25 results about "Granular computing" patented technology

The invention discloses a meta universe automatic driving obstacle identification method and system based on granular ball calculation. The method comprises the steps of extracting image space features; according to the local density of each feature point and a conditional purity criterion, carrying out particle-ball division; performing hierarchical aggregation on the pellets according to the spatial proximity relation and the feature similarity; taking each granular ball in the granular ball set corresponding to each image as a node, and determining edges among the granular balls according to spatial positions among the granular balls so as to construct a graph structure corresponding to each image; and inputting the node feature matrix and the adjacent matrix of the graph structure corresponding to the image into the trained graph neural network model to obtain a particle-ball-level obstacle recognition result, and mapping the recognition result back to the original image or the meta-cosmic space to realize pixel-level marking and classification of the target area.

The invention provides a power plant equipment sound abnormity identification and health prediction method based on granular computing and an LSTM network, and the method comprises the steps: employing an array pickup and a high-dynamic-range microphone array in a complex and high-noise background environment of a power plant, and combining an anti-interference filtering and beam forming algorithm, thereby achieving the sound abnormity identification and health prediction of the power plant equipment. According to the method, directional, multi-channel and non-contact sound acquisition is carried out on key parts of equipment, acquired sound signals are preprocessed, converted into time domain, frequency domain and time-frequency domain representations and coded into multi-dimensional numerical vectors, and compared with a rule-based expert system, the method has higher generalization ability and real-time response ability; a large language model is introduced, so that the output is closer to an engineering language and is suitable for operation and maintenance personnel to understand and execute; a self-defined knowledge base or safety semantic filtering is supported, and closed-loop deployment in an industrial field is facilitated; the system can be in butt joint with an intelligent inspection system, and full-link linkage of voice recognition, health assessment and suggestion generation is achieved.

The invention discloses a federated text open intention classification method based on granular ball calculation, which comprises a client training step, a server aggregation step and a client classification step which are completed in sequence, and is characterized in that the client training step uploads obtained pre-training language model parameters and a local granular ball knowledge base to a server; the server aggregation step constructs a global model and a global granular ball knowledge base, and issues the global model and the global granular ball knowledge base to each client; a client classification step: inputting a to-be-classified sample into the global model to obtain a global feature; and comparing the global features with a global granular ball knowledge base to realize intention classification. According to the method, the granular ball structured knowledge is constructed at the client side to realize efficient representation learning, the model and the knowledge are aggregated at the server side to support global reasoning and open recognition, and the method is suitable for a privacy protection scene of multi-client and multi-source heterogeneous text data; and accurate identification of known intentions and effective rejection of unknown intentions can still be realized.

The application provides a multi-modal collaborative evolution irony recognition method and device based on granular computing, and relates to the technical field of natural language processing. The method first extracts sample data from multi-modal, completes preprocessing and irony label labeling, forms a multi-modal irony data set aligned across modalities; then uses hierarchical granularity analysis to perform feature clustering in each modal feature space, constructs multi-modal multi-granularity knowledge representation; then filters a number of granularities with the highest dependency from each modality based on a rough set dependency function, generates a multi-modal optimal granularity matrix; then extracts the prototype mode vector of the known irony label and the test mode vector of the to-be-tested sample from the matrix, constructs an irony order parameter that characterizes the similarity between the two; finally, a collaborative neural network model is constructed based on the principle of synergetics, so that each modal order parameter evolves collaboratively under the mechanisms of self-excitation, self-inhibition and lateral inhibition, and the irony labeling mode corresponding to the highest order parameter is output after weight fusion, realizing irony recognition.

The application provides an information recommendation method, system, device and storage medium, comprising: by associating a special graph structure for different expert networks, the physical and logical decoupling of modal information is realized, and the embedding feature information of the target user and the candidate item in different semantic dimensions can be more accurately described. By calculating the data routing distribution with the user-item interaction instance as the granularity, and combining the progressive routing strategy with the prior routing distribution to obtain a hybrid routing distribution, accurate expert scheduling can be realized in different context environments, that is, it can automatically identify which modal features play a key role in user decision-making in a specific scenario, thereby accurately capturing the fine-grained preferences of users. Through the gating selection mechanism, the feature contribution of key experts is retained, effectively filtering the noise interference generated by irrelevant modalities, and further improving the accuracy of the prediction score and the reliability of the recommendation result.

The invention discloses a smart tourism recommendation method based on granular computing and a type-2 fuzzy set. The method comprises the following steps: acquiring multi-source heterogeneous tourism data; performing uncertainty modeling on the multi-source heterogeneous tourism data by using a Bayesian neural network, outputting posterior distribution, and mapping the posterior distribution into membership function parameters of a generalized type-2 fuzzy set to obtain multi-modal type-2 fuzzy information particles; based on a particle calculation framework, with coverage rate-specificity collaborative maximization as a target, performing optimal particle size distribution on the multi-mode type-2 fuzzy information particles to generate optimal particle size information particles; inputting the optimal granularity information particles into a two-channel deductive learning framework, realizing knowledge-data dynamic fusion by the two channels through a shared attention layer, and outputting a fused tourist preference feature vector; and based on the preference feature vectors, constructing a multi-granularity graph neural collaborative filtering model, respectively generating a personalized recommendation list, a group recommendation list and a socialized recommendation list, and outputting interpretable rules.

The invention discloses a resource semantic perception adaptive granularity network-on-chip atomic operation system and method, belongs to the technical field of network-on-chip, and aims to solve the technical problem of how to fundamentally reduce lock overhead, eliminate protection redundancy and improve resource utilization efficiency and chip concurrency performance through network-on-chip atomic operation. According to the technical scheme, the system comprises a resource semantic library, a plurality of on-chip network nodes, and a dynamic granularity generator, a semantic driving actuator, a source router or a target router which are deployed on the on-chip network nodes; the resource semantic database is used for storing semantic feature parameters of all competitive resources on a chip and providing a basis for granularity calculation and atomic operation strategy selection; and the on-chip network node is used for identifying an operation needing atomic protection according to upper-layer service logic, actively triggering an atomic operation request, packaging key information of the atomic operation into a request packet according to an NoC communication protocol, and sending the generated request packet to the source router through a network interface.

The application provides a virtual network function deployment method and a deployment device. The deployment method allocates resources for VDU required by a VNF to be deployed, takes the total VDU required as a whole group, calculates and schedules resources required by the total VDU in a calculation period in a group granularity, and until the whole group of VDU is scheduled to obtain corresponding resources or the whole group of VDU fails to be scheduled. The deployment position of the VDU of the VNF to be deployed is calculated in a group granularity, and the allocation mode not conducive to the overall deployment is recalculated and adjusted, so that the success rate of VNF deployment is improved.

The invention discloses a health degree assessment method and device for site assets, electronic equipment and a storage medium, and relates to the technical field of software and platforms or other related technical fields, and the method comprises the steps: collecting multi-source data of site assets in a target site, and extracting a feature index value; calculating the health degree score of the site assets under the asset type according to the asset granularity calculation rule and the characteristic index value, and calculating the health degree score of the target site under the asset type according to the site granularity calculation rule and the characteristic index value; and constructing a health degree view of each asset type, inputting the health degree score of the site assets under the asset type and the health degree score of the target site into an evaluation model corresponding to the asset type, and outputting the transformation priority of the site assets in the site through the evaluation model. According to the method and the device, the technical problems of relatively low evaluation efficiency and relatively low evaluation accuracy of a mode of evaluating the health state of the site assets through manual inspection in related technologies are solved.

This application provides a virtual network function deployment method and deployment apparatus. According to the deployment method provided in this application, when resources are allocated to VDUs required by a to-be-deployed VNF, all of the required VDUs are used as an entire group, to compute, in one computation periodicity at a granularity of a group, resources required for scheduling all the VDUs, until the corresponding resources are obtained for scheduling the entire group of VDUs or scheduling of the entire group of VDUs fails. When deployment locations of the VDUs for the to-be-deployed VNF are computed at the granularity of a group, an allocation manner that is not conducive to overall deployment is recomputed and adjusted, so that a success rate of VNF deployment is improved.

The invention discloses a dialogue generation method and system based on user portrait and mixed reward reinforcement learning, and the method comprises the steps: constructing a real user portrait based on real dialogue data; taking the distribution characteristics of the real user portraits as constraints, and generating corresponding virtual user portraits; taking the virtual user portrait as input, performing multi-round dialogue simulation through the dialogue interaction model after condition supervision fine tuning, and generating a virtual dialogue sample; based on the virtual dialogue sample, multi-dimensional rewards are calculated according to granularity and fused, and a mixed reward is obtained; and carrying out iterative updating on the dialogue interaction model by adopting a strategy optimization algorithm based on the mixed rewards. According to the method, various virtual user portraits are generated through potential spatial structure sampling, user coverage is expanded, and generalization ability is improved; and in combination with information entropy gain and other multi-dimensional mixed reward mechanisms, natural progressive expression of intentions and role stability are realized, and the authenticity, coherence and robustness of multiple rounds of conversations are remarkably improved.

The present application relates to the technical field of distributed storage. Disclosed are a metadata addressing method and apparatus, and a medium and a product. A target number of metadata service paths are obtained by filtering metadata feature parameters within a preset time, such that local management is performed on metadata in a large-scale distributed storage file system, and path information of the metadata is recorded and counted to improve the locality management of the metadata; moreover, a first path result is obtained by means of granularity calculation, and granularity selection is performed on the metadata paths to improve the temporal locality of the metadata; in addition, a second path result is obtained by evaluating and calculating the first path result on the basis of a path optimization strategy, such that locality processing is performed on the second path result on the basis of a locality function. Thus, the locality data structure of metadata is changed, thereby ensuring the spatial locality of metadata storage paths in a distributed file system, optimizing the paths for metadata operations in the distributed storage file system and improving the I / O performance of a storage system.

The application discloses a self-adaptive neighborhood granular clustering method suitable for mixed attribute data and belongs to the technical field of machine learning. In view of the problems that the traditional K-means clustering algorithm can only process numerical attribute data and the clustering result is unstable in the prior art, the application provides a self-adaptive neighborhood granular clustering method suitable for mixed attribute data, which comprises the following steps: S1, obtaining and inputting to-be-clustered data; S2, calculating neighborhood granular vectors of the to-be-clustered data by the self-adaptive neighborhood granular clustering method of mixed attribute data; S3, selecting initial clustering centers of the to-be-clustered data based on a dissimilarity measurement method of data; S4, updating the clustering centers by using a neighborhood granular K-means clustering algorithm; and S5, outputting a clustering result. Thus, the K-means clustering algorithm and granular computing are combined, mixed attribute data granulation is realized, and the self-adaptive neighborhood granular clustering method has the excellent characteristics of high applicability and high clustering performance.

The application discloses a kind of federal text open intention classification methods based on granular computing, including sequentially completed client training step, server aggregation step, client classification step, and the obtained pre-training language model parameter and local granular ball knowledge base are uploaded to server in client training step;Global model and global granular ball knowledge base are constructed in server aggregation step, and are issued to each client;Global feature is obtained by inputting the sample to be classified into global model in client classification step;And global feature is compared with global granular ball knowledge base, and intention classification is realized.The application constructs granular ball structured knowledge in client to realize efficient representation learning, and aggregation model and knowledge are supported in server side to support global reasoning and open identification suitable for privacy protection scene of multi-client, multi-source heterogeneous text data, under the premise that user data cannot be centrally summarized, still can realize accurate identification to known intention and effective rejection to unknown intention.

The invention discloses an electric power data transmission method and device based on Hash slicing, terminal equipment and a storage medium, and belongs to the technical field of data transmission, the method comprises the following steps: obtaining original electric power test data of electric power equipment, and carrying out division and Hash operation according to a preset time window to obtain a Hash segment with a Hash number; calculating a first segmentation position of the Hash segment of the non-sudden deformation waveform according to a preset slice granularity, calculating a second segmentation position of the Hash segment of the sudden change waveform according to a preset sudden change detection function and a preset sudden change threshold, and taking a key frame in the Hash segment of the video stream as a third segmentation position, after segmentation, obtaining a plurality of data slices and a slice number of each data slice; and finally, transmitting the data slices according to the preset priority weight of each data slice. By implementing the method and the device, the problem of low data processing efficiency during data transmission caused by a fixed and single data segmentation mode in the prior art can be solved.

The invention provides a long-term PM2.5 concentration prediction method based on granular computing and parallel LSTM, and belongs to the technical field of environmental data prediction and artificial intelligence application. The method comprises the following steps: preprocessing an original PM2.5 concentration time sequence; searching an optimal segmentation scheme by using a particle swarm optimization algorithm, and segmenting the time sequence to obtain a particle sequence; constructing three mutually independent LSTM neural networks, and performing training by taking a mean value sequence, a standard deviation sequence and a sample point number sequence in the particle sequence as input; the three trained LSTM neural networks are used to predict future features, and M prediction triples are obtained; and constructing Gaussian distribution for each prediction triad, generating prediction data points corresponding to each prediction triad, and splicing the prediction data points according to a time sequence to form a complete future PM2.5 concentration prediction sequence. According to the method, the problems of error accumulation and time sequence correlation weakening can be relieved, and the prediction accuracy is improved.

This invention discloses a method, device, and medium for measuring agricultural machinery operation area based on sphere calculation, relating to the field of agricultural machinery operation management and planning technology. The method includes: acquiring raw trajectory data of agricultural machinery operation, wherein the raw trajectory data is a latitude and longitude trajectory sequence collected by a GPS terminal; preprocessing the raw trajectory data to identify the effective operation area based on the preprocessed trajectory data; generating a set of spheres covering the effective operation area based on a sphere generation algorithm; calculating the area of ​​each sphere based on its centroid and radius; and aggregating the areas of all spheres to obtain the agricultural machinery operation area. This invention fits irregular operation areas through sphere coverage, automatically filters out non-operational trajectories, adaptively determines the sphere radius, and introduces Earth curvature correction, significantly improving the accuracy and automation level of agricultural machinery operation area measurement in hilly areas.

The application discloses a configurable control CRM business repeated handling method, comprising the following steps: receiving order creation request, obtaining time influence range and creation information; according to the time influence range and time granularity, the serial number of the influence time period is calculated; according to the creation information, the characteristic value is calculated; according to the serial number of the influence time period and the characteristic value, the characteristic node is generated; the application also discloses a configurable control CRM business repeated handling system; the application can prevent the same request from being repeatedly submitted, causing business repeated handling or abnormality. The characteristic value is obtained from the request parameter, and the unique characteristic node is composed of the time granularity. The unique check is realized by using the characteristic node table. The application eliminates the waste orders created due to repeated submission and the like in the previous system, improves the stability and efficiency of the system, and reduces the workload of system maintenance.

The invention provides a multi-modal co-evolution anti-recognition method and device based on granular computing, and relates to the technical field of natural language process.The method comprises the steps that firstly, sample data is extracted from multiple modals, preprocessing and anti-label labeling are completed, and a multi-modal anti-data set with cross-modal alignment is formed; performing feature clustering in each modal feature space by utilizing hierarchical granularity analysis, and constructing a multi-modal multi-granularity knowledge representation; a plurality of granularities with the highest dependency degree are screened from all the modals based on a rough set dependency function, and a multi-modal optimal granularity matrix is generated; extracting a prototype mode vector of a known antitag and a test mode vector of a to-be-tested sample from the matrix, and constructing a heat sequence parameter depicting the similarity of the prototype mode vector and the test mode vector; and finally, constructing a collaborative neural network model based on a synergetics principle, enabling each modal sequence parameter to co-evolve under a self-excitation, self-suppression and side suppression mechanism, and outputting an anti-vital mark mode corresponding to the highest sequence parameter after weight fusion to realize anti-vital mark recognition.

This invention discloses a method for evaluating the computational resources of a large-scale meteorological model, comprising: step (1) evaluation of the parameters of the large-scale meteorological model; step (2) evaluation of the FLOPs of the large-scale meteorological model; step (3) evaluation of the memory usage of the large-scale meteorological model; and step (4) evaluation of the distributed communication of the large-scale meteorological model. This invention proposes for the first time a multi-granularity joint evaluation framework for computational resources. This framework establishes a parameter calculation model, a spatiotemporal awareness FLOPs evaluation model, a memory usage model, and a distributed communication analysis model by dividing the data into modules, and combines these with the spatiotemporal heterogeneity characteristics of meteorological data to quantify the hardware resources required for the large-scale meteorological model.

The application discloses a new-onset myocardial infarction grading and classification prediction method and device based on granular computing, which comprises the following steps: establishing a new-onset myocardial infarction patient sample from real clinical data; performing single-feature modeling analysis on a plurality of clinical features through an xgboost model to obtain an AUROC value, and retaining a plurality of first clinical features; performing modeling on the plurality of clinical features through a plurality of tree models to output feature importance score of each tree model, and retaining a plurality of second clinical features; performing intersection processing on the first clinical features and the second clinical features to obtain target clinical features; constructing an integrated model and performing training; outputting feature importance score of the trained integrated model, and performing remodeling analysis on clinical features arranged in the front of a set number to output feature importance score of a newly constructed model, and screening out the most important clinical features according to the feature importance score; and verifying the newly constructed model through internal and external data. The application can reduce the cost of collecting data and improve the robustness of the model.

The application discloses a square superpixel generation method based on granular computing, comprising: dividing an input image into a plurality of square blocks under multiple granularities; calculating the purity value of each square block; screening out square blocks with purity values meeting a preset purity threshold as a candidate block set, selecting a set number of candidate blocks in the candidate block set, initializing a global coverage mask and recording the covered image area; processing each granularity in order from coarse to fine, aligning the global coverage mask scale to the current granularity resolution, performing purity calculation, candidate block screening and candidate block selection operations in the uncovered area, and completing the uncovered area at the finest granularity stage to form a multi-granularity square superpixel set covering the whole image and the corresponding global coverage mask. The method can adaptively represent the image as square superpixel blocks of different granularities, and select image blocks with more consistent and stronger representation through the purity evaluation mechanism.

The invention provides a multi-dimensional information time sequence abnormal behavior detection method and system based on granular computing. The method comprises the steps that time sequence behavior data of a to-be-detected user group is acquired and preprocessed; performing granularity division on the preprocessed time sequence behavior data of the to-be-tested user group to obtain a neighborhood relationship of each to-be-tested user, and performing neighborhood equivalent division; utilizing a Markov random walk model to calculate a stationary state of the time sequence behavior data of the to-be-tested user group, and obtaining a structural similarity score of each to-be-tested user according to the stationary state; calculating a low-order time sequence score and a high-order time sequence score of each to-be-tested user according to the neighborhood equivalent division result of each to-be-tested user; obtaining an attribute correlation matrix according to the neighborhood equivalent division result of each to-be-tested user so as to calculate an attribute score of each to-be-tested user; and constructing a self-adaptive multi-dimensional time sequence score by adopting an entropy weight method, and detecting whether an abnormal behavior exists or not based on the self-adaptive multi-dimensional score of each user to be detected.

The application discloses a low-quality medical image classification method, system and device based on granular computing, belongs to the field of low-quality medical image classification, and aims to solve the technical problem of low classification accuracy of existing technologies for low-quality images containing noise, blur and insufficient contrast. The method comprises the following steps: acquiring sample data, converting probability language, constructing and training a classification network model, and classifying images in real time; the constructed probability language term set describes different light and dark degrees of images through five probability languages, each pixel value in each single-channel gray image of a low-quality medical sample image is mapped and converted into a probability language representation through a trapezoidal membership function, and a multi-channel feature map of each sample image is obtained. Through the probability language term set based on granular computing and the trapezoidal membership function, the fuzzy expression can be adaptively adjusted according to the actual characteristics of the input data, so that the fuzzy information can be modeled more flexibly and accurately.