79 results about "Uncertain data" patented technology

The invention discloses a hypergraph feature representation method, system and device based on fuzzy logic and a medium, and the method comprises the steps: obtaining original data, and constructing a hypergraph structure containing vertexes and hyperedges according to the original data; the hypergraph structure is initialized, and an initialized hypergraph structure is obtained; and inputting the initialized hypergraph structure into a preset hypergraph convolutional fuzzy network model for feature representation processing to obtain a hypergraph structure after feature representation processing. According to the hypergraph convolutional fuzzy network model, fuzzy representation and the hypergraph technology are deeply fused, the membership core concept of fuzzy logic is introduced, the hard coding association limitation of an existing hypergraph representation learning method that no black is white is broken through, the technical problems that a traditional hypergraph model cannot capture association gradients and fuzzy semantics are lost are effectively solved, and the learning efficiency is improved. Therefore, the flexibility and accuracy of the model are effectively improved, and stable prediction is provided in an uncertain data environment.

The invention provides an equipment system mission task demand satisfaction degree evaluation method, system and device, and relates to the technical field of task demand satisfaction degree evaluation.The method mainly comprises the steps that a mission task is decomposed into subtasks, and the subtask demand importance degree # imgabs0 # is calculated; the subtasks are decomposed into meta-tasks, and a first relation matrix # imgabs1 # is constructed; decomposing the meta-task into equipment capability requirements, and constructing a second relation matrix # imgabs2; constructing a reliability rule base; inputting the equipment capability index data of the meta-task demand into a reliability rule base, and calculating the reliability of the capability demand satisfaction degree of the equipment system; based on the reliability and the equipment capability demand, calculating an equipment system underlying capability demand satisfaction degree # imgabs3 #; based on the # imgabs4 # and the # imgabs5 #, calculating a meta-task demand satisfaction degree # imgabs6 #; based on the # imgabs7 # and the # imgabs8 #, the sub-task requirement satisfaction degree # imgabs9 # is calculated; and based on the # imgabs 10 # and the # imgabs 11 #, calculating a mission task demand satisfaction degree # imgabs 12 #. According to the scheme, complex and uncertain data can be dynamically, effectively and comprehensively processed, and the accuracy and reliability of satisfaction degree evaluation can be remarkably improved.

The invention discloses a data classification method and system based on a deep multi-path attention adaptive graph convolutional network, and relates to the technical field of medical image processing. Rs-fMRI data is acquired and preprocessed to obtain a BOLD sequence, a function connection feature vector is constructed and input into a DMAGCN model, and finally an optimal model is acquired through five-fold cross validation for classification. The data quality is guaranteed through data preprocessing, and a foundation is laid for accurate analysis. Functional connection feature vectors effectively represent data, after a model is input, a Transform backbone network and an MLP branch network can extract multi-source domain features, non-imaging data are utilized in combination with a graph network, the model can learn rich features, and generalization and adaptability are enhanced. According to the method, the classification accuracy is improved, the performance is excellent when uncertain data classification such as autism spectrum disorder is processed, related disease research is promoted, and an efficient and reliable method is provided for medical data classification.

The embodiment of the invention provides a request processing method and device, a storage medium and a program product. In the request processing method, when a user has a service request, a main intelligent processing module can obtain request context information, and can automatically plan a target processing scheme based on the request context information. The main intelligent processing module can schedule the sub-intelligent processing modules corresponding to the processing links in the target processing scheme to process the service request, so as to dynamically distribute the processing task of the service request to the appropriate sub-intelligent processing modules, and reduce the dependence on manual customer service. A part of the sub-intelligent processing modules can process uncertain and fuzzy information by using the target language model, and can make a reasonable decision according to incomplete or uncertain data. And the main intelligent processing module and the sub intelligent processing module cooperate with each other, so that service requests of users in different scenes can be efficiently coped with. And furthermore, the automatic processing proportion of user complaints can be improved, and the labor cost brought by manual customer service is reduced.

A technique for locking a blockchain transaction based on undetermined data, using a blockchain network. A locking node may include a locking script in a blockchain transaction to lock a digital asset. The locking script includes a public key for a determined data source and instructions to cause a validating node executing the locking script to verify the source of data provided in an unlocking script by: a) generating a modified public key based on the public key for the determined data source and based on data defined in the unlocking script; and b) evaluating a cryptographic signature in the unlocking script based on the modified public key. The blockchain transaction containing the locking script is sent by the locking node to the blockchain network. The lock may be removed using a cryptographic signature generated from a private key modified based on the data.

A multi-field data analysis method based on a Bayesian information enhanced neural network comprises a BITRNN model, and a final optimization objective of the model combines expected cumulative rewards of reinforcement learning, an evidence lower bound (ELBO) of Bayesian reasoning and an exploration mechanism of information entropy. Through combination of Bayesian reasoning, information entropy and reinforcement learning, prediction and decision optimization in multi-field complex data are realized. The model provided by the invention can dynamically adapt to environmental changes and maintain efficient prediction performance in high-dimensional uncertain data. Through Bayesian reasoning, the model can effectively process parameter uncertainty; the introduction of the information entropy increases the exploratory performance of the strategy, and avoids falling into a local optimal solution. According to the method, theoretical advantages of Bayesian statistics, an information theory and reinforcement learning are fused, high-dimensional and high-uncertainty data can be effectively processed, the prediction precision and generalization ability of the model are improved, the method is suitable for various fields such as polymer material performance optimization, agricultural planting strategy making and financial investment decision making, and the method has wide application prospects. Wide application prospects and practical values are realized.

The invention discloses an electrical equipment defect detection method and system based on a multi-modal large model, and the method comprises the steps: analyzing a collected electrical equipment image, obtaining an equipment type and an equipment defect in the electrical equipment image, and marking the equipment type and the equipment defect; based on the equipment types and the equipment defects, respectively constructing corresponding tables of the equipment types and the equipment defects with attributes; constructing a self-attention equipment fault detection model; and inputting the marked electrical equipment image into the self-attention equipment fault detection model for training, and setting a basic task until the precision of the basic task reaches a specified threshold, thereby completing defect detection of the electrical equipment. According to the method, the problems of uncertain data preparation standard, indefinite training process, low professional adaptability and the like of the current large model in training and application in the power field can be solved, the accuracy of the large model in power equipment defect detection is effectively improved, and the operation and inspection quality efficiency is improved.

The invention discloses a new energy-energy storage combined operation strategy optimization method based on uncertainty modeling and distributed robust optimization. The method comprises the following steps: a) carrying out data acquisition and preprocessing on power generation data and power market prices of a new energy plant station; b) using a fuzzy set to express uncertainty factors in the data; c) based on the probability distribution of the uncertainty data, establishing an optimization model of new energy-energy storage joint operation through a distribution robust optimization method; d) modeling and solving the optimization problem by using a deep neural network or a convolutional neural network; and e) adjusting a scheduling strategy of the energy storage facility according to an optimization result, ensuring maximum consumption of new energy in an uncertain environment, reducing wind and light abandoning phenomena, and improving robustness of power grid scheduling. The method has the advantages that the intelligent level of scheduling is remarkably improved, and the risk and cost of a traditional scheduling method are reduced.

The present application is applicable to the technical field of model optimization, and particularly relates to a recommendation result optimization method and apparatus for a recommendation model, and a device and a medium. The method comprises: acquiring a classification boundary value used for classifying a prediction result generated by a recommendation model, and on the basis of the classification boundary value, determining a fuzzy interval that represents a classification error; performing screening on a training data set used during the training of the recommendation model, so as to obtain a target data set, the prediction result of which falls within the fuzzy interval, and performing prediction rule extraction on the target data set, so as to obtain at least one prediction rule; acquiring a prediction result of data to be recommended which corresponds to the recommendation model; and if it is detected that the prediction result of said data is within the fuzzy interval, performing recommendation prediction on said data on the basis of the at least one prediction rule, so as to obtain an optimized recommendation result corresponding to said data. Therefore, a prediction rule is extracted by means of analyzing corresponding data in a training data set, and the prediction rule is used to perform recommendation prediction on uncertain data, thereby improving the accuracy of a recommendation model.

The invention relates to a method for predicting the damage degree of an island building group after being hit, and the method comprises the following steps: dividing the island building group into n building samples, and distributing different damage evaluation weights for each building sample; random variables influencing the damage degree of the island building group are screened, and related parameters of the random variables are determined; establishing an island reef building group damage model; generating m groups of data of random variables based on a random sampling method, inputting the m groups of data into the island building group damage model for damage simulation, and obtaining damage data of each building sample; setting a damage threshold value, judging whether the damage data of each building sample reaches the damage threshold value or not, and determining the damage failure rate of each building sample; and determining the average damage target number of the island building group. The method can perform toughness evaluation on the anti-deliberate strike capability and damage degree of the deep sea island building group, considers the uncertain data boundary of the complex environment, and provides scientific data and information basis for the determination of the protection grade of the deep sea island.

The invention relates to the technical field of data processing and distribution, and discloses an internet of things data distribution engine algorithm. The method comprises the following steps: firstly, cleaning and standardizing collected original power data; performing feature extraction and semantic analysis, and generating a dynamic priority score representing the real-time emergency degree of the data and a static importance factor representing the inherent importance of the equipment by calculating a plurality of dynamic feature indexes of the equipment and combining the business metadata of the equipment; a hierarchical scheduling strategy is adopted, secondary judgment based on the change trend is started for uncertain data, and accurate routing of the data to channels with different priorities is achieved; finally, the system dynamically adjusts a distribution threshold value according to actual service utility feedback of data distribution, and closed-loop optimization is formed; according to the method, the problems of key information submerging, response delay and extensive resource allocation in massive Internet of Things data are effectively solved, and low-delay transmission of high-value data and self-adaptive optimization of system resources are realized.

The invention discloses an intelligent form processing method and system based on rule tree optimization and large model screening, and relates to the technical field of artificial intelligence and data automation. The problems that the processing efficiency is low due to static solidification of a rule system, redundancy of execution paths and dispersion of homogeneous rules in a traditional form processing method, and the screening accuracy is low and the labor cost is high due to the lack of effective judgment capability for complex, fuzzy or implicit condition data of semantic understanding are solved. By constructing and optimizing the rule tree structure and combining with the semantic comprehension ability of the large language model, automatic grading processing and intelligent screening of the form are realized. Firstly, deterministic rule matching and filtering are completed by a rule tree, then semantic judgment is carried out in a logic context by utilizing a large model for uncertainty data, and finally, a screening result is fused and output. According to the method, the efficiency, the accuracy and the automation level of form processing in a complex business scene are remarkably improved.

The present invention discloses a deep learning-based precipitation inversion method and system for satellite data. The method comprises: using data acquired from satellite data in a preset area within a specified time as the data to be analyzed; determining a first precipitation area using the determined data, performing uncertainty estimation on the uncertain data to obtain a second precipitation area, performing rainfall scale prediction on the data to be analyzed within the first and second precipitation areas to obtain a precipitation scale probability; extracting precipitation scale features from the data to be analyzed to obtain precipitation scale characteristics, and performing feature fusion on the precipitation scale probability and the precipitation scale characteristics to obtain fused data; constructing a deep learning precipitation inversion model based on the attenuation rate using the fused data, outputting the data to be inverted to the deep learning precipitation inversion model, and outputting an inversion result. This method not only improves the accuracy of satellite data precipitation inversion but can also be directly applied to satellite data precipitation inversion systems.

The invention discloses an industrial abnormal data detection method based on a weighted multilayer width learning system in the technical field of industrial data detection, which comprises the following three stages: firstly, performing n times of feature mapping on input data X to obtain mapping features; secondly, inputting the mapping features into an enhanced node layer, and performing random transformation activation to obtain enhanced features; and finally, the mapping feature nodes and the enhanced feature nodes are all connected to output, a model output weight W is calculated by using a least square method, and a weighted loss function is introduced. According to the method in the scheme, the feature learning ability is improved, and the negative influence of uncertain data such as training sample abnormal values and noise values on the training result is relieved.

The invention discloses a fatigue recognition method for a target person based on machine learning, and the method comprises the steps: taking data obtained from a worker database in a preset industrial scene as to-be-analyzed data; performing difference comparison on the to-be-analyzed data and the reference database, taking the to-be-analyzed data with difference as first data, and otherwise, taking the to-be-analyzed data as uncertain data; performing consistency comparison on the uncertain data by adopting the undetermined database, taking data without differences as problem data, and adding the problem data into the first data; performing fatigue feature extraction on the first data to obtain key features, performing personalized time sequence analysis on the key features to obtain second data, and performing multi-modal dynamic fusion on the second data by adopting an attention mechanism to obtain fused data; and performing fatigue judgment according to the fusion data based on a dynamic threshold value to obtain identification data, and constructing a target person fatigue identification model according to the identification data.

The present invention provides a method and system for detecting prefix hijacking for uncertain sources. The method comprises: obtaining data from the routing information base, resource public key infrastructure, and Internet routing registry in the Border Gateway Protocol; constructing prefix cloud droplets from the time, space, and data source dimensions based on cloud model theory, and deriving deterministic data and uncertain data based on the discreteness of the cloud model; performing spatiotemporal stability analysis on the deterministic data, dynamically scoring each prefix cloud droplet, and establishing a dynamic deterministic information base; for the uncertain data, performing online route detection using a custom dictionary tree; utilizing prefix coverage and matching rules in combination with a dual verification mechanism to determine whether the route is legitimate, thereby completing anomaly detection and online updating. The present invention proposes a prefix hijacking detection method based on cloud model theory and a Trie tree to overcome the characteristics of the Border Gateway Protocol, such as dynamic changes, complex and large-scale networks, and uncertain data.

The application provides a dynamic scheduling method for uncertain data-intensive workflow in a cloud environment, solves the scheduling problem caused by the lack of transmission data size information, and helps to simultaneously reduce the cross-data center data transmission amount of the workflow and the execution cost of the workflow. First, the workflow structure is abstracted to obtain a DAG graph; then, static task pre-allocation is performed under the condition of partial data size information missing, an executable task forest graph of each data center is obtained, each task node in the forest graph is sorted according to the saved transmission data size, the task node with the largest saved data transmission size is allocated in the corresponding data center, and the predecessor node and the successor node of the node in the data center are also allocated in the data center, until all tasks are pre-allocated; then, dynamic adjustment of task allocation is performed based on the static task pre-allocation result and the actual transmission data size generated after the execution of each task in the workflow, and finally, an allocation scheme is obtained.

The present application relates to the technical field of intelligent adjustment of air conditioner, in particular to a kind of air conditioner resource flexible regulation and control method of simplifying easy operation implementation, including establishing adjustment model, suggestion calculation model, establishing distribution model and closed loop regulation, beneficial effect is: by introducing the numerical concept of power transfer proportion, so only need to carry out preliminary data collection, power transfer proportion can be calculated based on the preliminary collection of data, so in subsequent adjustment process, the environmental data needed to be collected, only need to be calculated based on power proportion to realize the intelligent air conditioner adjustment based on power grid, and by calculating the power proportion data of different time periods, hedge a large number of uncertain data and factors, greatly reduce the difficulty of data collection and analysis, improve the simplicity of regulation.

The invention provides a graph convolution heart disease detection method fusing trust loss and decision reclassification. Belongs to the technical field of medical information intelligent processing and solves the problem that uncertain data affects classification precision in heart disease detection. According to the technical scheme, the method comprises the steps that firstly, a heart disease information data sample set is collected, then the data are preprocessed, and an adjacent matrix is constructed; inputting the processed heart disease information data into a graph convolutional neural network GCN for model training; and finally, the model enters a test stage, a preliminary classification result is obtained and then enters a decision-making layer, uncertain node information is screened out according to a threshold value, and a Gaussian kernel is used for mapping, so that the uncertain node information can be classified at a high dimension, and a re-classification effect is achieved.

The present invention relates to a heterogeneous platform parallel optimization method for a hierarchical step transformation loop with dependencies, belonging to the field of electronic information technology. It includes: 1) The CPU preprocesses the outer-layer data dependencies; in the hierarchical step transformation loop, the outer-layer dependent data is extracted from the entire hierarchical step transformation loop and independently executed for calculation on the CPU side; 2) The hierarchical loop is adjusted to a single-layer loop; 3) The remapping of the dependent array after extraction is performed. The present invention extracts the outer-layer dependent data from the entire loop body and performs pre-calculation in the CPU. Thereby, it avoids the repeated startup and shutdown of the kernel function, resulting in time waste. The present invention proposes a method and steps for changing the hierarchical step loop to a single-layer loop, and at the same time modifies the data access method, reducing the frequent transmission of uncertain data and improving the execution efficiency.

The invention provides a probabilistic nearest neighbor query method on high-dimensional continuous uncertain data, and relates to a method for quickly retrieving the nearest neighbor of the uncertain data by combining a locality sensitive hashing technology and probability calculation. The method comprises the following steps: firstly, constructing locality sensitive Hash for mapping, and mapping the uncertainty of a data object into a plurality of Hash tables through sampling; in a query stage, candidate adjacent objects are extracted from a given query point by using an index, and the probability that each candidate object becomes the nearest neighbor of the query point is calculated through Monte Carlo simulation. And finally outputting the neighbor object with the maximum probability and the probability value thereof. According to the method, the efficiency of high-dimensional uncertain data nearest neighbor query is greatly improved while the query accuracy is guaranteed, and the method can be widely applied to the fields of big data analysis, uncertain information retrieval and the like.

The application discloses a flotation condition recognition method and system based on a weighted K nearest neighbor algorithm, and the method comprises the following steps: setting fuzzy multi-neighbor particles for fuzzy and uncertain data and different distributions of various features in a bubble image data set; introducing the concept of entropy to calculate the information amount provided by the fuzzy multi-neighbor particles induced by a feature subset; fully considering the multiple relationships among the bubble image features under the fuzzy multi-granularity information theory framework; constructing a target evaluation function according to the multiple relationships among the features, calculating the function value of each feature, and sorting the features; weighting the K nearest neighbor algorithm by using the calculated feature function value; and selecting the feature subset with the highest classification accuracy as the optimal feature subset for the flotation condition recognition. The application fully considers the multiple relationships among the bubble image features, can fully utilize the useful information provided by the features, and has high classification efficiency and classification accuracy.

The application provides a kind of fusion trust loss and decision reclassification method for graph convolution heart disease detection.Belongs to medical information intelligent processing technology field, solved in heart disease detection, the problem of uncertain data affecting classification accuracy.Its technical scheme is: first, collect heart disease information data sample set, then, the above data is pretreated, and an adjacency matrix is constructed;Then, the processed heart disease information data is input into the graph convolution neural network GCN for model training;Finally, the model enters the test stage, gets the preliminary classification result and enters the decision layer, filters out the uncertain node information according to the threshold value, and maps it using the Gaussian kernel, so that it is high-dimensional separable, and achieves the effect of reclassification.

The invention discloses a power system steady-state operation control method and system driven by uncertain data, and the method comprises the steps: collecting historical operation data of a new energy power system, and carrying out the data preprocessing of the historical operation data, and obtaining first power data; inputting the first power data into a preset GAN enhancement model, obtaining corresponding analog data based on a generator, and inputting the analog data and the first power data as input data into a discriminator to obtain second power data similar to the first power data, the GAN enhancement model comprises the generator and the discriminator; and inputting the second power data into a preset dynamic optimization strategy network to obtain an optimal control strategy of the power system, and controlling the power system based on the optimal control strategy. According to the invention, the steady-state operation condition of the power system can be accurately and efficiently evaluated, and the steady-state operation of the power system after the new energy is accessed is ensured.

The invention relates to the technical field of operation and maintenance anomaly detection, and discloses an intelligent operation and maintenance anomaly detection method and system based on fuzzy logic, and the method comprises the steps: carrying out the modeling of operation and maintenance data through the fuzzy logic, so as to process the uncertainty and fuzziness of the data; analyzing the operation and maintenance data by using a fuzzy logic model to identify an abnormal mode; and outputting an anomaly detection result. According to the method, the fuzzy logic is deeply integrated into the whole anomaly detection process, an intelligent operation and maintenance anomaly detection mechanism with accuracy, adaptability and interpretability is constructed, the limitation of a traditional method in processing uncertain data is effectively overcome, and the intelligent level and fault response efficiency of an operation and maintenance system are remarkably improved.

The invention discloses a part quality grade classification method and device based on uncertain data pattern classification, equipment, a medium and a product, and relates to the field of artificial intelligence. The method comprises the following steps: firstly, acquiring part size data including multiple dimension characteristics of different parts and different processing stages of a part monitored by a sensor; performing data preprocessing on the part size data to obtain preprocessed part size data; a fitness function of a genetic algorithm is improved based on granularity and an elastic network, and a feature selection genetic algorithm is formed; screening a plurality of dimension features of the preprocessed part size data by using a feature selection genetic algorithm to obtain an optimal feature subset; extracting feature data corresponding to the optimal feature subset from the preprocessed part size data, and constructing a feature data set; based on the feature data set, part quality grade classification is carried out by using the evidence K neighbor model, so that the efficiency and accuracy of part quality grade classification in an uncertain data environment can be remarkably improved.

The invention provides an uncertain information source-oriented prefix hijacking detection method and system. The method comprises the following steps of: acquiring a routing information base, a resource public key infrastructure and Internet routing registration center data in a border gateway protocol; prefix cloud droplets are constructed from time, space and data source dimensions based on a cloud model theory, and deterministic data and uncertain data are obtained according to the dispersion degree of a cloud model; performing space-time stability analysis on the deterministic data, dynamically scoring each prefix cloud droplet, and establishing a dynamic deterministic information base; for uncertain data, a self-defined dictionary tree is used for carrying out online detection on a route; and judging whether the routing is legal or not by utilizing prefix coverage and matching rules and combining a dual verification mechanism, so as to complete anomaly detection and online updating. The invention provides a prefix hijacking detection method based on a cloud model theory and a Trie tree so as to overcome the characteristics of dynamic change of a border gateway protocol, complex network, large scale, data uncertainty and the like.

The invention discloses a distributed uncertainty multidisciplinary optimization design method and system for a carrier rocket, and aims to process uncertain parameters, convert high-dimensional uncertain data into low-dimensional independent variables, reduce the calculation amount and improve the optimization design efficiency. When the data in the quantization parameter matrix is reconstructed, the optimization problem is solved based on the quantized parameters to obtain the probability density function, compared with a traditional uncertainty quantization means, the method can more accurately describe the probability distribution of the uncertain parameters, a reliable data basis is provided for subsequent optimization design, and the probability density function is obtained. The later calculation result is more accurate and does not need to depend on design experience. The constructed subject uncertainty and subject analysis agent model replaces high-cost high-fidelity subject analysis, and by constructing an offline agent model and combining with high-performance parallel calculation, frequent calling of a high-fidelity model in a UMDO solving process is avoided, and the calculation efficiency is remarkably improved.

The invention discloses an uncertain industrial process robust fault diagnosis method based on interval distribution analysis. The method comprises the following steps: step 1, establishing an uncertain data interval representation model based on a kernel density estimation model; 2, uncertain process feature extraction based on a complete information principal component analysis algorithm: adopting an uncertain data driving algorithm based on complete information principal component analysis to carry out potential structural information feature mining on uncertain process data expressed in an interval form, therefore, extraction of uncertain fault feature information and multivariate variable dimension reduction are realized. And step 3, constructing a fault diagnosis model based on an interval radial basis function neural network algorithm: constructing a diagnosis model suitable for interval value fault data based on interval fault feature information extracted by a complete information principal component analysis algorithm constructed in the second part, and completing offline modeling of the diagnosis model by adopting a subclustering algorithm.

This application discloses a foreground data generation method performed at a computer device. The method includes: obtaining a target image, the target image containing a target object and a background; removing the background from the target image to obtain initial foreground data of the target object in the target image; expanding the initial foreground data and eroding the expanded foreground data; blurring the eroded foreground data to obtain certain foreground data and uncertain data from the initial foreground data; and segmenting the certain foreground data from the uncertain data, to obtain target foreground data of the target object in the target image.