10653results about How to "Reduce complexity" patented technology

The invention relates to the technical field of electrical safety, in particular to an electrical operation risk assessment and early warning system based on big data analysis, which comprises a data acquisition module, a data fusion module, a feature extraction module, a risk assessment module, a real-time monitoring and early warning module, a historical data storage and analysis module and a visual monitoring module. Multi-source data are collected in real time by deploying multiple sensors, risk assessment is carried out through Kalman filtering and support vector regression algorithm fusion processing, feature variable extraction and combination of a support vector machine and a random forest algorithm, real-time monitoring and risk level judgment of an electrical working environment are achieved, long-term risk trend prediction is supported, and the risk assessment accuracy is improved. The visual monitoring module provides a basis for optimizing alarm rules and decision support, and displays an evaluation result, early warning information and an analysis result through a visual interface; the system has the advantages of being accurate in data processing, efficient in risk assessment, timely in early warning and the like, and can be widely applied to risk monitoring and safety management of electrical operation.

The invention relates to the technical field of network time delay scheduling, and discloses a deterministic network time delay scheduling method and device based on a TSN and a storage medium. The method comprises the following steps: carrying out graph theory analysis on a TSN network topology structure to obtain a network node connection relationship and link bandwidth information; extracting time delay requirements and bandwidth requirement characteristics of the data streams, and generating a data stream priority queue and a resource requirement index matrix; inputting a deep Q network, establishing a state-action mapping relation including a network load state and a time slice distribution action, and generating an initial time delay scheduling strategy model; calculating queuing time delay, transmission time delay and processing time delay of each data stream, and generating a time slice resource redistribution scheme; generating a global resource allocation matrix based on the time slice resource reallocation scheme; and parameter feedback updating is carried out on the initial time delay scheduling strategy model to obtain a target time delay scheduling strategy model, and the utilization efficiency of network resources is remarkably improved through bandwidth borrowing and time slice reallocation.

The invention relates to a temperature control method, device and equipment of semiconductor equipment and a storage medium. The method comprises the following steps: performing finite element mesh division and modal coordinate transformation on a plurality of temperature control areas of semiconductor equipment to obtain a temperature control area dynamic model; collecting measurement data of a temperature sensor and performing time-frequency domain transformation to generate a temperature prediction data sequence; gaussian mixture modeling is carried out to obtain prior distribution, and temperature parameter probability distribution is obtained; performing sub-domain division on the temperature control range to obtain a temperature inter-domain feature mapping matrix; and a temperature interval thermal coupling coefficient matrix is calculated based on the temperature control area dynamic model, a temperature control objective function is constructed in combination with the temperature prediction data sequence and the temperature inter-domain feature mapping matrix, and a temperature control quantity sequence is solved by adopting a model prediction control algorithm. According to the invention, the accuracy and dynamic response speed of temperature control are improved, and the stability of temperature control and the energy utilization efficiency are ensured.

The invention provides a natural language SQL (Structured Query Language) conversion method based on a data platform and a large language model, which relates to the technical field of data processing and comprises the following steps of: performing vectorization storage on metadata, blood relationship, indexes and historical query information, performing intention judgment and vector matching on a user query request, and determining a query condition based on a matching result. An association path is constructed according to data consanguinity, and an optimal SQL statement is generated by utilizing a historical query optimization table connection sequence and combining index matching. According to the method, intelligent conversion from the natural language to the efficient SQL can be realized, the query efficiency is improved, and the use threshold of a user is reduced.

The invention discloses a multi-modal document structured processing and knowledge extraction method based on a large language model. The method comprises the following steps: S1, receiving an input multi-modal document; s2, performing preprocessing on the text data in the multi-mode document; s3, performing feature extraction on non-text data in the multi-mode document; s4, performing multi-modal data fusion on the preprocessed text data and the non-text data after feature extraction; s5, performing deep semantic analysis on the fused multi-modal data through a pre-trained improved BERT model, wherein the deep semantic analysis comprises key entity recognition, relation extraction and event detection; s6, based on a deep semantic analysis result, automatically constructing a knowledge graph by using the extracted information; and S7, outputting the data of the knowledge graph in a format for analysis or application. According to the method and the system, structured processing and knowledge extraction of unstructured and multi-modal documents are realized.

The invention relates to the technical field of raw material management systems, in particular to a raw material management system for building engineering construction. The management system comprises a raw material information management module, a purchase management module, an inventory management module, a use management module, an optimization allocation module, a raw material quality evaluation module, an environmental impact evaluation and material selection module, a supply chain transparency module, a life cycle management module and a raw material demand prediction and inventory optimization module. An automatic purchase and supplier collaborative optimization module; the system uniformly manages data information of links of raw material purchasing, inventory, use and the like through a centralized data platform, forms a complete material management chain, avoids information dispersion and lag, realizes real-time sharing of data of each link, improves transparency of management and accuracy of decision making, and meanwhile, greatly improves management efficiency. The complexity and error rate of manual operation can be remarkably reduced through automatic purchase order generation, intelligent inventory management and supplier collaboration.

The invention provides an intelligent power distribution cabinet safety early warning method, system and device and a storage medium, and relates to the field of electric power. The method comprises the steps of obtaining multi-source heterogeneous operation parameters of the intelligent power distribution cabinet, and performing feature extraction on the multi-source heterogeneous operation parameters to obtain operation feature parameters; performing parameter fusion on the operation characteristic parameters to obtain fused characteristic parameters, and inputting the fused characteristic parameters into a preset fault early warning model to obtain fault early warning data; and inputting the fault early warning data into a preset rule matching engine for rule matching to obtain a matching result, and generating an early warning signal according to the matching result. According to the method, the accuracy and timeliness of safety early warning of the intelligent power distribution cabinet are improved.

The invention discloses a cloth defect detection method based on multi-scale feature fusion and a diffusion pyramid network. The method comprises the following steps: S1, constructing a defective cloth image data set; s2, building a deep learning network model which comprises a backbone feature extraction network, a neck feature focusing diffusion pyramid network and a cross-stage multi-scale feature fusion module used in the backbone feature extraction network, the feature focusing module and the detection head structure are used in the neck feature focusing diffusion pyramid network; s3, training the deep learning network model; and S4, detecting a to-be-detected cloth image based on the trained deep learning network model. According to the invention, by introducing the cross-stage multi-scale feature fusion module, detail features of flaws in cloth can be effectively captured, so that the accuracy and robustness of detection are improved.

The invention relates to the technical field of prostate MRI (Magnetic Resonance Imaging) image segmentation, in particular to a prostate MRI image segmentation method based on Mamb-Unet, which realizes effective fusion of features of different scales between an encoder and a decoder, and fuses multi-scale information on a space axis by using a space attention bridge segment to fuse fusion features of different levels from the encoder, so that the segmentation accuracy of the prostate MRI image is improved. And then global and local information is captured through 1D convolution and full connection in the channel attention bridge segment, so that a channel attention map is generated, channel information is better integrated, and multi-level different-scale feature fusion is realized.

The invention belongs to the technical field of Linux kernel schedulers, and particularly relates to a Linux kernel scheduler parameter optimization system and method.The Linux kernel scheduler parameter optimization system comprises a data collection module, a large model training and optimization module, a kernel scheduler module, a code generation and verification module, a small model training module and a small model reasoning module; the large model is responsible for periodically providing a visual angle of global optimization, generating an initial weight and a scheduler prototype of the small model, extracting a rule from long-term accumulated data, and ensuring that an optimization strategy has a global visual field. Meanwhile, the small model serves as a local adapter to process the dynamic scheduling requirement of a kernel mode in real time. The layered optimization mechanism can balance short-term adaptability and long-term performance improvement.

The invention discloses a power equipment fault prediction method based on support vector regression, and aims to improve the prediction accuracy. The method comprises the following steps: firstly, collecting multi-dimensional operation parameter data of power equipment, and constructing an original monitoring data set; a data set is segmented through a sliding time window, wavelet transform is used for noise reduction, and then a normalization method is applied to generate a standardized data set. Time domain and frequency domain features are further extracted, a feature vector space is constructed, an optimal feature subset is screened through a mutual information method, and a training data set is generated; performing preliminary training on a support vector regression model by using the training data set to generate candidate support vectors, and screening a support vector set with the maximum contribution through an optimization algorithm; a grid search method is adopted to adjust kernel function parameters and penalty factors, and a support vector set is further optimized. And evaluating the performance of the model through cross validation, applying the optimized support vector regression model to power equipment fault prediction, and generating a fault occurrence probability prediction value.

The invention relates to the technical field of laser cutting, in particular to a laser cutting device and a control method thereof. The method comprises the following steps: collecting data of a laser cutting machine to obtain data of the laser cutting machine; cutting head moving speed data of the laser cutting machine data are extracted, and the cutting head moving speed data are obtained; performing power calculation on the moving speed data of the cutting head to obtain laser power data; spatial modeling is carried out based on the cutting head moving speed data and the laser power data, and a laser cutting machine three-dimensional model is generated; performing cutting path identification on the three-dimensional model of the laser cutting machine to generate cutting path data; and extracting first cutting data of the laser cutting machine data to obtain the first cutting data. Through the Internet of Things technology, the data mapping technology, the data analysis technology and the intelligent control technology, self-adaptive laser cutting control over different materials and thickness conditions is achieved, and therefore the cutting quality and efficiency are improved.

The invention relates to the technical field of high-voltage connectors, and discloses a high-voltage connector fault prediction method and system based on thermal management, and the method comprises the steps: collecting the temperature distribution data and electric signal transmission characteristic data of a high-voltage connector, and building a thermal-electric coupling network model comprising a temperature node and a signal transmission node; separating and optimizing the transmission characteristic function to obtain a temperature-transmission characteristic mapping relation; gaussian process regression calculation is carried out on the temperature time sequence data to obtain temperature dynamic characteristic statistical characteristics; inputting the statistical characteristics of the dynamic temperature characteristics into a specified time delay thermal management neural network for signal transmission delay prediction to obtain delay prediction characteristic parameters; according to the method, the weighted error value in the transmission feature space is calculated, and the signal transmission delay is dynamically adjusted to obtain the compensated signal transmission delay parameter, so that the accurate prediction of the signal transmission delay is realized, and the accuracy and robustness of the compensation effect are improved.

The invention discloses a rotational flow micro-mixing nozzle with built-in plasma combustion supporting and a micro-mixing combustor. The rotational flow micro-mixing nozzle comprises a tangential rotational flow part, a fuel pipe and a mixing pipe which are coaxially arranged, and plasma combustion supporting exciters arranged on the fuel pipe and the mixing pipe. The plasma combustion-supporting exciter is integrated in the nozzle, the structural design of the micro-mixing combustor can be simplified, and the size and complexity of the micro-mixing combustor are reduced; the plasma combustion-supporting exciter is installed at the top of the mixed gas channel of the rotational flow micro-mixing nozzle, stable adjustment of the combustion process under the variable working conditions can be achieved by controlling opening and closing and the discharge intensity of the plasma combustion-supporting exciter when the load changes, and meanwhile the response speed under the different working conditions is increased; air or an oxidizing agent can be fully mixed with fuel at a specific rotational flow degree through rotational flow air flow generated by the tangential rotational flow component, the combustion range can be widened, and the stable combustion effect under the extreme working condition is enhanced.

The invention provides a multi-protocol communication intelligent switch gateway control method and device, and the method comprises the steps: constructing a knowledge graph according to a network topological graph and protocol information of an intelligent switch, and obtaining a switch networking knowledge graph; determining an inference rule according to the switch networking knowledge graph to obtain an inference rule engine; acquiring a historical data packet of the intelligent switch in a preset time period, and analyzing and converting the historical data packet to obtain a data conversion module; integrating the inference rule engine and the data conversion module to obtain a protocol conversion module of the intelligent gateway; dynamically designing the protocol conversion module according to a preset implementation framework to obtain a dynamic protocol adaptation mechanism; evaluation and performance optimization are carried out on the dynamic protocol adaptation mechanism, an optimized protocol conversion system is obtained, and the optimized protocol conversion system is used for assisting the intelligent switch gateway to control the intelligent switch.

The invention provides an unmanned aerial vehicle visual language navigation method based on large model task analysis, and the method comprises the following steps: S1, decomposing a navigation instruction into a plurality of sub-tasks through a navigation task analysis module, and obtaining the text features of the currently executed sub-tasks through a sub-task switching module after the sub-tasks pass through a text encoder; s2, utilizing a visual encoder to obtain visual features of a current subtask input front view image; and S3, acquiring navigation action output corresponding to each moment of the current subtask through a multi-mode encoder by utilizing the visual text features. According to the method, the interpretability and the accuracy of visual language navigation action decisions can be improved.

The invention discloses a method and a system for realizing mine communication signal improvement based on edge computing, and relates to the technical field of mine communication signal improvement, and the method comprises the following steps: integrating a real-time monitoring system on each edge node, so as to continuously detect the fluctuation of communication flow and data request quantity, the real-time monitoring system identifies a potential communication peak period by collecting real-time data and generates an early warning signal to notify a network administrator or automatically trigger a load management program. According to the invention, through the real-time monitoring and early warning system on the edge node, active monitoring and peak prediction of mine communication are realized, the overload risk caused by traffic surge is reduced, and the stability of data transmission is ensured. And intelligent scheduling and dynamic resource allocation are combined, so that efficient execution of tasks under high load is ensured, and node overload is prevented. A self-adaptive load balancing and feedback mechanism enables the system to dynamically adjust distribution, quickly respond to a bottleneck and improve self-adaptability and stability.

The invention provides a distributed storage and data warehouse permission synchronization method and system, and relates to the technical field of permission management, and the method comprises the steps: analyzing user operation data of distributed storage, training a machine learning model, and generating a directory permission intelligent mapping table; analyzing the structure information of the data table in the data warehouse, and generating a multi-dimensional table authority evaluation model; generating a self-adaptive permission rule base by combining the two, and deploying the self-adaptive permission rule base to each node of distributed storage; when the permission is changed, the system calculates an influence range according to the self-adaptive permission rule base and generates a permission updating instruction sequence; and the system monitors the instruction execution state, identifies abnormal nodes, and performs repair based on the permission propagation tree to ensure the permission consistency. According to the method, automatic synchronization of distributed storage and data warehouse authority can be realized, the authority management efficiency is improved, the manual intervention cost is reduced, and the data security is enhanced.

The invention discloses a highway pavement temperature forecasting method based on physical constraints, which comprises the following steps of: collecting and preprocessing meteorological data of all stations in a region, historical observation data of pavement temperature and geographic data, and adding time information to construct time features; building a U-net neural network model, carrying out space field data correction on the multi-station multi-meteorological element forecast data, and outputting corrected meteorological data of each station; constructing a bidirectional LSTM network on the basis of the U-net model, processing time sequence data, and evaluating and optimizing model performance by using a loss function; physical constraints including the time relation of temperature change, seasonal and day and night change characteristics, material thermal characteristics, a thermal balance equation and the like are added into the model, and a model result is optimized; and evaluating the generalization ability and forecasting precision of the model by using a test set, and finely adjusting model parameters. According to the method, the precision and reliability of road surface temperature prediction are remarkably improved, and a reliable basis is provided for traffic management and safety early warning.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention provides a decoding and coding method, device and equipment, and the decoding method comprises the steps: decoding a first code stream of a current image block, and obtaining a coefficient hyper-parameter feature of the current image block; for each stage sub-block of the current image block, determining a probability distribution parameter of the stage sub-block based on the coefficient hyper-parameter feature, and decoding a second code stream of the current image block based on the probability distribution parameter to obtain a residual feature of the stage sub-block; based on a mean value prediction network, determining mean value features of the sub-blocks in the stage; determining a reconstruction feature of the stage sub-block based on the mean feature of the stage sub-block and the residual feature of the stage sub-block; performing feature aggregation on the reconstruction features of the sub-blocks in each stage to obtain the reconstruction features of the current image block; a reconstructed image block of the current image block is determined based on the reconstruction feature of the current image block. Through the scheme of the invention, the coding performance and the decoding performance are improved.

The invention relates to the technical field of channel estimation, and discloses an OTFS sparse channel estimation method based on unitary transformation and sparse Bayesian, which comprises the following steps: S1, forming a 2D data block in a time delay Doppler domain, and dividing the time delay Doppler domain; s2, acquiring a receiving signal of a time delay Doppler domain; s3, constructing a sparse sensing matrix and performing singular value decomposition; s4, setting a judgment condition for successful channel recovery; s5, calculating posterior distribution of a transmission channel by adopting a generalized approximate message passing strategy based on unitary transformation; s6, obtaining an optimal updating rule of the hyper-parameter in the estimation channel and the hyper-parameter in the noise signal; and S7, judging whether iteration is continued or not according to a set judgment condition. According to the invention, the approximation of the channel posterior distribution information is completed by using the generalized approximation message passing framework based on unitary transformation through the information carried by the pilot frequency position, so that the calculation burden is reduced, and the calculation resource is saved.

The invention discloses a network threat deduction system and method based on Transform and a graph attention network model. The system mainly comprises four modules including a threat intelligence extraction module, a network threat entity construction module, a network threat deduction module and a deduction result application module. The method comprises the following steps: preprocessing and mining network security threat intelligence data by utilizing a graph attention network (GAT) model, constructing a complete network threat intelligence knowledge graph based on a data association relationship, and determining a vulnerability utilization weight in the graph; modeling is carried out on a reasoned path sequence based on a Transform model, a next reasoning sequence is predicted in a generative mode, and rapid and efficient network threat intelligence reasoning is achieved. According to the method, the complexity of attack deduction calculation is reduced, the deduction efficiency is improved, and security risk investigation and early warning can be carried out for a large-scale network environment.

The invention belongs to the technical field of electromagnetic modeling of a target in a layered medium, particularly provides a planar multilayer medium electromagnetic scattering modeling method based on a layered medium Green function, and is used for solving the problems that the number of unknown quantities of a system is large and the calculation efficiency is low when electromagnetic scattering characteristics of the target are analyzed in the layered medium. The method comprises the following steps: firstly, carrying out mesh generation on a medium target in a planar multilayer medium; then, a layered medium Green function is calculated based on a space list and an interpolation method, a surface integral equation of the target is solved by adopting a moment method, and a matrix equation of target current and magnetic current is constructed; and finally, solving a matrix equation by applying an improved laminated off-diagonal low-rank matrix, and calculating a target scattering field. According to the method, a direct solving calculation method is adopted, and the problem that an ill-conditioned matrix is difficult to converge in a conventional iteration method is effectively solved; especially for a multi-port excitation problem, repeated calculation is not needed, the solving efficiency of a large-scale problem is improved, and calculation resource consumption is reduced.

The embodiment of the invention provides an access control strategy construction method and device, electronic equipment and a storage medium. The access control strategy construction method comprises the following steps: determining access control elements and attributes thereof of an application based on an acquired resource access scene; based on the access control elements and the attributes thereof, an access control strategy template is created according to different access control scenes, and the access control strategy template comprises preset access control elements and attributes; selecting the access strategy template based on a service demand and assigning a value to the attribute to create a corresponding access control strategy; and performing matching calculation on the access control strategy and the intercepted resource access request based on a strategy calculation engine, and if matching succeeds, executing a corresponding strategy action. Through the steps, access control configuration is more convenient and easier to understand, and access control is more flexible.

The invention provides a steel surface defect detection method and device, and belongs to the technical field of steel defect detection.The steel surface defect detection method comprises the steps that a defect detection network based on a YOLO architecture is constructed, and the defect detection network comprises a lightweight feature extraction network, a self-adaptive multi-scale feature fusion network and a head network; the lightweight feature extraction network comprises four layers of architectures, and each layer of architecture comprises a first convolutional layer and a space expansion feature module; the feature space expansion module comprises a second convolutional layer and an element multiplication operator module; the adaptive multi-scale feature fusion network comprises an adaptive fusion module and a feature space expansion module; training the constructed defect detection network based on the historical steel defect image, and performing channel pruning on the trained defect detection network by using LAMP (Loop-Mediated Isothermal Amplification); and acquiring a to-be-identified steel image, and inputting the to-be-identified steel image into the pruned defect detection network for defect detection. According to the method, the defect detection precision of the network can be improved while the network complexity is reduced.

The invention discloses a multi-class defect detection method and device based on a variational auto-encoder and a denoising network, and the method comprises the steps: obtaining an industrial product surface defect image, carrying out the preprocessing, and constructing a training set and a verification set; the training process of the method is divided into two stages: in the first stage, a preprocessed training image is input into a variational auto-encoder for training, feature distribution of the image is learned, and features of a normal image are captured by reconstructing the image; in the second stage, a denoising network and a feature guiding module are used for conducting secondary reconstruction on image features in the feature space of the variational auto-encoder, and therefore the reconstruction capacity and the multi-class reconstruction capacity of the model are enhanced; in the reasoning stage, multi-scale features of an original image and a reconstructed image are extracted through a feature extractor, feature comparison is carried out, and an abnormal scoring graph is generated. According to the method, normal information is reserved while the reconstruction capability of the variational auto-encoder is enhanced, and defect missing detection caused by reconstruction failure is effectively reduced.

The invention discloses a river pollution tracing method and system based on underground water quality pollution monitoring, and relates to the technical field of river pollution tracing. According to the river pollution tracing method based on underground water quality pollution monitoring, the real-time concentration value of each pollutant in underground water is obtained in real time and analyzed to obtain the underground water pollution real-time evaluation index; according to the method, a plurality of abnormal measurement points with abnormal concentration in a set area are identified, and the traceability position coordinate of each pollutant with abnormal concentration in underground water in a river is analyzed, so that the real-time evaluation index of underground water pollution can be continuously calculated by acquiring the pollutant concentration data of each measurement point in real time; and whether the pollutant concentration exceeds the preset range or not is judged in time, so that the defects of hysteresis and untimely data updating of traditional water quality monitoring are overcome, dynamic monitoring of the pollutant concentration can be realized, and quick response when the pollutant is abnormally increased is ensured.

The invention discloses a gas generator and energy storage battery hybrid energy supply scheduling strategy, belongs to the technical field of storage and charging, and mainly solves the problems that in an energy supply mode of a gas generator in existing oil and gas drilling operation, the power generation efficiency of the gas generator is low, and the gas generator is prone to flameout when coping with an impact load. Measuring and acquiring data of voltage and current of oil and gas drilling operation energy consumption equipment, the starting number of gas generators and the residual electric quantity of an energy storage battery; 2, calculating the real-time total power, the power abrupt change rate and the load rate of the gas generator according to the measured data; 3, the number of gas generators needing to be started and the charging and discharging state of energy storage electricity are determined according to the calculation and analysis result; and 4, repeating the steps 1-3 until the drilling operation is finished. The scheduling strategy depends on an existing energy management system, the starting number of the gas generators and the charging and discharging states of the energy storage batteries are reasonably controlled by combining the total power fluctuation characteristics of oil and gas drilling operation, comprehensive management of hybrid energy supply of the gas generators and the energy storage batteries in the oil and gas drilling operation is achieved, the power generation efficiency of the gas generators is improved, and the energy consumption is reduced. Meanwhile, flameout of the gas generator is avoided, and operation safety is guaranteed.

The invention provides an AI workflow automatic management method based on a creative scene, and relates to the technical field of data processing, and the method comprises the steps: obtaining initial data input by a user, extracting key elements from text data, analyzing related characteristics from non-text reference data, constructing an initial task chain, and analyzing a dependency relationship of task units, the method comprises the steps of generating a dependency path set, carrying out weight calculation on a dependency relationship among task units in the dependency path set, optimizing an initial task chain, generating a dynamic task chain table, selecting an applicable tool from an AI tool library, allocating a corresponding tool resource for each task unit, constructing an execution workflow, and dynamically running the execution workflow. Obtaining intermediate result data, detecting whether the intermediate result data meets the requirements of the logical relationship and the content style, and adjusting inconsistent parts to generate output result data; the intelligent degree of the workflow is improved.