212 results about "Data authenticity" patented technology

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 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 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 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 is suitable for the technical field of data processing, and particularly relates to a financial data processing and classification method, system and equipment based on verifiable computing, and the method comprises the steps: obtaining on-chain transaction data verified by a block chain consensus node storage and risk-related index data verified by an under-chain oracle machine; the problems that the authenticity of traditional financial data is difficult to verify and the data is split are solved; calling a risk assessment model through an intelligent contract deployed on a block chain, and placing an assessment process in a decentralized block chain network; performing verifiable calculation through an intelligent contract to determine a total performance score and a risk index, and realizing whole-course tracing and verification of a calculation process by utilizing the characteristics of verifiable calculation of a block chain; the total performance score and the risk index are stored in the block chain as non-tampering records, and the risk rating groups are automatically divided according to the fixed rating rule on the chain through the smart contract, so that the authenticity, objectivity, verifiability and fairness of financial data processing are guaranteed.

The invention relates to the technical field of visual inspection, and discloses an industrial product online visual inspection method based on cloud collaboration, which ensures data authenticity through edge side close-range acquisition of images, meets the real-time detection rhythm of a production line through a lightweight model, and quickly screens out most clearly qualified or unqualified samples. And for subsequent cloud collaboratively reducing invalid data transmission, the edge side single-frame image detection time is less than or equal to 30ms, the high-speed production line beat requirement is met, 80%-90% of clear samples are preliminarily screened out, burden is reduced for the cloud, and bandwidth congestion caused by total data transmission is avoided.

The invention provides a vehicle track generation method and system, and relates to the technical field of data processing. Obtaining vehicle trajectory data, and screening out vehicle following fragments with a traffic oscillation phenomenon; carrying out risk grade division on the vehicle following fragment, calculating a risk index value based on a peak speed variation coefficient, an aggregation approaching risk and an aggregation collision risk, and dividing the risk index value into discrete risk tags; a conditional diffusion Transform model guided by physical information is constructed and trained; and using the trained conditional diffusion Transformer model to generate high-fidelity and physically consistent vehicle trajectory data according to a specified risk tag. When the vehicle track is generated, the data authenticity and the physical rule consistency are ensured.

The invention is suitable for the field of data consistency verification, and provides a versioning consistency verification system and method for language generation, and the system comprises a knowledge unit generation module which is used for segmenting a text into knowledge slices and defining semantic vectors; the knowledge unit voucher generation module is used for giving a hash voucher; the versioned voucher storage database is used for storing the block contents, the corresponding semantic vectors, the block / vector identification codes and the hash vouchers; the access control module is used for authorizing and recording data access and modification operation of the knowledge base; the vector pre-reading storage module is used for storing corresponding block identification codes of the vector data; the question unit generation module is used for generating question slices, question semantic vectors and question blocks / vector identification codes; and the retriever is used for performing block / vector identification code retrieval. Therefore, the problem of potential safety hazards of an existing RAG system in the aspect of ensuring data authenticity and consistency can be solved.

The invention discloses an intelligent auxiliary method and system for graphic annotation depth analysis, and particularly relates to the field of image processing and image distinguishing, and the method comprises the steps: collecting an image sequence containing a target object, and synchronously recording environment data which comprises an illumination condition, a shooting visual angle parameter and a shielding position; combining the image sequence with environmental data to form an input substrate; according to the scheme, multi-source information such as image area complexity, texture features and edge curvature is subjected to comprehensive modeling in a sheltered environment, a structure response mapping and boundary consistency scoring mechanism is constructed, and credible completion candidate areas are dynamically generated and screened, so that verifiable completion and graphic marking consistency guarantee of sheltered targets is realized, and the accuracy of the sheltered targets is improved. Therefore, the problem that the authenticity of the graphic annotation data is difficult to maintain due to target missing of the existing graphic annotation mechanism is solved.

The invention belongs to the related technical field of fault diagnosis, and discloses a fault diagnosis sample generation method, application and equipment based on a physical constraint adaptive migration network, and the method comprises the steps: (1) converting original vibration signals of reference equipment and monitoring equipment into a time sequence spectrogram through continuous wavelet transform; (2) processing the time sequence spectrogram by adopting a spectrum feature encoder of the physical constraint adaptive migration network to extract multi-scale spectrum features; wherein a loss function of the physical constraint adaptive migration network comprises content loss, style loss and frequency band energy loss; (3) processing the multi-scale spectrum features by adopting the adaptive style normalization module so as to fuse the fault content features of the reference equipment and the machine style features of the monitoring equipment; and (4) processing the fused features by adopting a feature reconstruction decoder so as to reconstruct and generate a synthetic sample. According to the invention, the problem of insufficient data authenticity and physical consistency is solved.

The invention discloses a policy information crowdsourcing-oriented multi-level verification system and method, and relates to the technical field of data verification. A data source verification module; a content identification module; a distributed consensus verification module; and an abnormal behavior detection module. According to the policy information crowdsourcing-oriented multi-level verification system and method, the problem of data authenticity verification in government policy information crowdsourcing is solved, a four-level cascade protection framework is adopted as the core, and through four-level verification of data source verification, content identification, distributed consensus verification and abnormal behavior detection, the data authenticity verification efficiency is improved. The authenticity and compliance of the government policy information crowdsourcing data are ensured, the efficient, low-delay and anti-cheating system performance is achieved through algorithm optimization, and finally the core targets of 95% verification accuracy, 90% cheating detection rate and the like are achieved.

The invention provides a power plant carbon emission data full life cycle management method and device, and relates to the technical field of data processing, and the method comprises the steps: collecting production, fuel and coal quality test system multi-source data in real time through a standard interface, and forming a dynamic data flow; predicting the carbon content of the fire coal element based on a BP neural network, and calculating the carbon emission by combining an emission factor method; carrying out back calculation on the discharge amount by using a mass balance method, analyzing and verifying the relevance between data and an accounting result through multiple regression, and triggering correction when an error exceeds a threshold value; a double-stage attention deep learning network is adopted to predict the carbon price in a rolling mode, a carbon asset dynamic analysis result is generated in combination with a quota estimation model, real-time emission data, quota surplus and shortage and the carbon price trend are displayed through a visual interface, and graded early warning is conducted on abnormal data. According to the method, full-life-cycle automatic management is realized, the data acquisition real-time performance and accounting precision are improved, the data authenticity and traceability are guaranteed, and the carbon asset management decision support capability is optimized.

The invention discloses a block chain-based low-altitude airspace usage recording method. The method comprises the steps of performing registration and authentication of an aircraft and an operator; carrying out flight plan declaration and auditing; recording flight data in real time; carrying out change processing on the flight plan; the emergency situation is handled; and sharing and querying airspace use information. Through distributed storage and a consensus mechanism, flight plans, real-time flight data and supervision operation cannot be tampered with an overground chain, and the authenticity, integrity and traceability of data are achieved.

The invention discloses an unattended wagon balance monitoring method and system, and relates to the field of wagon balance monitoring, and the method specifically comprises the following steps: S1, constructing a data processing architecture; s2, triggering and receiving multi-source data; s3, carrying out data verification and credibility evaluation; s4, executing a graded early warning decision; and S5, realizing data archiving and model iteration. The unattended wagon balance monitoring method and the unattended wagon balance monitoring system provided by the invention can accurately identify behaviors influencing data authenticity, such as incomplete loading of a vehicle and cargo cheating, and guarantee the accuracy of commercial measurement data from the source. Meanwhile, for a localized dynamic correction mechanism of slightly abnormal data, a complete traceability mark is reserved while data validity is ensured, a traceable verification evidence chain is formed, a reliable basis is provided for solving commercial transaction disputes, legal rights and interests of two transaction parties are practically maintained, meanwhile, the manual intervention requirement can be greatly reduced, and the transaction efficiency is improved. Management personnel are liberated from tedious remote verification work.

The invention discloses an employee performance evaluation method based on behavior data driving, and relates to the technical field of human resource management, and the method comprises the following steps: constructing a multi-channel network fluctuation prediction matrix, generating an expected behavior arrival time scale based on the transmission state of a task switching node, and taking the expected behavior arrival time scale as a baseline for behavior sequence gap recognition; and under the guidance of an expected behavior arrival time scale, performing sliding window scanning on the received behavior sequence, generating a gap identifier when detecting that a time slot exceeds a threshold value, and injecting a confidence coefficient weight descending label. According to the method, a behavior gap is accurately identified through network fluctuation prediction and sliding window scanning, behavior reconstruction is realized in combination with self-supervised generation and residual reasoning, and dynamic evaluation correction is completed by using confidence projection and an intelligent regulation engine, so that the data authenticity is guaranteed from the source, and the objectivity and robustness of performance evaluation and the resource allocation precision are improved.

The invention provides a sequence decision strategy acquisition method and system for high-neuroplasm crowds. The method comprises the following steps: establishing a full-screen drawing canvas, and loading a task set comprising a decision map type, an emotion titer ratio, a reward parameter, a random seed and a sequence control parameter; collecting user gender selection and binding a corresponding emotion picture library; generating a decision map based on a Markov decision principle under the control of the parameter set, defining nodes and connecting edges, randomly scheduling and laying emotion pictures according to a titer ratio, and assigning rewards to form an emotion and reward coupling environment; based on the environment, recording a mouse position and a mouse path in an execution exploration stage, recording key pressing time, an action direction and an award in an execution stage, and establishing step sequence association according to a timestamp to form sequence decision data; a Latin square balance control task sequence is adopted, and the process is propelled under the trigger of a user; and finally, filing the data in a binary system and a text file for subsequent analysis. According to the invention, the authenticity and repeatability of data are improved.

The invention discloses a transparent solution system and method for an ESG (Environmental, Social and Management) key material supply chain. The transparent solution system and method for the ESG (Environmental, Social and Management) key material supply chain are used for realizing traceability, evaluation and information disclosure of key materials. The system comprises a data acquisition module, an evaluation module, a visualization module, a disclosure module and a material life cycle data center module. The data acquisition module acquires and verifies the multi-source heterogeneous data to ensure the authenticity and traceability of the data; the evaluation module performs ESG index evaluation based on the collected data and provides index management and early warning functions; the visualization and disclosure module is used for displaying and sharing evaluation results and supply chain information; and the data center module realizes data security storage and tampering prevention. The system can improve the supply chain management capability, realizes ESG risk management and control and transparent disclosure, and is suitable for enterprises, supervision organizations and third-party auditing organizations.

The invention provides a data sharing method, device and system, and belongs to the technical field of computers. Encryption protection is performed by binding shared data with a data protocol set by a data owner, digital signature is performed on ciphertext structure data and retrieval metadata as a whole, and a packaging signature and a signature public key certificate are bound on the ciphertext structure data and the retrieval metadata, so that the packaging data can be distributed in a circulation and sharing process, and the service life of the data is prolonged. The data authenticity can be verified, and confidentiality and integrity protection of shared data and data protocols in the circulation and sharing process is achieved. Besides, since the encapsulated data is generated by the trusted equipment trusted by the data owner, the sharing agent is trusted by a plurality of participants participating in data sharing, and the data is transmitted between the trusted equipment and the sharing agent through the secure transmission channel. And the security, confidentiality and integrity protection of the shared data and the data protocol in the export process is realized.

The invention discloses a whole-process traceability and authenticity identification method and related equipment for tobacco products, and relates to the technical field of data processing.The method comprises the steps that data are collected in a multi-source cooperative mode, the same production parameter is synchronously collected through at least three pieces of independent internet-of-things equipment, and equipment digital signatures, timestamps and positioning information are added; by means of the multi-device and multi-dimensional synchronous acquisition means, the problem of data distortion caused by faults or malicious tampering of a single sensor is effectively avoided, and the data transmission efficiency is improved on the basis of improving the data authenticity. And returning a Merkle tree path corresponding to the tobacco product, an associated equipment identity certificate and an operator behavior record to the user, thereby providing a full-process scheme for verifying data integrity and credibility of the collection main body for the user.

The invention discloses a protection check intelligent identification method based on an operation check mobile terminal. The method comprises the following steps: 1, completing basic configuration of field check, and ensuring data synchronization of a mobile terminal and a cloud terminal; 2, quickly positioning a to-be-checked pressing plate, and obtaining cloud standard data; 3, the current state of the pressing plate is accurately collected, and evidence information is attached; 4, uploading on-site collected data to a cloud end, and verifying the authenticity and integrity of the data through a block chain and a knowledge base; 5, on the basis of the intelligent knowledge base data, multi-dimensionally comparing the check data with the standard data, and automatically judging that the state of the protection pressing plate is normal / abnormal; 6, the abnormal information is accurately pushed to the corresponding personnel according to the early warning level, and the handling responsibility and time limit are determined; 7, exception handling is completed, the effect is verified, and a problem closed loop is ensured; 8, updating the knowledge base and the block chain data, precipitating the verification experience, and optimizing the subsequent process; the method has the advantages of being high in field checking efficiency, high in recognition and judgment precision, good in exception handling effect, credible in data and capable of continuously increasing value.

The application relates to the technical field of information security, and discloses a security control method and system of a cloud inventory management platform. The method comprises the following steps: obtaining cloud interaction data sets; performing abnormal operation identification by using a behavior benchmark template to obtain an abnormal operation set; matching the behavior characteristics in the set with historical risk patterns to generate threat possibility scores; extracting relevant inventory data records from data whose threat possibility scores exceed a preset score threshold to form a data group to be checked; comparing the field content and time sequence of the data group to analyze tampering signs and obtain a tampering possibility level; inputting the level into a risk processing rule library to generate and execute a security control strategy. The method can effectively identify and cope with potential data tampering risks by constructing a full-process security mechanism from user behavior monitoring to data authenticity verification and then to risk closed-loop disposal, thereby improving the security and data reliability of the cloud inventory management platform.

The application discloses a data verification system implementation method based on a block chain and zero-knowledge proof. In the data verification stage, the data use end pays the corresponding data verification fee to the on-chain smart contract according to the verification requirement, and the demand satisfaction and data real possession are verified through the interaction with the smart contract; in the data declaration stage, the data owner carries out the Merkle tree on the data and generates the characteristic zero-knowledge proof, and the on-chain verification and evidence storage of the data are completed through the interaction with the smart contract, and in the data verification stage, the data owner responds to the challenge of the data real possession, generates the existence proof of the challenge data block set and carries out the on-chain verification and evidence storage, so that the data verification can be independently executed without relying on the third-party service executable environment or the trusted hardware, the independence and safety of the data verification are ensured, and in the whole data real possession verification process, the data is always saved in the local security environment of the data owner, and the complete non-flow of the data is realized.

The invention relates to the crossing field of soil system remediation, digital smart agriculture, land comprehensive treatment and block chain technology, in particular to a soil treatment block chain authentication excitation system and method based on multi-source credible verification and mineral activation and heavy metal passivation technology. The system comprises a perception execution layer (deployed in a governance area and collecting multi-dimensional governance data), a credible verification layer (military-civilian fusion multi-source cross verification and credible uplink evidence storage), a block chain core layer (alliance chain architecture, including ecological value quantification smart contract), and an application excitation layer (automatic excitation distribution). According to the method, data are ensured to be real and cannot be tampered through multi-source cross verification, soil improvement indexes are converted into incentive integrals by using intelligent contracts, and immediate quantitative incentive of treatment contributions is realized. The problems of low data credibility, excitation delay and insufficient participation in traditional governance are solved, and a credible closed-loop system of'governance-income 'is constructed. Wherein the incentive points are only used for exchanging real object rewards or services (such as agricultural materials and technical support) in the system, do not have a currency function, and do not carry out public transactions or exchange legal currency.

The embodiment of the invention provides a full generation cycle carbon data credibility verification method and system and related equipment, and belongs to the technical field of information security. According to the method, the data abstract about the carbon data from the data acquisition terminal is acquired, and the zero-knowledge certification technology is adopted to perform carbon data credibility rule verification on the data abstract to generate the certification file, so that the privacy of the carbon data authenticity verification process is improved. The proof file and the data abstract are sent to the trusted mechanism equipment for digital signature operation, so that a signature file from the trusted mechanism equipment is received, the signature file carries a timestamp and comprises the signed proof file and the signed data abstract, the credibility of the carbon data is improved, and the reliability of the carbon data is improved. And the signature file is stored in an immutable log system adopting a hash chain recording mechanism, so that the carbon data can be prevented from being tampered, and the carbon data interaction efficiency can be improved through a centralized storage mode.

The application discloses a radar data generation and automatic labeling method and related devices, by acquiring the original radar data and the corresponding auxiliary data obtained by the radar equipment collecting the real target scene, and inputting the original radar data and the auxiliary data into the first coding network to obtain the first variable data, and applying physical consistency constraints to the first variable data to obtain the second variable data aligned with the actual physical quantity, and generating simulated radar data according to the second variable data, and outputting the dynamic state information, existence information and / or trajectory identification information label of the target according to the effective scene state variable in the second variable data, and screening the label according to the credibility score, so that the data authenticity of the simulated radar data and the labeling reliability can be considered without relying on the existing labeling of the original radar data.

The application relates to the technical field of data authenticity judgment, and particularly discloses a data monitoring method and system for auditing business software, which comprises the following steps: obtaining to-be-audited data based on a data statistical template; converting the to-be-audited data according to preset data conversion rules to obtain transit data; calculating the correlation characteristics of different to-be-audited units based on the transit data to obtain a correlation matrix; statistically analyzing the correlation matrix according to time sequence; determining the data change rate of each to-be-audited unit according to the statistical correlation matrix; and marking a target to-be-audited unit according to the data change rate. The to-be-audited data is converted, the correlation relationship of the converted data of different units is calculated, then it is judged whether the correlation relationship changes at each moment, and the unit for priority analysis is positioned according to the change of the correlation relationship; the positioning function is added to the original technical scheme, and the work efficiency of the staff can be effectively improved.

The invention belongs to the technical field of satellite communication data processing, and particularly relates to a satellite communication data prediction method, system and device and a storage medium. The objective of the invention is to solve the problem of insufficient data generation authenticity in the prior art. Beam service range division is unreasonable; and the adaptability of the demand prediction model is poor. Comprising the following steps: randomly distributing required flow in a geographic grid range covered by each beam according to beam configuration information to obtain analog data; analyzing the simulation data, and calculating influence data of each wave beam; according to the time dimension, the demand capacity in the geographic grid range served by each wave beam is counted, and historical demand time sequence data of each wave beam is obtained; constructing a prediction model and training to obtain a trained prediction model; and using the trained prediction model to predict the demand of each beam in the future time period, and correspondingly obtaining the demand prediction value of each beam in the future time period.

The invention provides an exercise data detection method with real-time identity authentication and an exercise bracelet, and the method comprises the following steps: obtaining a first fingerprint feature of a target user before the start of exercise, and generating a reference fingerprint template; collecting and analyzing motion data in a preset duration calibration window, and constructing a motion feature template; second fingerprint features and physiological data of the wearer are continuously collected in the movement process, and matching verification is conducted on the second fingerprint and the reference template; if verification is inconsistent, comparing the physiological data with the motion feature template to generate an identity consistency deviation degree; and comparing the deviation degree with a preset fault-tolerant threshold value, judging whether the associated real-time motion data is valid or not, and if not, executing a safety response. By implementing the technical scheme provided by the invention, the continuous high-reliability binding of the user identity and the exercise data is realized, the cheating on replacement is effectively prevented, and the data authenticity is ensured.

The invention relates to the field of manufacturing execution systems and intelligent manufacturing, in particular to a manufacturing execution system management method oriented to production data, which comprises the following steps: a multi-source data acquisition step: acquiring physical sensing state data, manual interaction time data and system scheduling data of a manufacturing execution system, and integrating to generate multi-source heterogeneous production data; a trust quantitative evaluation step of quantifying the distortion degree of the execution state based on the multi-source heterogeneous production data to obtain an execution trust entropy; a cross validation prediction step: comparing the physical sensing state data with the man-machine interaction time data to calculate execution trust entropy, and predicting system failure risk; a dynamic scheduling decision step: setting a scheduling degradation adjustment mechanism based on execution trust entropy, and outputting a target scheduling instruction to adjust the running state of the system; according to the method, the dynamic balance between pursuit of efficiency and maintenance of authenticity of underlying data is realized, and the vulnerability resistance of the complex manufacturing network is effectively improved.