46 results about "Data disclosure" patented technology

The invention relates to the technical field of data processing, and discloses an electronic manufacturing intelligent production data set generation system based on multi-modal data fusion, and the system comprises a system module assembly which comprises a data collection layer, a data processing layer and a quality management layer. The invention further provides an electronic manufacturing intelligent production data set generation method based on multi-modal data fusion, and the method comprises the following steps: S1, multi-modal data collection: collecting data including enterprise business data, public compliance data, sensor data and video monitoring data; and S2, data preprocessing and labeling. Through a multi-modal data fusion technology, limitation of a single data type is broken through, cross-modal alignment and feature association of images, texts, sensor data and industrial identification data are realized, and the utilization rate of electronic manufacturing scene data is improved.

The invention discloses an evaluation method for dynamically calculating the quality degree based on a commercial network. The evaluation method comprises the following steps: acquiring multi-source heterogeneous commercial network data; wherein the commercial network data comprises enterprise internal data, public data and authorization data; performing data processing and integration on the multi-source heterogeneous commercial network data to obtain a commercial network knowledge graph; analyzing the commercial network knowledge graph by using a graph neural network to obtain an association mode between enterprises; performing risk quantification on the commercial network knowledge graph by using a risk quantification model to obtain a risk assessment result corresponding to the enterprise; and performing enterprise high-quality scoring by using the commercial network dynamic high-quality evaluation model in combination with the risk assessment result, the association mode and the commercial network knowledge graph. In this way, dependence on subject credit and static financial data can be reduced.

Aspects of the subject disclosure may include, for example, receiving first data from a communication device over a mobile network, and determining that the first data is associated with a first user profile associated with the communication device. Further embodiments can include determining that the first user profile is associated with a first group of mobile network resources, and transmitting the first data across the mobile network utilizing the first group of mobile network resources. Other embodiments are disclosed.

Example methods disclosed herein include accessing public housing data for a set of public housing, the public housing data including return path data and small group meter data. Example methods disclosed also include accessing public housing data for a set of public housing, the public housing data including first return path data and respective small group meter data associated with individual public housing of the public housing; grouping the public housing data into viewing segments; classifying the viewing segments based on whether the return path data in individual ones of the viewing segments has matching small group meter data to determine labeled viewing segments; generating features from the labeled viewing segments; training a machine learning algorithm based on the features; and applying second return path data to the trained machine learning algorithm to determine whether a media device associated with the second return path data is on or off.

The invention provides a data protection method and device, a data access method and device, equipment, a medium and a product. The data protection method comprises the steps that first data extracted from to-be-protected target data is publically published to a block chain; encrypting second data extracted from the target data, and storing the encrypted second data; generating a key question and a key answer corresponding to the key question according to the content, which is not disclosed in the first data, in the target data; and storing the key question and the key answer in a smart contract of the block chain, so that the smart contract sends the key question to a second terminal when the second terminal requests the block chain to access the first data, and in response to receiving answer information matching the key answer from the second terminal, transmitting a key for decrypting the encrypted second data to the second terminal.

An artificial intelligence (AI)-based system for detecting malware in endpoint devices using a multi-source data fusion and method thereof are disclosed. The AI-based system collects at least one of: endpoint visibility data, publicly available information, and sandbox analysis data using the multi-source data fusion. By using the collected data, the AI-based system generates one or more numerical embeddings for at least one of: one or more files and one or more applications of each endpoint device using one or more AI models. The AI-based system is configured with the one or more AI models to generate one or more dynamic directed graphs based on the generated one or more numerical embeddings to compute a maliciousness risk score. The maliciousness risk score of at least one of the: one or more files and one or more applications is used for detecting the malware in the one or more endpoint devices.

The invention discloses a block chain-based verification method and electronic equipment, and the method comprises the steps: carrying out the analysis of a transaction request, and obtaining identity certificate data carried by the transaction request; wherein the identity certificate data is generated based on the relationship between the transaction account and the account blacklist and the version information of the account blacklist; calculating the identity data according to a preset function to obtain to-be-verified data; generating first verification data according to a public key corresponding to the account blacklist and the to-be-verified data, and generating second verification data according to the public generator and the to-be-verified data; the public generator is a preset public parameter; and verifying the transaction request according to the identity proof data, the first verification value and the second verification value. According to the technical scheme, the volume of the verification data is compressed, the data transmission amount and the calculation amount of the block chain node in the verification process are reduced, and the verification speed of the transaction request can be remarkably improved.

Methods, apparatus, systems and articles of manufacture are disclosed to project ratings for future broadcasts of media. Disclosed example methods include normalizing, with a processor, audience measurement data corresponding to media exposure data, social media exposure data and programming information associated with a future quarter to determine normalized audience measurement data. Disclosed example methods also include classifying a media asset based on the programming information to determine a media asset classification. Disclosed example methods also include building, with the processor, a projection model based on a first subset of the normalized audience measurement data, the first subset of the normalized audience measurement data associated with a first time frame relative to the future quarter, the first subset of the normalized audience measurement data based on the media asset classification, and applying, with the processor, the programming information to the projection model to project ratings for the media asset.

Aspects of the subject disclosure may include, for example, providing a standard request for usage of one or more frequencies to an automated frequency coordination (AFC) system, where the request includes a location of a low power indoor transmitter that is a subject of the request. The aspects may include receiving data identifying at least one frequency channel and a power limit for the at least one frequency channel from the AFC system, where the at least one frequency channel and the power limit are determined by the AFC system to reduce interference with existing standard power access points based on the location of the low power indoor transmitter. The aspect may include providing the data identifying the at least one frequency channel and the power limit to the low power indoor transmitter. Other embodiments are disclosed.

Techniques for managing data stored within a database, such as a decentralized database are provided. Some techniques involve managing some data within a lower-trust database and some other data within a higher-trust database. A higher-trust database may be a decentralize database including a blockchain. A lower-trust database may store references to data within the blockchain, and optionally other data in association with those references. Disclosed techniques include WHERE clause query handling in databases with reference values, replacement of distinct data in a relational database with a distinct reference to that data, number line storing for secure indexing, APIs for databases, and consensus operations for private blockchain networks.

The utility model discloses a quantum key distribution system terminal based on zero trust. The quantum key distribution system terminal comprises an Alice sending end, a Bob receiving end and a zero trust server, the zero-trust server comprises an identity authentication server and a key management server; the utility model discloses a quantum key distribution system terminal based on zero trust, which respectively performs identity authentication on an Alice sending end and a Bob receiving end through an identity authentication server, effectively prevents malicious operations such as malicious impersonation, resource access and data leakage of a user, realizes continuous verification and monitoring on the user, equipment and a system, and improves the user experience. The current complex and changeable network environment can be met, and the safety and reliability of communication between nodes in the QKD network are improved.

The present application relates to the field of Internet of Things security and computing scheduling, and particularly relates to a kind of end edge cloud multistage multi-domain access control method, which method includes: first according to service level requirement and compliance clause generates intention constraint description and establishes session root and one-time session token;Each layer carries remote proof and execution commitment and submits bid in parallel, and control plane verifies to form a bid package set;Under the constraint, joint arrangement solution is determined, stage is settled and cross-domain data path is determined, and minimum data disclosure rule and minimum feasible plan are generated, and stage token is issued;Each stage executes according to token and generates execution proof, and control plane verifies and enters account after verification, and switches between backup plan and current plan according to verification and operation monitoring, and completes verifiable and traceable operation closed loop.

The application discloses a supplier green dynamic evaluation method and system based on carbon footprint data driving, and relates to the technical field of environmental protection and low carbon, wherein the method comprises the following steps: obtaining effective data of a supplier and green certification data of an industry to which the supplier belongs; performing carbon footprint accounting based on the effective data and the green certification data, obtaining carbon performance results of the supplier, and updating green evaluation information of the supplier according to the carbon performance results; listening to a target event message, refreshing the green evaluation information based on the target event message; and calculating a comprehensive green score of the supplier based on the target event message and the effective data. The scheme can improve the data updating timeliness and industry adaptation accuracy of the supplier green evaluation, timely respond to carbon data disclosure policy requirements, and thus help enterprises improve the green risk management level of a supply chain.

The present invention relates to the technical field of energy management, and specifically provides an operation identification method and device for a user-side energy storage device. The method includes: obtaining the original data of a target enterprise and a control enterprise; wherein, the control enterprise has put an energy storage device into operation. Performing data processing on the original data to obtain the to-be-verified data of the target enterprise and the labeled data of the control enterprise. Inputting the labeled data into a clustering algorithm to output the clustering results before and after the energy storage device is put into operation. Obtaining the identification thresholds before and after the control enterprise puts the energy storage device into operation according to the clustering results. Identifying whether the target enterprise has put the energy storage device into operation according to the identification thresholds and the to-be-verified data. The present invention discovers the inherent structural characteristics of data unsupervised by introducing a clustering algorithm, providing support for the operation research of enterprise energy storage devices. It overcomes the limitation of data disclosure on energy storage operation identification, has good accuracy, and provides a basis for energy management and optimal scheduling.

The disclosed system may include a non-transitory memory and one or more hardware processors configured to execute instructions from the non-transitory memory to perform operations including determining online data and offline data from a mobile application, wherein the online data is determined based on the mobile application being online and the offline data is determined based on the mobile application being offline, determining travel distance data from a remote device associated with the vehicle, aggregating at least a portion of the online data, at least a portion of the offline data, and at least a portion of the travel distance data, generating data associated with the aggregation of the portion of the online data, the portion of the offline data, and the portion of the travel distance data, and causing the mobile application to display the generated data. Other methods, systems, and computer-readable media are disclosed.

The invention relates to the technical field of energy management, and particularly provides an operation identification method and equipment for a user side energy storage device. The method comprises the following steps: acquiring original data of a target enterprise and a contrast enterprise; wherein the control enterprise is put into operation of the energy storage device. And performing data processing on the original data to obtain to-be-verified data of the target enterprise and annotation data of the contrast enterprise. And inputting the labeled data into a clustering algorithm, and outputting clustering results before and after the energy storage device is put into operation. And according to a clustering result, obtaining an identification threshold before and after the energy storage device is put into operation by the contrast enterprise. And identifying whether the target enterprise is put into operation of the energy storage device according to the identification threshold and the to-be-verified data. According to the method, the clustering algorithm is introduced to discover the internal structure characteristics of the data in an unsupervised manner, and support is provided for operation research of energy storage devices of enterprises. The limitation of data disclosure limitation on energy storage operation identification is overcome, the accuracy is good, and a basis is provided for energy management and optimal scheduling.

This invention relates to the field of artificial intelligence technology in mining, and particularly to a method, apparatus, and equipment for fine-tuning and distillation of a coal mine underground model. The method includes: initial supervised fine-tuning of a teacher model and a target model using a precisely labeled dataset; performing multi-channel parallel inference on an unlabeled dataset using the initial teacher model and the target model, and constructing a high-quality distillation dataset after consistency verification and quality screening; fusing precisely labeled data, publicly available general data, and distillation data to form a comprehensive training set, and performing secondary supervised fine-tuning of the target model using multi-loss fusion; iteratively updating and quality-evaluating the distillation dataset based on the optimized target model, and performing enhanced fine-tuning using a thought chain inference template to obtain a final model adapted to edge devices. This invention effectively utilizes multi-source underground data and reduces labeling costs, significantly improving the model's adaptability, inference efficiency, and robustness in anomaly detection in complex underground environments, achieving a synergistic optimization of high accuracy and low resource consumption.

In various examples, contextual data may be generated using structured and unstructured data for conversational AI systems and applications. Systems and methods are disclosed that use structured data (converted to unstructured form) and unstructured data, such as from a knowledge database(s), to generate contextual data. For instance, the contextual data may represent text (e.g., narratives), where a first portion of the text is generated using the structured data and a second portion of the text is generated using the unstructured data. The systems and methods may then use a neural network(s), such as a neural network(s) associated with a dialogue manager, to process input data representing a request (e.g., a query) and the contextual data in order to generate a response to the request. For instance, if the request includes a query for information associated with a topic, the neural network(s) may generate a response that includes the requested information.

A method for protecting against sensitive data disclosure includes receiving a user request to launch a web application in a web browser; loading code for the web application in the web browser; requesting session initialization from a web server; and receiving a dynamically and randomly generated URL map specific to a session from the web server, wherein the URL map identifies a plurality of randomly generated URL positions and for each URL position a randomly generated number of characters to skip. The method further includes modifying sensitive information associated with a URL in accordance with the URL map; transmitting the URL with the modified sensitive information to the web server; and receiving a requested data object from the web server, wherein the web server decoded the URL with the modified sensitive information to recover the sensitive information and used the decoded URL to access the data object.

The invention belongs to the field of data encryption, and relates to a multi-data source technology layout strategy generation method, which comprises the following contents: constructing a training database, and taking the training database as a data source, the training database comprising public data and private data, the public data comprising patent data and paper data, and the private data comprising private technology experiment data; private data are encrypted through a data preprocessing module, encrypted training data are obtained, and in the training process, encrypted language conversion is carried out on the encrypted training data through an encryption large model; constructing an encrypted language interpreter, and converting the encrypted training data into a common language through the encrypted language interpreter; and carrying out technical layout strategy generation model training on the encrypted large model through public data and a common language converted and output by the encrypted language interpreter, and obtaining a technical layout strategy generation model through training.

Aspects of the subject disclosure may include, for example, a drone service retrieving network state information describing a network state of at least a portion of a communication network, determining an impact of the network state on operation of an unmanned aerial vehicle (UAV), selecting a carrier frequency to be used for communication by the UAV, and providing the data describing the carrier frequency to the UAV and / or to communication network nodes. Other embodiments are disclosed.

The application relates to the technical field of battery passport, and discloses a battery data cross-border transmission method, device, equipment and readable storage medium. The method comprises the following steps: obtaining battery data of a target battery, and generating a full-amount supply chain knowledge graph corresponding to the target battery based on the battery data; performing data review on the basis of preset cross-border transmission regulations and the full-amount supply chain knowledge graph by a preset game intelligent agent, determining a legal quasi-outbound data set and an unexportable confidential data task list; performing zero-knowledge proof processing on the unexportable confidential data task list, generating a zero-knowledge proof string, and encapsulating the legal quasi-outbound data set and the zero-knowledge proof string to generate a battery data compliance digital certificate; and sending the battery data compliance digital certificate to an overseas node. When the battery data is cross-border transmitted, the overseas data disclosure requirement can be met, and core confidential data leakage can be avoided.

The present invention is aimed at large-scale unlabeled image data under complex noise erosion, and discloses a robust multi-view image clustering method, system and device based on smoothed anchor graph learning, which belongs to the field of information technology. The method includes: 1. Acquiring large-scale multi-view image features eroded by noise; 2. Performing graph filtering smoothing and denoising on the acquired image features; 3. Generating a representative anchor set, and adaptively learning the anchor graph between each view anchor set and the smoothed image features; 4. Applying a local popular learning method to fuse the anchor graphs of different views to obtain a weighted anchor graph; 5. Smoothing and denoising the weighted anchor graph under the Frobenius norm to obtain a consensus anchor graph; 6. Obtaining a consensus spectrum embedding and the corresponding clustering results. When processing the clustering task of real large-scale multi-view image data containing complex noise, the method of the present invention can significantly improve the clustering efficiency and the robustness to complex noise.

This network node: receives, from a first node, a message that requests terminal model training data and that includes an application server identifier and a terminal group identifier; derives a terminal identifier that belongs to a terminal group which is indicated by the terminal group identifier; receives, from a second node and for each terminal identifier, a message that includes information indicating whether or not a user consent related to data disclosure to an application server which is indicated by the application server identifier is given; transmits, to a third node, a message that requests terminal model training data and that includes a list of terminal identifiers to which the user consent is given; receives, from the third node, a message that includes terminal model training data corresponding to the list of the terminal identifiers to which the user consent is given; and transmits, to the first node, a message that includes the terminal model training data corresponding to the list of the terminal identifiers to which the user consent is given.

1. Name of this design product: Graphical user interface for a production control platform for electronic equipment. 2. Purpose of this design product: for information display and interaction, data disclosure, content exchange, etc. 3. The key design point of this design product lies in the content displayed on the graphical user interface on the screen. 4. The picture or photo that best illustrates the key points of the design: main view. 5. This display screen panel is of existing design, and other views are omitted. 6. Purpose of the Graphical User Interface: This Graphical User Interface is used for displaying and interacting with the Graphical User Interface of the production control platform. 7. Human-computer interaction mode of the graphical user interface: Click "Production and Shipping" in the main view to jump to the interface change status diagram.

Various aspects of the present disclosure relate to generating data and analytics based at least in part on both internal wireless communications system data and on external data. The internal wireless communications system data is, for example, internal data of the wireless communications system that describes operation of the wireless communications system. The external data is, for example, data external to the wireless communications system that the apparatus has access to (e.g., data that the wireless communications system accesses with authentication from an owner of the external data, publicly available data, and so forth). An entity of the wireless communications system receives requests from a 3rd party consumer, generates the requested data and analytics, and returns the requested data to the 3rd party consumer.

Aspects of the subject disclosure may include, for example, assigning each of a plurality of devices with a first configuration, the first configuration being associated with a first priority level in terms of a priority of access to resources of a communication network or system, subsequent to the assigning, allocating a first portion of the resources to a first device included in the plurality of devices based on the first device utilizing a second configuration, the second configuration being associated with a second priority level in terms of the priority of access to the resources, wherein the second priority level is greater than the first priority level, and based on the allocating of the first portion of the resources, obtaining first data from the first device. Other embodiments are disclosed.

The invention provides an Android application third-party library privacy policy analysis and generation method, which comprises the following steps of: firstly, constructing a relationship between an ontology and mapping; then, static code analysis is carried out on the example application of the third-party library, the behavior of the third-party library for accessing the user privacy data in the source code is obtained, and the behavior is output in a triple form, namely, the behavior of the third-party library for accessing the user privacy data; extracting user data disclosure information about the third-party library from the application privacy policy set by using a natural language processing technology, merging and de-duplicating the obtained multiple pieces of user data disclosure information, and outputting the merged and de-duplicated information as a quintuple; and finally, combining a static code analysis result with a final result of post-processing extracted by a natural language processing technology to generate a third-party library privacy policy. According to the method and the device, the privacy policy can be accurately generated for the third-party library without the privacy policy.

The invention discloses an enterprise credit granting processing method, device and equipment, a medium and a product, and relates to the field of financial science and technology. The method comprises the following steps: acquiring public financial data and public property data of a target enterprise and industry technical information of a target industry; determining at least one enterprise technology point of the target enterprise according to the public known production data and the industry technology information; determining the attention weight of each enterprise technology point relative to each technology hotspot in the industry technology information based on the public known production data and each enterprise technology point; and determining a credit granting result of the target enterprise according to the public financial data, the technical point of each enterprise and the attention weight. According to the technical scheme of the embodiment of the invention, the multi-dimensional heterogeneous data processing efficiency and the credit extension accuracy in the credit extension process can be effectively improved, and the method has good universality and adaptability.