triggering events to generate a scheduling update scheme for the low-altitude emergency response task. This invention can improve the overall success rate, resource utilization efficiency, and system robustness of low-altitude emergency response tasks.

The present application belongs to the technical field of residential area extraction, and discloses a large-range remote sensing image rural street-type residential area extraction method and system fusing target detection and visual attention mechanism, which comprises the following steps: generating a residential area candidate area detection frame on a large-range remote sensing image through a YOLOv7 model detection; and performing accurate extraction of the residential area based on the candidate area detection frame by using a PgNet model to obtain a rural residential area on the large-range remote sensing image. The present application combines the candidate mechanism of the target detection technology and the visual attention mechanism method, and roughly removes the vegetation and terrain, which is conducive to the rapid and automatic extraction of the rural residential area and the buildings thereof, and improves the search efficiency. After obtaining the candidate frame, the PgNet efficient extraction mechanism is used, so that the rural street-type residential area can be rapidly and automatically extracted in remote sensing images of different sizes.

This invention belongs to the technical field of aircraft manufacturing engineering and intelligent scheduling optimization. It discloses an aircraft final assembly scheduling method and equipment based on an improved genetic algorithm. The steps are as follows: (1) Construct a scheduling model based on process and worker data with the optimization objectives of minimizing the final assembly completion time and minimizing the number of workers involved; (2) Encode the process priority and worker skill slots obtained from the scheduling model using an improved genetic algorithm in the encoding stage, and use the encoding as chromosomes to form an initial population; (3) Perform event-driven decoding on the individuals in the initial population to obtain an initial scheduling scheme; (4) Calculate the fitness value of each chromosome according to the total completion time and the number of workers involved corresponding to the initial scheduling scheme, and screen all chromosomes according to the fitness value to obtain a new population, and then finally obtain the aircraft final assembly scheduling scheme. This invention realizes the collaborative optimization of completion time and the number of workers involved.

The application discloses a model-based mode switching combined control method for a shaft fan variable cycle engine. Firstly, a constant rotating speed control is carried out on a low-pressure output shaft of an external rotor, a low-pressure rotor rotating speed cascade controller is designed, and parameters of the cascade controller in the mode switching process are on-line set through a fuzzy theory. Secondly, through analysis of the relationship between the residual power of the low-pressure shaft and the rotating difference and the compressor surge margin in the mode switching process, a closed-loop controller for an internal connotation nozzle area is designed to compensate the output power of the low-pressure shaft and reduce the engine thrust; in order to inhibit the disturbance caused by the clutch engagement / disengagement and the pitch angle change in the mode switching process, a nozzle area pre-opening strategy and a pitch angle feedforward are introduced, and the surge margin of the low-pressure compressor in the mode switching process is improved through bleeding regulation. The application can provide a technical reference for the engineering development of a new generation of high-speed long-range helicopter power device in China.

The application provides a Chinese-oriented named entity recognition method and system, and belongs to the technical field of natural language processing; comprising: an encoding stage step, context encoding of input Chinese text; a feature extraction stage, a double-branch structure of global semantic branch and local boundary branch in parallel is adopted, wherein the local boundary branch carries out boundary feature enhancement through multi-granularity convolution combined with a learnable Gaussian Laplacian operator; a feature fusion and prediction stage step, global and local features are fused through a gating mechanism, and multi-label classification prediction is carried out based on a preset six-tuple grid label system; a decoding stage step, named entities are obtained through bidirectional search strategy decoding according to predicted relationship probability; through boundary perception feature extraction and enhanced label system, the recognition accuracy and recall rate of nested entities and discontinuous entities in Chinese text are significantly improved.

This invention belongs to the field of wheat breeding, specifically relating to a wheat Fusarium head blight resistance locus and molecular breeding method. This invention identified five stable Fusarium head blight resistance-associated loci, Qfhb.jaas-2D, Qfhb.jaas-3A, Qfhb.jaas-3B.1, Qfhb.jaas-5A.2, and Qfhb.jaas-6D, on chromosomes 2A, 2D, 3A, 5A, and 6D. For the identified resistance loci, this invention designed KASP molecular markers. These markers have high specificity and sensitivity, enabling accurate and rapid detection of resistance genotypes in wheat materials, providing a powerful tool for marker-assisted selection (MAS). This invention verified the effectiveness of the screened resistance loci. The results showed that the above five loci were significantly associated with Fusarium head blight resistance, providing a reliable basis for the breeding of resistant varieties.

This invention discloses a method and system for fault diagnosis of industrial equipment under complementary label constraints, belonging to the field of industrial intelligent operation and maintenance and fault prediction health management technology. The method includes: collecting and preprocessing equipment vibration signals; constructing a dual-head deep network based on one-dimensional residuals as a complementary label learning model; generating a joint probabilistic loss function using complementary labels based on maximum likelihood estimation, enabling the model to learn fault features from "negative" labels; and achieving high-precision fault classification using the trained model. This invention applies the complementary label learning paradigm in weakly supervised learning to industrial equipment fault diagnosis, achieving a diagnostic accuracy of over 90% on multiple standard bearing datasets such as CWRU and MFPT. This method significantly reduces the data labeling threshold and effectively solves the pain points of scarce fault samples and difficult labeling in industrial scenarios, possessing significant practical application value.

The invention relates to the technical field of computer programs, in particular to an experiment processing method and device, computer equipment, a storage medium and a product. The method comprises the following steps: generating an experiment operation flow meeting experiment requirements according to the experiment requirements fed back by a user; verifying the validity of the experimental operation process; under the condition that the validity verification of the experiment operation process is passed, the experiment operation process is executed, and the experiment result corresponding to the experiment demand is obtained, the dependence on artificial experience in the experiment processing process is reduced, the determination efficiency of the experiment operation process is improved, and then the experiment result is obtained under the condition that the validity verification of the experiment operation process is passed. And the experiment operation process is executed to obtain the experiment result corresponding to the experiment requirement, so that the experiment operation process can be smoothly carried out.

The invention discloses a deepwater structure health monitoring method and system. The method comprises the following steps: establishing a discrete model and constructing a unit strain matrix; collecting single-side surface strain data and establishing a mapping relation between single-side surface theoretical strain and node displacement; constructing a unilateral inverse finite element data fitting item comprising a surface strain fitting item and a lateral shear strain penalty item; constructing a geometric guidance hybrid regularization term consisting of a membrane regularization term and a bending regularization term; assembling a global equation to solve full-field displacement; and recovering full-field strain and stress and outputting a monitoring result. The system comprises a data acquisition module, a preprocessing and inverse reconstruction module, a stabilization solving module and a visual output module. According to the full-field response sensing scheme based on unilateral inverse finite element and geometric guidance mixed regularization, stable and high-precision inversion of full-field displacement, strain and stress of the structure can be realized under the condition of only depending on unilateral sparse sensing data, and the dependence of a traditional method on bilateral sensing arrangement is effectively broken through.

The invention provides an airfoil flow field sensor optimization arrangement method and system, and the method comprises the steps: obtaining the surface flow field parameter data of an airfoil under different working conditions, and constructing a data matrix; complementing the data matrix by using the matrix complementing model; and searching and optimizing the arrangement position of the sensor by taking the completion error of the matrix completion model as an optimization target to obtain an optimal sensor arrangement scheme. The method further comprises the steps that the reconstructed complete pressure distribution is compared with actual measurement data or historical simulation data of the partial redundant sensors, and if errors exceed a preset threshold value, re-optimization of the optimal sensor arrangement scheme is triggered. According to the method, the data detected by the sensor is recovered and extracted by using the matrix completion algorithm, so as to obtain information as much as possible; the genetic algorithm is used for optimizing the configuration position of the sensor so as to maximize the information carried by the sensor detection data; the problems of sensor efficient configuration and data reconstruction in flow field detection are solved.

Based on the improved grey wolf optimization algorithm, the PMU optimal configuration method for distribution network is proposed. Under the condition of system full observability, the PMU optimal configuration mathematical model is established. Considering the influence of zero injection node and the system observability under single PMU interruption and single line outage, the constraint conditions of the established PMU optimal configuration mathematical model are modified. On the basis of grey wolf optimization algorithm, the population initialization, nonlinear convergence factor and position update weight coefficient are improved. The improved grey wolf optimization algorithm is used to solve the PMU configuration model under different scenarios. The method builds the optimal configuration model under different scenarios to achieve the goal of system full observability, and uses the improved grey wolf optimization algorithm to solve the PMU optimal configuration scheme. Compared with other algorithms, the method has better optimization effect.

The invention belongs to the technical field of embedded systems and real-time control, and relates to a high-precision modular event timing system based on a microTCA.4 standard and a Zynq SoC architecture, which is suitable for scenes with high requirements on time synchronization precision and system stability, such as industrial control, and adopts a Zynq SoC timing main logic AMC board card conforming to the microTCA.4 standard and a ZYNQ-7045 SOC industrial chip; a Zynq SoC-based heterogeneous firmware module is provided, a PL end supports nanosecond event timing precision, and a PS end is in high-speed communication with the PL end through an AXI bus; a point-to-point signal real-time transmission and sharing mechanism based on mLVDS is provided; a standard backboard signal output board card and a plurality of types of high-speed photoelectric conversion terminal matching board cards are provided; according to the invention, the advantages of the FPGA are fully exerted, the core function is realized, a high-precision, flexible and extensible system is constructed, and the performance of an event timing system is improved.

The invention discloses an enterprise human resource management evaluation method and system. The method comprises the following steps: acquiring, analyzing and evaluating management process data to obtain an index evaluation value of each preset monitoring index; determining a weight corresponding to each preset monitoring index, and performing calculation based on the weight and the index evaluation value to obtain an initial evaluation value of enterprise human resource management; obtaining management result data of the enterprise for analysis, and determining result parameter data related to enterprise human resource management; evaluating the enterprise human resource management result based on the result parameter data to obtain a management result evaluation value, and determining a correction coefficient based on the management result evaluation value; and correcting the initial evaluation value based on the correction coefficient to obtain a comprehensive management evaluation value. According to the invention, the transformation of human resource management from an empirical type and qualitative type to a data type and quantitative type is realized, an intelligent evaluation system capable of self-calibration and continuous optimization is established, and powerful technical support is provided for enterprises to improve the human resource management level.

The invention discloses an event camera video reconstruction method and system based on active aperture modulation, and the method comprises the steps: introducing an aperture modulation strategy for the first time, and reconstructing an initial frame with good quality by periodically adjusting the opening and closing of an aperture and actively triggering a dense global event signal; the problem of low background reconstruction quality caused by sparse events in a static region in the prior art is solved, and good prior information is provided for subsequent dynamic scene reconstruction. By constructing a forward-reverse bidirectional network, rich intensity information is provided for a static scene, and the problems of background disappearance and error accumulation in long-time operation in a traditional method are effectively solved. And meanwhile, high-time-resolution capture of a dynamic region is kept, and high-fidelity and high-dynamic-range video reconstruction is realized.

The invention discloses a control method for realizing bus current fluctuation suppression by a switched reluctance linear motor for gravity energy storage. The method is based on a hyper-local model predictive control algorithm, a cost function in the algorithm is adjusted, and the switching state of an overlapping region is modulated according to the current flow directions of different asymmetric half-bridge arms corresponding to different switching states. On the basis, the current ratio of the predicted electromagnetic force is introduced to redistribute the reference electromagnetic force so as to reduce the current difference of the conduction phase of the overlapping region. Experiments verify that the method can effectively suppress bus current fluctuation and improve the operation efficiency of the switched reluctance linear motor for gravity energy storage.

This invention relates to the field of power equipment safety monitoring technology, and addresses the problems of existing technologies such as reliance on physical tags, insufficient accuracy in key point positioning, unstable depth estimation, and difficulty in end-side deployment. The invention constructs a database of key points and depth estimation in substations, designs a lightweight key point detector, and achieves accurate positioning of key points in the risky areas of workers and energized parts of equipment. A stereo perception module outputs dense depth information, mapping two-dimensional key points to three-dimensional space, calculating the minimum safe distance between human key points and energized parts, and combining threshold values ​​to achieve graded early warning and visualization. This invention eliminates the need for additional physical tags, achieving significant improvements in detection accuracy, real-time performance, lightweight deployment, and engineering reliability through key point detection and stereo vision collaborative perception. It effectively solves the problems of insufficient accuracy and difficult deployment in safety distance monitoring in complex substation scenarios.

This invention belongs to the field of image processing and segmentation technology, specifically relating to a method for segmenting corrosion defects on the exterior of nuclear power plant equipment based on an attention mechanism and edge detection operators. The method includes the following steps: acquiring exterior images of key nuclear power plant equipment; performing semantic-level annotation on the acquired images to construct a nuclear power equipment corrosion segmentation dataset; using the TransUnett model as the basic network framework and obtaining pre-trained weights from the dataset; obtaining an improved TransUnet model; obtaining the final weight file suitable for nuclear power equipment corrosion defect segmentation; inputting the image of the nuclear power equipment to be detected into the trained model, which simultaneously outputs a semantic segmentation mask and edge response results; and generating the final corrosion defect segmentation result through post-processing. The beneficial effect is that it can simultaneously utilize a combination of semantic segmentation and edge detection to achieve defect segmentation, solving the shortcomings of existing model structures in edge blurring and inaccurate segmentation under complex environments.

This invention relates to a method for energy consumption modeling and process parameter optimization in laser cleaning processes, belonging to the field of advanced manufacturing and automation technology. The method includes the following steps: establishing an energy consumption model for the laser cleaning process based on energy consumption analysis of the laser system, robot system, cooling system, and dust removal system; building a real-time power monitoring platform to obtain experimental parameters of the energy consumption model; establishing an energy-oriented laser cleaning process parameter optimization model considering energy efficiency, surface roughness, and surface oxygen content; solving the model using an improved lion herd optimization algorithm; and providing case studies. This invention is simple and practical, and fully considers energy consumption and cleaning quality during modeling, providing excellent support for energy consumption optimization in laser cleaning processes.

The application discloses a kind of personalized federated learning method and system based on multi-dimensional adaptive aggregation and local dynamic representation alignment, the method in federated learning process, first by multi-dimensional adaptive aggregation mechanism, comprehensive historical local characteristics, round attenuation and model structure characteristics, to build personalized training starting point for client;Subsequently in the local training process, introduce local dynamic representation alignment mechanism, dynamically update class prototype and build contrast loss, to enhance feature discrimination ability;Further, the importance of each layer parameter of the model is evaluated by parameter importance fair quantization mechanism, and a layer importance mask is generated to guide the next round of refined aggregation.Based on the above technical solutions, the application effectively alleviates the personalized and generalization imbalance problem under the condition of data heterogeneity, while improving the model accuracy, effectively reducing the communication round and communication overhead.