2963 results about "Complete data" patented technology

Systems and methods for computing time series predictions are provided, including a two-stage classifier-and-regressor processor. The classifier is trained on the complete data set while the regressor is trained on a pruned dataset. The classifier includes an Extreme Gradient Boosting classifier. The regressor includes an attention mechanism and a Long Short-Term Memory (LSTM) neural network. For a series of successive time period computations, a current output of the LSTM neural network is recursively fed back as an input to the attention mechanism for a subsequent time period computation. The output of the regressor is scaled by the output of the classifier to adjust for overfitting caused by the pruned training dataset.

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention discloses a drilling key parameter automatic regulation and control method based on an improved DDPG algorithm. The method comprises the following steps: collecting and preprocessing drilling data, and constructing a complete data set for training; a drilling speed (ROP) time sequence prediction model is constructed and trained on the basis of a self-attention mechanism (self-attention); building a simulation environment by using the trained ROP prediction model, and defining a state space, an action space and a multi-target reward function of a reinforcement learning DDPG algorithm model; a deep deterministic strategy gradient algorithm (DDPG) is adopted to train a reinforcement learning model, through an Actor-Critic network structure, an intelligent agent learns an optimal bit pressure (WOB) and rotating speed (RPM) regulation strategy in a simulation environment, and dynamic optimization of the drilling speed (ROP) is achieved. According to the method, through automatic and intelligent parameter regulation and control, the efficiency and stability of the drilling process are improved, the drill bit wear rate and the operation cost are reduced, and the method has good adaptability and multi-target optimization capacity and is suitable for drilling operation under the complex stratum condition.

The invention discloses a data security sharing method and system, and relates to the technical field of data security, and the method comprises the steps: receiving to-be-shared data uploaded by a data provider; fragmenting the data by adopting a biologically inspired adaptive fragmentation algorithm; generating a digital fingerprint for each fragment by using a multi-dimensional dynamic Hash algorithm; encrypting data fragments by adopting a post-quantum encryption algorithm and storing the data fragments in a decentralized network; recording the access authority, the digital fingerprint and the storage position information to a block chain security account book; when a data request is received, the access permission is verified; carrying out identity verification by adopting multi-mode biological feature recognition; after the verification is passed, obtaining a digital fingerprint and a storage position from the block chain; acquiring the encrypted fragments from the decentralized network and decrypting the encrypted fragments; verifying the integrity by using the digital fingerprint; and recombining the verified fragments to obtain complete data and returning the complete data to the requester. According to the method, efficient data sharing is realized while the data security is guaranteed, and the data value and privacy protection are effectively balanced.

Disclosed in the present invention is a Chinese national cryptographic algorithm-based identity authentication and data encryption method for CoAP, comprising: pre-configuring identity authentication data of a client and a server; and performing identity authentication on the basis of the pre-configured information, determining the security of the client and the server, performing key agreement to obtain a session key, and if it is determined that the security meets a requirement, completing data transmission on the basis of the session key. According to the present invention, identity tables are established for a client and a server respectively, and important data can only be accessed by both the client and the server by looking up the tables; by means of a pre-shared key, SM4 and SM3 algorithms are used to complete bidirectional identity authentication and key agreement before data transmission between the client and the server, and by using only two handshake messages, the confidentiality of the important data and the integrity of messages during identity authentication can still be guaranteed; replay attacks are prevented by using timestamps and random numbers; and data transmission is performed by using an observer mode that reduces overhead to the greatest extent.

The invention discloses a power system source network interaction data security encryption verification method, system, device and medium, and belongs to the technical field of power system communication encryption, and the method comprises the following steps: a source side device and a network side device complete identity authentication through a bidirectional digital certificate verification mechanism; and an identity binding label is generated in combination with the device identifier, the timestamp and the position information. And after the authentication is passed, the two parties perform key negotiation based on an elliptic curve algorithm, and generate a temporary encryption key in combination with an authentication result. A source side device carries out structural packaging on original interaction data, adds a random number, a timestamp and a hash abstract, then encrypts the original interaction data in an AES-GCM mode, and carries out signature binding on key fields through digital signatures to form a complete data packet. And a receiving end performs compliance judgment on the timestamp and the random number in the data packet, and after the timestamp and the random number are confirmed to be in an effective time window and are not replayed, signature verification, decryption processing and Hash consistency verification are sequentially completed, so that closed-loop processing of a data flow is ensured.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention discloses a welding detection method and application thereof in welding monitoring of an LNG (liquefied natural gas) storage tank. According to the scheme, 3D point cloud data, surface image data and cross-sectional image data acquired from a welding seam by combining a 3D laser scanner, an industrial camera and an ultrasonic phased array probe are subjected to multi-modal data fusion; the fusion data is input into a trained defect recognition model, so that the weld joint can have a more perfect data source in quality detection classification, and a reliable data basis is provided for weld joint detection; besides, according to the scheme, a welding seam model based on digital twinning is constructed on the basis of fusion data, in this way, when the welding seam has defects caused by abnormal welding, the welding seam model constructed by the digital twinning model can be used for positioning the defects and outputting local welding seam model data with the defects, and meanwhile, by combining a detection result, the defect detection accuracy is improved. And more visual and reliable detection feedback can be provided for background management personnel.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention discloses a large model data distributed management method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: segmenting large model training data to obtain a plurality of data blocks, and determining a predicted access frequency based on a long short-term memory network model and the historical access frequency of the data blocks; caching the large model training data corresponding to the data block to a corresponding data cache layer by utilizing the predicted access frequency; constructing a resource portrait by using the static attribute and the dynamic index of the GPU node, and allocating the large model training task to a target GPU node by using a preset hybrid strategy and the resource portrait based on the predicted access frequency and the storage position corresponding to the data block in the data cache layer; when it is monitored that the large model training task on the target GPU node is executed, periodic snapshot is conducted on the large model training task through a distributed snapshot algorithm, and the obtained complete data state is stored in a distributed storage center. And the resource vacancy rate is reduced.

The invention relates to the technical field of electrical equipment analysis, and particularly discloses an electrical equipment operation state monitoring system and method, and the method comprises the steps: the system is deployed at an electrical equipment site, integrates a low-power-consumption processor and an FPGA acceleration module, and is used for collecting multi-source data in real time, and executing lightweight model calculation and local early warning; and storing full data and operating a global analysis model, and communicating with the edge computing node to realize data collaborative analysis. According to the electrical equipment operation state monitoring system and method provided by the embodiment of the invention, through edge computing lightweight deployment, data preprocessing and local early warning are completed on the equipment site, and high efficiency of monitoring response and low-power-consumption hardware adaptation are realized; by means of an electromagnetic interference dynamic evaluation module, an acquisition strategy is intelligently adjusted, noise is suppressed, the reliability of multi-source data is guaranteed, the efficiency, precision and intelligent capability of electrical equipment state monitoring are remarkably improved, and technical support is provided for reliable operation of power system equipment.

Provided are a data analysis method and system, and a related device. In the method, a physical mapping relationship between metadata and table data of an underlying physical table is established; a semantic mapping relationship between nodes and the metadata is established, wherein the semantics of labels of the nodes are related to the semantics of the metadata; and a model mapping relationship between an analysis model and the nodes is established. In this way, a plurality of mapping relationships are established, so that the analysis model can be decoupled from underlying table data, an underlying data change does not affect the analysis model, and the analysis model is thus highly flexible and adaptable; in addition, since nodes in a knowledge graph include semantic labels, a user can complete data querying by means of natural language.

The invention relates to the technical field of data distributed storage, and discloses a distributed storage method and system for clinical test data, and the method comprises the steps: carrying out the ring signature encryption processing of the patient test data of a plurality of data centers, and obtaining a patient anonymization identifier and standardized clinical data; performing two-stage random storage node configuration to obtain an optimal storage node configuration scheme; constructing an LPPMM-DA data aggregation model based on the optimal storage node configuration scheme, and performing fragmentation processing on the patient test data to obtain a plurality of target data fragments; performing dynamic operation envelope analysis and alliance graph game calculation on the multiple target data fragments to obtain a storage resource scheduling scheme; according to the storage resource scheduling scheme and the anonymization identifier, distributed data block storage and access authentication are executed on the multiple target data fragments, data block storage distribution information is generated, a complete data access control system is constructed, and safe and reliable storage of clinical test data is ensured.

The invention discloses a ship abnormal track identification method based on AIS data, which belongs to the technical field of AIS data mining processing, is used for identifying an abnormal ship track, and comprises the following steps: collecting and sorting AIS historical abnormal track data and marking, and constructing a graph structure of the track data; dividing the abnormal trajectory data into a training set and a test set, training a graph convolutional neural network model by the training set, evaluating model performance by the test set, and adjusting hyper-parameters of the model; performing matching according to mmsi to obtain complete AIS data, and establishing a graph structure of trajectory data; classifying ship trajectories to be classified by using the model, and finding out individual abnormal trajectories; and processing the ship trajectory points based on a neighbor mapping Haversine grid algorithm, and finding out a group abnormal trajectory. Compared with the prior art, the method has stronger automatic feature learning ability, can effectively capture the space-time relationship and the complex trajectory mode, and improves the recognition performance and adaptability.

The invention relates to the technical field of metadata generation, and discloses a data platform metadata automatic generation method based on a large model, which comprises the following steps of: firstly, performing unified standardization and preliminary grammatical analysis on an original SQL (Structured Query Language) code to obtain a structured intermediate representation; on the basis, preliminary blood relationship analysis based on rules is carried out, and simple column references are quickly identified and processed. For complex expressions which are difficult to accurately analyze by a traditional method, code snippets and context information of the complex expressions are accurately extracted and submitted to a large language model for deep semantic understanding and complex blood relationship analysis. And finally, integrating the complex consanguinity analyzed by the large model with the initial consanguinity list to form a comprehensive and accurate field-level consanguinity, and further generating complete data platform metadata. In this way, the defect that a traditional analysis tool understands complex semantics is effectively overcome, and the accuracy and integrity of metadata generation are remarkably improved.

The invention relates to an offshore wind turbine generator data acquisition method and system, and the method comprises the steps: dividing a target offshore wind power plant into a plurality of inspection regions, and obtaining the structure parameters and topographic data of a wind turbine generator of each inspection region; according to the wind turbine generator structure parameters and topographic data, respectively constructing an unmanned aerial vehicle flight path of each inspection area, and presetting a surrounding radius and a vehicle position angle; based on the flight path, the surrounding radius and the camera position angle, controlling the unmanned aerial vehicle to carry out surrounding flight on the wind turbine generator in each inspection area, and collecting a wind turbine generator image and point cloud data in the surrounding flight process; and constructing a wind turbine generator three-dimensional model based on the wind turbine generator image and the point cloud data, constructing a three-dimensional map in combination with the topographic data, and fusing a flight path into the three-dimensional map to complete data acquisition of the target offshore wind plant. According to the invention, autonomous intelligent inspection of the offshore wind turbine generator can be realized.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention relates to the technical field of power systems, and discloses an industrial park distributed power utilization network simulation analysis power utilization behavior evaluation method comprising the following steps: S1, data acquisition: power load data, equipment operation state data and environment data of each power utilization unit in an industrial park are acquired in real time, and the environment data comprise temperature and humidity; s2, data preprocessing: preprocessing the collected data to obtain complete data; and S3, simulation modeling: establishing a power load flow model based on the park information and the real-time data, and performing simulation modeling to generate a dynamic model under various power consumption situations. According to the invention, through real-time data acquisition, dynamic load scheduling and a deep reinforcement learning algorithm, flexible optimal distribution of electric power resources is realized, the efficiency and stability of park electric power management are improved, stable operation of equipment is ensured, and energy waste is reduced.

The invention discloses a multi-unmanned ship cluster cooperative tracking control method based on data driving, and belongs to the field of unmanned ship cluster intelligent control. The method comprises the following steps: firstly, constructing a communication topology for cluster distributed cooperative control of multiple unmanned ships; constructing a single unmanned ship speed and course system dynamic linearization data model with interference terms based on unknown external interference and nonlinear uncertainty; constructing a smooth anti-interference trajectory tracking algorithm of a single unmanned ship tracking dynamic target point; constructing a pseudo-Jacobian matrix estimation and distributed disturbance compensation controller for distributed model-free adaptive control of the multiple unmanned ships; constructing a reset algorithm and a controller of an actual unmanned ship; and finally, multi-unmanned ship cluster cooperative tracking control is realized. The method breaks through the limitation of a traditional control method, does not need to depend on complex system model information, only utilizes input and output data of the system, and achieves a complete data-driven intelligent control strategy.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention discloses an industrial internet data processing method based on big data, which comprises the steps of designing a visual data processing flow arrangement tool, allowing a user to flexibly define logic and steps of data processing, and realizing common data processing operation through a predefined data processing operator library in the flow arrangement process. A user is allowed to select a required data processing component through dragging operation through a visual configuration interface, and corresponding data types and processing conditions are set to form a complete data processing flow chart; acquiring data type information configured by a user, performing primary condition filtering judgment on the data according to the information, and screening out original data meeting conditions; judging whether data needs to be dynamically calculated along with time or not according to whether conditions are changed or not; a real-time data processing engine is introduced, and data are collected, cleaned, converted and analyzed in real time; through a visual interface, NLP interaction, real-time processing and intelligent scheduling, the flexibility of data processing is improved.

The invention discloses a graph database query method based on runtime filtering, which belongs to the technical field of graph data, and comprises the following steps: analyzing a graph database query request to obtain an original physical execution plan; identifying each connection operator meeting a runtime filter generation condition as a target connection operator, and verifying that a scanning operator capable of receiving the runtime filter exists at the upstream of the detection side of each target connection operator; a runtime filter transmission channel is established, and the output end of the construction side of each target connection operator is connected to the input end of the corresponding scanning operator; during operation, the construction side operator completes data processing and then sends the data to the runtime filter, and the corresponding scanning operator receives the runtime filter, executes optimized scanning by using the structured storage characteristics of the graph storage engine, and outputs graph elements matched with the runtime filter. According to the method, the RuntimeFilter is introduced into the physical execution plan of the graph database query request, so that the data scanning amount can be greatly reduced, and the query speed is improved.

The invention discloses an automatic input vital sign detection method and system, and relates to the technical field of medical information processing, and the method comprises the steps: collecting vital sign data of a target individual, and carrying out the synchronous processing; a vital sign data set is formed through cleaning and multi-channel data fusion; uploading the encrypted data to a medical information system to finish classified storage and updating of the data; performing feature extraction and analysis to generate a health risk prediction result; health management suggestions are generated, and data display is completed through a dynamic feedback mechanism. According to the method, the privacy and the integrity of the vital sign data are guaranteed through a dynamic updating and verification mechanism in a mode of combining symmetric encryption and asymmetric encryption, and the risks of data leakage, data tampering and version management disorder in a traditional system are overcome. By introducing an intelligent analysis method combining a classification model and a regression model, the limitation of traditional single-dimensional static assessment is solved, and a comprehensive health risk report is generated by integrating logic association among multi-modal feature data.

The invention discloses a project side end data fragmentation recombination and service breakpoint resuming method in a weak network environment, and the method comprises the steps: receiving original service data collected on site through a project side end, carrying out the fragmentation according to the division of a construction region and a time interval, and generating a fragmentation data package with a time-space identifier and a check code; verifying the received fragmented data packet, and generating a fragmented cache set with a processing progress; when all fragments of a certain region-time fragment in the fragment cache set are received, the preprocessing progress of the corresponding service is read from the persistent file, and remaining service processing continues to be executed from the progress interruption position; and integrating the recombined original data of each region-time slice, the business processing result, the breakpoint resume record and the fragment transmission log, and generating a business data file containing a complete data chain and a processing track. By utilizing the embodiment of the invention, the data transmission efficiency and the service continuity in a weak network environment can be improved, and the reliability requirement of a project side end scene is met.

The invention discloses a DBA management method and device in an FTTR scene and an optical line terminal. The method is applied to a PON MAC chip, wherein the PON MAC chip comprises a CPU unit and a logic unit. The method comprises the following steps that: a CPU (Central Processing Unit) determines the uplink service bandwidth of each AllocID in each sub-period in the current DBA period; the CPU unit takes at least part of the sub-periods as sub-distribution periods, and determines BWMap data of the first sub-distribution period and the second sub-distribution period; the CPU unit sends the length of the silent window, the BWMap data of the first sub-allocation period, the BWMap data of the second sub-allocation period and the total length of the current DBA period to the logic unit; the logic unit determines the number of the remaining sub-allocation periods in the current DBA, and takes the BWMap data of the second sub-allocation period as the BWMap data of each remaining sub-allocation period to obtain complete BWMap data in the current DBA period; and the logic unit sends the BWMap data of the current DBA period, so that the problem that the DBA cannot adapt to the use requirement of the FTTR scene is solved, and the low-delay requirement of the DBA is met.

The invention discloses a bar code and RFID dual-mode fused tamper-proof data storage system, and relates to the field of data security storage, which comprises the following steps: uploading complete data to be stored into a scanning monitoring platform, splitting the data into static data and dynamic data, writing the static data into a bar code module, and writing the dynamic data into the bar code module; randomly distributed initial fluorescent particles are generated on the surface of the bar code data label through a scanning monitoring platform, a unique initial optical fingerprint is generated, if a bar code module is tried to be written or modified, the distribution position of the initial fluorescent particles is modified, an abnormal optical fingerprint is obtained, and an alarm is given; and writing the dynamic data into the RFID module, generating a dynamic key in each communication, and performing cross validation on the hash value based on the static data and the dynamic data. The method has the advantages that a three-dimensional protection system is constructed through a dual tamper-proof mechanism of a physical layer of fluorescent particle displacement and a password layer of a dynamic key in combination with cross validation of a hash tree, and the tamper-proof capability is improved.

The invention discloses a dynamic printing data minimization splitting method based on topological entropy mapping. The method comprises the steps that S1, a complete data set of a printing task is acquired; s2, establishing a topological network for the divided data blocks; s3, calculating the three-dimensional fractal dimension of each data block in the topological network by using an improved spatial fractal algorithm; s4, recording topological entropy values of the data blocks and priority indexes of the data blocks in the network; s5, calculating the comprehensive priority of the data blocks in a weighted fusion mode; s6, performing dynamic minimization splitting on the data blocks according to the comprehensive priority; s7, dynamically adjusting the transmission sequence and the splitting granularity; s8, carrying out local compensation on missing data generated by splitting; s9, optimizing subsequent transmission and splitting strategies; and S10, performing supplementary transmission on the data blocks marked as low priorities according to the integrity requirement of the task. According to the invention, efficient resource allocation is realized, and the integrity and accuracy of the printing task are ensured.

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.