1844 results about "Data verification" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

The invention discloses a power distribution network collaborative management method and system based on artificial intelligence, and relates to the technical field of electrochemical detection, and the method comprises the steps: constructing a distributed edge computing node network, deploying nodes at key positions of a power distribution network, achieving the collection and preprocessing of local power data, and reducing the cross-regional data transmission pressure; an AI real-time communication scheduling model is established based on the preprocessed data, communication resources are dynamically allocated according to the operation state of the power distribution network, and fault data transmission is guaranteed preferentially; seamless interaction of multi-protocol equipment is realized through a self-adaptive protocol conversion mechanism containing protocol identification, format conversion and data verification; training a fault diagnosis model by using a federated learning framework, and enabling edge nodes to only upload parameters to a coordination center for aggregation and updating, so as to balance model precision and data privacy; when a fault is detected, a millisecond response mechanism is started, and a processing strategy is generated and executed in combination with edge local decision and central global optimization.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention relates to the technical field of pulsed ion beam processing, and discloses a positioning system for a pulsed ion beam processing optical element, which can accurately predict nonlinear structure deformation in a complex thermal environment and eliminate compensation deviation caused by a traditional linear model. A multi-sensor data fusion mechanism effectively suppresses the interference of local measurement noise on a control decision, and improves the reliability of a compensation strategy. And a closed-loop control system realizes real-time verification and dynamic optimization of the compensation effect, and machining precision reduction caused by error accumulation is avoided. The submicron executing mechanism ensures that the thermal drift compensation amount is accurately converted into platform pose adjustment, the high-precision optical element machining requirement is met, and the problem that a traditional positioning system is incomplete in model input due to the single temperature field information collection dimension is solved. Meanwhile, the dynamic characteristics of the temperature-displacement coupling relation are verified through high-precision displacement data.

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance / digital medical treatment, and discloses a multi-source heterogeneous data verification and tracing method and device and related equipment. The method comprises the following steps: acquiring multi-source heterogeneous data from a service data source of a target service; preprocessing the multi-source heterogeneous data, extracting a first data field in each data source, and matching the first data fields with the same semantics in different data sources based on a preset semantic mapping rule; performing consistency verification on the matched first data fields to obtain second data fields with inconsistent verification results; performing root cause tracing on the second data field based on a preset difference analysis rule, and determining a difference type corresponding to the second data field according to a root cause tracing result; and generating a multi-source heterogeneous data verification and tracing result according to the business data source, the second data field, the root cause tracing result and the difference type. According to the method, matching, difference identification and root cause positioning of multi-source heterogeneous data are realized.

The invention provides an artificial intelligence auxiliary antenna design method based on a Gaussian regression process, and the method comprises the steps: employing a script interface to interact with a simulation platform, automatically completing the parameterized modeling process of an antenna model, and setting an excitation condition and a radiation boundary; a multi-dimensional space stratified sampling method is adopted to generate a uniformly distributed sample point set in the multi-dimensional parameter space, and electromagnetic simulation is carried out; preprocessing the data output by simulation to form a data structure suitable for machine learning model processing; integrating the trained MLNN model into a grey wolf optimization algorithm, and optimizing antenna design parameters; and performing electromagnetic simulation on the optimal parameter obtained by optimization, comparing a prediction result with actual simulation data, and verifying the performance of the optimization engine in the target frequency band. According to the method, the problems of insufficient sample generation efficiency, insufficient parameter sensitivity analysis, limited model generalization ability, low convergence speed of an optimization algorithm and the like in the prior art can be solved.

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention relates to a full-life-cycle digital passport management system and method for electromechanical equipment, which take dynamic data in an operation and maintenance stage as a core and depend on linkage of an intelligent algorithm and full-cycle data. The problems of equipment parameter deviation, performance degradation and the like can be accurately identified based on five-dimensional full-period data integrated by a data passport construction module; and a fault influence range and associated equipment are positioned according to physical, logic and power dependency relationships presented by the equipment dependency graph construction module, so that a basis is provided for decision correction. Besides, through unique identification of a gene coding module, historical data and responsibility subjects of equipment can be traced, BIM model dynamic updating, feedback design and construction stage data verification optimization are driven, and a closed loop of operation and maintenance feedback-model correction-full-cycle data iteration is formed. The module solves the problems of traditional operation and maintenance data isolation and model static state, enhances the timeliness and accuracy of block chain evidence storage data, and supports applications such as intelligent service layer predictive maintenance and fault self-repairing.

The invention discloses a pressure injury risk early warning method and equipment, and relates to the technical field of medical monitoring instruments. The pressure sensor array layout can be self-adaptive to the body shape of a patient, a key osseous process part is covered, no monitoring dead angle is ensured, a temperature compensation mechanism is built in, environmental interference is reduced, the measurement precision is improved, an abnormal pressure threshold value can be dynamically set based on individual medical record data, data verification is performed before early warning is triggered, false alarm is avoided, and the array structure is light, thin and flexible. The mask is comfortable to fit skin, supports long-term wearing and is easy to disinfect and maintain; environment temperature and humidity parameters and pressure data are subjected to fusion analysis, the influence of a high-temperature and high-humidity environment on skin microcirculation can be recognized, the risk of pressure sores can be evaluated in an auxiliary mode, a calibration algorithm is built in a sensor, environment fluctuation interference is reduced, and data reliability is ensured; and the early warning unit comprises sound-light alarm equipment which supports a multi-stage alarm mode and is used for triggering an early warning signal and pushing information when the risk index exceeds a threshold value, so that the early warning timeliness is ensured and the false alarm risk is reduced.

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.

The invention provides a data verification method based on two-dimensional code third-party login and national secret authentication, and the method comprises the steps: obtaining a user identity bill and obtaining a private key password corresponding to the user identity bill by an encryption end when a login request sent by a user end through scanning a login two-dimensional code of a login end is received; the encryption end encrypts the private key password to obtain an encrypted key password, and sends the encrypted key password to the login end; the encryption end determines collaborative signature response data according to the collaborative signature request and the user identity bill and sends the collaborative signature response data to the login end; and the encryption end verifies the complete signature through the verification request, and sends a verification result to the authentication end. By arranging the encryption end, national secret authentication and two-dimensional code third-party login are combined, and the safety of logging in a third-party website when the two-dimensional code is scanned is guaranteed.

The invention discloses a multi-modal fusion bank receipt intelligent processing system and method based on vision and NLP, and the method comprises the steps: receiving a bank receipt picture or a PDF document, and completing the text detection, direction correction and character recognition through a visual processing engine; a three-level receipt independent segmentation mechanism is applied, and independent receipt records are divided through spatial clustering analysis, semantic analysis, visual verification and cross-page association processing; jointly extracting text features and layout features of each receipt through a double-flow multi-modal fusion model, and fusing the text features and the layout features; field-level data extraction is executed through a field extraction engine, and financial data verification including account number, amount, date and abnormity quadruple verification is carried out; and generating structured JSON output to obtain a bank receipt processing result. According to the invention, the innovative five-layer processing architecture realizes high-precision analysis of the bank receipts through an independently researched and developed receipt independent segmentation engine, a vision-semantic fusion model and a financial data verification system.

The invention discloses an operation data processing method and system based on a compliance verification library and a risk control plan, and belongs to the technical field of data compliance and risk control, and the method comprises the steps: obtaining an execution plan of a target operation task and risk control strategy information of the execution plan, analyzing operation data items, distributing corresponding compliance verification time windows and levels, and forming an operation data verification scheduling table; according to the verification level and the business domain, a rule set is extracted from a compliance verification library, and rules comprise predefined rules, model output rules and historical violation rules; field-level matching, logic consistency verification and cross-source linkage verification are executed in the verification time window; when the verification result meets the triggering condition, generating an abnormal event, and executing response operations such as delay, recheck or interception; and finally, writing the execution state and the verification result into an abnormal log queue for risk control strategy parameter updating or re-evaluation. According to the method, the dynamic compliance verification and strategy closed-loop optimization of the operation data are realized, and the intelligent compliance control capability of the system is improved.

The invention discloses a block chain technology-based review data information security tamper-proofing method, and relates to the technical field of information security, the tamper-proofing method comprises the following steps: a data encryption storage step, a data evidence storage step, a trusted computing step and a data verification step, when a computing request is received, the trusted computing step is executed, and the trusted computing step is executed; whether the identity of a requester conforms to the access control strategy is verified through the smart contract, after verification is passed, the smart contract generates a calculation task event, the oracle monitors and captures the event, then a calculation task is transmitted to the trusted execution environment, ciphertext data is obtained in the trusted execution environment and decrypted, and the calculation task is sent to the target server. The method has the advantages that the balance of data privacy protection and integrity verification is realized through a hierarchical architecture combining on-chain evidence storage and off-chain encrypted storage, sensitive review data is stored in an off-chain database in an encrypted form, the security of the review data is improved, the security of the review data is improved, and the security of the review data is improved. Only data fingerprints are recorded on the block chain, data privacy is protected, and data integrity can be verified through the block chain.

The invention relates to an article label anti-counterfeiting authentication method and system and computer equipment. Comprising the following steps: reading first label information containing an identification area, a coding area and a password area through an external network base station to realize data preliminary verification and label authentication; and the second label information only containing the coding area is read by the intranet base station for re-verification, so that data multiple verification and anti-counterfeiting verification are provided for electronic label data interaction in a network isolation environment, information comprehensiveness and security are considered, the accuracy and reliability of target object identification are improved, and the user experience is improved. And meanwhile, data security is guaranteed through partition information management.

The invention discloses a cross-heterogeneous database data migration method and system, and the method comprises the steps: building a multi-data-source connection among multiple data sources, and enabling the multi-data-source connection to be used for representing the incidence relation between a source database and a target database; obtaining a source data table based on the migration sql, and querying source data corresponding to the source data table from a source database; converting the source data based on a preset model mapping rule to obtain target data; determining a target database into which the source data is migrated according to the multi-data source connection, and writing the target data into the target database to realize data migration; and performing data verification on the target database according to the checking rule to obtain a migration result, and generating a result report according to the migration result. According to the method, the source data is converted into the target data through the preset model mapping rule, migration obstacles caused by structural differences among heterogeneous databases are effectively solved, the consistency of the data is ensured through the checking rule, and the accuracy of data migration is improved.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention discloses an underground water seepage analysis and prediction method based on physical-data cooperative driving, and belongs to the field of underground water seepage, and the method comprises the following steps: establishing a hydrogeological numerical model, carrying out seepage simulation calculation, and carrying out comparison verification with field monitoring data to realize accurate mapping; meanwhile, a machine learning data set is constructed by utilizing a numerical simulation result, a prediction model based on a Stacking integrated learning structure is established in combination with field data of a construction roadway, a water curtain layer and an oil storage cavern layer, and the water seepage amount or the underground water level after excavation of a rock mass in front of a tunnel face is predicted by taking geological, hydrological and construction parameters as input. According to the method, a physical mechanism and data driving are fused, the prediction precision and interpretability are remarkably improved, and a scientific basis is provided for cave depot project grouting and excavation optimization.

The invention discloses an agent assisted testing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring demand information of a Web system to be tested; analyzing the demand information and generating a test case set; performing hierarchical labeling on the test case set to form a hierarchical case set containing the basic operation type case, the data verification type case and the complex logic type case; distributing the basic operation type use case to an execution module of the agent system, distributing the data verification type use case to an interface automation test module, and distributing the complex logic type use case to an artificial interaction terminal; and when the execution module or the interface automatic test module monitors that the case execution fails or the state is abnormal, an exception processing strategy is matched according to an exception type and an exception source, corresponding processing operation is executed, and feedback information used for optimizing the layered case set is generated. According to the method, the test tasks can be intelligently allocated and accurately executed, and automatic adaptation and optimization can be performed when an exception occurs, so that an efficient man-machine collaborative test is formed.

The invention relates to a data verification method, a data verification device and a storage medium. The method comprises the following steps: firstly, through semantic analysis, establishing a mapping relationship between a first position corresponding to first text data in an editable format in a to-be-verified first document and a second position of second text data in a target format in a second document, so that an original to-be-verified text and a to-be-verified text after format conversion form a position corresponding relationship; then, verifying the second document through semantic analysis, and taking second text data which does not pass verification in the second document as annotation data; and finally, based on the mapping relationship, determining the labeling position of the labeling data in the first document, and generating an editable error prompt at the labeling position. Therefore, the time for manually searching the error position of the original document is shortened, and the verification and correction efficiency is improved, so that the data verification efficiency is improved.

The invention relates to a data integrity joint verification method and system for a trusted data space, and relates to the technical field of industrial data verification, and the method comprises the steps: taking the data importance, the Hash calculation frequency and the uplink evidence storage frequency in a preset time zone as an adaptive data integrity verification strategy; processing a real-time operation state data stream of the industrial equipment, generating a data integrity verification certificate, and storing the certificate to a credible data space distributed account book; when a maintenance analysis request is initiated, performing integrity verification on the obtained original operation state data stream based on a data integrity verification voucher on the distributed account book; and if the verification is passed, performing equipment health state diagnosis and targeted maintenance according to the original operation state data stream. The method solves the problems that a conventional data integrity verification method mostly adopts a fixed verification strategy, is not adaptive to data importance and network states, and cannot meet the requirements of predictive maintenance on efficiency and real-time performance while guaranteeing data credibility.

The invention discloses a carbon emission abnormity traceability method and device, a medium and equipment, and belongs to the field of carbon emission, and the method comprises the steps: receiving a traceability request containing a to-be-verified data identifier, positioning target link data from a block chain storage, and executing signature verification and Hash consistency verification, thereby guaranteeing the authenticity and consistency of the data. And if the verification of the target link data is passed, verifying the inclusion relation of Merkle tree root hash of each link upwards layer by layer to confirm the integrity of the full-chain data. And if verification failure of any level is found, the feature data of the level is combined with an anomaly detection model based on an isolated forest algorithm, and an abnormal data node is positioned. And then, according to the signature information of the abnormal data node and the preset tracking secret key, a group signature verification and private key derivation process is applied, the responsibility participant is traced, and the tracing process of the carbon emission abnormity is completed, so that the problem that the carbon emission abnormal point cannot be accurately and efficiently traced in the prior art is effectively solved.

The invention discloses a dynamic balance monitoring system and method for a water network. According to the invention, through real-time analysis and correlation analysis of the three-level index system by the dynamic equilibrium algorithm module, the system realizes accurate pre-judgment of a water quality fluctuation rule, and the early warning capability of abnormal working conditions is effectively improved. And the membrane state prediction module fuses the mechanism model and the operation data, constructs a membrane element full life cycle management model, and strengthens the predictive maintenance capability of the performance degradation of the key equipment. The anomaly detection module greatly reduces interference of manual input errors on system judgment through a multi-dimensional data verification mechanism, the authenticity and reliability of monitoring data are ensured, the intelligent management level of the industrial water treatment process is remarkably improved, a dynamic threshold value early warning mechanism converts traditional passive treatment into active intervention through a gradient response strategy, and the system reliability is improved. Equipment loss caused by the fact that technological parameters exceed the standard is reduced, and stability and economical efficiency of water network operation are comprehensively enhanced.

The invention discloses a data verification method and electronic equipment, and relates to the technical field of electric digital data processing, metadata can be scanned to identify a target entry point of the metadata, and a plurality of associated verification items of to-be-verified data corresponding to the target entry point can be determined through annotations of the target entry point and corresponding target verification rules, so that the verification efficiency of the to-be-verified data is improved. And verifying the to-be-verified data by using the target verification rule and the plurality of associated verification items, thereby solving the technical problems that in related technologies, association between business logics cannot be perceived, a static verification technology is difficult to meet dynamic business logic requirements, and thus the user experience is affected, reducing the complexity of computer readable instructions, and improving the user experience. According to the method and the device, a plurality of associated verification items needing to be verified are dynamically determined, so that a very complex business verification scene is processed, and the technical effect of efficient and reliable data verification is achieved.

The invention provides a data verification method, electronic equipment and a computer program product. The method comprises the steps of obtaining a to-be-verified target bill from an upstream first database; monitoring log change information of successful library falling of the target bill in a downstream business database, and obtaining original log data corresponding to the target bill from the business database; performing format conversion processing on the original log data to obtain target log data, and storing the target log data in a second database; and performing consistency comparison verification on the target log data obtained from the second database and the target bill.

The invention discloses a secure key injection method and system, which are applied to electronic equipment with a rich execution environment and a secure virtual machine environment, and the method comprises the following steps: receiving a key injection request in the rich execution environment, and loading and starting the secure virtual machine environment; forwarding the key injection request to a secure virtual machine environment; generating a key pair in the secure virtual machine environment, and sending a public key certificate and an identity certificate of the key pair to a key management background through a rich execution environment; the key management background returns response data after verification is passed, and the response data is forwarded to the secure virtual machine environment through the rich execution environment; verifying the response data in the secure virtual machine environment; and after the verification is passed, storing the to-be-injected key material in the response data in the secure virtual machine environment. According to the invention, end-to-end security protection of the key material is realized through dual-environment cooperation, and the anti-attack capability and the data confidentiality of the injection process are effectively improved.

The invention relates to the technical field of big data, and provides a data verification method and device in big data cluster migration, equipment and a medium, and the method comprises the steps: obtaining a source data cluster and a target data cluster; dividing the source data cluster and the target data cluster into a plurality of data layers according to a standard data warehouse layering strategy, and binding different data comparison strategies for each data layer; establishing a fingerprint baseline table and constructing a sampling model feature library; performing data fragmentation processing on each data layer, and controlling each data layer to execute a data comparison task in parallel; and if any data layer of the target data cluster and any data layer of the source data cluster are detected to have difference, recording difference data and performing data restoration. According to the method, the source data cluster and the target data cluster are subjected to hierarchical parallel fragmentation comparison, a plurality of fragments of each layer are subjected to data comparison tasks in parallel at the same time, different comparison strategies are bound to the data layers, the comparison process can be achieved, and the overall data processing efficiency is improved.

The invention is applied to the technical field of engineering cost, and discloses a BIM parameterized model-based cost fine control and pre-settlement auditing system, which comprises a data acquisition module, a calculation module, a calculation module and a calculation module, the data comprises building component geometric parameters, building material market real-time price data, cost quota standard data and historical project cost auditing data, the building component geometric parameters cover the size, the material and the number, and the building material market real-time price data comprises supplier quotation, freight and tax; the cost quota standard data comprises national and local quota sub-items and rate standards. According to the construction cost fine control and pre-settlement auditing system based on the BIM parameterized model, the data acquisition module is automatically connected with a multi-source database and performs standardized processing on unstructured data, and the data verification unit is combined to perform logic verification on data such as geometric parameters of building components, building material prices and the like, so that complexity and errors of traditional manual calculation are avoided.