508 results about "Information quantity" patented technology

The invention relates to the field of multi-modal large models, and discloses an optimization method and system for a self-adaptive multi-stage fine-tuning multi-modal large model. The method comprises the following steps: constructing a multi-modal data set; coding modal data in a single group of samples based on a pre-trained multi-modal basic large model to generate feature representation; calculating a task correlation score, a feature information amount score and a gradient scale score of each modal data feature to generate a single-modal score; calculating a modal alignment degree score, an information complementation degree score and a collaborative gain degree score among different modals to generate a cross-modal interaction score; constructing a depth adjustment demand index and an interaction strength demand index according to the two scores, selecting a combination strategy of depth fine adjustment / shallow fine adjustment and strong interaction / weak interaction according to index values, and performing adaptive optimization on the multi-modal basic large model; and verifying whether the model performance meets the requirements or not so as to iteratively optimize to meet the requirements. According to the method, computing resources are saved, and the representation and generalization ability of the model is improved.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

The invention relates to the field of knowledge base construction, and discloses a knowledge base construction method for assisting digital employee question answering, and the method comprises the following steps: collecting internal multi-modal data of an enterprise, including document data, voice data and metadata; carrying out information amount analysis on the collected data, and screening high-value data through an information entropy theory; performing interactive verification on the screened multi-modal data to analyze data consistency; dynamically correcting the verified data, and reducing data noise in combination with a prediction model and an observation model; and performing semantic extraction and clustering optimization on the corrected data to generate knowledge entries. Through the closed-loop process of multi-modal data collection, dynamic correction, semantic extraction, distributed updating and intelligent question and answer, the data processing efficiency, the knowledge base quality and the question and answer accuracy are remarkably improved, and the problems that in the prior art, unstructured data processing efficiency is low, updating is lagged, and dynamic optimization capacity is lacked are solved.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention discloses an image region analysis method based on entropy driving feature enhancement, and relates to the technical field of image analysis and feature enhancement. According to the method, the local channel information entropy is used as a core feature statistical magnitude, and adaptive weighting and strengthening of different importance region features are realized through explicit quantification of image feature information amount; weight distribution is dynamically adjusted according to the characteristic values, the response of a high-information dense area is remarkably enhanced, and meanwhile low-information and noise interference areas are effectively restrained. On the basis, a cross-layer attention mechanism based on entropy prior is designed, feature statistical information is embedded into a gating and weight generation process, and attention distribution with feature significance as guidance is achieved. According to the method, through an entropy-driven adaptive feature enhancement mechanism, the perception capability, the feature discrimination capability and the analysis precision of the model on the salient region of the image are effectively improved.

The invention relates to the technical field of intelligent retrieval of papers, and discloses a paper review expert intelligent recommendation method and system.According to the method, word paraphrasing is carried out on review expert text information and to-be-reviewed paper text information, semantic connotation supplementation and extension can be carried out on text information with limited information amount, and therefore the content of paper review experts is improved. Richer and more useful information is provided; multi-source information fusion is carried out on the expanded text information, the expanded rich multi-source text information can be summarized, extracted and summarized, and comprehensive text information with sufficient information content and high distinction degree is provided; and performing overall vectorization on the comprehensive text information, performing similarity calculation and sorting on the review expert text vector and the to-be-reviewed paper text vector, and screening out review expert information with relatively high similarity as output. The method can effectively improve the matching precision of the paper'small peer 'review experts, reduces the rejection rate caused by inconsistent research directions, and provides technical support for improving the review quality and shortening the review period.

The invention provides a multi-modal content generation AI model construction method and device based on a diffusion model, and relates to the technical field of content generation. The method comprises a multi-source data processing module, a modal priority analysis module, a serialized modal processing module, a diffusion parameter adaptive adjustment module, an interactive feedback integration module and a collaborative output adjustment module. According to the method, multi-modal data input is integrated, model parameters are dynamically adjusted, information entropy is used for evaluating the information amount of the data, the processing priority is set according to the information amount, the recognition capacity of the model for data importance is enhanced, the model parameters are adjusted in real time to adapt to the characteristics of different data modalities, and the accuracy of data processing is improved. And the generation parameters are dynamically adjusted through user feedback, so that the generated content better fits the actual demand and preference of the user, and the consistency and integration of the content are ensured, the attraction of the content is improved and the propagation effect of the content is enhanced by coordinating the style, theme and emotion of the output content.

The invention relates to an event camera target detection method based on sparse Mama, and the method comprises the steps: adaptively discarding no-event and noise marks based on time-space continuity evaluation, capturing a global dependency relationship in space and channel dimensions, and showing the optimal balance between accuracy and efficiency; an information-first local scanning strategy (IPL-Scan) is provided and is used for guiding the model to pay attention to marks with high information content in the scanning process, so that the space context modeling capability is improved; and a global channel interaction module (GCI) is designed to aggregate channel information from the perspective of global space, and global interaction is expanded to a 3D feature space, so that the global modeling capability is further improved.

The invention discloses a method and device for deducing data frequency based on SQL query prices, and the method comprises the steps: constructing queries Q1 and Q2 for data v, obtaining prices p1 and p2, and directly purchasing and returning the frequency of the data v if any price is 0; otherwise, when the pricing algorithm is based on the usage amount, constructing a group of LIMIT queries Qk and querying prices pk corresponding to queries under different k values, and positioning the first element k meeting the condition that pk is equal to p1 as the frequency; and when the pricing algorithm is based on the information amount, constructing a group of HAVING queries Qi and inquiring the price pi of the corresponding query, determining the first element i of which pi is 0, constructing a group of new HAVING queries Qj, inquiring the price pj of the corresponding query, calculating the mean value of all the elements j meeting the condition that pj is equal to p2, and rounding down to obtain the frequency. The method can deduce the occurrence frequency of the original data from the SQL query price, the precision loss is less than or equal to 1, and the method is efficient.

The invention provides a geological surveying and mapping information extraction method and system based on multi-source data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source geological data, including satellite remote sensing data, geological exploration data, geophysical data, geochemical data and ground actual measurement data; preprocessing the multi-source geological data to obtain preprocessed multi-source geological data; based on the amount of information provided by each data source, the frequency of data updating and the accuracy of historical data, calculating a dynamic adjustment factor of each data source; fusing the preprocessed multi-source geological data by using a dynamic adjustment factor to obtain fused geological data; based on the fused geological data, extracting target geological surveying and mapping information by using a geological information extraction algorithm; and performing information screening on the extracted target geological surveying and mapping information to form a final geological surveying and mapping information extraction result. According to the invention, the precision and efficiency of data fusion can be improved.

The invention discloses an RAG information retrieval method based on tree-shaped mixed retrieval, and relates to the technical field of intelligent questions.The method includes the steps that after a to-be-traversed structure tree is initialized according to a hierarchical structure tree of document content in an external knowledge base, the priority of all nodes in the to-be-traversed structure tree is maintained; according to the RAG information retrieval method, traversal is carried out by combining the priority of each node on the basis of a traditional DFS method, and a to-be-traversed structure tree and the priority of the nodes are dynamically pruned and updated in the traversal process, so that the RAG information retrieval method can preferentially retrieve a better traversal path and dynamically prune the nodes with relatively low association degree with user query Query; according to the method, the related information of the Query queried by the user can be quickly read and effectively positioned in the document content with a large amount of information, the advantages of the recall rate and the response speed are integrated, the RAG information retrieval efficiency can be improved, and particularly, the outstanding effect is achieved in a large-scale document quick retrieval scene.

The invention relates to the technical field of target detection, and discloses a multi-stage refined camouflage target detection method based on wavelet enhanced recognition, which overcomes the defect that detection details are omitted due to the fact that global information is not effectively utilized in an existing method and solves the problem of feature redundancy caused by the fact that multi-layer information is not processed. And a position target detection task can be better completed. Firstly, a visible light image is preprocessed through a pre-training model, a feature quantity is expanded through pyramid operation, the calculated amount is controlled while the information quantity is guaranteed, part of initial features are selected for feature enhancement, and discrimination features are screened through a wavelet discrimination enhancement module; selecting the highest-layer features and sending the highest-layer features to an asymmetric convolution Mangbar module to obtain features with global information; processing global information features and distinguishing features through a feature inversion decoder composed of a plurality of guide units to obtain a prediction map, and training the model; when a specific detection task is executed, the trained model is adopted to obtain the prediction map, and the detection task is completed.

A tactile reproduction system is made more efficient by achieving a data amount reduction of a tactile signal while ensuring reproducibility of a tactile sense. A decoding apparatus according to the present technology includes a decoding unit configured to decode tactile coded data obtained by performing encoding of compressing an information amount, on a tactile signal using higher-order perception in a tactile sense. Therefore, a data amount reduction of a tactile signal can be performed in accordance with a tactile characteristic of a human.

The invention is suitable for the technical field of computers, and provides an SATD identification method based on a query strategy combining representativeness and uncertainty, and the method comprises the following steps: data preprocessing and representation; constructing an initial training set; data selection based on a query strategy; stopping training; and analyzing a result. According to the method, the SATD recognition model trained on the basis of the BERT model is superior to an original method in F1 score index; a representative and uncertainty combined query strategy and an active learning method are adopted, a model with excellent performance can be obtained only by training part of data rich in information amount, and the manpower labeling cost is greatly reduced; the specific value of the number of all marked SATD annotations to the number of SATDs in all training sets serves as a stop threshold value, and flexible strategies are provided for different application scenes so as to obtain different SATD recognition models. According to the method, the SATD in the code annotation can be accurately and efficiently identified under the condition of using less data and label cost.

The invention relates to a dynamic desensitization processing method and device for biological medicine classification variable data. The method comprises the following steps of S1, performing data characteristic analysis on classification variables in original biological medicine information; s2, performing random recoding on the original classification variable data to obtain encrypted classification variable data; s3, increasing the number of types of encryption classification variables to exceed the number of types of original codes; an additional category is introduced into the encryption classification variable; s4, establishing a mapping file; and S5, verifying the data. According to the method, characteristic analysis and dynamic desensitization processing are carried out on the data of the classification variables, the risk that sensitive data information is leaked and recognized by a third party is greatly reduced while the real data information amount and statistical characteristics are reserved, and the mapping file is established to record the data corresponding relation before and after desensitization processing; and subsequent result interpretation is facilitated, and the data traceability is enhanced.

The invention provides a page content display method, a related device and a medium. The method comprises the steps that in response to sliding in a first direction on a first content page, a content container sequence is displayed on the first content page, and the content container sequence comprises a target content container; when the target content container is displayed on the first content page, the first target container is displayed in the first direction, and the first content is displayed in the first target container; after the first target container is displayed on the first content page, responding to a second sliding operation in the first direction, displaying a second target container in the first direction, and displaying second content in the second target container. The information amount of the content page of the information flow application can be increased, and the effective information display efficiency can be improved. The method and the device can be applied to various scenes such as page layout, content delivery and information technology.

The invention discloses a power system multi-scene adaptive transient stability evaluation method based on active transfer learning, and the method comprises the following steps: constructing a transient stability evaluation model based on a Transform encoder, building a mapping relation between data and a stable state, and obtaining the source domain data of a power system; constructing a pre-training model based on the source domain data, and when it is detected that the pre-training model enters a new electrical system scene, performing short-term simulation to obtain an unlabeled target domain sample; calculating the information amount of each target domain sample through an active learning strategy; and carrying out fine adjustment on parameters of the model by utilizing transfer learning, selecting a transfer scheme by considering a difference degree between data distribution, enabling the transient stability evaluation model to quickly finish final updating training, evaluating the transient stability of the power system by using the updated model, and outputting a transient stability evaluation result. According to the method, the transient stable state of the power system can be accurately evaluated, and the model updating efficiency is higher.

The invention relates to the technical field of machine learning models, in particular to a geological disaster risk dynamic assessment method based on multi-source data fusion, which comprises the following steps: constructing a basic geographic information database; dividing geological disaster risk areas by adopting a machine learning algorithm; calculating the contribution degree of each environment factor to the geological disaster through an information amount model; fusing the dynamic rainfall data, carrying out weighted fusion on the dynamic rainfall data and the static geological disaster factors, and calculating a comprehensive risk value; dividing risk levels according to the risk values, and generating a geological disaster risk zoning map; compared with the prior art which mainly depends on single static geological data for analysis and has the problems of incomplete evaluation dimensions and poor timeliness, the scheme realizes multi-dimensional fusion analysis of geological conditions and real-time meteorological factors by constructing the geographic information database integrating the multi-source environmental factors and the dynamic rainfall data; and the comprehensiveness and the momentality of risk assessment are obviously improved.

The invention belongs to the technical field of traction network line fault detection, and particularly relates to a traction network line lightning arrester health state evaluation system which comprises a data acquisition layer, a data calculation layer, a state evaluation layer and a terminal database. The data acquisition layer acquires and sends the characteristic information amount of the operating environment and the working condition of the lightning arrester to the data calculation layer; the data calculation layer is used for calculating and processing the characteristic information amount of the lightning arrester and sending related data to the state evaluation layer; and the state evaluation layer is used for evaluating the health state of the lightning arrester and comparing and predicting the future state in combination with the current influence factors. And the terminal database is used for storing data required by the training model and an evaluation prediction result. Lightning arrester leakage current fluctuation caused by traction network load change is converted into a constant component capable of being monitored, state evaluation and future prediction of the lightning arrester are achieved, the operation and maintenance efficiency of equipment is effectively improved, and the situation that faults occur to affect train and personnel safety is avoided.

The invention relates to the technical field of geological disaster assessment, in particular to a non-landslide sample selection optimization method and system based on information amount, and the method comprises the steps: collecting historical landslide data and environmental factor data in a to-be-predicted region, carrying out the importance evaluation of environmental factors, and screening out landslide characteristic factors which have large impact on the landslide susceptibility; carrying out information quantity analysis on the screened landslide characteristic factors, carrying out weight assignment on the factor information quantity according to the importance of the landslide characteristic factors to obtain comprehensive information quantity, and selecting reliable non-landslide samples in an area with relatively low comprehensive information quantity; inputting the data into a prediction model to obtain a prediction result; and carrying out landslide susceptibility evaluation on the to-be-predicted region, generating a landslide susceptibility partition map, and formulating targeted disaster prevention and reduction measures. According to the method, a machine learning algorithm and an information amount method are combined, the objectivity of weight assignment of the influence factors is improved, so that more representative non-landslide samples are selected, and the precision of a landslide susceptibility prediction model is improved.

The invention relates to the technical field of computers, discloses an astronomical sensitive data security processing method, device and system based on privacy calculation, and a storage medium, and aims to solve the problems that in astronomical data cross-mechanism joint analysis, original data is easy to leak, the cooperation efficiency is low, and a traditional desensitization or encryption method is difficult to consider security and availability at the same time. The method specifically comprises the following steps: each participant deploys a private computing agent node locally, and original data is not out of a domain; the task coordination center issues an analysis task; the proxy node extracts and preprocesses local data, and loads a corresponding secure multi-party computing protocol template; according to the method, share segmentation is carried out on multi-modal data such as images, spectrums and star catalogues by adopting addition homomorphic secret sharing, and distribution is carried out through a national secret SM4 encryption channel; and multiple parties cooperatively execute task-oriented security calculation in an encrypted state, and the result is aggregated and returned with the minimum information amount. The method has the effect of realizing high-precision safe collaborative analysis which is available and invisible.

The invention discloses a geological disaster early warning method based on information amount. The geological disaster early warning method comprises the following specific steps: determining a reference rainfall threshold value of a research area; dividing the research area based on ArcGIS to obtain a plurality of drainage basin units; selecting a plurality of index factors, and calculating the information amount of each index factor based on an information amount model to obtain the information amount of each drainage basin unit; constructing a relationship between the information amount and a threshold adjustment coefficient, and obtaining the threshold adjustment coefficient of each drainage basin unit based on the information amount of each drainage basin unit; obtaining the rainfall threshold value of each drainage basin unit based on the reference rainfall threshold value of the research area and the threshold value adjustment coefficient of each drainage basin unit; obtaining forecast rainfall data, and dividing rainfall grades based on the rainfall threshold value of each drainage basin unit; and determining a geological disaster early warning result based on the rainfall level. According to the method, the rainfall threshold value of each drainage basin unit is obtained based on the reference rainfall threshold value, the information amount of each drainage basin unit and the threshold value adjustment coefficient, so that one unit has one threshold value, and the geological disaster precision and the coverage rate are improved.

The basis of calculating memory capacity of modern computing is bit. Thus, the number of bits is the unit of information quantity of modern communication. The number of neurons (node number) in the neural networks in human brain is not the unit of the memory capacity of the human being. The complexity of neural network is much greater than the bit capacity. Hence, the current AI, which tries to imitate the human brain using computing with the basis on bits, performs inherently different processing of information from the human brain. In addition, computing based on bit number is always facing the limitation of integration. The present disclosure provides a system of information memory without relying on bits using three-dimensional neural networks. By replacing the electrical connection of non-volatile memory cells, which are distributed in a three-dimensional array, the mechanism of the information processing of the human brain can be imitated.

The invention provides a communication system resource allocation method and system based on semantic information, and relates to the technical field of wireless communication. According to the method provided by the invention, a to-be-transmitted image is processed to obtain a semantic importance score of each semantic feature vector corresponding to the to-be-transmitted image; then determining the semantic importance weighted information amount of each semantic feature vector; sorting the plurality of semantic feature vectors from large to small according to the semantic importance weighted information amount to obtain a first sequence; and according to the first sequence, sequentially determining a channel with the minimum communication interruption probability corresponding to each semantic feature vector, wherein the available transmission information amount of the channel is greater than the required transmission information amount. According to the method provided by the invention, the importance scores of the plurality of semantic vectors included in the to-be-transmitted image are determined, and then the channels with small interruption probability are allocated to the semantic vectors with high importance based on the importance scores of the plurality of semantic vectors, so that the information transmission efficiency can be improved.

The invention belongs to the technical field of artificial intelligence and power system crossing, and discloses a marketing and distribution fusion scene-oriented multi-modal large model efficient fine tuning method and system, and the method comprises the steps: firstly constructing a small amount of high-quality multi-modal seed data with a thinking chain by field experts; performing domain knowledge injection and multi-modal alignment on the base model in stages by adopting a parameter efficient fine tuning technology; the method comprises the following steps of: selecting an unlabeled sample, further introducing an active learning iterative loop based on hybrid uncertainty perception, automatically screening the unlabeled sample with the most rich information amount by quantifying cognitive uncertainty and accidental uncertainty of a model, and labeling the unlabeled sample by an expert, so as to expand a data continuous optimization model and form a'fine tuning-evaluation-labeling 'closed loop. According to the method, rapid and accurate adaptation of the multi-modal large model in a marketing and distribution fusion complex scene is realized with extremely low expert labeling cost, the service reliability and safety of model output are ensured, and a long-acting mechanism of sustainable evolution of the model is established.

The invention relates to the technical field of artificial intelligence, and discloses a judgment method and device for AI academic text generation and related products. The method comprises the steps of classifying academic texts to be judged to obtain a target subject category; rewriting the academic text to be judged by using the large model to obtain a rewritten text; calculating the target information amount of the academic text to be judged and the average information amount of the rewritten text, and subtracting the target information amount and the average information amount to obtain a target information amount difference value; based on the first difference value distribution condition data, calculating a first AI generation vote number and a first human writing vote number by utilizing the target subject category and the standardized target information amount difference value; calculating a possibility score that the academic text to be judged is an AI generation text based on the two types of votes; and judging whether the academic text to be judged is generated by the AI or not according to a preset threshold value. According to the method, the source of the academic text to be judged can be accurately judged by using one piece of difference value distribution data without independently training a model for each subject category.

The invention discloses an image retrieval method based on difficult-to-negative sample representation synthesis. The method comprises the following steps: constructing a difficult-to-negative sample representation generation network capable of synthesizing rich information; a batch image constructed by a category equalization sampling strategy is used as network input, batch image representation is extracted through an image representation extraction module, and then the batch image representation is input into an inter-sample global association learning module for learning; inputting the mutually negative sample pairs into a correlation perception channel diversity interpolation module to synthesize difficult-to-negative sample characterization; training the difficult-to-negative sample representation generation network capable of synthesizing rich information, and jointly training an image representation extraction module by using synthesized difficult-to-negative sample representation and real sample representation; performing image retrieval by using the trained image representation extraction module; by combining the global correlation learning ability between samples, difficult-to-load sample representation with more information amount is synthesized, and the image representation extraction module is guided to extract image representation with higher discrimination so as to enhance the retrieval performance.

The invention provides a wind power prediction method based on feature reconstruction. Original meteorological data and original unit data are subjected to expansion processing to generate first input characteristics with time delay, nonlinearity or average characteristics, so that meteorological information and unit operation information can be indicated from multiple aspects, after the first input characteristics are obtained, dimensionality reduction and recombination are carried out on the first input characteristics, dimensionality with large information amount is reserved, and the system performance is improved. In addition, optimization can be carried out in real time in the feature reconstruction process to obtain the optimal reconstruction parameter, the feature reconstruction process is executed according to the optimal reconstruction parameter to obtain the second input feature with the optimal performance, and finally the second input feature is input into the target model to execute the corresponding prediction task. According to the method, the comprehensiveness of the input information is improved through feature expansion, and the data dimension is reduced and redundant and noise information is removed through feature reconstruction, so that the data volume is reduced, the data quality is improved, and the effectiveness and efficiency of wind power prediction can be improved.

The invention discloses a media resource pushing method and device and electronic equipment. The method comprises the following steps: acquiring multi-modal data associated with a to-be-processed resource pushing task; in a graph structure in which each type of media resource data is mapped into nodes, determining an edge weight of an edge in the graph structure based on mutual information amount among the media resource data; under the condition that the edge weight of each edge in the graph structure is determined, determining a current mutual information total amount matched with the graph structure and a fusion feature vector corresponding to the graph structure by utilizing the data feature vector of the media resource data corresponding to each node in the graph structure and the edge weight; acquiring a resource recommendation coefficient corresponding to the fusion feature vector under the condition that the current mutual information total amount meets a convergence condition; and determining a target media resource matched with the resource pushing task according to the resource recommendation coefficient. The technical problem that the recommended content is inaccurate in the prior art is solved.