1800 results about "Data integrity" patented technology

The invention relates to the technical field of hard disk testing and verification, in particular to a multi-thread-based solid state disk reliability automatic testing method and system.The method comprises the steps that firstly, SMART information is deeply analyzed through microsecond-level high-granularity continuous performance monitoring, and multi-thread parallel processing is assisted; according to the method, fine performance fluctuation of the solid state disk under the concurrent load can be quickly captured, a fault mode can be identified, then early warning is realized by utilizing the extracted multi-dimensional features and a machine learning model, and a detailed fault diagnosis report is generated; and through dynamic error correction code strength verification and data integrity verification under pressure, an internal error correction mechanism of the solid state disk is actively detected and optimized. And finally, in combination with prediction reliability modeling, the system can estimate the remaining service life and predict faults, and provides product optimization suggestions for design, manufacturing and firmware optimization of the solid state disk, so that automation, intelligence and full life cycle management of the fault detection reliability of the solid state disk are realized.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention discloses an urban building three-dimensional automatic modeling and visualization method, and belongs to the technical field of building three-dimensional modeling. The method comprises the steps that point cloud data, high-resolution images and geographic information system data of urban buildings are acquired, data cleaning, registration and alignment are carried out, and preliminary building digital representation is formed; accurately segmenting each building, and identifying the contour and main structural features of the building; based on the data integrity and the building complexity, adaptively selecting a proper reconstruction strategy to carry out three-dimensional reconstruction; in the reconstruction process, the geometric structure is analyzed and optimized in real time, and potential topological problems are repaired; automatically generating missing details based on a predefined architectural style library and a component library, and performing material inference and texture mapping; a graph structure is used for representing the relation between the buildings, and the positions and orientations of the buildings are adjusted through a global optimization algorithm; a rendering engine supporting multi-level detail switching is developed, and smooth visualization and interaction of a large-scale city scene are achieved.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

The invention discloses a behavior analysis early warning method and system based on AI situation awareness, and relates to the technical field of data recognition. The method comprises the following steps: collecting multi-source sensing data and extracting a numeralization feature vector; an edge correlation degree dynamic threshold self-learning algorithm is adopted to screen effective edges to construct a dynamic space-time diagram, and space-time correlation features are extracted; a modal weight coefficient is calculated based on historical detection accuracy, data integrity and scene adaptation degree, and multi-modal features are weighted and fused; inputting the deep reinforcement learning model, outputting a threat risk value and a safety response strategy, and executing; and updating the dynamic weight, the threshold value and the strategy parameter according to the early warning accuracy and the threat interception success rate to realize closed-loop optimization. The method solves the technical problems that multi-source heterogeneous data lacks uniform feature representation, cross-modal relevance is lost, a space-time association relationship cannot be dynamically updated, multi-modal feature fusion weight is fixed, fusion effectiveness is affected, threat assessment lacks adaptability, and performance is degraded due to model parameter updating lag.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

Techniques are described for enabling concurrent cross-region replications and garbage collection while maintaining consistency and data integrity among file systems. In some embodiments, techniques for garbage collection fencing utilize a system-level garbage fencing key (GC fencing key) and one or more job-level GC fencing keys in a source file system that perform one or more cross-region replications with one or more target file systems, one replication and one job-level GC fencing key per target file system. In some embodiments, one job-level GC fencing key in a source file system and one job-level GC fencing key in a source file system together provide garbage fencing for a cross-region replication. In certain embodiments, the metadata information in a GC fencing key can inform, instruct, or be used to configure garbage collectors to skip garbage collection for a range of snapshots in a file system.

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 relates to the technical field of mobile communication transmission, and discloses a heterogeneous network multilink parallel mobile communication transmission method and system. A dynamic link aggregation module of the system collects link state parameters including signal strength, delay jitter and packet loss rate from a plurality of heterogeneous network interfaces in real time; a flow distribution decision module calculates available bandwidth weight of each link according to the data and generates a dynamic flow distribution strategy; the data fragment packaging module splits to-be-transmitted data into a plurality of data fragments according to the strategy, and adds a sequence identifier and a link identifier to each fragment; the multi-channel concurrent transmission module sends corresponding data fragments in parallel through a specified heterogeneous network link; and the receiving recombination verification module sorts the recombination data fragments according to the sequence identifiers, and detects the data integrity through a verification mechanism. According to the system, a set of complete multi-link parallel transmission solution is formed, and the problems existing in a traditional single link and an existing multi-link transmission technology can be effectively solved.

The invention relates to the technical field of oil well fault monitoring, and discloses an oil well indicator diagram real-time fault prediction method and system. The method comprises the following steps: acquiring an oil well sensor data stream, buffering and checking data integrity through a sliding window, and aligning multi-channel sensor data by applying a dynamic time warping algorithm to generate a standardized data stream; extracting time domain features based on the data stream, and comparing the time domain features with a historical feature library after principal component analysis dimension reduction to generate a feature difference index; triggering a multi-level threshold strategy according to the difference index, collecting an incremental training data set, finely tuning the model by adopting an elastic weight preserving algorithm, and generating a hot switching ready model; after the model is loaded, a fault probability value is generated through GPU accelerated reasoning, and an early warning event with a timestamp is generated; and finally analyzing the message into an early warning protocol message edge for transmission, and dynamically optimizing system resources based on logs. According to the method, the delay problem of high-frequency data flow is effectively solved, and the fault prediction accuracy and the system response speed are remarkably improved.

The invention provides a real estate full life cycle intelligent management method and system based on Internet of Things perception, and the method comprises the steps: distributing a unique hardware identifier of equipment, combining a lightweight digital certificate with a pre-shared key to achieve two-factor authentication, and storing a root certificate in a cloud end; an access authentication process is optimized, an identity label and a certificate signature request need to be submitted when a node accesses for the first time, a temporary session key is generated after the gateway verifies, and abnormal access is immediately isolated and reported; data integrity verification is implemented, and after a sensor collects data, an abstract is generated and data encryption transmission is carried out; after decryption, the receiving end recalculates the abstract for comparison, and if not, the abstract is discarded and nodes are marked to be abnormal; a data anomaly detection model is constructed, energy consumption fluctuation is determined based on historical data, a frequency baseline is collected, data reasonability is monitored in real time, secondary authentication is triggered when the data is abnormal, and node data access is suspended if the data fails; a hierarchical key system is established, hardware is solidified by a root key, session keys are alternated for 24 hours, and data encryption keys are dynamically derived and distributed through encryption channels.

The invention discloses a live broadcast e-commerce compliance auditing method and system based on penetration type supervision, and relates to the technical field of e-commerce compliance supervision, and the method comprises the steps: carrying out the Hash abstract calculation of a judgment result data set and a multi-source data set, carrying out the partitioning storage of the data set in a block chain, verifying the storage process through a timestamp and a multi-node consensus protocol, and carrying out the verification of the verification result; forming an evidence storage data chain; automatically matching the evidence storage data chain with a compliance rule, identifying potential illegal behaviors and risk levels, and forming a compliance result set; performing fusion calculation on the compliance result set, historical violation records and user feedback data to generate a comprehensive risk score, and automatically triggering real-time compliance intervention measures according to the comprehensive risk score to form an intervention result set; and re-storing the intervention result set, the comprehensive risk score and the violation behavior in the block chain to form a closed-loop evidence storage data chain. According to the invention, supervision transparency is enhanced, data integrity is ensured, and reliable support is provided for real-time intervention and closed-loop auditing.

The invention relates to the technical field of data integrity protection, in particular to a property right registration core data security protection method based on a block chain technology, which comprises the following steps: acquiring an anchor point on an identity data generation chain, verifying a historical block to construct a topological structure, and matching a permission mapping field write-in path. And verifying the node permission and generating a data integrity verification report. According to the method, identity anchoring data is generated by binding a registrant identity credential with an address on a chain, structured identity mapping is realized, historical blocks form a logic closed loop through pointer and serial number verification, the integrity of data circulation is enhanced, a field access boundary is established by matching a field set with a node identifier through an authority level, and the accuracy of access control is enhanced. Field contents are written into nodes according to an addressing protocol and a distributed path index is generated, the flexibility and addressability of data positioning are improved, a verification link is fused with a certificate and an authority label to match and screen credible nodes, and integrated verification of path compliance and access security is realized.

The invention discloses an edge computing-based industrial Internet of Things equipment data encryption transmission method, which comprises the following steps of: constructing an edge node alliance comprising at least two cross-regional edge gateways, completing bidirectional identity authentication through an alliance root certificate, and establishing an encrypted communication link; industrial equipment collects original data and then transmits the original data to an edge gateway which the industrial equipment belongs to, and the gateway extracts data features and divides the data features into a core control level, a process parameter level, a common monitoring level and a security level; differential encryption strategies are adopted for different levels of data; each edge gateway uploads the encrypted data to a distributed key management node, and obtains and synchronizes a periodically updated key chain; during cross-region transmission, performing secondary packaging and encryption to generate a data packet containing identifications such as a security level and the like; and the receiving end verifies the legality of the node, decrypts and verifies the integrity of the data, distributes the data if the decryption is passed, and blocks the alarm if the decryption The method is suitable for scenes such as single-area monitoring and multi-area manufacturing, data security and transmission efficiency balance are achieved, and all-weather stable transmission is guaranteed.

The invention provides an energy efficiency optimization method and system based on a multi-device coupling relationship, and is applied to the field of data processing. According to the method, the change rate of the collection frequency is dynamically judged by combining various working parameters related to the energy consumption equipment and the coupling relation, accurate regulation and control are performed based on the frequency peak value, the minimum holding time period and the change coefficient, quick response can be performed when the frequency fluctuates abnormally, data over-collection or data missing is avoided, and the accuracy of data collection is improved. And furthermore, by combining emergency process matching and state flag bit identification, the acquisition tasks are layered according to safety influence degrees, and high-frequency monitoring in a high-risk state and low-frequency acquisition in a low-risk state are realized, so that data integrity and resource saving are both considered.

The invention relates to the technical field of carbon emission, in particular to a full-life-cycle carbon footprint tracking management platform for power equipment. The platform collects carbon emission parameters in the whole process from raw material input to scrap recovery in real time through a monitoring equipment network and a sensor array to generate an original monitoring data stream; performing multi-source fusion and deviation correction on the data stream to generate a standardized carbon footprint sequence; a carbon emission trend model is constructed based on sequence extraction correlation features, and a dynamic adjustment strategy is generated when an integration fracture risk or emission abnormity is detected; transmitting an abnormal signal through a mobile terminal interface, receiving an adjustment instruction, and generating a closed-loop control signal; storing the extended carbon footprint data to construct a full life cycle file; estimating emission fluctuation to generate a stability strengthening strategy; and a monitoring-analysis-control-prediction four-order optimization link is formed. According to the invention, accurate tracking and dynamic optimization of carbon emission are realized, data integrity and management timeliness are improved, and technical support is provided for green manufacturing of power equipment.

Disclosed are systems and methods for maintaining data integrity (e.g., of a database, a data lake, etc.). In some embodiments, one or more processing circuits identify an update, insertion, or deletion of unstructured data stored in an upstream source. The processing circuits determine a new schema of the unstructured data using an inferring function and further determine a divergence between the new schema and a previous schema of the unstructured data based on comparing the new schema to the previous schema of the unstructured data. In an example, the processing circuits store the structured data in a database. The database includes an established data channel between the database and a downstream source, and the structured data is a unique representation within the database of the update, insertion, or deletion of the unstructured data.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The present invention relates to a method for quantitatively evaluating the quality health of pharmaceutical manufacturing sites; specifically, the invention pertains to a method that measures audit readiness, data integrity compliance, and overall operational performance of the pharmaceutical manufacturing sites.

The invention relates to a PCB (Printed Circuit Board) online packaging defect deep learning identification method, which comprises the following steps of: synchronously acquiring defect images and process parameters of each station, marking station indexes and timestamps, and realizing multi-dimensional structured filing; features such as multi-scale textures and edges are extracted and aggregated, integrated heterogeneous sub-models are utilized, confidence coefficients are fused, and comprehensive judgment facing stations and process parameters is formed; and meanwhile, feature contribution analysis is introduced to realize classification interpretability, linkage with real-time process data is realized, and accurate positioning and tracing of defects are supported. According to the method, data integrity and flow traceability are ensured, and the production line quality control capability is enhanced.

The invention discloses a knowledge graph generation method based on multi-source data integration, and relates to the technical field of knowledge graph generation. The method comprises the following steps: collecting multi-format data, and performing cleaning, standardization and desensitization preprocessing to ensure that the data is integrated; using the fusion model to extract entities and relationships, and adapting to multi-source data types; similarity is calculated in combination with multi-dimensional features, and entity alignment disambiguation is achieved; carrying out weighted fusion on multi-source knowledge to construct a triple, and processing relation conflicts; evaluating the quality of the atlas through multiple indexes; triggering conditions are set, incremental updating and version management are adopted, and dynamic iteration of the atlas is guaranteed. According to the method, the multi-source data preprocessing quality is improved, the entity recognition and alignment precision is enhanced, relation conflicts are solved, and a high-quality time sequence knowledge graph is constructed; incremental updating is efficient and energy-saving, version management is traceable, and multi-field dynamic application requirements are met.

The invention discloses a PDF (Portable Document Format) drawing recognition and information structured extraction method. The method comprises the following steps: generating a high-resolution bitmap through image preprocessing; positioning and classifying a text region, a table region and a symbol region in the drawing based on a target detection model of transfer learning; hough transform is combined with SIFT feature matching to identify engineering symbols, and sub-pixel positioning is realized through an RANSAC algorithm; after the oblique text is corrected through affine transformation, the content is extracted through OCR; reconstructing a table structure based on OPTICS clustering and projection analysis; constructing an RDF knowledge graph according to a coordinate association rule; and using U-Net difference to detect and position an omission area and complementing the omission area. According to the method, deep learning and image processing technologies are fused, the problems of low rotating text recognition rate, table structure loss and semantic association deficiency in a traditional method are solved, through lightweight model compression and TensorRT acceleration, the analysis accuracy is remarkably superior to that of the traditional method, and the method can be widely applied to the fields of constructional engineering, petrochemical engineering and the like and has wide application prospects. And the drawing information processing efficiency and the data integrity are improved.

The invention relates to the technical field of storage, and particularly discloses a solid-state electronic disk soft destruction method, device and system, which are mainly used for solving the systematic vulnerability problems of key metadata single-point failure, power-on deadlock, single recovery strategy, insufficient core component redundancy protection and the like of the existing solid-state electronic disk in an extreme application scene. The method comprises the following steps: monitoring metadata integrity, PCIe links and power-on progress in real time by anomaly perception, and generating an abnormal data packet; the decision arbitration generates an instruction packet according to an abnormal matching recovery strategy; and the execution layer dispatches hardware to complete repairing or clearing operation and feeds back a result. According to the scheme, the deep fault of the solid-state electronic disk can be intelligently sensed, accurately arbitrated and effectively recovered without physical intervention, and the reliability, availability and safety of the solid-state electronic disk are remarkably improved.

The invention relates to the technical field of tunnel defect monitoring, in particular to a tunnel monitoring system based on an autonomous robot, which realizes autonomous inspection and multi-modal data acquisition in a tunnel through a multi-module collaborative autonomous robot system and an integrated image acquisition and ground penetrating radar device, and improves the defect detection efficiency and continuity. Compared with a traditional mode depending on image recognition, a radar scanning image and a representation image are fused, feature extraction and defect reasoning are carried out through a deep learning model, the type and the extension area of the structural hidden defect are recognized, and the recognition capacity of deep structure abnormity is enhanced. Meanwhile, in combination with defect types and extension prediction, the robot is guided to execute supplementary scanning, and data integrity is ensured. Furthermore, cause classification analysis is carried out through a plurality of complete defect detection data, intelligent closed-loop monitoring from defect identification to cause diagnosis is realized, the monitoring cost is effectively reduced, and the accuracy and automation level of cause judgment of tunnel structure diseases are improved.

A blockchain-based system for managing music rights and royalties is disclosed. The system enables artists and contributors to upload music, assign revenue percentages, and track usage via smart contracts on a distributed ledger. Play counts are immutably recorded across distributor platforms. A contributor dashboard provides real-time access to royalty data. The platform incorporates Majority Reports to validate metadata matches and calculate royalties, and Minority Reports to flag discrepancies without payments, thereby ensuring data integrity. A Golden Box smart contract module replicates song rankings and applies predictive classification models to identify features associated with musical success. These mechanisms improve transparency, accuracy, and fraud prevention in royalty distribution.

The invention belongs to the technical field of communication, and particularly relates to an NFC (Near Field Communication) encrypted IMS (IP Multimedia Subsystem) server security data exchange method, which comprises the following steps of: in response to a near field communication connection event, acquiring an original service data message of service equipment to be opened and physical environment data monitored when the event occurs, forming a seed data set, executing Hash operation to generate an initial data fingerprint, and sending the initial data fingerprint to a server; and generating a symmetric session key through a key derivation algorithm by combining a device identifier in the original service data message, so as to encrypt the original service data message, packaging an encrypted data packet and the initial data fingerprint into a transmission data packet, sending the transmission data packet to an IMS server, reconstructing the session key by the server based on expected service parameters stored locally, and sending the session key to the IMS server. According to the method, decryption verification of the encrypted data packet is carried out, and service opening is executed after the verification is passed, so that deep binding of an encryption process and data integrity verification is realized, and the security of data exchange and the system processing efficiency are improved.

The present invention relates to systems and methods for proactive real-time anomaly detection in cross-environment RPC (Remote Procedure Call) communications within computing systems. Utilizing an Intelligent GraphRPC Method, this invention integrates advanced graph analysis techniques to enhance fault detection and workflow management. The method features a dual-graph approach, employing both real-time and aggregated dependency graphs, which allows for continuous monitoring and analysis of RPC interactions to detect and prevent unauthorized or misconfigured RPC calls between staging and production environments. An ingestion pipeline further supports the system by aggregating and archiving call graph data, providing beneficial insights into service dependencies and potential security risks. This proactive anomaly detection system is designed to seamlessly integrate into existing monitoring and alerting frameworks, providing a robust solution to safeguard data integrity and operational stability, thereby minimizing losses and reputational damage due to data breaches and system disruptions.

The invention provides an agricultural product supply chain traceability data integrity verification method based on machine learning, and belongs to the technical field of agricultural product supply chains. An agricultural product supply chain data acquisition system is established, layering is carried out according to timeliness, a data classification processing framework is constructed, and original data is classified according to complexity; establishing an agricultural product damage prediction model to identify a vulnerable link, and designing a double-layer game optimization model consisting of an upper layer model taking data integrity maximization as a target and a lower layer model taking calculation efficiency optimization as a target, a liquid neural network is adopted to construct a data fusion verification model, liquid neuron state parameters are dynamically adjusted according to the number of supply chain nodes, data timeliness and other parameters, the parameter range of each evaluation matrix is determined through a small-scale verification experiment, and finally integrity verification is executed to calculate and output a comprehensive score result. The technical problems that the agricultural product supply chain traceability data integrity verification accuracy is not high and the calculation efficiency is low are solved.

The invention relates to a time series data acquisition method and device based on a dynamic time window and fingerprint deduplication. The method comprises the following steps: establishing a sliding time window with a variable length by taking the current system time as a reference, and only querying incremental time sequence data in the window; adaptively zooming the window by calculating the ratio of the collected data volume to the expected volume in real time; setting the trigger interval as half of the length of the sliding time window to form a time overlap, thereby capturing out-of-order data; key features of each piece of time series data are extracted, and data fingerprints are generated; a bloom filter is used for primary screening, and accurate duplicate removal is carried out in a key-value storage system; compared with an existing fixed polling scheme, the method has the advantages that resources can be dynamically adjusted along with data flow rate, system load is reduced, real-time performance is improved, data integrity is guaranteed by overlapped windows, zero repeated acquisition is guaranteed by fingerprint two-stage duplicate removal, and the method is applicable to efficient incremental synchronization scenes of transactional log-free time sequence databases such as InfluxDB and the like.

The invention provides a regional interference signal detection method and device based on a Beidou router, and the method comprises the steps: obtaining reference broadcast ephemeris data of a network platform and local ephemeris data received by the Beidou router, carrying out the data integrity calculation, and judging whether the local ephemeris data are missing or not; performing same-epoch data comparison on the reference broadcast ephemeris data and the local ephemeris data, and judging whether the local ephemeris data has deception signals or not; calculating a visible satellite integrity rate in a fixed time window based on the observation data of the Beidou router, and judging whether interference signals exist in the observation data or not; analyzing by utilizing the cycle slip ratio and the carrier-to-noise ratio of the observation data, and judging whether interference signals exist in the observation data or not; under the condition that local ephemeris data do not have missing and deception signals and the observation data do not have interference, whether the observation data have the deception signals or not is judged based on pseudo-range single-point positioning and inter-epoch carrier phase difference velocity measurement, and the method can improve the reliability of Beidou signal interference detection.