70 results about "Indicator vector" patented technology

The invention discloses an industrial equipment interconnection and intercommunication method based on an industrial control platform, and relates to the technical field of industrial equipment interconnection and intercommunication, and the method specifically comprises the following steps: calculating a behavior entanglement degree between a resending data frame with repeated content and a resending data frame, and if the entanglement degree exceeds a preset threshold value, determining that the resending data frame is the resending data frame; if yes, extracting a sequence offset, a time delay amount and a field fluctuation amplitude, and generating an index vector used for describing the aging degree of the data identifier; the generated index vector is input into a pre-trained graph embedding algorithm model, a first parameter and a second parameter are generated, the first parameter is used for describing the consistency of the data frame and a historical evolution path, and the second parameter is used for describing the aging degree of the data frame identifier. According to the method, the problem that the reissued data of the industrial equipment cannot be accurately identified is solved, and timeliness judgment and validity identification of the state data are realized based on the causal atlas and the graph embedding model.

The invention relates to the technical field of data processing, and discloses a power sample data acquisition and classification method and system. The method comprises the following steps: synchronously acquiring active power, reactive power fluctuation and voltage harmonic data through a multi-point terminal, and constructing six types of characteristic index vector groups; a power load genetic optimization algorithm is used to optimize a classification threshold under power flow constraint; based on the optimal threshold value, clustering analysis is carried out through an EFC-KMeans algorithm in combination with impedance matrix characteristics; and inputting the clustering center into a multi-head power topology attention mechanism modeling node coupling relationship to realize power sample data classification and identification. The physical constraint conditions of the power system are effectively fused in the power sample data acquisition and classification process, so that the physical feasibility and engineering practicability of the classification result are improved.

The invention discloses a construction method of an intelligent question answering system based on a lightweight large model, and relates to the technical field of natural language processing, and the method comprises the steps: receiving a natural language question of a user, carrying out vector coding through a lightweight BERT model, calculating the cosine similarity of the natural language question and a business index vector, and generating a structured semantic map; converting the structured semantic map into a scene feature vector, and injecting the scene feature vector into an adapter parameter block to construct a lightweight scene adaptation model; the lightweight scene adaptation model is combined with a real-time data interface to obtain a structured multi-modal response and construct an index tracking tree; and based on the unexpanded nodes of the index tracking tree, actively initiating scene migration type questions through a questioning strategy engine, obtaining target scene feature vectors, performing deep analysis, and generating a deep question and answer analysis report. According to the method, the lightweight scene adaptation model is constructed, calculation logic is flexibly adjusted according to different service scene features, and the self-adaptive processing capability of a single model to multiple service scenes is achieved.

The invention provides a software quality evaluation method and device for a power grid dispatching automation system. Belongs to the technical field of power dispatching automation. The method comprises the following steps: acquiring running state data of power grid dispatching automation system software, and extracting a third-level index from the running state data; determining subjective and objective fusion weights of the third-level indexes; aggregating the third-level indexes related to the same quality feature according to the subjective and objective fusion weights of the third-level indexes to obtain corresponding second-level indexes; an index vector composed of the second-level indexes is input into the dynamic fuzzy neural network model, and the first-level indexes of the corresponding subsystems are output to serve as comprehensive quality evaluation scores; and determining the software quality grade of the business subsystem according to the comprehensive quality evaluation score and a preset grade threshold value. The method is suitable for power grid dispatching automation system software quality evaluation oriented to complex operation scenes, and comprehensive evaluation and self-adaptive optimization of dispatching software under multi-dimensional and dynamic conditions can be achieved.

The invention provides a reverse design method of a starch hydrogel microstructure, and belongs to the technical field of computer material science. A standardized data set is constructed by collecting different starch hydrogel microstructure images and corresponding target performance index vectors. And constructing a starch hydrogel microstructure reverse generation model, and establishing nonlinear mapping between a macroscopic mechanical property index and a microscopic topological structure in combination with a multi-scale self-attention mechanism and a double-flow physical consistency identification strategy. Meanwhile, a differentiable physical performance prediction agent model and a multi-dimensional physical consistency coupling loss function are designed, adversarial iterative optimization under physical constraints is executed, and a prediction physical performance vector of a generated structure is forced to strictly approach a target performance index vector through a self-supervised feedback closed loop. According to the method, accurate anchoring of the generated structure on the mechanical property is achieved, design scheme recommendation and morphological quantitative analysis based on confidence are provided, and the intelligent level and scientificity of bio-based material design are improved.

The invention relates to the technical field of unmanned aerial vehicle cluster security and collaboration, in particular to an LLM-based multi-machine collaboration abnormal data detection system, which comprises a generation module used for generating at least one abnormal detection score by using an airborne end; the processing module is used for extracting features from a first historical database in the edge server to obtain first historical features and binary classification anomaly detection tags, calculating performance index vectors and uploading the binary classification anomaly detection tags and the performance index vectors to the cloud; and the prediction and analysis module is used for performing long-time sequence prediction by using the cloud to obtain a long-time sequence prediction result, and performing security analysis by using the cloud to obtain a security analysis result. Therefore, the problems of mechanism deficiency, data processing bottleneck, insufficient threat coping, poor system expansibility and the like of the existing unmanned aerial vehicle cluster in the aspect of multi-aerial-vehicle cooperation security are solved, efficient multi-aerial-vehicle cooperation is realized, and data processing and utilization are optimized.

The invention discloses an index query method and device based on a large language model and a storage medium, and the method comprises the steps: carrying out the semantic word segmentation operation of an index query instruction, and obtaining at least one index query keyword; performing a first matching operation on an index vector corresponding to the at least one index query keyword to obtain a target vector matched with the index vector, the target vector including a reference index vector matched with the index vector and a reference dimension vector matched with the index vector; performing second matching operation on the at least one index query keyword to obtain a target keyword matched with the at least one index query keyword; generating a query statement corresponding to the query information through a pre-trained large language model, and performing function verification on the query statement; and under the condition that the query statement passes the function verification, executing the query statement to obtain the index data. The technical problem that the accuracy of index query is low is solved.

The invention discloses a government affair data query method and device, electronic equipment and a storage medium, and is used for solving the problems of relatively high cost, inaccurate query result and relatively slow response of a related government affair data query technology. The method comprises the steps of obtaining a question statement input by a user; performing intention recognition on the question statement based on a pre-trained large language model to obtain a recognition result; under the condition that it is determined that data of government affair indexes need to be queried in a government affair database according to the recognition result, matching corresponding index aperture vectors in a preset government affair index vector library based on the question statements, and determining query index vectors corresponding to the index aperture vectors; wherein the query index vector and a plurality of index aperture vectors corresponding to the query index vector are stored in a government affair index vector library in an associated manner; and through a large language model, according to the query index vector and a preset prompt word project, generating an SQL statement.

The invention discloses a large model safety performance evaluation method based on an analytic hierarchy process and Shapley value fusion, and belongs to the technical field of artificial intelligence safety. Comprising the following steps: setting a large model safety performance evaluation index, collecting data and carrying out standardization processing; constructing a hierarchical structure of large model safety performance evaluation, and setting a comparison matrix; constructing a feature equation based on the comparison matrix, solving the feature equation, and normalizing feature vectors to obtain the AHP weight of each safety evaluation index; calculating the Shapley value weight of each safety evaluation index in the safety evaluation index vector; the AHP subjective weight and the Shapley objective weight are fused, a model safety score is calculated, and the model safety score is used for evaluating the model safety performance. According to the method, the hierarchical decision framework of the AHP is creatively combined with the cooperative game theory of the Shapley value, and through dynamic fusion of subjective and objective weights, an explainable and verifiable quantitative weight distribution scheme is provided for large model safety performance evaluation, and accurate optimization of a safety governance strategy is realized.

The invention discloses a micro-service architecture-oriented fault identification method and system without fault marking, and the method comprises the following steps: constructing a micro-service topological potential energy field based on full-link call data collected in real time, and obtaining the potential well depth of each node depended by a whole network; based on a business semantic type extracted from the call chain; calculating an index vector health consistency coefficient of each node and a direct upstream node under the same service semantic type; performing joint calculation according to the overall dynamic semantic entropy break variable, the potential well depth and the health consistency coefficient to obtain a fault score; when the overall dynamic semantic entropy break variable, the health consistency coefficient and the fault score meet preset triggering conditions at the same time, it is judged that a corresponding node breaks down, and an alarm is output; according to the method, the micro-service calling topological graph with the weight is constructed in the sliding time window, so that the utilization degree of structural information during node state judgment is improved.

The invention relates to a flexible resource adaptive configuration method based on multi-scene fusion. The method comprises the steps of obtaining wind power, photovoltaic and load power predicted values of a regional power grid; constructing a scene recognition target function by taking the weighted sum of the flexibility characteristic indexes of the power balance, peak regulation and frequency modulation scenes as a minimum target, and solving the scene recognition target function based on the scene recognition constraint to obtain a flexibility characteristic index vector; based on the flexibility characteristic index vector, a scene identification result is obtained through judgment; if the scene is one or more of power balance, peak regulation and frequency modulation, constructing a two-stage adaptive configuration model with minimum flexible resource capacity configuration and resource control loss of each side of the source network load storage as a target; based on a scene identification result and the first-stage constraint, solving to obtain a flexible resource configuration scale; and solving and obtaining a flexible resource operation plan based on wind power, photovoltaic and load power predicted values, the newly added flexible resource configuration scale and the determinacy and uncertainty constraints of the second stage.

The invention provides a longitudinal federated lifting tree method for privacy protection, and relates to the technical field of longitudinal federated learning. The method comprises the following steps: combining a secretly shared binary matrix and gradient information to obtain a shared matrix and secretly sharing the shared matrix; each training terminal performs row permutation on the sharing matrix based on the permutation triad and secretly shares a permutation result; the linear gain scores of the candidate split are calculated cooperatively and shared secretly, and the indication vector of the split corresponding to the maximum linear gain score is calculated; according to the candidate split number, selecting corresponding columns and indication vectors from the matrix to calculate sub-node samples and a permutation matrix of sub-nodes; and recursively executing a calculation splitting step, completing splitting from the root node to the leaf node, calculating the weight of the leaf node, and storing the weight into a preset set. Updating the binary permutation matrix of the root node of the next decision model based on the leaf node weight, and continuing calculation until all decision tree models are generated; the problem that an existing longitudinal federated gradient boosting tree protocol is low in efficiency can be solved.

The invention relates to the technical field of geological disaster prevention and control, and particularly discloses a freeze-thaw collapse event risk early warning method based on an adaptive weighting algorithm and LSTM, and the method comprises the steps: S1, obtaining and collecting multi-source spatio-temporal data, and obtaining a unified data set; s2, obtaining an index vector of each moment; s3, obtaining a normalized feature vector; s4, obtaining a weighted feature vector at the moment; s5, obtaining prediction risk sequences from a short period to a middle period; s6, obtaining risk early warning output for emergency response and a repair strategy; according to the method, an adaptive weighting algorithm based on contribution degree evaluation is introduced, so that the model can dynamically adjust the relative importance of each input index under different time windows and environment conditions. Compared with a static weight method, the mechanism can automatically amplify physical driving factors directly associated with freeze-thaw collapse, such as the influence of short-term snow melting rate, ground temperature gradient or sudden rainfall accumulation, and meanwhile, inhibits event-independent or noise indexes.

The present invention discloses a dynamic path planning method and system for an unmanned boat based on an improved D* algorithm, which is applicable to the technical field of path planning. First, the present invention conducts a rasterized modeling on the sea area map where the unmanned boat is located, clusters the obstacles according to the position coordinates, and then divides the map into regions; then, corresponding obstacle complexity quantization index vectors are established for each region to improve the cost function in the D* algorithm; and the path search behavior of the unmanned boat is combined with the improved D* path planning algorithm for dynamic path planning; finally, the Bezier curve is used for optimization to improve the smoothness of the path. The present invention can solve the problems that it is impossible to obtain all the sea area information and it is difficult to meet the requirements of global optimality and real-time obstacle avoidance when conducting path planning in a complex marine environment in the presence of unknown obstacles, and realizes the dynamic smooth optimal path planning of the unmanned boat.

The application provides an engineering education system and method fusing explainable multi-objective intelligent optimization, and relates to the technical fields of intelligent education and artificial intelligence optimization. The method comprises the following steps: obtaining an initial engineering design scheme of a user, and obtaining a standardized multi-dimensional index vector through physical simulation analysis and logical analysis; configuring weights with structured reasons for each index, identifying critical weight values leading to decision reversal through sensitivity analysis, and then determining optimization objectives and constraint conditions; performing multi-objective optimization search using an explainable multi-objective optimizer, generating a Pareto optimal solution set and outputting explainable data; simultaneously generating a behavior interaction sequence of the user; generating a capability evaluation report of the user according to the behavior interaction sequence; and adjusting a preset teaching logic sequence according to the capability evaluation report to form a closed-loop teaching process. The application is suitable for cultivating and evaluating the decision-making ability of students in balancing technical performance and environmental sustainability in engineering design.

The invention relates to a historical case similarity-based quality evaluation method, which belongs to the field of software development, and comprises the following steps of: screening out a prior index and a posterior index; collecting data of each stage in the research and development process of each software historical version as historical cases; the specific numerical value of each index is counted; obtaining quality problem source data of the historical cases; taking the historical cases with the labels as input samples, performing model training by adopting an SVM classification algorithm, and constructing a quality problem classification model; constructing a prior index vector matrix for each historical case and the newly-added case, and calculating the similarity between the newly-added case and each historical case; automatically classifying the newly added cases and setting labels; calculating a risk value of each similar case; and averaging the risk values of all the similar cases to obtain a risk coefficient value of the newly added case. According to the method, the influence of manual subjective deviation on index interpretation can be avoided, and an objective quality evaluation standard is established through quantitative similarity analysis.

In some aspects, a computing system can train a risk assessment model, using a training process, for determining a risk indicator. The training process can include: accessing a set of features; determining, using a selector network, a set of selected features and an indicator vector; and training the risk assessment model using the set of selected features and the indicator vector. The computing system can determine the risk indicator for a target entity using the trained risk assessment model. The computing system can transmit, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.

The invention provides a method for constructing a data quality evaluation model, and belongs to the technical field of quality evaluation, and the method comprises the steps: collecting a to-be-evaluated data set, and obtaining a business demand and an application scene of the to-be-evaluated data set; determining an assessment index vector of the to-be-assessed data set based on the business demand and the application scene of the to-be-assessed data set; based on the to-be-evaluated data set and the evaluation index vector, determining a training quality feature matrix and a test quality feature matrix; and constructing a quality evaluation model based on the training set and the training quality feature matrix, and evaluating and optimizing quality evaluation performance based on the evaluation index vector, the test set and the test quality feature matrix. The method can enhance the capturing capability of the model for the data quality characteristics, achieves the high-precision evaluation of dynamic weighting, improves the intelligence and stability of the quality evaluation model, and improves the adaptability of the model in the quality management and analysis scenes of complex data.

The present invention relates to the field of integrated control training and evaluation technology, and discloses a method and system for intelligent evaluation of integrated control training results based on personalized learning curves, including: collecting training data of controllers, constructing training intelligent evaluation indicators; performing data processing, calculating indicator vectors corresponding to evaluation indicators; constructing a training evaluation model through a multi-layer perceptron, capturing the relationship between deviation and score, and generating a training result score vector; determining the weight of the evaluation indicator, and calculating the training result; constructing a corresponding learning growth curve, identifying weak knowledge points in training, and formulating targeted plans for future training. The present invention can improve the accuracy and reliability of evaluation results, can comprehensively analyze the ability change trend of controllers, accurately identify their learning achievements and ability shortcomings at different training stages, provide a scientific basis for personalized training design and training program optimization, and thus improve the adaptability and comprehensive operational capabilities of controllers.

The invention belongs to the technical field of unmanned ship data analysis, and discloses an intelligent weight prediction method for a dynamic scene, and the method comprises the steps: obtaining unmanned ship data; obtaining an index vector of each unmanned ship according to the unmanned ship data; inputting each index vector into a local attention network to obtain a corresponding first attention weight; calculating a corresponding local attention aggregation result according to each first attention weight; inputting each local attention aggregation result into a global attention network to obtain a corresponding second attention weight; calculating a corresponding global attention output result according to each second attention weight; splicing all global attention output results to obtain an output head vector; and inputting the output head vector into a full-connection feed-forward layer to obtain a weight prediction result. According to the method, the relationship among the parameters in the index vector can be captured to match different formation working scenes, and the index weight prediction precision is remarkably improved.

The invention provides a motion axis fault diagnosis method based on OSPCA-Resformer of a current signal, and the method comprises the steps: collecting a steady-state current signal of a drive end of a motion axis of a machine tool under a normal working condition, extracting a peak value, a root-mean-square value and a kurtosis signal feature value of the current signal, carrying out the dimension reduction of data through a PCA principal component analysis method, and selecting a plurality of feature values as feature index vectors; according to the method, a non-intrusive detection method is adopted, unsupervised online detection and supervised offline model diagnosis are combined into a whole, the calculated amount is greatly reduced, and the method has practical possibility for actual industrial production online detection.

The invention relates to a sustainable development assessment method, belongs to the technical field of sustainable development assessment, and solves the problems that in the prior art, an assessment subject is difficult to acquire index system data, the assessment cost is high, and sustainable development assessment cannot be quickly and accurately realized due to lack of experience. Obtaining user input indexes for data preprocessing, and converting the user input indexes into a first index vector set; performing similarity comparison on the preprocessed user input index, the first index vector set and a second index vector set of each index system to obtain a first matching index system; inputting the first index vector set into a similarity prediction model to obtain a second matching index system; if the two index systems are consistent, taking the two index systems as a matching system; acquiring a corresponding characteristic index based on the matching system; and calculating a sustainable development score based on the characteristic values of the characteristic indexes. According to the method, the optimal index system adaptive to the user demand can be obtained, and the sustainable development evaluation can be completed.

The present invention discloses an e-commerce marketing prediction method and system based on big data analysis, and relates to the field of data analysis and prediction technology. It includes collecting user behavior trajectory data of an e-commerce platform, performing dynamic window segmentation processing on the user behavior trajectory data to generate time series behavior fragments; inputting the time series behavior fragments into an encoder, and using improved Huffman coding at the event type layer to generate a behavior path coding vector; and calculating a value index vector in real time based on transaction record data; performing multi-stage coupled clustering analysis on the behavior path coding vector and the value index vector to generate a user grouping label that integrates the behavior pattern and the value level; based on geographic location feature data, according to the user grouping label, generating a time-space weighted user behavior prediction map. The present invention can better mine regional differences and time-space laws, greatly improving the personalization and accuracy of marketing predictions.

Mechanisms are provided for compressing ciphertext data for data transmission. A sparse vector is received, comprising a plurality of vector elements and a tree is built from the sparse vector where each leaf node corresponds to a vector element in the sparse vector, and each subsequent level of the tree is built from a child level below it in the tree. Nodes of a subsequent level have values determined based on values of child nodes connected to them. The mechanisms execute a level-based copy-and-recurse operation on the tree from a root node of the tree to leaf nodes of the leaf node level. The level-based copy-and-recurse operation computes, at each level of the tree, an indicator vector and a selection matrix that identifies which nodes to recurse into. The mechanisms generate the compressed ciphertext data based on the indicator vectors and the sparse vector.

The invention belongs to the technical field of information system resource control, and particularly discloses an intelligent information system resource control method and system based on a large model, and the method comprises the steps: extracting the text data of a request intention, a task type and a task priority label based on a user request log; extracting external event text data based on the external environment data; splicing the index vector and the text vector to obtain an input matrix; predicting the input matrix by using the large model to obtain a task-level resource demand map and a resource control instruction sequence; and taking the task-level resource demand, the resource control instruction sequence, the current resource state of the system, the dependency relationship between the tasks and the historical performance indexes of the system obtained by the large model as input, and obtaining a final task queue priority ranking result and a resource allocation result through a reinforcement learning algorithm. The large model can continuously learn and improve the prediction accuracy and the effectiveness of the control strategy, and self-adaptive optimization is realized.

This invention discloses a dynamic interactive response method and system for e-commerce applications based on sensor data, comprising the following steps: collecting and preprocessing data from the e-commerce application's operation to generate a standardized interactive input data set; extracting continuous state features and discrete event features to construct a dual-path coupling control sequence; performing stage correlation calculations to generate interactive stage indicator vectors; inputting the dual-path coupling control sequence and interactive stage indicator vectors into an improved NCDE model to generate a dynamic interactive hidden state sequence; performing response sensitivity modulation to construct a dynamic interactive response strategy set; dynamically adjusting the interface based on the dynamic interactive response strategy set to generate dynamic interactive response results; collecting subsequent feedback and writing back updates to optimize the dynamic interactive hidden state sequence and the dynamic interactive response strategy set. This invention improves the real-time performance, accuracy, and adaptive optimization capabilities of e-commerce interactive responses.

The invention discloses a digital economy network flow prediction method and system based on artificial intelligence, and relates to the technical field of digital economy monitoring. Comprising the following steps: acquiring historical network traffic time sequence data of a plurality of digital economic entity nodes, corresponding digital economic characteristic data and economic index data aligned with the historical network traffic time sequence data; based on the digital economic characteristic data, the economic index data and the historical network flow time sequence data, constructing a dynamic adjacency graph evolved along with time; the method comprises the following steps: processing historical network traffic time sequence data through a multi-scale time encoder to obtain a node time representation containing a multi-granularity time dependence feature; after node time representation and digital economic feature data are fused, graph volume accumulation combination is carried out in combination with a dynamic adjacency graph, a joint attention mechanism based on an economic index vector generation offset item is introduced, and node representation fused with space-time and economic semantics is generated; and performing initial traffic prediction based on node representation fusing time-space and economic semantics.

This invention belongs to the field of sensors and signal processing, specifically relating to a wavelet kernel-scale sensitivity-guided denoising method for abrasive particle induced voltage signals. The method includes: acquiring abrasive particle induced voltage signals and performing harmonic cancellation to obtain a preprocessed signal; constructing a wavelet kernel function and calculating a kernel-scale guided spectrum using it and the preprocessed signal; constructing a sparse joint denoising model based on the kernel-scale guided spectrum; processing the sparse joint denoising model to obtain a convex optimization objective function; solving the convex optimization objective function using an adaptive step-size gradient descent method and an adaptive iterative shrinking threshold method to obtain a weight vector characterizing the distribution of abrasive particle characteristic signals; binarizing the weight vector characterizing the distribution of abrasive particle characteristic signals to obtain a feature indicator vector; performing a Hadamard product between the feature indicator vector and the preprocessed signal, followed by low-pass filtering to obtain a denoised signal. This invention can adaptively and non-destructively enhance and denoise abrasive particle characteristic signals under strong interference environments.

The present invention relates to the field of data management technology, and specifically to a data center data management method and system based on the TensorFlow ecosystem. The method and system collect historical multi-source heterogeneous data and real-time multi-source heterogeneous data in the data center and perform preprocessing to obtain first historical multi-source data and first real-time multi-source data; construct a data partitioning management model to analyze the first historical multi-source data, obtain data partitioning results, and generate a gradient baseline; calculate the real-time gradient baseline based on the first real-time multi-source data, calculate and obtain the reconstruction error of the gradient baseline, dynamically adjust the threshold in real time, and obtain an abnormal TensorFlow ecosystem gradient; construct an abnormality analysis management model to analyze the abnormal TensorFlow ecosystem gradient, obtain a TensorFlow ecosystem abnormality indicator vector, and obtain the specific abnormal factors that cause the abnormality; and evaluate and issue an alarm for the abnormal factors.

The application provides a key PVT corner evaluation method and device based on probability integral transformation and equipment. The method comprises the following steps: simulating N design samples randomly sampled to obtain a performance index vector to form an original data set S; modeling the performance index vector of each PVT corner by using a kernel density estimation method to obtain a probability density function and a cumulative distribution function of a circuit performance parameter; mapping the performance index vector into a standard normal distribution variable through a probability integral transformation; constructing a multivariate Gaussian distribution model based on the mapped data and obtaining new samples, combining the new samples with the original data set S to form a complete data set, counting the frequency of each corner as the worst PVT corner in the complete data set, and sorting the frequency to obtain a key PVT corner set, wherein the highest frequency is the first key PVT corner. The embodiment of the application can simplify simulation calculation, significantly reduce the calculation cost, accurately lock the PVT corner with the greatest impact on the circuit performance, and improve the design reliability.