258 results about "Information dispersal" patented technology

Techniques for generating AI-powered responses tailored to a specific entity's communication style. The techniques involve selecting a particular AI agent associated with an entity, receiving a user query, and generating an embedding from it. This embedding is used to retrieve relevant content from the entity's knowledge database. The entity's communication type is then determined. A large language model (LLM) prompt is created, combining the retrieved content and instructions to apply the entity's communication style. This prompt is submitted to an LLM service, which generates an output. A response based on this output is returned to the user. The techniques enable the creation of AI-generated responses that are both informative and aligned with the entity's preferred communication style, enhancing the consistency and effectiveness of AI-powered customer interactions or information dissemination for the entity.

The invention discloses an artificial intelligence data analysis method and system based on machine learning, and the method comprises the following steps: 1, collecting and standardizing original data, and carrying out the embedding processing, and obtaining a data feature vector; 2, mapping the data feature vector into a Riemannian manifold space; 3, calculating the distance between every two sample points by using Gromov-Hausdorff measurement, and establishing an initial causal latent map; 4, constructing a causal potential energy tensor field; 5, obtaining an evolved causal latent map by adopting a Ricci curvature disturbance mechanism; 6, calculating a geodesic distance in the evolved causal latent map as an information propagation path; 7, constructing a causal relationship model; and 8, executing anti-fact reasoning based on the causal relationship model, simulating the causal influence of input variable disturbance on an output result, and performing visual output. According to the method, Riemannian manifold embedding and curvature evolution methods are fused, and intelligent data analysis of causal structure modeling is realized.

The invention discloses a shipping container freight rate prediction method based on artificial intelligence, and relates to the technical field of freight rate prediction.The method comprises the steps that a port state prediction model is constructed through a graph neural network GNN, a port congestion index is calculated based on the port throughput, the ship queuing condition, the berth utilization rate and the transportation scheduling condition, and a port network graph is constructed to predict the freight rate of a shipping container; describing a connection relationship between ports by using an adjacent matrix, performing information propagation by using GNN, and calculating updated port features; a port congestion index is adopted to train an LSTM regression model, the future port state is predicted, and therefore the mutual influence between ports is dynamically captured; and based on the predicted port state, a reinforcement learning RL training agent is adopted to construct a cargo circulation prediction model, a Markov decision process MDP is adopted to define a cargo circulation state, and a deep Q network DQN is adopted to optimize a cargo circulation path, so that the agent can adjust a cargo circulation strategy in different port states, and the cargo circulation prediction accuracy is improved. Therefore, market change is adapted and prediction reliability is improved.

The invention discloses a hotspot mining system and method based on Internet finance and economics information and a medium, and relates to the field of Internet finance and economics hotspot mining systems, and the system comprises an information collection module which collects finance and economics information in a multi-source manner, determines a collection period according to source characteristics, and carries out the screening and storage; the text preprocessing module is used for cleaning and analyzing texts, standardizing terms and carrying out classification labeling; the hotspot evaluation index calculation module is used for calculating indexes such as classification weighting, TF-IDF and the like; the hotspot mining and analyzing module is used for setting threshold mining hotspots, analyzing emotion and the like; and the visual display module is used for visually displaying the hot spots and supporting interaction and pushing. According to the invention, financial information is integrated, and hotspots are mined accurately. The information acquisition efficiency is improved, and decision-making investment and research are assisted. Information spreading is promoted, and financial field development is promoted.

The invention provides an equipment log fault trend prediction method and system based on deep learning, and relates to the technical field of fault prediction, and the method comprises the steps: building equipment, fault and maintenance entity nodes and associated edges thereof through obtaining equipment operation historical data; and performing information propagation and aggregation on the node attribute information and the time sequence attribute information of the edge by using a graph neural network to obtain a node representation vector, performing time sequence segmentation by using a sliding time window to obtain a node dynamic feature, calculating a time sequence autocorrelation coefficient to identify a fault rule and an evolution mode, and constructing a fault prediction model to output a prediction result. According to the method, the graph structure and the time sequence information are fused, the accuracy of fault prediction is improved, and effective guidance can be provided for equipment maintenance decisions.

The invention belongs to the field of geotechnical engineering, and provides a geotechnical engineering investigation data analysis method and system based on machine learning, and the method comprises the steps: obtaining geotechnical engineering investigation data; taking the sampling points as nodes of the rock-soil data graph; according to the spatial proximity relation and the geological similarity, determining edges of the rock-soil data graph, and endowing the edges with weights, so that nodes with similar spaces and similar geological characteristics are connected; carrying out sparse processing on the rock and soil data graph; performing weighted fusion according to the spatial distance weight and the geological similarity weight to form a final rock-soil data graph; a graph convolutional neural network is adopted, and the initial features and the adjacency relation of the nodes are used for information propagation; neighbor information of each node is aggregated through multi-layer graph convolution calculation, and node features are updated step by step; a semi-supervised learning or supervised learning method is adopted to train a GNN model, and prediction error minimization is taken as a target; and deducing rock and soil parameters of an unknown area through the trained GNN.

An information verification method based on a cloud security technology is provided. Before transmitting to-be-verified information including user information, a terminal device performs encryption processing on the to-be-verified information by using random numbers and a public key issued by a verification server, to obtain to-be-verified ciphertext information, which is to be used throughout transmission for logging into a server of a third-party application. The server of the third-party application cannot decrypt the to-be-verified information and the to-be-verified information may be obtained only by the verification server. The verification server transmits an application login success message to the terminal device based on decryption processing on the to-be-verified ciphertext information. In this way, the server of the third-party application may be logged in to without exposing privacy information of a user to the third-party application, thereby improving the security of identity verification.

The invention provides a carbon emission calculation method and device based on power grid prediction, and the method comprises the steps: constructing a graph structure containing a plurality of nodes and edges based on the topological information and operation data of a power grid; the nodes and the edges respectively represent the connection relationship between the devices in the power grid; on the basis of the graph neural network model, feature extraction and information spreading are carried out on the graph structure, a prediction result is obtained, and the prediction result comprises state information of each node; a physical constraint mechanism is arranged in the graph neural network model, and the physical constraint mechanism is used for constraining a prediction result to conform to a physical rule of power grid operation; and outputting a prediction result conforming to the physical rule of power grid operation, thereby solving the problem that the prediction result in the traditional scheme is unavailable and uncredible in the key industrial field, enabling the prediction result to be seamlessly connected to the power grid dispatching and decision-making process with extremely high requirements on safety and stability, and improving the reliability of the power grid. Therefore, the practicability of the intelligent prediction technology of the power grid is greatly improved.

The invention relates to the technical field of multivariable time series prediction, in particular to a time series prediction method and system based on dynamic hypergraph and multi-scale coding. The method comprises the steps of performing data preprocessing on acquired data; constructing a multivariable time series prediction model, wherein the multivariable time series prediction model comprises dynamic clustering hypergraph construction and correlation information spreading and variable output; performing multi-scale time representation learning on the output variable; carrying out iterative training on the multivariable time sequence prediction model; and performing data prediction by using the trained model. According to the method, through dynamic clustering hypergraph learning, precise modeling of the complex space-time dependency relationship is realized.

The invention belongs to the technical field of wind generating set operation parameter monitoring, and aims to solve the problem that information propagation time is different due to the fact that the distance of the relation between devices is different. The invention discloses a wind generating set time sequence data anomaly detection system and method. The system comprises a multi-scale generation module, a graph structure self-learning module, a scale adaptive fusion module and a decoding module. The method comprises the steps of generating data representations of different time scales, representing a relationship between sensors in a graph structure form, self-learning a weight coefficient of each scale layer through a neural network training mode, mapping features of a potential space back to an original space through a three-layer full-connection layer, generating reconstruction data, and obtaining the reconstructed data. The loss is calculated through the mean square error of the reconstructed data and the original data, and the model parameters are optimized by using a back propagation algorithm. According to the invention, diversified interaction modes between sensors can be effectively captured, so that the accuracy and robustness of anomaly detection are improved.

The invention discloses an iterative collaborative learning method for node characterization and a topological structure, which comprises the following steps: acquiring initial graph data containing a node set and an initial edge set, and initializing node characterization; constructing a dynamic edge predictor, calculating the structure confidence degree between the node pairs based on the current node representation, generating a candidate edge weight matrix, and obtaining an optimized adjacent structure through a sparsification strategy; inputting the optimized adjacent structure into a graph neural network encoder, performing information propagation and aggregation in combination with the node feature matrix, and outputting enhanced node representation; the enhanced node representation is fed back to the dynamic edge predictor, and the edge weight and the node representation are iteratively updated until the convergence condition is met; and outputting a final optimized graph structure and node representation for downstream graph analysis. According to the method, through iterative collaborative optimization of the graph structure and the node representation, the graph topology can be dynamically corrected, the discrimination capability of the node representation is enhanced, the method is suitable for various complex scenes such as a noise graph and a sparse graph, and the performance of a downstream graph analysis task is improved.

The invention relates to the technical field of experiment reuse in the field of information spreading, and provides a field scientific experiment-oriented behavior gene framework description method, which comprises the following steps of: analyzing a behavior model to obtain a behavior level description file of the behavior model; determining a behavior subject in an action layer behavior model in the behavior model from the behavior level description file based on the behavior level formalized description template; the action layer behavior model is a behavior gene of the simulation model; and constructing a behavior gene description file of the simulation model based on the behavior subject on the modeling granularity and the modeling structure of the behavior subject. According to the method, the behavior gene description file can be reasonably adjusted and constructed under the condition that new action modeling and original action adjustment requirements are continuously provided for the simulation model, flexible and dynamic adjustment of new achievements of scientific experiments is achieved, and a self-developed behavior model can directly drive a heterogeneous entity model to run.

The invention provides a day-ahead prediction method and device for wind power cluster power, equipment and a storage medium. Relates to the technical field of wind power generation prediction. The method comprises the steps that clustering is completed based on historical multivariate information of all stations in a wind power cluster, cluster virtual nodes are set, and the visual angle of global information is expanded for cluster modeling; and information propagation characteristics between nodes are explored based on a cluster prevailing wind direction. Constructing an adjacent matrix of the dynamic graph, and quantifying the dynamic evolution process of the cluster topology; and finally, mining cluster dynamic information by using a frequency domain channel attention mechanism and STGCN, and completing short-term power prediction of the wind power cluster. The method is a prediction method which considers multivariate information space-time diversity and is suitable for the day-ahead wind power cluster power, and is simple in calculation, high in prediction performance, clear in physical significance, effective in prediction result and high in practicability.

The invention discloses a robot environment identification method based on multi-modal fusion, and the method comprises the following steps: collecting image data, point cloud data and acoustic data in a robot operation environment, and carrying out the preprocessing; inputting the structured multi-modal input sample into a modal perception type quantum encoder of a multi-modal quantum graph neural network; constructing a modal coupling quantum diagram based on the modal quantum state representation set; inputting the modal coupling quantum diagram into an entangled modal propagation unit, and performing cross-modal information propagation of nodes of the modal coupling quantum diagram through a parameterized quantum circuit; quantum measurement operation is executed on the fusion quantum graph embedded representation, and an environment recognition result is output; and inputting an environment identification result into a robot control module, and driving the robot to execute path adjustment, obstacle avoidance or action response. According to the method, the multi-mode quantum graph neural network is adopted, and autonomous recognition of the robot in a complex environment is achieved.

The invention discloses a double-layer high-order network structure reconstruction method and system based on maximum likelihood estimation, and relates to the technical field of network reconstruction. The method comprises the following steps: constructing a double-layer network model based on an information transmission model and a disease transmission model; a likelihood function is constructed based on the node state time sequence, and items associated with an information diffusion layer and items associated with a disease transmission layer are decomposed; according to the method, likelihood function approximate solution is carried out through a state average field approximation method, a second-order Taylor expansion method and a two-stage reconstruction strategy; according to the first-order Taylor expansion method, likelihood function approximate solution is carried out through a state average field approximation method and the first-order Taylor expansion method; and network topology reconstruction is carried out on the basis of a solving result. A dynamic coupling process of two layers is established through an interlayer coupling mechanism; layered optimization is realized by decomposing a likelihood function; and finally, the likelihood function is converted into a linear equation or an equation set for solving, so that the calculation complexity is reduced, and the reconstruction precision is ensured.

The invention is suitable for the technical field of information propagation prediction, and provides a user generated content popularity prediction method based on retrieval enhancement prediction. According to the method, a user generated content (UGC) knowledge vector library is constructed, a retrieval enhanced prediction (RAP) method is designed, and a dual-path retrieval, high-speed recall and retrieval content decoupling noise reduction architecture is adopted, so that the performance bottleneck problem of existing UGC popularity prediction is effectively solved. Wherein the two-way retrieval and rearrangement recall architecture solves the problem of long text characterization deviation; a decoupling noise reduction method for dynamically adjusting a feature fusion weight according to confidence in the downstream deep learning network module solves the problem of retrieval noise interference; the overall architecture design overcomes the strong coupling defect of the model, so that the model can adapt to dynamic updating of the retrieval library, and the universality and generalization ability are improved.

The invention discloses a physical field data restoration method and system based on diffusion and iterative resampling, and the method comprises the steps: initialization: filling a missing region with random noise in to-be-restored physical field data, and obtaining an initial noisy field; forward diffusion: gradually adding noise into the complete data until the complete data becomes complete random noise data; noise model pre-training: training random noise added in each step in forward diffusion; inverse diffusion denoising: gradually restoring the image from the initial random noise through the noise predicted by the model and an iterative formula; and iterative resampling correction: in the reverse diffusion process, iterative circulation is carried out in a de-noising-noise adding-re-de-noising mode, so that the model can be effectively forced to spread information of a known area to a missing area, and the physical consistency and smooth transition of a repair result and a known data boundary are ensured. Any prior information of the missing area does not need to be preset, the repairing precision is high, and the physical consistency is good.

The invention discloses a short video platform-oriented multi-modal event information propagation strategy identification method and system, and the method comprises the steps: S1, data collection: obtaining data from a short video platform, including video image data, audio data, subtitle text data and user interaction data; s2, data processing: processing the collected video image data, audio data and subtitle text data, and establishing a feature library comprising the video image data, the audio data, the subtitle text data and user interaction data; s3, propaganda strategies are defined and classified; and S4, performing directional analysis according to feature requirements of different propaganda strategies, and performing event information propagation strategy identification. Through multi-modal data analysis, the characteristics of video images, audios and texts are integrated, the limitation that in the prior art, analysis only depends on single dimension is broken through, and the accuracy and comprehensiveness of propaganda strategy recognition are remarkably improved.

The invention relates to a multi-hop RAG question and answer method and system based on path exploration and hyperbolic refining. The method and system are used for solving the problems of reasoning path stiffness and evidence noise interference in the multi-hop question and answer process. The method comprises the steps that complex multi-hop query is received, a multi-level path exploration search strategy is adopted, a hierarchical inference tree is constructed through Monte Carlo tree search, and atomic operations such as direct answering, retrieval answering and sub-question decomposition are sequentially executed to explore an optimal inference path; performing hyperbolic refining on candidate evidences retrieved in the construction process, constructing an evidence graph and performing information spreading by using a graph attention network, thereby performing dynamic reordering and noise suppression on the evidences; and recursively aggregating the processed intermediate question and answer results from bottom to top, and generating a final answer by combining an original query calling language model. According to the method, the continuity and accuracy of multi-hop reasoning are effectively improved, and the method is suitable for natural language processing application such as complex questions and answers and knowledge reasoning.

The invention discloses a pseudo label generation method, system and device based on pathological image segmentation and a medium, and the method comprises the steps: segmenting a pathological full-slice image into a plurality of square small-block images, and carrying out the multi-label labeling of the square small-block images; inputting the marked square small block images into a pre-trained segmentation model, and extracting features of the marked square small block images to obtain a feature map; based on the feature map, information spreading is carried out by using an attention mechanism, and feature representation of the nodes is updated; according to the feature representation of the updated node and the spatial information of the node, generating probability distribution of each node belonging to different categories, and obtaining a pseudo label; refining the pseudo labels; and optimizing the refined pseudo tag to obtain a final pseudo tag. The image attention network and the global classification maximum pooling technology are introduced, and the accuracy and stability of pathological image segmentation are improved by establishing the context relation between pixels, enhancing transmission of weak supervision signals and optimizing the pseudo label generation process.

The invention relates to the technical field of artificial intelligence, can be applied to service system platforms of medical health, financial science and technology and the like, and discloses a recommendation method based on an enhanced graph neural network. Calculating node attributes and associated edge weights according to the processed user multi-source information, and constructing a graph structure through the node attributes and the associated edge weights; generating a node importance evaluation result according to the graph structure and the processed user multi-source information; dynamically adjusting an information propagation path and a node propagation weight according to a node importance evaluation result and feature correlation; and carrying out cooperative processing on the target recommendation task according to the information propagation path and the node propagation weight through an attention mechanism and a graph neural network, and outputting a recommendation result. According to the method, user behavior changes are captured in real time through a dynamic graph structure, invalid information is filtered in combination with a self-adaptive propagation mechanism, and accurate personalized recommendation is realized through cooperation of an attention mechanism and a graph neural network.

To compare information transmission in an unspecified field with information extracted from another information source.SOLUTION: A non-transitory computer-readable recording medium storing a program for operating a computer, the program causing a processor of the computer to execute: receiving designation of a text; and extracting, from an information source available to an artificial intelligence system, at least one source text including a description similar to a designated text that is the designated text. Generating a prompt including an instruction to output information about the source sentence and an instruction to compare the extracted source sentence with a specified sentence, and presenting, to a user, the information about the source sentence and a result of the comparison between the specified sentence and the source sentence, based on an answer obtained from a large scale language model provided by an artificial intelligence system by inputting the prompt to the large scale language model.SELECTED DRAWING: Figure 10

The invention discloses a detection and positioning method for multi-modal image-text deep counterfeiting based on a Mangbar module. The method comprises a multi-modal feature alignment module, a multi-modal feature fusion module and a counterfeiting area detection and positioning module. The method comprises the following steps: firstly, searching a corresponding relationship among different data modals of the same instance through a multi-modal feature alignment module, then, fully fusing features in a multi-modal feature fusion module, learning feature representations among the features, and finally, inputting the fused features into a forged region detection and positioning module, so as to detect the forged region. And specific information of detection, area positioning and forgery methods is given. The method can effectively detect the multi-mode deep counterfeit information, can give a counterfeit area and a counterfeit mode, can prevent the transmission of false information, and guarantees the reliability of an information source.

To solve the problem that people trying to gain a new idea have difficulty expanding a new idea exclusively by a singular function such as obtaining new knowledge, and unable to achieve any refined process.SOLUTION: Provided is an ideation assistance system comprising a processor and a storage device. The storage device holds one or more ideas. The processor executes idea divergence processing to increase the number of ideals held in the storage device and idea convergence processing to reduce the number of ideas after having been increased by divergence processing. In the idea convergence processing, the processor calculates, for each of a plurality of ideas, a degree of importance that decreases as a possibility of replacement with another idea included in the plurality of ideas increases, and selects an idea having a low degree of importance as an idea to be delete, thereby reducing the number of ideas. For ideas which have not been selected as ideas to be deleted among the plurality of ideas held in the storage device, The processor determines, on the basis of the degree of mutual similarity, whether or not the ideas have converged. When it is determined that the ideas have been converged. The processor executes the idea divergence processing again.SELECTED DRAWING: Figure 1

A multilingual text cognitive rumor detection method, system, product and medium relate to the field of electrical digital data processing, the method comprises: obtaining source cognitive response data of a user to a source text in a source language area, the source text being changed into a target text through an information propagation process; obtaining target cognitive response data of a user to the target text in the target language area, comparing the source cognitive response data with the target cognitive response data to obtain cognitive difference data, and when the information understanding deviation value exceeds a preset deviation threshold value, sending the cognitive difference data to the user; positioning a difference fragment according to the semantic feature mapping relationship between the source text and the target text, and determining a tampering position and a tampering degree of the difference fragment; if the tampering degree of the target text is greater than a preset degree threshold value and the number of the tampering positions is greater than a preset number threshold value, judging that the target text is a rumor text; and sending rumor early warning information to an information issuing platform. By implementing the method, the rumor detection accuracy can be improved.

New systems for ensuring the reliable function of systems for laser powering and information propagation using receivers located in premises frequented by the public, such as a store or a public office, using information displays. The systems employ a wireless power transmitter which has a detector for determining whether the premises is operating in a situation characteristic of normal working hours, with people present who may interfere with its correct operation, or whether the situation is typical of closed premises, with low or zero occupancy, enabling the system to operate reliably in what may be termed an “after-hours” mode. Operations which must be performed reliably include charging the receivers, updating of the displays, maintenance of the devices, and software updates. The system can detect the presence of human activity directly, or by implication by the cessation of services such as lighting or air conditioning in the area.

The invention provides a wireless communication method based on a cellular Internet of Vehicles technology, a communication terminal and a vehicle, and relates to the technical field of vehicle communication. The method comprises the following steps: adding a first vehicle-mounted communication terminal and a second vehicle-mounted communication terminal of a target following vehicle in preset formation vehicles to a communication network of a target multicast group, and generating a target multicast routing table, so that a leading vehicle adopts a multicast communication mode, the multicast data can be sent to the second vehicle-mounted communication terminal of each target following vehicle in a targeted manner through the first vehicle-mounted communication terminal, so that the data is prevented from being sent to other vehicles. Due to the adoption of the multicast communication mode, the vehicle-mounted communication terminal of any vehicle in the target multicast group sends the multicast data, and the vehicle-mounted communication terminals of the other vehicles can receive the multicast data, so that unnecessary repeated transmission is avoided, the risk of network congestion is remarkably reduced, the information spreading efficiency is improved, and the user experience is improved. And meanwhile, all target following vehicles can receive the latest instruction in time, and synchronization and stability of a motorcade are maintained.

The invention belongs to the technical field of network information processing, and aims to provide a cross-platform information propagation analysis method, system, equipment and product. The method comprises the following steps: performing multi-modal data acquisition processing from different platforms to obtain multi-source data, and constructing a knowledge graph according to the multi-source data; performing event topic mining processing and sentiment analysis processing on the knowledge graph to obtain event topic distribution information and a sentiment analysis result, and constructing a propagation graph according to the event topic distribution information and the sentiment analysis result; and obtaining an entity type set, an inter-entity relationship and an entity attribute according to the propagation atlas, and constructing a path tracing database according to the entity type set, the inter-entity relationship and the entity attribute so as to carry out cross-platform information propagation tracing based on the path tracing database. According to the method, cross-platform information propagation analysis can be realized, and the propagation path analysis capability is high.

The invention provides a model training method, an information delivery method and device and electronic equipment. The method comprises the following steps: selecting at least one simulated delivery node from candidate delivery nodes in an information propagation network through a first reinforcement learning model; and generating a simulated delivery strategy for each simulated delivery node through the second reinforcement learning model. And respectively generating simulation release information according to the simulation release strategy of each simulation release node, and simulating a forwarding process of a forwarding node based on the simulation release information released by each simulation release node so as to generate a simulation propagation result of the information propagation network. And updating parameters of the first reinforcement learning model and the second reinforcement learning model by taking the forwarding times of the forwarding node in the simulated propagation result as rewards.

To prevent spread of unreliable information.SOLUTION: A system is provided, comprising means of allowing generative AI to intervene in spread of information to prevent spread of unreliable information.SELECTED DRAWING: Figure 1