340 results about "Information quantity" patented technology

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 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 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 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 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 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 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 embodiment of the invention provides a data processing method and electronic equipment, and the method comprises the steps: obtaining the length information of target data and the target information amount corresponding to the length information of the target data, and when the target information amount is greater than the target residual capacity, obtaining the target residual capacity of the target data; according to the length information of the target data, the target information amount and the target residual capacity, the target data is packaged to obtain a first data packet, the first data packet is sent to a receiving device, and the length information of the target data is carried in a data header frame of the first data packet and at least one data frame of the first data packet. By means of the data transmission method and device, the problem of data packet loss or analysis errors caused by incomplete length information or incapability of indication when large-capacity data is transmitted due to the fact that the frame capacity of the data header is limited is solved, and then the effects of improving the efficiency of processing the large-capacity data and ensuring the accuracy of data transmission are achieved.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention relates to image processing, in particular to a complex environment-oriented power transmission line inspection image processing method, which comprises the following steps of: screening key frames with high geometric information content by quantitatively evaluating inter-frame motion amplitude and feature tracking quality; extracting features of each key frame by using a pre-trained visual model; performing intra-frame feature aggregation on the features of each key frame by using an intra-frame self-attention mechanism to obtain corresponding intra-frame feature representation; performing global feature aggregation on the intra-frame feature representations of all the key frames by using a global self-attention layer to obtain inter-frame feature representations; constructing a multi-task learning network, and carrying out end-to-end collaborative optimization on depth estimation, image defogging and high-level semantic segmentation; inputting the inter-frame feature representation into a multi-task learning network, and obtaining a restored clear image through deep fusion of scene depth information and image degradation priori; according to the method, the defect that the dual requirements of quality improvement and feature retention of the power transmission line inspection image in a complex environment cannot be met can be overcome.

The invention discloses a privacy enhanced CPPS anomaly detection method based on longitudinal federated learning, and relates to the field of CPPS anomaly detection. According to the method, through longitudinal federated learning, an SCINet model and a Transform model are deployed at clients of a physical side and an information side respectively and are used for local deep feature extraction; in the feature uploading stage, feature compression processing is performed on the physical side data deep features and the information side data deep features, so that effective compression of the uploaded features is realized, and meanwhile, the sensitive information amount possibly leaked in the middle features is greatly reduced. In addition, a bidirectional collaborative optimization mechanism between the client and the server is constructed, and a local feature extraction strategy can be optimized in real time. According to the method, the data privacy protection effect is remarkably improved while the anomaly detection accuracy is guaranteed, the defects of a traditional anomaly detection method in the aspects of privacy security, feature compression effectiveness, bilateral collaborative optimization and the like are overcome, and the practicability and security of CPPS anomaly detection are effectively improved.

The invention discloses a power corridor disaster risk assessment method and system based on an entropy weight method, and particularly relates to the technical field of power corridor disaster risk assessment, and the method comprises the steps: obtaining original multi-source point location data, related to disaster risk assessment, of a power corridor region in the southwest region, carrying out the coordinate unification and time alignment, and carrying out the coordinate unification and time alignment; an original multi-source point location data set is obtained and subjected to quality control processing, the consistency and stability of the original multi-source point location data set are checked, missing values and abnormal values are removed, and the multi-source point location data set is output; on the basis of electric power corridor axis segmentation, double-threshold amplitude limiting conditions of an information amount retention rate and an extreme value order are set, a segmentation optimal interpolation kernel and a time window parameter set are constructed, and an interpolation scale and time aggregation granularity are dynamically backtracked and adjusted, so that risk integral offset caused by terrain fracture and window averaging is eliminated, and the power corridor axis segmentation accuracy is improved. And the credibility of the information amount of entropy weight evaluation is ensured.

The invention relates to the technical field of privacy protection, in particular to a dynamic data privacy protection evaluation method and system based on big data analysis, and the method carries out the analysis of information importance based on the privacy type condition, the correlation derivation condition and the information amount condition of information in a corresponding scene. The corresponding information risk is evaluated through the analysis of the stealing condition of the corresponding information in the scene and the stealing means in the scene, and the information stealing hazard is evaluated through the information importance analysis result, the information risk evaluation result and the hazard condition generated by stealing. The encryption means are dynamically allocated according to the information stealing hazard assessment results of different levels, and the encryption means are dynamically allocated according to the information stealing hazard assessment results of different levels, so that waste of resources can be avoided.

The invention discloses a performance self-improving large model recommendation system and method, and the system comprises an initialization module which is used for pre-training a large language model through supervision and fine tuning, and generating an initial recommendation model; the self-optimization module comprises three iteratively executed sub-modules; the sample selection sub-module is used for screening historical data samples of which the information amount is higher than a threshold value on the basis of comparison between the model prediction probability and the preset threshold value; the response fusion sub-module is used for generating K candidate responses for a selected sample and generating a preference data set based on model evaluation; and the DPO optimization sub-module is used for updating model parameters by utilizing the improved DPO loss function and generating an optimized recommendation model. According to the method, the dependence on static preference data in the prior art is broken, the recommendation quality and robustness are improved, and adaptive optimization is realized.

The invention provides a meteorological radar image sequence prediction method based on self-adaptive physical equation embedding, and belongs to the technical field of meteorological prediction.The method comprises the steps that firstly, a historical meteorological radar image sequence is obtained and input into a depth sequential network model for spatio-temporal feature extraction; constructing a radar image sequence prediction framework based on an adaptive physical equation; training a constructed radar image sequence prediction framework through a composite loss function; and outputting a prediction result of the future radar image sequence. Physical equation guidance is added in the loss function, information is supplemented from the mechanism level, the problem that the information amount of a data-driven deep learning method in long sequence prediction is insufficient is solved, and the effect of a model in long sequence prediction is improved.

A method and apparatus for automatic outlier detection in data sets are provided. An ensemble of outlier detection operations is generated by selecting particular features of the data set, selecting particular algorithms to process those features, and running the selected algorithms using the selected features to identify potential outliers. Feature selection and algorithm selection can be based on a variety of factors, such as measurements of correlation, information content, effectiveness and diversity. Information content may indicate the amount of information in a feature which is a candidate for selection, and may be measured using an information theoretic entropy or potential data compression rate. Diversity and correlation may measure the extent to which different features, algorithms, or combinations thereof produce different information or results.

The invention discloses an industrial mechanical arm joint rigidity online identification method based on an optimal excitation track, and belongs to the field of mechanical arm parameter identification. The method comprises the following steps: firstly, establishing a mechanical arm rigid-flexible coupling dynamic model, and constructing a linear regression equation taking joint rigidity and viscous damping as parameters; a Fisher information matrix is derived based on the regression model to quantify the amount of information of the excitation trajectory. And further, parameterizing the excitation trajectory by using finite term Fourier series, and solving the optimal excitation trajectory by using a scalar index of a Fisher information matrix and a sustainable excitation condition as optimization targets under the condition of meeting joint motion constraints. And finally, the mechanical arm is driven to execute the track, motion data are collected and input into parameter identification algorithms such as unscented Kalman filtering and recursive least square, and online and high-precision estimation of the time-varying joint stiffness is achieved. According to the method, the excitation track is designed directly from the information theory, and the precision, robustness and real-time performance of joint rigidity identification are effectively improved.

The invention discloses a dual-channel collaborative unknown signal reconstruction error control method, and belongs to the technical field of wireless communication. The method solves the problem that the existing method cannot give consideration to both the signal reconstruction precision and the information content contained in the signal. The invention provides a scheme of combining dual-channel collaborative quantization and error control of a receiving end, a dual-channel quantization mechanism realizes information complementation through different quantization characteristics, single-channel error accumulation is inhibited, and the signal detail retention capability is improved. And in combination with an interpolation processing method, estimating and compensating quantization information, optimizing reconstruction output, and completing high-fidelity signal recovery. According to the method, the reconstruction precision and fidelity of unknown signals can be remarkably improved, the robustness and adaptability of the system under the condition of lack of prior information are ensured, meanwhile, extreme dependence on the performance of a single-channel quantizer can be reduced, and then efficient balance between computing resources and reconstruction quality is achieved. The method can be applied to the field of wireless communication.

The application provides a multi-view clustering method and system based on a graph attention automatic encoder, relates to the technical field of multi-view clustering, and specifically includes the following steps: selecting a view with the largest information quantity from different views of the same group of nodes; learning a graph structure and node content by using a trained graph attention encoder based on the view with the largest information quantity and node content information, so as to obtain a node feature representation; performing specific constraint on the node feature representation by using an l1,2-norm penalty, so as to obtain a constrained node feature representation; inputting the constrained node feature representation into a self-optimizing clustering module to perform clustering, so as to obtain a final clustering result; and the application applies the graph attention network to multi-view graph clustering, simultaneously reconstructs the graph structure and the node content, and makes the latent representation well preserve the graph structure and the content information of the nodes, so that the application is more suitable for cluster tasks.

The invention provides a glioblastoma patient lifetime prediction method and system, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: obtaining and coding a digital full-slice image of a patient; screening high-information-content image blocks based on information entropy; executing a dendritic calculation rule and a structural plasticity rule through internal learnable dendritic neurons by using a dendritic morphological neural calculation model, and extracting global and local features from the image blocks; fusing the features through feature fusion neurons to obtain depth features; and finally, predicting the total lifetime through the output neurons. By simulating hierarchical calculation and plasticity of neuron dendrites, a double-level feature learning model capable of integrating global context and local details of a full-slice image at the same time is constructed, and the technical problems that an existing prediction method is insufficient in feature integration and limited in model calculation structure are effectively solved. And more comprehensive and accurate prediction of the lifetime of the glioblastoma patient is realized.

The invention discloses an information guide identification and form filling system and method, belongs to the technical field of intelligent information interaction and data processing, and aims to solve the problems of fuzzy demand positioning, fragmentation of information acquisition, low form filling efficiency and insufficient data credibility in traditional information interaction. After initial basic information of a user is collected, a question and answer association graph is constructed through a graph neural network, the initial information is mapped to graph generation node association features, and a guide label is generated through clustering analysis; matching a core guiding main line based on guiding labels and node weights, dividing interaction stages, generating a verbal skill construction guiding plan, starting self-adaptive question and answer to construct an original demand pool, and verifying and integrating the original demand pool into an initial demand information set; and matching a multi-modal acquisition target to generate a standardized data set, correcting structured information to position a target demand, matching a scale template, and realizing intelligent form filling through information quantitative conversion and deletion completion. According to the method, the cross-scene information interaction efficiency and the data availability are remarkably improved.

The invention discloses a model increment training method and device based on dynamic storage, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the model increment training method based on dynamic storage comprises the steps: obtaining at least one piece of original sample data, and determining an information amount score corresponding to the original sample data; determining the original sample data with the information content scores higher than a preset score threshold as training sample data, storing the training sample data in a preset memory, and accumulating the information content scores of the training sample data in the preset memory to obtain a corresponding accumulated information content score; and performing incremental training on the to-be-trained model by utilizing the training sample data in the preset memory to obtain a target model in response to the condition that the accumulated information amount score or the sample number corresponding to the training sample data in the preset memory meets a preset triggering condition. The technical problem that the training effect of a traditional field small model is affected due to data distribution offset of training data is solved.

The invention relates to the technical field of education evaluation, in particular to an English reading understanding ability self-adaptive evaluation method and system. The method aims at solving the technical problems that the traditional fixed difficulty test is easy to cause inaccurate capability estimation, the existing adaptive test is rough in subject description difficulty, the reading skill diagnosis is fuzzy, and the question bank security is insufficiently considered. According to the technical scheme, the method comprises the steps that a pre-training language model is used for conducting word, syntax, chapter and subject multi-dimensional difficulty feature extraction on a reading material; correcting question parameters based on an item reaction theoretical model in combination with answer data; dynamic evaluation is realized by adopting a self-adaptive test engine based on Bayesian capability estimation and maximum information amount topic selection; outputting the fine-grained skill mastering degree through the cognitive diagnosis model with the Q matrix constraint; a question exposure control strategy is integrated to balance measurement precision and question bank safety. And the system finally outputs a capability level, a skill diagnosis radar map and personalized reading recommendation.

The application belongs to the technical field of data processing, and discloses an information pushing method, device, equipment, storage medium and product. The application determines the information quantity corresponding to the information pushing event in response to the information pushing event triggered by the terminal equipment, then selects the corresponding to-be-pushed multimedia information from the information cache pool based on the information quantity, the information cache pool is constructed after being layered based on the initial pushing request requested by the information pushing event, and then the to-be-pushed multimedia information is pushed to the terminal equipment. The application can determine the information quantity corresponding to the information pushing event when the user triggers the information pushing event on the terminal equipment, can obtain the information quantity pushed to the terminal equipment, and then selects the to-be-pushed multimedia information from the information cache pool based on the information quantity, so that the to-be-pushed multimedia information pushed to the user can be effectively selected, and the overall information promotion value is improved.

The present invention optimizes the amount of input information with respect to a language model for determining the confidentiality of information, and enables identification of confidential information with excellent efficiency and accuracy. This information processing system 100 is configured to comprise: an auxiliary storage device 203 for retaining information about sentences; and a processor 201 that calculates, on the basis of the information about the sentences, an anticipated computational cost and certainty factor during confidentiality identification with regard to respective sets of a specific sentence and a peripheral sentence, that generates a peripheral information list indicating peripheral sentences selected on the basis of results thereof, and that inputs specific sentences and peripheral sentences to a language model on the basis of the peripheral information list to thereby identify whether the specific sentences are confidential information.

The present application relates to the field of image processing. The processing method of dark current modeling and outlier detection based on Gaussian mixture model simulates the dark field condition of actual observation environment in the laboratory, and continuously shoots a group of photos at different temperatures according to the exposure time interval of the camera working in the actual observation environment; in each group of photos shot at different temperatures, the gray scale of each exposure time pixel is taken as a vector, and the characteristic vector of the pixel is modeled; the characteristic vectors of all pixels in the photos are clustered by using the Gaussian mixture model, and the Akaike information quantity BIC and the Bayesian information quantity AIC under the clustering are calculated. The present application compares the non-target part of the actually shot picture with the same part of the image shot in the simulated environment, obtains the temperature of the actual observation environment, and further obtains the dark current of the camera shot in the actual observation environment.

The present application relates to the technical field of cross-modal retrieval, and particularly relates to a strong correlation unsupervised cross-modal retrieval method guided by information quantity, which is realized through the following steps: firstly, image local features and global features, and text features are extracted; the image local features and global features are enhanced; the enhanced local features are subjected to regularization processing; then, the image global features and local features are subjected to orthogonal fusion using an image feature fusion network; next, the image features and text features are fused according to a different modal feature information quantity conversion ratio principle using a multi-modal fusion network; finally, different modal features are mapped into hash codes, and Hamming distance is used for similarity sorting, so as to obtain a retrieval result. The present application focuses on the enhancement and fusion of data features, can obtain more semantic information, and improves the retrieval efficiency.