18 results about "Information science" patented technology

The application relates to the technical field of medical care information science, and discloses an old-age health prediction method and system, which comprises an information collection module, a phase-locked analysis module and a trend determination module, the information collection module synchronously acquires a behavior perturbation sequence and a physiological response sequence; the phase-locked analysis module calculates dynamic phase offset characteristics between the behavior perturbation sequence and the physiological response sequence, and determines a logical deviation residual of a measured phase offset relative to a reference mapping track by using a logic transfer check unit; and the trend determination module monitors a second-order cumulative change rate of the logical deviation residual in a sliding window, and outputs a warning signal when the second-order cumulative change rate exceeds a stability threshold value; the application can perceive the deterioration of body feedback regulation flexibility before a physiological parameter appears abnormal, and avoids prediction failure caused by a physiological compensation mechanism of an old individual by quantifying the information transmission efficiency of a feedback loop.

This invention relates to a multi-temporal radiation signal inversion method based on Conditional Transformer, belonging to the field of satellite remote sensing data processing / spatial information science and technology. The method includes: acquiring meteorological time-series data of the target area to establish a dataset; designing a Conditional Transformer deep learning network; combining the target time-series data and meteorological data encoded by time and meteorological code generators with the output of the Encoder layer, and inputting this combination into the Decoder layer to train the Conditional Transformer deep learning network to obtain a surface radiation signal inversion model; inverting surface radiation brightness temperature data and evaluating the inversion accuracy; efficiently capturing long-distance spatiotemporal dependencies of radiation signals; flexibly inverting radiation changes according to the target time and meteorological conditions; removing nonlinear interference from the atmosphere on radiation; and more accurately inverting the true radiation characteristics of the surface.

This invention discloses a method for probabilistic quantification of mixed states in urban functional areas by integrating multi-source heterogeneous data, belonging to the fields of geographic information science, smart cities, and artificial intelligence. The method includes: S1, constructing a traffic analysis area; S2, acquiring multi-source data; S3, preprocessing the multi-source data acquired in S2; S4, generating true probability distribution labels; S5, extracting socio-semantic features; S6, extracting dynamic activity features; S7, extracting physical scene features; S8, feature fusion and distribution prediction; S9, model training; and S10, determining the mixed state and outputting the results. This invention achieves refined quantification of the mixed states of urban functional areas, outputting a continuous probability distribution vector rather than a single category label, significantly improving the information density and application value of the recognition results.

The application discloses a kind of perfect preset area history flood disaster database method and system, belong to flood disaster information science and big data technology field;Including the following steps: obtaining preset area history hourly precipitation grid data, generates hourly grade grid map according to rainstorm threshold classification, connects the rainstorm area of corresponding moment of adjacent same grade grid;Combined with the process of spatial and temporal splitting and merging, complete rainstorm process is aggregated;Matching the corresponding city of preset area rainstorm flood related social media data, associate rainstorm process with city, extract corresponding moment data to generate pseudo document to determine city flood event;Linkage disaster city, build cross-city transmission chain;Based on flood propagation chain, flood event number caused by rainstorm, the number of affected cities, the start and end time of each city flood and cross-city chain are written into history flood disaster event database;The application can accurately identify and track flood events in space and time.

This invention relates to an automated penetration testing method based on reinforcement learning, belonging to the field of cyberspace security in computer and information science and technology. First, this invention utilizes environment-related parameters to construct the state space, action space, and reward function of the reinforcement learning model to generate training data. Second, it applies the training data and vulnerability exploitation components to train and generate a reinforcement learning model. Finally, based on a vulnerability exploitation component library, the reinforcement learning model recommends optimal vulnerability exploitation information, thereby automating the entire penetration testing process. This invention addresses the problems of manual reliance in penetration testing and the rigid application of vulnerability exploitation components by employing artificial intelligence technology to effectively improve the accuracy of vulnerability exploitation component recommendations, significantly enhancing the efficiency of penetration testing.

The application discloses a spatial analysis code large model fine-tuning method and system based on data distillation, and belongs to the field of geographic information science. The method first designs a prompt engineering framework that integrates knowledge fusion, data perception and diversity enhancement, and guides the teacher model to generate standardized triplets. Then, high-quality data is screened through multi-layer progressive quality control to form a knowledge distillation dataset, and the student model is fine-tuned based on the dataset to learn the professional ability of the teacher model. The knowledge distillation effect is verified through multi-dimensional comparison and evaluation, and the student model that meets the standard is determined as the final spatial analysis code large model to provide intelligent spatial analysis code automatic generation services. The application realizes the effective transfer of the professional ability of the teacher model to the student model, significantly improves the accuracy and professionalism of the student model in the spatial analysis code generation task, and provides an innovative teacher-student framework knowledge distillation fine-tuning scheme for the intelligent development of geographic information systems.

This invention discloses a method for measuring human brain cognitive maps using long-term temporal behavioral trajectory big data, comprising the following steps: acquiring data, extracting dwell events and movement events, and constructing a "state-action" trajectory sequence; calculating the built environment features of each dwell location; designing grid cell-inspired absolute and relative position codes to convert geographic coordinates into multi-scale, translation-invariant embedding vectors; constructing a Transformer-based encoder-decoder architecture; adopting a Bayesian iterative learning scheme based on the AVRIL framework; using the SHAP method to decompose the contributions of geographic location and built environment; inferring rewards for unvisited locations and generating a mental map heatmap; and outputting prediction performance indicators and interpretability analysis results. According to this invention, the structure is simple, the theoretical foundation is solid, and it possesses advantages such as high performance, high interpretability, and low manual intervention costs, showing broad prospects for promotion and application in the interdisciplinary fields of cognitive science, data science, and spatial information science.

The application discloses a broken surface attribution determination method and device, equipment and a storage medium, and relates to the technical field of geographic information science, comprising: obtaining a to-be-processed broken surface and a plurality of peripheral polygons within a first preset radius of the to-be-processed broken surface from the polygon data set of a target area; establishing an association relationship list based on the to-be-processed broken surface and each peripheral polygon; obtaining a comprehensive score of each peripheral polygon according to the association relationship list, and determining a candidate merging object based on the comprehensive score; verifying the candidate merging object based on the ownership information and data level of the to-be-processed broken surface, and obtaining a verification result; when the verification result is verified, it is determined that the to-be-processed broken surface belongs to the candidate merging object, so that the broken surface merging of the target area is performed according to the to-be-processed broken surface and the candidate merging object. The problem of insufficient attribution determination accuracy caused by single determination decision dimension and lack of compliance constraints in the prior art is solved.

The application relates to a website service classification method combining a graph attention network and contrast learning and belongs to the field of computer and information science. First, the BERT is used to extract text semantic features of a website; second, a webpage DOM parsing tree is constructed according to HTML codes of the webpage, and a webpage relationship graph is generated according to URL links; then, the graph attention network is used to extract structural features of the webpage DOM parsing tree and the webpage relationship graph in combination with a contrast learning method, so that a website structure representation is generated; and finally, the text semantic features and the structure representation of the website are fused to perform website service classification. The application aims at the problem that existing methods do not sufficiently utilize website structure information, and proposes the website service classification method combining the graph attention network and the contrast learning, so that the website structure features are strengthened, and the website service classification accuracy is improved.

The present application relates to a binary function similarity detection method using hypergraph twin neural network, belonging to the field of computer and information science technology. The present application firstly constructs a directed heterogeneous hypergraph containing data flow and control flow hyperedges based on the abstract syntax tree of binary functions; then uses the improved hypergraph twin network to obtain hypergraph embedding vectors, and calculates the cosine distance as the similarity measure of function pairs, effectively learns the structure and semantic features of long-distance statements through multi-layer convolution, uses the initial residual connection and identity mapping to retain the initial features of the statements and avoid the performance degradation caused by the stacking of network layers; finally, the similarity measure is compared with the threshold to determine whether the function pairs are similar. The present application aims at the problem that the existing methods do not generate sufficient information representation of function semantics and structure, and it is difficult to accurately model the mutual influence between long-distance statements in the function, and proposes a binary function similarity detection method using hypergraph twin neural network, which improves the detection accuracy.

The present application relates to the technical field of geographic information science, and discloses a land space multi-element comprehensive observation network construction method, comprising: acquiring characteristic parameters of remote sensing satellites and performance parameters of sensors in a target land space, and determining satellite observation plans and sensor layout schemes; using a consistency checking algorithm to perform multi-source data fusion on the characteristic parameters of remote sensing satellites and the performance parameters of sensors, and realizing dynamic linkage analysis among multiple elements; based on a feature matching and weight distribution algorithm, aggregating multi-source data and eliminating redundant features; performing radiation reference alignment on the multi-source data, constructing a unified space-time reference frame to coordinate the consistency of time stamps, spatial coordinates, spectral bands, observation angles and polarization characteristics, and integrating the consistency coordination results to generate a multi-dimensional data cube. The present application significantly improves the data comprehensiveness and timeliness, and provides a scientific basis for land space planning, ecological protection and disaster warning.

The present application embeds high-sensitivity quantum gravimeter in large equipment which needs to verify identity, measures the gravity characteristics of the environment where the equipment is located in real time, and compiles the gravity characteristics into a complex gravity characteristic signal; when the identity authentication of the equipment in other places is initiated, the gravity characteristic signal needs to be uploaded in addition to the input password, and the network administrator or identity authentication verifier needs to compare and verify the uploaded gravity characteristic signal by referring to the existing gravity field database in addition to verifying the password, so as to approve the real location of the equipment in other places which initiates the identity authentication, and prevent the equipment which steals the password and is in an unexpected location from impersonating identity to access a network with high security requirements or obtain information with high security level. The physical means provided by the present application is an important supplement to the existing information science means and biological means, and provides a new method with stronger reliability for identity authentication in other places, and greatly improves the management and control ability of the communication network to the access equipment.

This invention relates to an intelligent address parsing method based on adversarial error correction using a word segmentation engine, belonging to the fields of natural language processing and geographic information science and technology. The method includes: collecting multi-source heterogeneous address data, standardizing the format to obtain standardized addresses, constructing an address corpus, storing it in a structured database, and simultaneously establishing an incremental update mechanism to ensure the timeliness of the corpus; generating adversarial interference samples based on the corpus, constructing an intelligent address parsing model, generating a noisy training set in real-time at the input layer, outputting a fused feature vector at the feature engineering layer, capturing local and global features at the convolutional and LSTM layers respectively, and performing a second verification at the output layer using pre-trained language methods for initial error correction and adversarial training error correction, outputting a structured result with confidence and error correction records; and batch verifying and labeling errors in the parsing results. This method achieves accurate handling of the diversity and non-standardization of Chinese addresses, reduces reliance on manual intervention, and improves parsing robustness, accuracy, and scenario adaptability.

The application relates to the technical field of medical care information science, and discloses an AI-based old-age care system and method, which comprises the following steps: a signal acquisition module acquires physiological signals of an audience; a feature segmentation module extracts physiological feature vectors by using a physiological feature vector library; a topological modeling module constructs a physiological state directed topological structure; a search matching module maps the physiological topological structure into a binary hash search code by using a local sensitive hash function cluster, and matches a reference topological feature in a physiological normal state library to determine a Hamming distance; and a care instruction generation module determines a physiological abnormal state according to the Hamming distance and generates a care decision data packet. In the application, waveform evolution is converted into a topological atlas, and a high-dimensional search mechanism is introduced, so that the false alarm problem caused by physiological parameter dynamic drift is effectively overcome, and the reliability of the identification of hidden recession is improved.

This application provides a fuzzy geographic detector and its usage method, belonging to the fields of geographic information science and spatial intelligent computing technology. The fuzzy geographic detector includes: a fuzzy spatial stratification unit, configured to stratify the spatial units of the study area using a fuzzy spatial stratification method, and calculate the membership degree of each spatial unit to each stratum; a spatial attribution analysis unit, configured to calculate the representative value of the target variable for each stratum based on the membership degree, and accordingly calculate the reconstructed value of the target variable for each spatial unit; constructing fuzzy statistics based on the estimation error between the reconstructed value and the observed value of the target variable; and an interaction identification unit, configured to superimpose the fuzzy spatial stratification results of multiple explanatory variables using fuzzy intersection operations, and compare the changes in fuzzy statistics before and after superposition to identify the interaction types between the explanatory variables. This scheme can improve the spatial attribution capability and complex mechanism explanation capability of the geographic detector while maintaining compatibility.

The application discloses a kind of wild animal habitat suitability evaluation method based on geographical weighted random forest classification model, belong to ecological information science, geographic information science, wild animal protection and machine learning technical field.The present application is aimed at the defects that existing global model cannot capture the spatial non-stationarity of environment-species relationship and traditional geographical weighted model is difficult to deal with nonlinear classification problem, constructs a spatial self-adaptive local random forest classification model system, completes the whole process of data library construction, spatial weight definition, optimal bandwidth optimization, global suitability prediction, result grading and driving factor analysis.The application significantly improves the habitat assessment accuracy in large-scale, high-heterogeneous habitat areas, can simultaneously analyze the dominant driving factors of species distribution in different regions, provides accurate scientific basis for wild animal habitat protection and nature reserve planning, and adapts to the evaluation needs of multiple types of wild animals.

The application relates to the technical field of medical care information science, and discloses a nursing auxiliary system based on artificial intelligence, which comprises a data verification module, a reference benchmark generation module, an alarm decision module and a monitoring and dispatching module. The system acquires bed displacement parameters, calculates displacement deviation variance, determines a weight adjustment factor, and then generates physiological benchmark reference data by using weighted recursive logic. Meanwhile, the system compares local mean values of physiological parameters with standard evolution vectors, so that the physiological benchmark reference data can be reset. The application dynamically modulates data weights based on physical displacement variance, fits the physiological relaxation process of a patient, solves the invalid alarm problem caused by the disconnection between physiological monitoring data and clinical context, and realizes the optimized scheduling of medical monitoring resources.

The application relates to the technical field of information science, and discloses a real-time network quality evaluation and adjustment method, which comprises the following steps: collecting real-time network quality multi-modal data, performing data fusion and standardization processing on the collected multi-modal data, generating a network quality fusion feature data set, guaranteeing the comprehensiveness and consistency of evaluation basic data, simultaneously using a data cleaning and feature fusion algorithm to process noise and abnormalities in the original data in real time, effectively overcoming the quality evaluation conclusion distortion problem caused by data source deviation and feature missing, guaranteeing the accuracy of real-time network quality evaluation, reducing evaluation errors, and being capable of correcting in real time when evaluation deviation occurs, guaranteeing the dynamic alignment of evaluation criteria and optimization targets, so that the accuracy of network quality perception and the timeliness of decision-making are ensured.