2402 results about "Verification system" patented technology

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention discloses an AI agent design platform decision optimization method based on big data, and particularly relates to the field of artificial intelligence, comprising multi-modal data sensing layer construction, a streaming feature calculation engine, a dynamic index fusion center and an adaptive decision matrix. According to the method, accurate synchronous monitoring of the utilization rate of hardware resources and dynamic collaborative optimization of heterogeneous computing units are achieved, and the resource scheduling efficiency in a complex computing scene is remarkably improved; knowledge system degradation caused by long-term learning is effectively prevented, and the continuous reliability of a cognitive system is ensured. The provided multi-dimensional decision credibility verification system is fused with interpretability penetration analysis, environment coupling modeling and logic drift detection technologies, the limitation of a traditional single credibility index is broken through, the risk prediction and fault-tolerant capability of the decision process is remarkably enhanced, and a full-dimensional safety decision guarantee system is constructed for an intelligent agent.

The invention discloses a wide-area landslide rapid identification method based on an interpretable intelligent algorithm, and relates to the field of remote sensing science and technology, and the method comprises the steps: building a dual-channel feature extraction architecture through multi-source spatio-temporal data fusion and knowledge graph dynamic weighting: capturing image local textures through a lightweight CNN, modeling geological spatial correlation through a graph convolutional network, and carrying out the recognition of the landslide. Combining the SHAP value and causal reasoning to generate an interpretable contribution degree thermodynamic diagram and a rule chain; a knowledge graph bidirectional verification system is introduced, spatial logic contradictions are verified by using prior rules, and co-evolution of a model and a rule base is triggered based on misjudgment samples; outputting a multi-dimensional credibility report, quantifying uncertainty by Monte Carlo Dropout, and customizing interpretation granularity according to roles; a terrain-adaptive block-stream processing architecture is adopted, and edge lightweight deployment and federated learning are combined, so that wide-area real-time early warning and model dynamic updating are realized. According to the scheme, the limitation of a traditional black box model is broken through, and a disaster prevention closed loop with physical driving, transparent decision and second-level response is formed.

The invention discloses a multi-modal bill processing method based on dynamic knowledge enhancement. The multi-modal bill processing method comprises the following steps: S1, constructing a dynamic knowledge base containing an aging weight; s2, synchronously processing text, image and format features of the bill by adopting a multi-modal feature fusion network to generate a composite feature vector; s3, semantic-level, format-level and timeliness three-level fusion retrieval is carried out based on the composite feature vector, and a three-level fusion retrieval engine comprises dynamic weighted sorting with timeliness attenuation, a difference degree triggered artificial review mechanism and a policy sensitive slope adjustment algorithm; s4, setting a multi-expert cooperative verification system, wherein the multi-expert cooperative verification system comprises cooperative work of a rule engine, a large language model and a logical reasoning module; s5, implementing a dynamic knowledge updating mechanism, and automatically triggering incremental learning of the knowledge base when policy change or format update is detected; and S6, outputting structured data, and synchronously generating an auditing traceability chain containing a decision path. According to the method, the key field identification accuracy can be improved, and auditing traceability and non-perceptual increment updating in the whole process are realized.

The invention provides a right and interest exchange verification system, belongs to the technical field of right and interest management systems, and effectively deals with complex right and interest fraud through multi-modal dynamic behavior trust chain construction. The system comprises a multi-modal behavior acquisition module, a real-time acquisition input behavior, equipment and environment characteristics, a federated behavior modeling module, a cross-platform construction dynamic behavior baseline library and trust score output module, a scene risk assessment module, a hierarchical verification strategy supporting module, a dynamic scheduling execution module, and an intelligent resource allocation and auditing module. Data desensitization and feature extraction are realized through lightweight edge calculation; federated learning fuses local and global credit scores, and combines a dynamic attenuation model and scene perception weight adjustment; the multi-modal cross validation is used for coping with different risk levels; the intelligent resource scheduling resists the DDoS attack; the system realizes balance between high-precision fraud identification and user experience through behavior continuity protection, confrontation sample detection and interpretable scoring atlas.

A behavioral authorship verification system captures and analyzes multi-modal behavioral patterns during content creation to authenticate human authorship. The system comprises a processor executing behavioral analysis modules that generate comprehensive behavioral fingerprints distinguishing genuine human authors from AI-generated content and impostor authorship. A sentence progression mapping module detects sentence boundaries and captures intermediate composition states including additions, deletions, and modifications. A multi-modal input analysis module monitors keystroke dynamics including flight time and dwell time while detecting paste events and input method transitions. A behavioral pattern recognition engine generates user-specific baselines from historical sessions and computes deviation scores using statistical distance metrics. An anomaly correlation module aggregates behavioral deviation signals using weighted fusion algorithms to detect sophisticated mimicry attempts. An authorship scoring engine synthesizes outputs into unified confidence scores while maintaining temporal authorship chains. The system enables real-time authorship verification during content creation rather than post-hoc analysis.

Techniques for verifying authenticity of a cosmetic product using a distributed ledger include generating a transaction including identification information for a cosmetic product indicating a manufacturer of the cosmetic product. The transaction is augmented with a cryptographic signature from the manufacturer to prove the identity of the manufacturer. Additionally, the transaction is transmitted to at least one other participant in a distributed ledger network of participants maintaining the distributed ledger. A unique identifier is embedded on the cosmetic product or on packaging for the cosmetic product that references the identification information for a cosmetic product in the distributed ledger.

The invention relates to a steel structure component virtual pre-assembly method based on three-dimensional laser scanning. Designing a self-adaptive hierarchical scanning strategy, and dynamically adjusting parameters of scanning equipment to obtain data of each region of the component; abnormal points are detected and eliminated by fusing scanning data of different levels; datum points are extracted, coordinate calibration is carried out, data of all levels are spliced, and a full-component three-dimensional point cloud model is formed; classifying the point clouds by using a multi-dimensional data analysis algorithm; establishing a closed-loop data verification system; designing a multi-feature point matching algorithm; performing iterative docking and position calibration through an algorithm; quantitative analysis is carried out on spatial deviation between the components in the assembling process; after the deviation is detected, a fine adjustment scheme is generated, and the position of the component is adjusted; a virtual pre-assembly path is generated according to the shapes, the connection modes and the installation sequence of the components, and the assembly sequence and the layout of spatial positions are controlled; and the assembly state of each component is monitored in real time, and the path is optimized and adjusted through a feedback mechanism.

The invention discloses a food safety sampling inspection data verification method and system and a computer storage medium thereof, particularly relates to the technical field of food safety supervision informatization, and is used for solving the problem of false consistency caused by silent failure when an external data source fails in an existing verification system. Field-level binding is realized by establishing a dependency mapping table of a sampling inspection system and an external data source; constructing a service response causal graph model, and dynamically marking a failure data source based on the deviation between the anti-fact response and the actual response; analyzing the verification field set and positioning a key verification field; degrading the weight of the key field to be below a threshold value and activating a manual auditing flag bit; scanning a preset coupling relationship between fields, freezing associated fields which do not trigger rules, verifying and constructing a conflict topology; when the check engine is executed, dynamically skipping the weight reduction field, suspending the freezing field, and generating a to-be-rechecked report in combination with the artificial flag bit and the topological graph; error data transmission is blocked from the source, and the food safety risk missed judgment rate is remarkably reduced.

The invention relates to an engineering document index consistency proofreading method and system based on a multi-modal large model, and the method comprises the steps: Q1. OCR detection and recognition: carrying out the optical character recognition and format analysis of a source document, converting an uploaded PDF document into a processable text message in a Markdown format, and carrying out the format discrimination of a table, a formula and a plain text; and Q2, table and formula processing: adopting a hierarchical processing strategy, intelligently selecting an optimal processing mode according to the complexity of the table, and converting table information into a descriptive long text through a language large model and cue words. According to the method, accurate, reliable and efficient document index checking service can be provided for a user, the quality and efficiency of professional document processing are remarkably improved, the efficiency and quality of knowledge graph construction are remarkably improved, a knowledge verification system capable of being evolved continuously is established, and the method is suitable for popularization and application. And a reliable technical support is provided for knowledge management and professional decision-making in a complex field.

The invention belongs to the technical field of steel and iron material design, and discloses a stainless steel strength and toughness collaborative optimization method and system based on heterogeneous integration, and the method comprises the steps: constructing a database of stainless steel components, process parameters and mechanical properties; according to the database, establishing a stacked heterogeneous integration model; quantizing contribution weights of input variables of the stacked heterogeneous integrated model to mechanical properties based on an SHAP method, identifying key factors, and setting a multi-target strength and toughness collaborative optimization index to guide an optimization direction; optimal components and processes are screened according to SHAP analysis, a sample is prepared through vacuum melting, hot rolling and heat treatment, the performance is verified according to the ASTM standard, and a final optimization scheme is determined; and a cross-scale digital twinborn verification system is constructed, and collaborative optimization of alloy components, process and macroscopic performance is realized. According to the method, the problems of low efficiency and insufficient generalization ability of a single model of a traditional trial and error method are solved, and an efficient and explainable intelligent optimization scheme is provided for development of high-performance stainless steel.

The invention discloses a network risk assessment model construction method and system based on an AI large model, and relates to the technical field of artificial intelligence and network security, and the method comprises the following steps: S1, a participating node splits a large model into a plurality of semantic independent fragmentation units through a dynamic fragmentation mechanism based on local multi-source heterogeneous data; according to the network risk assessment model construction method and system based on the AI large model, through a dynamic fragmentation federated distillation learning framework, the inherent contradiction between data privacy protection and AI large model training efficiency in cross-mechanism cooperation is effectively solved. A ten-billion-level parameter model is disassembled into semantic independent units by adopting a self-adaptive parameter fragmentation mechanism, the communication overhead is reduced on the premise of ensuring physical isolation of sensitive data in combination with a local differential privacy and parameter confusion technology, and meanwhile, cross-domain fusion of threat features and attack surface blind spot detection are realized through a layered distillation verification system. And the APT attack detection rate is improved.

The invention relates to the technical field of network security, in particular to an equipment identity verification system based on an MAC (Media Access Control) address, which comprises a hardware feature acquisition module, a binding MAC address generation module, an authentication code encryption module, an authentication server communication module, an equipment behavior monitoring module and a security response module, wherein the hardware feature acquisition module is used for generating a unique equipment hardware fingerprint; the binding MAC address generation module is used for mapping the equipment hardware fingerprint into a binding MAC address uniquely corresponding to the equipment; the authentication code encryption module is used for generating an equipment authentication code; the authentication server communication module is used for sending the equipment authentication code to an authentication server for verification; and the equipment behavior monitoring module is used for identifying potential abnormal behaviors. According to the invention, through combination of hardware feature binding of the MAC address and multi-dimensional behavior monitoring, uniqueness, security and active protection capability of equipment identity verification are realized, and the security protection level of the system is significantly improved.

The invention relates to the technical field of BOM data analysis, and discloses a collaborative BOM data analysis and verification system and method based on an AI model.The collaborative BOM data analysis and verification system based on the AI model comprises an intelligent document sensing and structure analysis module, the intelligent document sensing and structure analysis module monitors and identifies a list file, and sends the list file to a server; and the AI assistant dialogue engine module adopts a language model to interact with an engineer to analyze the document in the unified format field by field. According to the method, a field-level approval mechanism is introduced, each suggestion can be modified after being confirmed by a user, and the user can execute operations such as rejection, remark and pending instead of rechecking only in the final stage, so that the sovereignty, the safety and the traceability of the user are improved, the liability boundary of AI aided design is cleared, the AI is evolved from a tool to an engineering partner with learning ability, and the work efficiency is improved. Interaction intelligent experience is obviously enhanced, and adaptability is high.

The invention discloses an SQL statement structure verification system based on large model and knowledge graph fusion enhancement. The SQL statement structure verification system comprises a knowledge graph construction module, an input design module, a verifier design module and an output design module. According to the method, a knowledge graph is introduced as a structured semantic support, and a closed-loop enhancement processing flow of SQL statements from generation, verification to optimization is realized in combination with a structure perception cue word design, a rule verification mechanism and a Function Call feedback interface; the defects that an existing Text2SQL method is insufficient in structural understanding, lack of semantic reasoning, weak in generalization ability, dependent on computing power and the like are overcome. According to the method, triple guarantee of semantic perception, rule driving and model assistance is realized, the accuracy, robustness and interpretability of SQL generation are remarkably improved, and the cognitive load and operation risk of non-professional users in database interaction are effectively reduced.

The invention relates to the technical field of chip verification, and discloses a chip verification method, test equipment and a chip verification system.The method comprises the steps that a template file is built, a verification environment and a test case are generated based on the template file, a configuration file is obtained, and parameters in the verification environment and the test case are updated through parameters in the configuration file; according to the method, the verification environment and the test case are updated, the updated verification environment and the updated test case are obtained, regression testing is carried out on the chip on the basis of the updated verification environment and the updated test case, the test result is obtained, the verification environment can be rapidly generated on the basis of the template file, parameters can be rapidly replaced, and the chip testing efficiency is improved.

The invention relates to the technical field of block chains, and discloses a block chain and privacy computing collaborative verification system, which comprises a data fragmentation module, a privacy fusion module, a collaborative verification module, a consensus correction module and a fragmentation graph optimization module. The data fragmentation module constructs a multi-stage data collaborative topology through a preprocessing node and a dynamic fragmentation protocol; the privacy fusion module realizes privacy protection by using a ciphertext analysis unit to switch links in a multi-state manner and a zero-knowledge verification unit; a collaborative verification module generates a reference verification strategy through a consensus modeling node multi-dimensional fusion model, and a state optimization unit dynamically aggregates and sorts fragmentation states; the consensus correction module corrects the fragmentation state through an isolation verification unit and a delay compensation unit and calibrates ciphertext parameters; and the fragment atlas optimization module constructs a stability evaluation network monitoring synchronization rate and performs closed-loop adjustment on a verification strategy. The system realizes dynamic fragmentation, privacy fusion and intelligent verification, and improves the efficiency, security and stability of block chain data processing.

Systems and methods for staking instrument cryptocurrency rewards and validator management are disclosed. The systems and methods include generating validator pods associated with cryptocurrency staking validation and generating validator keys configured to be utilized by the validator pods for validating proof of stake operations. A subsets of the validator keys may be assigned to the validator pods and a request to perform the proof of stake operations may be received. A validator pod may be provisioned for validating proof of stake operations, and the validator node may be utilized to validate proof of stake operations based on the selected validator node being a compliant validator node.

Systems and methods described herein relate to the real-time verification of data purges. A first subprocess of a data purging process is executed to purge a plurality of data items. A system accesses purge result data providing an indication of a result of the first subprocess. The system determines, based on the purge result data, that the first subprocess was not executed in accordance with a purge policy associated with the data purging process. In response to determining that the first subprocess was not executed in accordance with the purge policy, the system adjusts a state of the data purging process. A second subprocess of the data purging process is then executed according to the adjusted state.

The invention discloses a high-availability testing method and system for task scheduling of a computing power host management platform, belongs to the technical field of high-availability testing of computers, and aims to solve the technical problems that in the prior art, high-concurrency scene testing coverage rate is insufficient, cross-domain scheduling, failover and self-healing scene verification are imperfect, and testing efficiency is high. According to the technical scheme, the method structurally comprises the steps of hybrid load testing, wherein the elastic capacity expansion and contraction capacity and the performance stability of a system are verified by simulating a long-time stable load and burst flow scene; cross-regional intelligent scheduling: tasks are dynamically scheduled based on cost, time delay and regional strategies, and optimal resource allocation and service continuity are ensured; resource fault migration recovery: simulating node-level or region-level faults, and verifying the automatic detection, migration and recovery capabilities of the system; monitoring and data collection: collecting affairs, resources and stability indexes in real time, and providing multi-dimensional data support for verification; and analysis and optimization: identifying a bottleneck based on test data to generate optimization suggestions, and continuously improving the high availability of the system.

The invention relates to document verification, in particular to a heterogeneous document set-oriented cross-modal semantic alignment and logic consistency verification system, which is used for heterogeneous document input, supports multi-format document input and comprises multi-modal elements including texts, pictures, tables and charts. The document analysis module is used for carrying out structured extraction on document contents; extracting multi-modal elements, identifying and classifying various elements in the document, and establishing position and type labels of a foundation; the knowledge graph construction module is used for uniformly modeling heterogeneous elements into a multi-modal knowledge graph; the graph neural network semantic alignment module is used for realizing accurate cross-modal semantic alignment by using a specially designed graph neural network based on the multi-modal knowledge graph; the hybrid consistency verification engine is used for performing logic consistency verification in combination with a symbol logic verification mechanism and a semantic consistency verification mechanism; according to the method, the defect that accurate cross-modal semantic alignment and logic consistency verification are difficult to carry out on professional documents with multi-modal elements can be effectively overcome.

Systems and methods for verifying an applicant. An example method includes, receiving, from a user device, an application that includes an identifier associated with an applicant; determining a digital profile for the applicant based on the identifier, processing the digital profile through an assessment model to determine a metric for the applicant; and providing, to the user device, validation data for the application based on the metric for the applicant.

The invention discloses a low-vacuum circulating water heating dynamic load matching method based on AI regulation and control, and the method comprises the following steps: step S100, multi-source data real-time collection: building thermal environment parameters are synchronously collected through a temperature sensor array, a vacuum pressure transmitter, a flowmeter and an environment monitoring unit which are deployed in a distributed manner; according to the method, the control period is compressed to the minute level, compared with traditional PID control, the response speed is increased by several times, the indoor temperature can be controlled more accurately, and meanwhile the failure rate of equipment is reduced. According to the sudden weather coping mechanism, heat supply strengthening can be started 30 minutes ahead of time under extreme weather such as snowstorm weather, and the stability of the system is ensured. The method is characterized in that the prediction advantage of deep learning and the optimization capability of model prediction control are deeply combined, and the control problem of multivariable strong coupling of the low vacuum system is solved through a verification system of multi-source data fusion and virtual-real combination.

A security device maintenance system is provided. The aspects include one or more processors configured to assess security data quality by conducting one or more tests that compare the security data to one or more predetermined thresholds, assign one or more anomaly indicators to security devices if the security data quality fails to meet the one or more predetermined thresholds, and generate an automated maintenance report for the one or more security devices including the one or more anomaly indicators. The aspects can also include periodically retrieving one or more security data from the one or more security devices, utilizing an artificial intelligence (AI) model to detect one or more anomalies, and generating one or more predictive maintenance suggestions.

The invention, which relates to the technical field of network security protection, discloses a photovoltaic power station 5G network security protection device comprising the following components: a data acquisition module, an intelligent fusion analysis and verification system and a protection mechanism triggering module. According to the invention, by integrating an intelligent fusion analysis and verification system, multi-dimensional data such as photovoltaic power station operation data, 5G network characteristic parameters, external environment factors and personnel operation behaviors can be comprehensively collected and deeply analyzed, and a potential association relationship and a mutual influence mechanism among different factors can be accurately mined; meanwhile, a dynamic association rule self-adaptive updating mechanism can adjust the association rule in real time according to newly collected data and a mined new association relationship, the accuracy and timeliness of an analysis result are ensured, the credibility of the data is improved, and network security risks can be found and coped with in time.

The present disclosure provides a verification system, a verification method, an electronic device and a storage medium. The verification system includes a simulation verification device, a first portion and a second portion. The first portion includes a first master module and a second slave module. The second slave module includes a memory unit connected to a memory access interface at a periphery of a design under test. The second portion includes a first direct programming interface, a second direct programming interface, a function library module, and a test case module. The first direct programming interface communicates with the first master module and is configured to call a first function to implement front door access to a register of the design under test. The second direct programming interface communicates with the memory unit and is configured to call a second function to implement back door access to the memory unit.

The invention discloses an industrial field-oriented incremental knowledge graph updating and consistency verification system, which comprises the following steps of: monitoring original industrial data to obtain new data, old data and a data change set; respectively converting the new data and the old data into structured data according to a preset knowledge extraction rule set, and respectively mapping the structured data into triads according to a preset mapping rule to obtain a triad change set; calculating update priorities of a plurality of attributes in the triple change set, and obtaining a first update atlas according to the update priorities; performing multiple consistency verification on the first update atlas, and modifying the first update atlas according to a verification result to obtain a second update atlas; and constructing a knowledge change link diagram according to the first update map and the second update map, and carrying out rollback verification on the knowledge change link diagram. The invention relates to the technical field of industrial knowledge maps, and solves the technical problems of high resource consumption, difficulty in guaranteeing knowledge consistency and the like in a traditional full-quantity updating mode.

The invention discloses a distributed data encryption and verification system and method based on a block chain, and relates to the technical field of block chains. The method comprises the following steps: dynamically matching an initial processing node and a transmission path according to data characteristics and node capability; selecting an optimal path and an encryption strategy by combining the node capability, the network state and the data security requirement; data fragmentation encryption transmission is executed, and the transmission quality is evaluated in real time; future transmission risks are predicted based on historical data, and path sustainability is evaluated; and adjusting the path according to the real-time verification value and the prediction feasibility, and updating the block chain record. Full-link traceable auditing is realized through block chain evidence storage, the dynamic encryption strategy and predictive path optimization are combined, the transmission efficiency is improved, the security risk is reduced, the energy consumption is reduced, and the method is suitable for a high-security demand scene.

The invention belongs to the technical field of logistics information, and discloses a multimodal transport trusted data space construction method based on privacy calculation and a block chain. The method comprises the following steps: deploying a modular intelligent data gateway at a core node of a multimodal transport line, unifying the format of multi-source heterogeneous original data, and chaining a hash value; after the multi-source heterogeneous data is subjected to customization processing, key metadata attributes of all the data are registered and linked in real time; constructing a core ontology model, and establishing a cross-domain semantic interoperation capability; establishing a privacy calculation and block chain collaborative dual-channel architecture, and cooperatively training an ETA prediction model in a local data isolation environment by adopting transverse federal learning to ensure that original trajectory data is not out of a domain; constructing a dispute arbitration and credible verification system, and realizing an arbitration process of judicial verification when a transportation dispute occurs; data minimization disclosure and compliance sharing are achieved through dynamic access control. According to the method, multimodal transport data can be available and invisible, and the whole operation process can be audited.

The invention relates to the technical field of transaction verification, in particular to an electronic commerce transaction verification system based on a block chain, and the system comprises a transaction identity verification module, a fund flow monitoring module, an order payment state confirmation module, an abnormal transaction comparison module and a risk control strategy adjustment module. According to the invention, identity verification is carried out by using the hash calculation of the timestamp, the device identifier and the user public key and the block chain technology, so that the authorization and override accuracy of the transaction request is ensured, dynamic monitoring and trend analysis are carried out on the fund flow direction, including the measurement of the fund flow path and the evaluation of the fund transfer change rate, and the fund transfer accuracy is improved. The method effectively monitors the stable flow condition of funds, improves the recognition capability of transaction abnormity through integrating multi-source data analysis, enhances the real-time performance and accuracy of risk management, improves the safety and transparency of transaction, enhances the rapid response capability of abnormal behaviors, and improves the safety and reliability of the transaction. And higher-level trust and security assurance are brought to the user and the platform.