368 results about "Data authenticity" patented technology

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (AI) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

The invention discloses a personal data cross-border circulation method based on a block chain. The method comprises the steps of data acquisition and local storage, third-party neutral institution witness and data fingerprint chaining, data transmission and use authorization declaration, fingerprint verification and data verification on a receiver chain, and full-link tracing and dynamic auditing. Aiming at the problems that trust guarantee of personal data cross-border circulation depends on a single technical means such as centralized institution authentication or encrypted transmission or a static legal protocol, a trust transfer chain is lengthy, and cross-national legal mutual recognition is fragile, a data fingerprint anchoring system constructed based on a block chain is designed; aiming at the problems that traditional data cross-border authorization depends on a manual signing protocol or static strategy configuration, response lag exists, rule execution deviation exists, privacy protection and compliance auditing are difficult to consider and the like, a data use rule is dynamically constrained through an intelligent contract; a user defines a data use range, timeliness and an operation white list through a declarative intelligent contract, and contract codes are automatically executed after being subjected to consensus verification of nodes on a chain.

The invention provides a carbon transaction intelligent management and control method and system based on a block chain, and relates to the technical field of carbon transactions, and node analysis and block chain network construction are performed by analyzing carbon transaction participation associated information; acquiring carbon emission monitoring network sensing data, encrypting the data, uploading the data to a distributed account book of a participating node on a chain, and performing consensus verification; if the verification result meets the preset verification standard, carrying out history backtracking, activating a transaction contract in combination with transaction constraints, and carrying out quota division and quota updating; a plurality of carbon transaction platforms are connected, when a transaction request is activated, transaction opponent matching and transaction rate evaluation sorting are performed based on a transaction contract, and a first matching result of the sorting is used as a current transaction opponent for recommendation, so that the problems of low transaction credibility, low transaction efficiency and poor real-time performance and safety during carbon transaction are solved, and the transaction efficiency is improved. The technical effects of improving the transparency and security of the carbon transaction and improving the transaction efficiency and the response speed while ensuring the data authenticity are achieved.

A system and method for optimizing automated textual prompts in artificial intelligence (AI) conversational systems is disclosed. The system comprises a network interface, processors, and memory-storing instructions for performing operations to optimize prompts. These operations include receiving and preprocessing input data, tokenizing the data, verifying data authenticity, performing temporal analysis, calculating prompt complexity scores, and selectively expanding or refining prompts based on complexity thresholds. The system further incorporates context-aware optimization, multi-faceted prompt refinement, variation generation, and evaluation using machine learning models. Additional features include a technological hub with advanced processing capabilities, sensor-augmented input apparatus, device-specific prompt optimization, AI model selection, multimodal context integration, and an AI-driven creativity booster. The system provides interactive prompt visualization, certification, and uniqueness verification modules. This comprehensive approach ensures the generation of optimized, contextually relevant, and creative prompts for various AI applications while maintaining data integrity and user engagement.

The invention discloses a high-precision instrument assembly fault backtracking method and system, belongs to the field of precision manufacturing, and aims to solve the problems that in a traditional backtracking method, assembly data are scattered and unreliable, fault root positioning is fuzzy, and new scene adaptation depends on a large amount of data. The method comprises the following steps: collecting assembly structured data, video images and environment data in a multi-source manner, filtering out low-quality images, and distributing unique identifiers for products; fusing the multi-modal data to generate a depth feature matrix; constructing an anomaly detection model to output a risk score and a label; hashing the data and then storing the data into a product exclusive private block chain; when a fault occurs, extracting data on the chain through a unique identifier, reconstructing an assembly process by using a graph neural network, and comparing a standard positioning root; and based on the fault report incremental training model, parameters are optimized in combination with meta-reinforcement learning. According to the method, the data authenticity is guaranteed, the fault backtracking precision and efficiency are improved, a new scene is quickly adapted, the production rework rate is reduced, and the stable assembly quality is maintained.

The invention discloses an OBD tamper-proof two-way verification method, which comprises the steps of obtaining OBD equipment data and vehicle sensor data, registering to a cloud end based on the OBD equipment data, obtaining registration data, obtaining key data and non-key data according to the vehicle sensor data, symmetrically encrypting the key data by using a session key in the registration data, and verifying the key data and the non-key data. Performing approximate homomorphic encryption on the non-key data, obtaining a double-entropy source dynamic salt value according to the time entropy of the ciphertext and the OBD equipment operation entropy, reversely reconstructing the dynamic salt value by the cloud according to the timestamp and the registration data, verifying the ciphertext structure by using the dynamic salt value and generating a random challenge containing a salt value check code, and sending the random challenge to the cloud; the equipment verification result and the challenge value are signed and then transmitted back, the OBD equipment carries out reverse verification on the salt value according to the signature and the equipment verification result, and the cloud generates an authority token based on the reverse verification result. According to the method, the data authenticity and integrity of the diagnostic equipment are guaranteed through packet encryption, dynamic salt value generation and bidirectional identity verification of transmission data.

The invention discloses an AI-based enterprise benefit policy intelligent matching and declaration auxiliary system, and relates to the technical field of policy declaration auxiliary systems. The data collection module collects policy and enterprise data in a multi-modal fusion manner, and dynamically updates according to activeness; the preprocessing module performs cleaning through a high-performance cluster, analyzes a policy through sentiment analysis and performs AES encryption; the feature extraction module uses GAN enhancement and cognitive calculation mining to update a knowledge graph; the matching calculation module calculates the matching degree in combination with a complex network, and the declaration auxiliary module demonstrates the process through VR and AR and answers questions through intelligent customer service. The risk assessment module assesses the risk by using a block chain and the like; the user interaction module supports a brain-computer interface and the like and adaptively adjusts the interface; and the data updating and feedback module records feedback by means of the block chain and optimizes a prediction mechanism. The system is advantaged in that multi-channel accurate acquisition is facilitated, enterprise matching policy bonus is facilitated, a declaration link is simplified, the cost is reduced, data authenticity is guaranteed, cooperation is promoted, continuous optimization is carried out according to feedback, and enterprise benefit policy landing is promoted.

The invention discloses an industrial wastewater monitoring data authenticity verification method based on multi-modal feature fusion. The method comprises the following steps: carrying out adaptive preprocessing and feature extraction on monitoring data; performing integrity verification based on the spatial-temporal feature sequence; generating an encrypted two-dimensional code through multi-modal feature fusion; and evaluating data consistency and generating early warning information. According to the method, a complete verification chain from sensor physical feature extraction to data consistency evaluation is constructed, so that the authenticity of industrial wastewater monitoring data is comprehensively guaranteed; by adopting the technologies of adaptive data processing, multi-head attention feature fusion and the like, the key problems of data source anti-counterfeiting, feature extraction accuracy, multi-modal feature fusion and the like are effectively solved, and the reliability of data verification is improved.

The invention provides an information security risk management method and management system. The system is composed of four core modules: a multi-modal data acquisition layer: probes deployed at a terminal and a server support heterogeneous data acquisition of network traffic, operation logs and an API (Application Program Interface) call chain; according to the block chain log storage layer, a private chain is constructed based on an improved BFT consensus algorithm, and each block comprises a timestamp hash value and a preorder block fingerprint; the dynamic risk assessment engine adopts an LSTM-Transform hybrid neural network, and input dimensions comprise a user behavior baseline, a vulnerability library version and threat intelligence feed; and the intelligent response decision module is used for integrating a reinforcement learning algorithm, automatically generating a disposal strategy and triggering the SDN controller to execute network isolation. According to the method, a local chain-alliance chain-audit chain three-level structure is applied to data island treatment and data integration in organization, the problem of internal data fragmentation is solved through the local chain structure, unified storage and rapid source tracing of heterogeneous logs are achieved, and data authenticity and time sequence integrity are ensured.

The invention discloses a bank intelligent risk control system based on a block chain and multi-modal artificial intelligence. A data acquisition module acquires and integrates bank internal data, third-party data and cross-chain data; the edge computing module is provided with a plurality of edge computing nodes, and high-frequency transaction data is processed in real time by using a lightweight model; the block chain network module adopts a relay chain to connect a bank chain and a supply chain, and performs verification based on zk-SNARK zero knowledge proof; the artificial intelligence module constructs a multi-modal risk assessment model, and performs risk assessment in combination with an XGBoost integrated learning algorithm, GNN deep learning and federated learning; and the application decision module performs risk early warning, dynamic processing, visual monitoring and on-chain auditing according to the evaluation result of the artificial intelligence module. According to the scheme, the problems of information isolated island, difficulty in guaranteeing data authenticity, risk identification lag and the like in the existing bank risk control system are effectively solved, and the bank risk prevention and control capability and the market competitiveness are improved.

The invention discloses a data security sharing method and device for verifiable bilateral access control based on an attribute threshold, computer equipment and a storable medium, and aims to realize data privacy protection and source authenticity guarantee in an open cloud-edge collaborative environment. According to the method, an attribute threshold matching mechanism is introduced, a bilateral access control strategy is designed, decryption can be performed only when intersection of attribute sets of a data sender and a data receiver and access attribute sets specified by each other meets a preset threshold, and access authorization under fuzzy matching is supported. In order to ensure the authenticity and non-repudiation of the data, the attribute signature of the sender is embedded into the ciphertext structure, and the credibility of the data source is ensured. According to the method, a constant-size ciphertext structure is adopted, and an offline / online double-stage encryption technology is combined, so that the encryption delay and the calculation burden are reduced. At a decryption end, data verification and partial decryption tasks are outsourced to edge nodes, and a result verification mechanism is introduced, so that malicious nodes are effectively prevented from returning wrong results.

The invention relates to a video data encryption method and a video data storage sharing system based on a block chain, and the video data encryption method comprises the steps: carrying out the encryption processing of video data through a SECMPEG algorithm model, and carrying out the encryption of the encrypted video data through combining a homomorphic encryption model and an attribute-based searchable encryption model. Calculation and analysis of the data in an encryption state and security of private data are guaranteed, and authenticity and reliability of the data in a storage sharing process are guaranteed. According to the video data storage and sharing system based on the block chain, the block chain is used as a core layer, the video data is encrypted according to a video encryption method, in the storage and sharing process, the data security is ensured, and the data authenticity and reliability are ensured in the query and downloading process of a user.

The invention is suitable for the field of data processing and analysis, and provides a medicine cold chain on-line fast verification system and a real-time data processing and intelligent analysis method thereof.The medicine cold chain on-line fast verification system has the beneficial effects that the medicine cold chain on-line fast verification system comprises a wireless temperature and humidity recorder, an intelligent management host and upper computer server platform software, and the wireless temperature and humidity recorder adopts an SHT40 sensor; the wireless management host receives encrypted data of the recorder, then transfers the encrypted data to the W25Q256JVEIQ storage chip, and encrypts and uploads collected environment parameters and equipment state information to the upper computer server, an online real-time verification technology is introduced, instant monitoring and response to overtemperature early warning can be achieved, rapid deviation correction can be achieved, repeated verification of traditional offline verification is avoided, and the verification efficiency is improved. And the verification period is greatly shortened. The automatic data processing system realizes full-chain unmanned intervention, reduces manual operation time, guarantees data authenticity, and improves verification efficiency and result objectivity.

The invention belongs to the technical field of military and camp management, and particularly relates to a smart camp management method and system based on big data, a terminal and a storage medium. The intelligent camp management method based on big data comprises the steps of S1, acquiring real-time environment data and real-time personnel data of each area of a camp to generate real-time acquisition data; s2, according to the real-time acquisition data, generating a real-time verification instruction to a corresponding real-time area; s3, executing verification collection operation according to the real-time verification instruction to generate verification collection data; and S4, according to the real-time collection data, the verification collection data and database record data, obtaining a collection management result of data authenticity. According to the invention, the monitoring management efficiency can be improved.

The invention discloses a trust evaluation data storage tracing method and system based on a block chain technology, and particularly relates to the technical field of data storage management, and the method comprises the following steps: recording trust evaluation data in a block form based on the distributed storage characteristics of a block chain, automatic management of data submission, verification, storage and updating is realized by using an intelligent contract, data authenticity and non-tampering are ensured, time stamps are added for data through a timestamp function, a time-ordered operation chain is formed, a data tracing function is provided, an authorized user is allowed to inquire a data source and historical operation records, and the user experience is improved. Based on data access characteristics, a storage area is dynamically adjusted, and efficient utilization and optimal management of storage resources are achieved; according to the method, the storage position of data can be dynamically adjusted, hot data are stored in a high-performance storage area, cold data are migrated to a low-cost storage area, the access efficiency of high-frequency data is guaranteed, and the storage cost is optimized.

The invention relates to the technical field of bridge monitoring data storage, in particular to a bridge digital twin data storage and tracing platform based on a block chain. According to the technical scheme, the system comprises a multi-mode tamper-proof acquisition module, a credible data preprocessing module, a component-level block chain evidence storage module, a data-driven twin modeling module and a judicial traceability verification module, wherein the credible data preprocessing module adopts an improved ICP algorithm to realize submillimeter registration of laser point cloud and a BIM model; and the component-level block chain evidence storage module realizes millisecond-level credible evidence storage based on a fragment chain architecture divided by bridge components. According to the method, through hardware fingerprint binding, multi-modal data fusion, fragment chain evidence storage, dynamic twin modeling and judicial-level evidence chain construction, whole-process credible management of bridge monitoring data from collection to tracing is realized, and data authenticity, fusion precision, evidence storage efficiency and model dynamics are remarkably improved; and a real-time and intelligent solution with judicial efficacy is provided for bridge health monitoring.

The invention provides a snail bioactive peptide quality tracing system and method based on big data, and belongs to the technical field of snail bioactive peptides. Comprising a data acquisition module used for acquiring various data in the production process of the snail bioactive peptide, a big data processing module used for integrating and cleaning the data, and an intelligent quality analysis module used for analyzing the quality of the snail bioactive peptide. The intelligent quality analysis module is used for carrying out quality prediction and abnormal early warning on the snail bioactive peptide, and the block chain tracing module is used for carrying out quality tracing on the snail bioactive peptide. According to the method, data islands of cultivation, production and detection links can be broken, cross-link association analysis is realized, manual sampling inspection is upgraded to AI-driven real-time early warning and process optimization, data authenticity is guaranteed through a block chain technology, international compliance requirements are met, and consumer trust and brand price are improved by adopting three-dimensional visualization and scientific data.

The present application relates to the technical field of computers, and provides a data sharing method, a device, and a system. Shared data and a data protocol set by a data owner are bound for encryption protection, ciphertext structured data and retrieval metadata are integrally digitally signed, an encapsulation signature and a signature public key certificate are bound on the ciphertext structured data and the retrieval metadata, so that the data authenticity can be verified during circulation and sharing of encapsulated data, and confidentiality and integrity protection of the shared data and the data protocol during circulation and sharing are realized. In addition, the encapsulated data is generated by a trusted device trusted by the data owner, a shared agent is trusted by a plurality of participants participating in data sharing, and data is transmitted between the trusted device and the shared agent through a secure transmission channel, so that the security, confidentiality and integrity protection of the shared data and the data protocol during exporting are realized.

The invention provides a product whole-process supervision method and system based on a block chain, belongs to the field of computers, and solves the problem that the block chain cannot verify the authenticity of input data in a commodity traceability system. The method comprises the following steps: acquiring initial state information after agricultural and sideline products are produced, wherein the initial state information comprises growth data, initial appearance data and initial weight data; obtaining full-process environmental condition parameters of storage, transportation and sales and stage state information of the agricultural and sideline products at each process transfer node, wherein the stage state information comprises stage appearance data and stage weight data; and on the basis of the initial state information or the stage state information and each process environment condition parameter, predicting ideal state information of the agricultural and sideline products at the next process transfer node, so that the agricultural and sideline products can be obtained under the condition that the matching degree of the ideal state information and the recorded stage state information is lower than a preset matching degree. And generating a process environment condition parameter authenticity alarm and recording the process environment condition parameter authenticity alarm to the block chain system.

The invention relates to the technical field of data processing, in particular to a screw air compressor state monitoring method, which comprises the following steps: acquiring monitoring data of M dimensions of each monitoring object in an air compressor to obtain multi-dimensional real-time monitoring data and multi-dimensional historical monitoring data; screening suspected real multi-dimensional monitoring data from the multi-dimensional historical monitoring data, and obtaining the data real degree of each dimension in the suspected real multi-dimensional monitoring data; n reference dimensions of any monitoring object at the current moment are obtained, and the data authenticity of each reference dimension in each piece of suspected real multi-dimensional monitoring data and the difference between each piece of suspected real multi-dimensional monitoring data and the multi-dimensional real-time monitoring data are calculated according to the data authenticity of each reference dimension in each piece of suspected real multi-dimensional monitoring data. Obtaining the state voting weight of each piece of suspected real multi-dimensional monitoring data for any monitoring object; and according to the state voting weight of each piece of suspected real multi-dimensional monitoring data, a state evaluation result of any monitoring object at the current moment is obtained by utilizing a KNN algorithm, and the running state of the current air compressor is accurately obtained.

The embodiment of the invention provides a data evidence storage verification method and device, equipment and a medium. The method comprises the steps of collecting to-be-stored data of various data sources, and performing preprocessing and primary processing on the data; receiving the primarily processed data and encrypting the data to obtain encrypted data and a key pair; adding a timestamp to the encrypted data to obtain encrypted evidence storage data with the timestamp, and carrying out evidence storage recording on the data; receiving an evidence storage verification request, performing verification and authorization management, routing the request to a corresponding verification execution channel according to a verification type, and providing a data access permission; calling encrypted evidence storage data with a timestamp according to the instruction, and performing integrity verification, time sequence verification and source verification on the data; a precise timestamp is obtained, reliable data backup is performed in combination with a burning technology, and data confidentiality is guaranteed by applying an encryption algorithm to ensure data authenticity and integrity.

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (Al) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

The invention provides an image authenticity detection model training method and an image authenticity detection method. According to the implementation scheme, image features are decoupled into semantic features, unique features of a forgery algorithm and universal features of the forgery algorithm through a forgery algorithm type classifier and a data authenticity classifier. Determining a symbolic feature in the universal features of the forgery algorithm based on the feature difference between the unique features of the forgery algorithm and the universal features of the forgery algorithm, and performing variation on the feature in the universal features of the forgery algorithm; and finally, reconstructing the combined features of the semantic features, the unique features of the forgery algorithm and the varied universal features of the forgery algorithm to obtain a new image so as to determine the training loss of this time, and performing parameter adjustment by using the loss. Therefore, the learning of the data authenticity classifier on the general counterfeit features among the counterfeit algorithms can be enhanced, so that when the data authenticity classifier is used as a target image authenticity detection model, the accuracy and generalization of the data authenticity classifier on image authenticity detection can be improved.

The invention discloses a privacy protection truth value discovery method based on reputation in a block chain. The method provided by the invention aims at the problems of perception data quality reduction, participant privacy information leakage and the like caused by lack of a trusted participant screening mechanism and insufficient privacy protection in the crowd intelligence network. Therefore, participants with high creditworthiness are screened on the premise of privacy protection to complete a data perception task, so that high-quality data are obtained. According to the method, firstly, a double-chain collaborative architecture is constructed based on a block chain, and privacy protection is performed on creditworthiness of participants through exclusive or homomorphic encryption; in the subsequent task allocation stage, the high-reputation participants are preferentially selected to execute the data acquisition task in combination with multi-round encryption communication and a dynamic screening mechanism; and finally, on the basis of the data of the high-credibility participants, calculating the task scores of the participants with unknown reputation, dynamically updating the reputation of the participants, and ensuring the authenticity and accuracy of the data.

A method for enhancing detection of fraudulent authentication data includes receiving, by an electronic device, data during an authentication transaction, computing a feature vector from the received data, and normalizing the feature vector. The method also includes encoding the normalized feature vector into qubits, expanding, using at least one quantum algorithm, the normalized feature vector into a high-dimensional space, and detecting in the high-dimensional space anomalies indicative of fraud based on the qubits. Furthermore, the method includes calculating, based on the detected anomalies, a confidence score reflecting a likelihood that the received data is genuine and comparing the confidence score against a threshold value. In response to determining the confidence score fails to satisfy the threshold value, the method determines that the received data requires secondary authentication.

The invention discloses a multi-dimensional credit asset traceability system and method. The system comprises a data acquisition and encryption module (10) which is used for performing encryption and digital signature on credit asset data based on a trusted execution environment (TEE) and a hardware security module (HSM) at a data generation end; the parallel Hash processing module (20) is used for performing parallel Hash operation on the encrypted data and generating leaf node Hash values based on GPU acceleration and a multi-thread technology; the nested hash and zero-knowledge proof module (30) constructs leaf node hash values into a compression type Merkle tree and generates a zero-knowledge proof (ZKP), so that a third party can verify the authenticity and consistency of data without accessing full data; the on-chain evidence storage module (40) is used for recording root Hash (RootHash) and zero knowledge proof of the compression type Merkle tree to the block chain; the dynamic authorization and secure multi-party computing module (50) performs access clipping on the credit asset data based on the access intention and realizes verification of the minimum data dimension through secure multi-party computing (SMPC). Through combination of technical means such as trusted hardware acquisition, parallel hash calculation and privacy protection verification, rapid verification is realized on the premise of not exposing original data, and verification performance, data security and privacy protection capability are remarkably improved.

The invention discloses an intelligent irrigation water and fertilizer application device, and relates to the technical field of agricultural fertilization, the intelligent irrigation water and fertilizer application device comprises a data acquisition mechanism and a user interaction system, the data acquisition mechanism is externally connected with a data acquisition processing system, and the data acquisition processing system is electrically connected with the user interaction system; the system further comprises an intelligent judgment system, an execution control system, a water and fertilizer blending mechanism and an irrigation mechanism. According to the invention, in combination with a data validity judgment mechanism, data authenticity is ensured, misjudgment caused by sensor faults is avoided, the unmanned aerial vehicle acquisition assembly performs automatic sampling when soil is abnormal, and in combination with manual detection and multi-source data fusion processing, the response capability to complex soil changes is improved; the image acquisition unmanned aerial vehicle generates differential water and fertilizer schemes by analyzing plant leaf states and growth conditions and cooperating with soil data, and the problem of matching of nutritional requirements of plants in different physiological stages is solved.

The invention belongs to the technical field of power grid data processing, and particularly relates to a power grid data interaction and auditing method based on multi-source heterogeneous distributed data, which comprises the following steps of: identifying and accessing data sources; designing a data authentication protocol; compressing the multi-source heterogeneous distributed data; constructing a representation model of the multi-source heterogeneous distributed data; performing data processing on the data of different data sources to extract data features; on the basis of a cross validation idea of deep learning, compressed data records of different sources are compared through an algorithm, and the data consistency is verified so as to evaluate the authenticity of the data; establishing an audit log and a chain structure; deploying auditing rules and algorithms; an evaluation mechanism of audit results; according to the method, a charging facility and power grid multi-source heterogeneous data high-performance sharing interaction model is constructed, heterogeneous interfaces and protocols, multi-type data representation and compression, cross check and auditing are supported, and high-throughput and high-concurrency data interaction and cooperative execution of multiple types of nodes are achieved.

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (AI) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

Disclosed in the present invention are a method and system for blockchain storage optimization based on erasure coding. The method comprises the following steps: acquiring blockchain network nodes; grouping the blockchain network nodes by using a dynamic short-distance node grouping algorithm based on a fuzzy C-means clustering algorithm so as to obtain a consensus unit; and on the basis of the consensus unit, performing storage allocation on coding blocks by using a random coding block placement policy based on node performance, and performing global processing on a block file. According to the present invention, the storage load of a single node is reduced, and the problem of loss of features such as decentralization, data security, data authenticity, and data credibility of the blockchain due to heavy storage load of nodes is mitigated.