634results about How to "Avoid misjudgment" patented technology

The invention discloses a respiratory disease oxygen precise conveying system based on a sensor, and relates to the technical field of medical equipment, and the system comprises a sensing module which integrates multiple sensors to collect and process airway and physiological data of a patient; the disease matching module identifies disease types through database comparison; the dynamic adaptation module calculates exclusive oxygen concentration based on a multi-mode fusion initial algorithm; the conveying module executes adjustment; the closed-loop feedback module continuously fine-tunes the oxygen concentration through a closed-loop stepped fine-tuning algorithm to form a closed-loop link, so that accurate dynamic control of oxygen delivery is realized; according to the invention, multi-sensor collaborative acquisition and disease precise matching are combined with a dynamic algorithm and closed-loop feedback, so that individuation and dynamic oxygen delivery are realized, the system avoids misjudgment by relying on a standardized database, and the oxygen concentration, flow and pressure are ensured to be adjusted in real time along with physiological indexes of a patient through multi-parameter collaborative adjustment and stepped fine adjustment; the treatment comfort, the effectiveness and the long-term adaptability are improved.

The invention discloses a heat supply network pipeline hydraulic and thermal working condition abnormal fluctuation intelligent identification and fault early warning method, and relates to the technical field of computer processing, and the method comprises the following steps: S01, collecting hydraulic and thermal parameters of a heat supply network pipeline in real time; s02, acquiring abnormal data of the time sequence sample set based on the thermodynamic mechanism model, detecting and repairing the abnormal data; s03, extracting characteristic parameters capable of reflecting abnormal fluctuation of the hydraulic and thermal working conditions of the heat supply network pipeline from the repaired time sequence sample set; s04, establishing an abnormal fluctuation identification model by utilizing an intelligent algorithm, and judging whether the hydraulic and thermal working conditions of the heat supply network pipeline have abnormal fluctuation or not; and S05, when the abnormal fluctuation is identified, giving a grading early warning mechanism and risk prevention and control suggestions of the damage degree grade of the associated pipeline. According to the invention, by fusing multi-source data and an intelligent algorithm, full-chain intelligent management of the hydraulic and thermal working conditions of the heat supply network pipeline from abnormal accurate sensing, fault intelligent diagnosis to risk active early warning is realized.

The invention discloses a power distribution network fault positioning method and system based on multi-source sensing fusion, and relates to the technical field of intelligent power distribution, and the method comprises the following steps: collecting high-frequency fluctuation signals corresponding to a plurality of sensing points around the transient high-frequency disturbance generated at the initial stage of a power distribution network fault, expanding each high-frequency fluctuation signal under a unified time reference, and obtaining a plurality of high-frequency fluctuation signals; and extracting starting and ending moments of protruding segments in the high-frequency fluctuation signal, and forming a local amplification fingerprint set based on the protruding segments. According to the invention, by introducing local amplification fingerprint identification and propagation consistency constraint, the pseudo propagation influence is eliminated, so that the fault trace is more consistent with the real propagation law, and the positioning accuracy and stability are improved; and meanwhile, through bandwidth convergence type peak clipping and energy boundary retention, in combination with time and space anchoring of a main fault trace, accurate locking and dynamic isolation are realized, and the fault response speed and the system reliability are improved.

The invention relates to the technical field of coal mill powder pipes, in particular to a powder leakage defect automatic identification method based on digital twinning and AI video analysis, and the method comprises the steps: obtaining operation parameters and environmental parameters of a coal mill powder pipe, calculating a powder leakage risk characterization coefficient, and judging whether there is a potential risk of powder leakage; image data of the surface and the surrounding area of the powder pipe in the initial starting period are collected through an AI video analysis module, powder leakage characteristic parameters are extracted, a characteristic change curve segment is drawn, similarity comparison is conducted on the characteristic change curve segment and the reference characteristic curve segment, and whether a suspected powder leakage area exists or not is judged; actual operation parameters and model prediction parameters of the powder pipe in a stable operation period are obtained based on a digital twin model, the fluctuation degree of parameter deviation is calculated, and whether the powder leakage defect exists or not is comprehensively judged by combining a suspected powder leakage area judgment result; the early-stage accurate identification of the powder leakage defect is realized, the detection efficiency and precision are improved, and the operation and maintenance cost and the safety risk are reduced.

The invention discloses a vehicle distance measurement and calibration method based on laser radar and binocular vision fusion, relates to the technical field of measurement and calibration, and is used for solving the problem that the precision of a vehicle distance calibration result is reduced when a laser radar used for calibrating a highway vehicle distance snapshot system is under the conditions of rain, snow, wet and slippery or waterlogging road surfaces. Joint modeling is carried out on spatial distribution and echo energy characteristics of laser radar low-height point cloud, a point cloud echo coefficient and a low-height number proportion are introduced, a specular reflection state code is constructed, a self-calibration mechanism is adaptively triggered accordingly, and a specular reflection state is obtained. The discrete characteristic of a laser radar vehicle distance time sequence and the degradation trend of binocular vision parallax connectivity are subjected to quantitative analysis, the dominant source of ranging abnormity is judged through symbolic analysis, and an offset calibration or fusion correction strategy is selected according to the dominant source. Reliability and engineering adaptability of vehicle distance measurement under the wet and slippery road surface condition can be improved under the condition of not depending on an additional sensor.

The invention discloses an electromagnetic wire self-adhesion performance data acquisition and intelligent analysis method, and relates to the technical field of data analysis. Multi-source data such as turn-to-turn gap, paint layer residual thickness, tension fluctuation and conduction state are synchronously acquired in the winding process; and analysis mechanisms such as a gap reduction rate, paint layer thinning distribution, a synchronous evolution track and a gap strength response curve are introduced, so that continuous modeling and quantitative characterization of the autohension performance evolution process are realized. Compared with the prior art which only depends on a single gap threshold value or a post mechanical test to judge the bonding performance, the method can be started from a complete physical chain from gap change to paint layer extrusion to metal contact to bonding failure, and the change trend of the bonding performance under different gap levels can be accurately revealed; and tension output is corrected in real time through a self-adaptive algorithm, closed-loop optimization of the bonding quality is achieved, and the product percent of pass and the production efficiency are improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent control system for a freeze-drying process based on an intelligent algorithm, which comprises an acquisition module, a freezing adjustment module, a primary adjustment module and a secondary adjustment module. According to the method, the corrected plate layer temperature after incomplete freezing is accurately recognized according to double criteria of the freezing center temperature and the temperature drop trend, and after the drying efficiency deviation degree is calculated and the abnormal type is determined by utilizing the preset efficiency interval, the related preset process conditions are adjusted; meanwhile, stagnation and overheating runaway are independently diagnosed and analyzed on the basis of time sequence correlation of the water content decreasing rate and the center temperature increasing rate, the preorder freezing defect is traced when secondary drying is normal but overtime does not reach the standard, and the slab layer temperature is reduced in an equal proportion according to the freezing defect degree; the problem that the process efficiency and the product quality stability are difficult to consider at the same time due to dependence on artificial experience, stage splitting and lack of dynamic feedback is effectively solved.

The invention provides an intelligent pipeline inner wall damage identification method based on voiceprint feature extraction, and relates to the technical field of sound wave detection.The method comprises the steps that pipeline acoustic time sequence data, medium flow velocity and pressure data, medium type identification and structure parameter information are collected; performing multi-scale feature extraction on the acoustic signal, constructing a voiceprint feature expression model and a damage evaluation model based on a voiceprint feature learning network, introducing a structural voiceprint stability coefficient, a damage type voiceprint separation coefficient and a damage evolution risk coefficient, and combining a multi-level threshold judgment mechanism to determine the damage type of the acoustic signal. Graded early warning, damage type identification and evolution risk discrimination of the inner wall damage of the pipeline are realized; when an uncontrollable damage evolution risk is identified, a maintenance strategy is automatically triggered, and manual nondestructive testing and repairing are guided; and adaptive optimization is carried out on model parameters to form a closed-loop correction mechanism for pipeline inner wall damage identification and risk assessment, so that the pipeline operation safety and the intelligent level of maintenance decision are improved.

The invention provides a remote intelligent operation and maintenance monitoring method and system for a new energy power station, and relates to the technical field of operation and maintenance monitoring, and the method comprises the steps: constructing the photoelectric hysteresis loop area density and the topological thermoelectric resonance deviation degree according to an ontology response time-space alignment data set; a synchronous matching degree aging phase space is constructed, and the photoelectric hysteresis loop area density and the topological thermoelectric resonance deviation degree are taken as coordinate axes, and a state point is obtained by calculating the trajectory curvature and divergence coefficient of the state point in the synchronous matching degree aging phase space; dividing the synchronous matching degree aging phase space into a plurality of mutually exclusive state sub-regions according to the judgment condition, and determining the mutually exclusive state sub-region to which the state point belongs, so as to generate an aging degradation index; generating a risk conduction score based on the aging degradation index to identify a key risk conduction link; and according to the risk conduction score, backtracking a fault source and generating a remote monitoring diagnosis report. The defects of fuzzy fault positioning and single operation and maintenance strategy in the prior art are thoroughly solved.

The invention discloses an internet information intelligent monitoring method and system, a medium and a product, and relates to the technical field of data processing. The method comprises the following steps: firstly, acquiring internet information data containing text, image and audio contents, extracting features of the three contents respectively, and constructing a semantic map; calculating semantic similarity among different map nodes, and establishing a cross-modal mapping relation to form a mapping set; node attribute characteristics are extracted for the mapping relation to calculate consistency, and mapping lower than a threshold value is marked as a conflict relation; counting the conflict node proportion of each map to obtain a modal conflict degree, calculating the similarity with the reference map to obtain a modal matching degree, and calculating the modal credibility according to the modal matching degree; normalizing the modal credibility to obtain a weight coefficient, and performing weighted fusion on the map structure features to obtain a fusion feature vector; and finally, matching the fusion feature vector with a monitoring rule base to obtain a monitoring result. By implementing the technical scheme provided by the invention, the monitoring accuracy of Internet information can be improved.

The invention relates to the technical field of antenna detection, in particular to a laser cutting antenna line type defective product detection method and system, and the method comprises the steps: obtaining processing batch information, building a coiled material motion coordinate system, and storing target antenna parameters and reference information of a dispensing region; acquiring image data of the antenna, and aligning the image data with the reference information in the coiled material motion coordinate system; extracting an antenna boundary according to the image data, and calculating a geometric deviation based on the antenna boundary and the reference information; obtaining a defect coordinate position according to the geometric deviation, and comparing the dispensing area with the geometric deviation to judge the severity of the defect; mapping the coordinate position to a coiled material motion coordinate system to obtain bad position data, and executing differentiation processing according to the severity; and storing the bad position data and the processing batch information corresponding to the differentiation processing, and generating a detection result. According to the invention, the antenna boundary and geometric deviation are analyzed through the image data and the reference information, defects in the antenna cutting process are identified, and the detection accuracy is improved.

The invention discloses an English teaching writing error diagnosis method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: obtaining an English composition text, and obtaining initial writing data; performing grammatical structure analysis according to the initial writing data to obtain grammatical features; performing vocabulary error analysis according to the grammatical features to obtain vocabulary errors; performing syntactic and semantic consistency analysis according to the vocabulary errors to obtain syntactic and semantic errors; performing article logic analysis according to the syntactic and semantic errors to obtain article logic errors; and performing comprehensive diagnosis according to the article logic error to obtain a diagnosis result. According to the method, the conjunction matching degree and the semantic compatibility are combined, cross-sentence logic analysis is achieved, misjudgment of isolated sentences is avoided, a complete error set is formed, the comprehensiveness and accuracy of diagnosis are improved, and practicability and guidance are enhanced.

The invention relates to the technical field of batteries, and discloses a battery self-adaptive safety protection method and system based on data driving, and the method comprises the steps: extracting parameter features of a transient segment, a steady segment and a fluctuation segment after segmentation, constructing a comprehensive feature matrix based on the parameter features, all environmental factors of the battery are screened based on the environmental influence model, the working condition of the battery is determined based on the working condition identification model, the environmental influence factors and the state feature sequence, the working condition is verified based on historical data, and a corresponding monitoring strategy is selected based on the verified working condition or the re-determined working condition. And the acquisition interval is adjusted based on the monitoring strategy, the risk direction of the battery is determined based on the relationship between the standard state feature sequence and the state feature sequence, and whether power-off protection is performed on the battery is judged based on the association relationship of all the risk directions. According to the invention, through data driving of historical data, the flexibility and reliability of battery safety protection are ensured.

The application discloses a kind of key feature transformer health state detection systems, it is related to power equipment monitoring technical field, including: data acquisition module, data processing module, confidence calculation module, fault determination module and result output module;Data acquisition module synchronously acquires transformer oil tank surface vibration signal and neutral point direct current data;Data processing module calculates the phase difference of vibration signal and the coherence coefficient of vibration signal and direct current data;Confidence calculation module calculates mechanical fault confidence;Fault determination module judges whether mechanical fault confidence exceeds preset threshold, exceeds and then carries out winding loosening positioning processing, generates loosening coordinate;Otherwise, the multi-band resampling of vibration signal is carried out and the coherence coefficient data is recalculated;Result output module outputs loosening coordinate or interference warning signal;The application can improve the accuracy of transformer health state detection, ensure the reliability of fault positioning.

This invention provides a rapid rewarming and circulatory support system for frostbite and hypothermia in high-altitude environments, relating to the field of treatment device technology. It includes: a first temperature acquisition module for acquiring and preprocessing the current first ambient temperature, the first frostbitten skin surface temperature, and a first skin surface image; a control module connected to the first temperature acquisition module for analyzing the preprocessed first ambient temperature, the first frostbitten skin surface temperature, and the first skin surface image to determine the corresponding first target temperature and a first target warming rate; and a temperature regulation module connected to the control module for gradually warming the skin to the optimal preset temperature according to the warming rate. This invention achieves dynamic temperature control, continuous protection, and intelligent decision-making for frostbite and hypothermia in high-altitude environments.

The application discloses a primer combination, a kit and a method for detecting Aspergillus fungi based on targeted sequencing, and belongs to the technical field of microorganism detection. The primer combination comprises a primer pair for detecting common species of Aspergillus, and further comprises a primer pair for detecting at least one cryptic species of Aspergillus, wherein the common species of Aspergillus comprises Aspergillus fumigatus, and the cryptic species of Aspergillus comprises Aspergillus lentulus, Aspergillus udagawa, Aspergillus viridinutans and Aspergillus thermomodestus. The primer combination can be used for detecting Aspergillus fungi based on targeted sequencing, has high specificity and high sensitivity, can effectively distinguish Aspergillus fumigatus from the cryptic species, avoids misjudgment, and thus accurately guides drug use.

The invention relates to the technical field of wearable intelligent equipment, and further relates to a method and device for determining the home position of a user and wearable intelligent equipment. The method comprises the following steps: when the wearable intelligent equipment is in an indoor environment, acquiring wireless signal data and multi-source sensing data; determining wireless signal feature data according to the wireless signal data, and determining a standing proportion, a charging behavior similarity and a position aggregation degree according to the multi-source sensing data; performing weighted summation on the wireless signal feature data, the standing proportion, the position aggregation degree and the charging behavior similarity; and when the weighted summation result is not less than the total threshold, determining that the target coordinate is the user home position. According to the method, the wireless signal data and the multi-source sensing data are effectively fused, the problem that a single judgment condition is prone to interference or misjudgment is avoided, the accuracy and reliability of user home position recognition are remarkably improved, a solid foundation is provided for subsequent intelligent services based on home scenes, and therefore the user experience is optimized.

The invention relates to the technical field of electric power, in particular to a power supply zero-sensing switching method and system based on phase real-time tracking, and the method comprises the following steps: collecting the communication connection state of each inverter node in a micro-grid group, constructing a communication topology model of the micro-grid group, and analyzing a sparse communication connection relationship among the nodes in the communication topology model, and generating a Laplacian matrix reflecting node connectivity according to the sparse communication connection relationship. According to the method, the system state can be comprehensively evaluated from the two dimensions of external alignment precision and internal stability, and misjudgment caused by a single index is avoided. Dual errors are input into a switching stability evaluation model to calculate a stability index, and a switching instruction is generated only when the index exceeds a grid-connected permission threshold value, so that quantitative decision and strict control of a grid-connected opportunity are realized, and the micro-grid and a main grid are highly synchronous in phase and frequency at the moment that a circuit breaker is closed; and transient impact and circulation at the switching-on moment are eliminated.

The application belongs to the technical field of mechanical processing, and particularly relates to a mechanism for detecting the axial arc surface center point size of an interlocking rod. The mechanism mainly comprises a measurement standard base (1), the axial center of the measurement standard base (1) is consistent with the center size of each arc surface of the standard interlocking rod, each measurement standard base (1) comprises a center through hole, the measurement standard base (1) has a lateral positioning hole with a through hole on the side edge, which is used for installing a first wedge block (2), and has a top edge positioning hole with a through hole on the top edge, which is used for installing a second wedge block (3). The interlocking rod installed in the center through hole is adjusted through the two wedge blocks, the end of the interlocking rod is pressed against a touch screen instrument (4), and the size of the arc surface center of the interlocking rod (5) deviating from the axial center of the measurement standard base (1) is determined through the touch screen instrument. The application can quickly and accurately determine the axial size deviation of the four arc surface center points of the interlocking rod.

The application discloses a ransomware identification method and system based on multi-source data fusion, and relates to the field of ransomware detection. The method obtains file system event data, system resource running indexes, process behavior data and industrial control system behavior data, identifies file features, system running behavior situation features, process features and industrial scene features from the data, judges whether each feature meets the essential behavior features of ransomware encrypted files, and divides the features into strong indication features, medium indication features and basic situation features. Different mapping methods are used to determine sub-risk scores for different features, and finally a weighted scoring mechanism is used to obtain an overall urgency score, based on which a ransomware attack is determined, and then suspicious process operations are automatically triggered and terminated in parallel, and network connections are blocked to prevent the spread of ransomware attacks. The application can accurately identify known ransomware and unknown variants, and has good interpretability.

The application provides a pipeline scene recognition method based on a convolutional neural network, comprising the following steps: collecting audio data of multiple pipelines in different scenes to form a label set of scene categories; selecting audio data recorded by the multiple pipelines under different labels to form an original audio file set D; constructing a recognition model; training and evaluating the recognition model; collecting audio data of unrecognised pipelines and performing feature extraction; and recognising the unrecognised pipelines by using the model. According to the pipeline scene recognition method provided by the application, pipeline audio data can be collected by an audio device, and the environment of the pipeline can be recognised without further manual measurement.

This invention discloses a data synchronization method and system based on front-end orchestration management, belonging to the field of data synchronization. The method includes the following steps: selecting the data table and field information to be monitored through the front-end interface, generating configuration information, and orchestrating the synchronization process logic according to business needs; storing the configuration information in real time to the configuration center to generate real-time monitoring rules; capturing changed data of the data table and field information, encapsulating it into standard data units and sending it to a message queue; preprocessing the standard data units in the message queue; the synchronization adaptation module matching the corresponding driver type according to the synchronization process logic to generate a synchronization instruction set, and executing data synchronization operations based on the preprocessing results; performing full-link monitoring of the synchronization process, and performing task lifecycle management based on the lifecycle rules configured on the front end. This invention improves synchronization efficiency through dynamic threshold verification, multi-source field fusion weighting, and dynamic encryption strategies, and the full-link monitoring further enhances front-end manageability.

The invention relates to a rail traffic unmanned aerial vehicle emergency patrol method based on an optical fiber monitoring and signal system, and the method is characterized in that the method comprises the steps: S1, obtaining all abnormal events in a target event interval and the position information of each abnormal event based on real-time physical parameters collected by a monitoring optical fiber in combination with train operation data; s2, grading the abnormal events based on the real-time physical parameters corresponding to the abnormal events to obtain grades of the abnormal events; s3, based on the position and the remaining endurance of each unmanned aerial vehicle, pairing the unmanned aerial vehicles for each abnormal event in combination with the position and the grade of each abnormal event, and performing flight path planning on each unmanned aerial vehicle based on the position of the paired abnormal event; and S4, controlling each unmanned aerial vehicle to inspect each abnormal event. Compared with the prior art, the accuracy of the abnormal event can be improved by combining the train operation data of the signal system.

The application discloses a kind of tangential frost heaving force test methods of cold region pile foundation, belong to cold region pile foundation frost heaving prevention and control technical field, the test method of the application is in the top of test pile Design load is applied and is kept, and in the time point when freezing depth and frost heaving reach maximum, obtain annual maximum unit tangential frost heaving force, by the determination of dangerous section, obtain the maximum tangential frost heaving force that pile body bears, namely the most unfavorable load state that pile foundation faces in a year, can be directly used as the key design control value of pile foundation security guarantee, fundamentally avoid the problem of data randomness and insufficient representation caused by improper test timing, so that key design parameter originates from real annual most unfavorable working condition, provide more scientific and reliable basis for subsequent frost heaving prevention and control and stability checking.

The present application relates to the technical field of heavy-duty gas turbine, and particularly relates to a heavy-duty gas turbine high-temperature alloy welding defect intelligent identification and repair method. By acquiring a welding area infrared thermal image sequence to identify an abnormal temperature drop area as a candidate defect area, then performing defect verification and type determination through ultrasonic phased array scanning, calculating a repair heat input curve according to the defect type, executing pulse modulation cladding repair and adjusting parameters according to real-time temperature field and acoustic emission signals, automatic identification and accurate repair of welding defects can be realized, and the repair quality and efficiency are improved.

A signal processing method includes the following steps: receiving an input signal through a signal receiving circuit and parsing the input signal to generate multiple bit codes; temporarily storing the multiple bit codes of the first part according to a time sequence through a shift register, and starting a decoder when the shift register is full; when the multiple bit codes of the first part conform to the decoding table rules and boundary detection rules, performing boundary calibration through the decoder based on the multiple bit codes of the first part.

This invention discloses a multi-channel electromagnetic induction detection method and system based on uniform magnetic field constraint. A uniform magnetic field is constructed within the detection area of ​​the structure under test. Electromagnetic induction response signals are acquired through multiple channels under the constraint of the uniform magnetic field, and the spatial coordinates of each acquisition channel are marked. The raw electromagnetic induction signals acquired by each channel are preprocessed, including noise reduction filtering, envelope extraction, and amplitude normalization. A deep learning model is then used to extract structural features from the preprocessed multi-channel electromagnetic response signals. Based on the spatial position information of each acquisition channel, magnetic field uniformity parameters, and response characteristics, a channel correlation weight matrix is ​​constructed to describe the spatial consistency and physical coupling relationships between channels. A multi-channel joint optimization model is constructed, and the equivalent electromagnetic parameter distribution inside the structure under test is reconstructed through iterative solution. This invention improves the reliability, repeatability, and engineering applicability of electromagnetic induction detection results.

The application relates to the technical field of circuit breaker detection, and specifically discloses a circuit breaker action characteristic detection system based on artificial intelligence, which comprises a data acquisition layer, a data preprocessing layer, a data analysis layer, a decision warning layer and a system management layer; the data acquisition layer is responsible for collecting the action characteristic parameters of the circuit breaker. Through filtering, normalization and feature extraction, the data quality and availability are improved, laying a foundation for subsequent analysis; the data analysis layer uses comprehensive calculation and analysis to evaluate multidimensional parameters in mechanical action, obtains a mechanical action state evaluation coefficient, and accurately grasps the operation state of the circuit breaker; the decision warning layer accurately determines the operation state and timely warns according to the evaluation result and a threshold value, helping operation and maintenance personnel to quickly respond; and the system management layer is used for dynamically optimizing a feedback determination threshold value, so that the overall scheme effectively improves the accuracy, reliability and intelligent level of circuit breaker detection, reduces fault risks, and guarantees the safe and stable operation of a power system.

The embodiment of the application provides a straight-arm type aerial work platform, and relates to the technical field of engineering machinery. At least comprising: a vehicle chassis, an arm support, a rotating mechanism, an electronic control unit and a driving unit; wherein a middle position detection switch is arranged between the vehicle chassis and the rotating mechanism; the middle position detection switch is used to determine the driving direction of the straight-arm type aerial work platform; a distance measuring sensor is arranged at the end of the rotating mechanism connected with the arm support; the distance measuring sensor is used to determine the distance between the straight-arm type aerial work platform and an obstacle; the electronic control unit is used to receive the switch signal of the middle position detection switch, the distance signal of the distance measuring sensor and the driving state of the driving unit; and to control the driving of the straight-arm type aerial work platform according to the generated vehicle control instruction; the vehicle control instruction is generated according to the switch signal, the distance signal and the driving state. The safety of the straight-arm type aerial work platform during transportation can be ensured, and collision with a container can be avoided.

The invention relates to the technical field of ship traffic management and marine monitoring, and discloses a multi-source data-based false AIS signal detection method, which comprises the following steps of: firstly, extracting a historical track to construct a coverage grid mapping table containing radar detection attributes; receiving real-time data and dividing the real-time data into an AIS to-be-detected group and a radar truth value group; non-radar blind area targets are screened according to the mapping table; retrieving an associated target in the radar truth value group to execute existence reverse verification; a dynamic time warping algorithm is adopted to align time sequences and calculate feature differences, and fine comparison verification is executed; constructing a neighborhood flow field model, and executing flow field consistency verification according to the motion vector deviation; and outputting a false AIS signal list in combination with multi-stage verification results. According to the method, through physical coverage modeling and environmental dynamics constraint, high-simulation-degree false signals in a radar blind area can be effectively identified, so that the false alarm rate is reduced.