15859 results about "Original data" patented technology

A modeling method for three-dimensional real-time measurement reconstruction, comprising: on the basis of a three-dimensional camera, a laser radar, and an infrared sensor device, generating a multi-source original data set; performing data preprocessing; performing feature extraction and data fusion; performing three-dimensional space construction; by means of a deep machine learning algorithm, performing real-time dynamic adjustment and optimization on a model; and, by adopting an advanced image processing algorithm, generating a high-precision three-dimensional model.

The invention provides a building dynamic structure health monitoring method. The method comprises the steps that original data of a multi-mode sensor is acquired and preprocessed; constructing topology sensing network representation reflecting the spatial distribution and structure connection relation of the sensors; extracting multiple spatio-temporal scale features; local-to-global structure state mapping is realized by using a recurrence plot evolution predictor; integrating structural dynamics knowledge into the neural network model to perform physical constraint correction; and constructing a multi-dimensional health index set and outputting damage probability evaluation. According to the invention, accurate mapping from the local dense monitoring area to the global structure state under the non-uniformly distributed sensor network is realized, and the accuracy and reliability of structure health monitoring are improved.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention discloses an intelligent operation and maintenance management and alarm system based on a large model agent, and the system comprises a data collection and fusion module which is used for processing original data collected from an IT system to obtain an operation and maintenance data graph, and the original data at least comprise logs, indexes and link tracking data; the anomaly detection module is used for performing analysis based on the preprocessed data and the time sequence characteristics to determine abnormal data; the alarm optimization module is used for analyzing the semantic information corresponding to the abnormal data so as to determine the priority of the corresponding alarm; the root cause analysis module is used for performing fault reasoning on the abnormal data based on a pre-constructed knowledge graph and generating fault interpretation according to data corresponding to the abnormal data; and the automatic operation and maintenance decision module is used for generating an automatic repair strategy according to the abnormal data and the fault interpretation and executing the automatic repair strategy to repair the abnormal data.

The invention discloses a cross-modal information fusion and retrieval method and system driven by semantic comprehension, and the method comprises the steps: obtaining text, image and audio original data, and extracting an initial feature set of each modal through a deep neural network; dynamically distributing each modal weight coefficient based on an attention mechanism, and performing weighted fusion on the initial feature set to obtain cross-modal fusion feature representation; through a cross-modal semantic association analysis model, high-dimensional semantic association features are extracted from the fusion feature representation, and semantic enhancement feature vectors are generated; constructing a cross-modal semantic graph network based on the vector, complementing missing modal features, and generating an optimized multi-modal feature set; and inputting the optimized feature set and the query sample into a contrast learning model, calculating a semantic similarity score, and generating a cross-modal retrieval result sorting list according to the score.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal sensor fusion inspection method and system, and the method comprises the steps: collecting the multi-modal original data of power equipment through a multi-modal sensor in an inspection robot, and constructing a feature vector set; performing adaptive weight calculation on the multi-modal sensor according to the feature vector set to obtain a sensor weight set; carrying out conflict identification and resolution on the multi-modal original data to obtain a fusion data set; performing abnormal feature extraction on the power equipment based on the fused data set to obtain an abnormal feature set; and carrying out routing inspection trajectory optimization based on the abnormal feature set to obtain a target routing inspection path sequence, and carrying out equipment state joint prediction in combination with historical equipment routing inspection data to obtain an equipment fault prediction result. And thus, more accurate equipment state joint prediction is realized.

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention discloses a method and a system for collecting decision data by an intelligent agent driven by a multi-modal large model. The method comprises the following steps: collecting original data of a predetermined sensor, calculating information entropy and a normalized mass score, and generating a fusion feature vector through self-adaptive multi-modal information fusion; time sequence sensitive multi-mode alignment processing is carried out on the image, and time sequence alignment features are obtained by combining timestamp embedding and a self-attention mechanism; based on this, decision distribution is generated by using a Bayesian neural network, uncertainty is evaluated, and a final decision and index set is generated; evaluating a task score in combination with task description, and making an execution plan according to a resource state; initializing the model, generating synthetic data, optimizing the model and storing experience. According to the invention, the efficient decision-making process of the intelligent trolley is realized, and the execution capability and task completion precision of the intelligent trolley are improved.

The invention discloses an intelligent file classification and retrieval method and system, and the method comprises the steps: carrying out the unified format conversion and feature extraction of text, image and audio data according to the original data format of a file through a multi-modal data fusion algorithm, and obtaining a standardized file feature data set; based on a deep learning adaptive classification model, performing classification processing on the standardized archive feature data set to generate multi-level archive classification labels; analyzing the semantic relationship between the archive classification labels through an enhanced semantic network construction technology, generating a semantic association map, and optimizing the semantic association map to obtain an optimized semantic association map; and according to a retrieval request input by a user, performing semantic matching and correlation sorting by utilizing the optimized semantic correlation graph, and outputting archive information most related to the retrieval request. By utilizing the embodiment of the invention, the relevance and accuracy of retrieval can be improved, and modernization and intellectualization of archive management are promoted.

The invention relates to the field of digital twinborn modeling, and discloses a digital twinborn visual modeling method and system based on a neural network, and the method comprises the steps: collecting the multi-dimensional perception data of a target physical system, carrying out the format unification and normalization processing of the original data of a sensor through a heterogeneous data fusion module, and obtaining a data fusion module; constructing a high-dimensional input feature set for neural network modeling by combining a structured embedding algorithm; carrying out stability pre-evaluation on the constructed input feature set, and screening core features by adopting a disturbance sensitivity analysis mechanism; a dynamic residual feedback mechanism is introduced to carry out enhanced training on the preliminary twin model, and modeling is carried out on the space-time dependency relationship of different components through a graph neural network; tracking a stability index of visual representation in real time in a model training process; and according to a multi-dimensional visualization result output by the final twin model, performing interpretation in combination with an industrial scene semantic rule base. The method has the advantage of improving the practicability of the twin model in the industrial scene.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention discloses a medical image encryption and secure storage method and system based on a block chain, and the method comprises the steps: obtaining the original data of a medical image, segmenting the original data of the medical image into a plurality of segments through a fragmentation technology, carrying out the encryption of each segment through a symmetric encryption algorithm, generating an encrypted segment set, and storing the encrypted segment set in a database; determining an integrity check value of the encrypted fragment set; according to the encrypted fragment set, adopting a distributed storage protocol to distribute each fragment to a plurality of storage nodes, recording a storage position and an integrity check value of each fragment to a block chain account book, and generating a storage index containing a storage path and check information; and establishing an access control strategy through the block chain smart contract, and if the obtained requester identity identifier and the authority level conform to the access control strategy, generating a dynamic authorization token including an access range and time limitation. According to the method, the privacy of the patient is protected, and meanwhile, the safety and availability of medical data are improved.

The invention discloses a collaborative sharing method, system and device on an energy data chain fusing multi-party security calculation and zero knowledge proof and a storage medium, and belongs to the field of energy data security sharing and block chain privacy protection, and the method comprises the steps: executing desensitization processing on original data according to types, and generating a cryptographic commitment value and a data hash identifier; encrypting and fragmenting the desensitized data, registering a task structure body through an intelligent contract, and uploading the task structure body; and each node decrypts the ciphertext, executes calculation and generates a zero-knowledge proof, and submits the zero-knowledge proof to the block chain to be verified by the smart contract. According to the method, data privacy protection and result verifiability unification are realized, cross-mechanism cooperation efficiency is improved, an auditing system on a whole process chain is constructed, and safe sharing of energy data is guaranteed.

The invention provides a disaster area unmanned aerial vehicle cluster dynamic task allocation and cooperative control method and system. The method comprises the steps that S1, original data of a disaster area scene are collected in real time through ground IoT equipment; s2, performing dynamic task priority marking on the original data, and integrating multi-dimensional information of the unmanned aerial vehicle; s3, tasks are allocated to the unmanned aerial vehicle based on a hybrid allocation strategy, and an initial obstacle avoidance path is generated; s4, when the unmanned aerial vehicle executes the task, the flight path is updated in real time, temporary obstacles are avoided, path conflicts are coordinated through a conflict detection algorithm, and task redistribution or degradation is carried out if necessary; s5, periodically, globally and dynamically adjusting task priorities or correcting paths; and S6, constructing a self-adaptive closed-loop control mechanism, and tracking multi-dimensional information and task attributes of the unmanned aerial vehicle in real time. According to the method, the global resource utilization rate is maximized while the second-level response of the high-priority task is guaranteed, and finally, the robustness and the adaptive ability of the unmanned aerial vehicle cluster in a complex scene are realized.

The invention discloses a cross-industry data security sharing method and system based on data desensitization and a medium, and relates to the related field of privacy computing, and the method comprises the following steps: carrying out dynamic desensitization processing on original data, generating a shared data set for real-time recording, constructing an access behavior characteristic spectrum to configure a dynamic access strategy, and establishing a third-party security data sharing platform, storing the shared data set in a trusted execution environment in a fragmented manner, obtaining fragmented encrypted data, executing a dynamic access strategy, performing conjoint analysis on the fragmented encrypted data through a security multi-party computing protocol, and generating a data security sharing suggestion. The technical problems that in cross-industry data sharing, the privacy leakage risk is high, the static desensitization rule is rigid, and the multi-party calculation efficiency is low are solved, and the technical effects of breaking data islands and constructing safe and mutual-trust data circulation ecology through fusion of dynamic hierarchical desensitization and a trusted execution environment are achieved.

The invention provides an intelligent electric energy meter fault prediction method based on multi-modal sensor fusion. The method comprises the following steps: collecting original data of a multi-modal sensor and preprocessing the original data to obtain a standardized data stream; establishing a sensor health state model and monitoring the working state of the sensor; performing feature extraction and pattern recognition on the standardized data to generate an electric energy meter state feature vector; combining a sensor health assessment result and an electric energy meter state feature vector, and applying a double-layer causal reasoning framework to realize separation and identification of a sensor fault and an electric energy meter fault; multi-source uncertainty is quantified, diagnosis reliability is evaluated, and a fault evaluation report is generated. The method can effectively distinguish the sensor fault from the electric energy meter fault, improves the fault prediction accuracy, reduces the false alarm rate, and optimizes the maintenance decision.

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention discloses a bolt looseness online monitoring method and system, and the method comprises the steps: collecting an original data set containing a vibration signal of bolt connection, temperature gradient data and structural stress distribution through a multi-mode sensor array, and carrying out the time-space alignment and frequency domain decomposition processing of the original data set, obtaining a multi-dimensional feature matrix of the bolt nodes; based on the multi-dimensional feature matrix, a graph neural network model is adopted to carry out bolt looseness probability calculation, and a real-time looseness probability value of each bolt is output; generating a risk level map based on time evolution according to the real-time loosening probability value; and the risk level map is mapped in real time through a three-dimensional visual interface, and when it is detected that the bolt loosening risk level exceeds a preset threshold value, an early warning message is generated and uploaded to an operation and maintenance platform, and closed-loop monitoring response is completed. According to the embodiment of the invention, accurate assessment, dynamic prediction and visual early warning of the loosening risk can be realized, and the intelligent level of structure safety monitoring is improved.

The invention provides a cross-domain data integration and fusion method based on a large model, a terminal and a storage medium, and the method comprises the steps: carrying out the format conversion and cleaning of cross-domain original data through a unified data access adaptation layer, and outputting a standardized data set; carrying out semantic understanding integration by utilizing a large model, analyzing field names, generating field semantic tags in combination with a business term knowledge base, judging business relevance by calculating similarity, and generating an inter-domain field mapping relationship; and performing technical standard conversion and data fusion processing based on the mapping relationship, and performing intelligent quality treatment by adopting a mode of combining a quality treatment standard library and a large model. According to the method, a large model is introduced to assist data understanding and treatment, so that the technical problems of insufficient semantic understanding, non-uniform standards and the like in existing cross-domain data integration are effectively solved, and the accuracy and automation level of data integration are remarkably improved.

The invention relates to the technical field of data encryption, and discloses a data encryption protection system and method for intelligent controller hardware, and the method comprises the steps: generating a random confusion factor based on a real-time operation state through constructing a dynamic confusion algorithm, and carrying out the nonlinear transformation of original data; implementing a fragment encryption strategy, dividing fragments according to data sensitivity levels, and adopting a heterogeneous encryption algorithm; generating a dynamic binding key in combination with the environment parameters and the hardware unique identifier; hardware-level integrity check is executed, and data and hardware are verified through cyclic redundancy check codes and hardware fingerprints; establishing a self-adaptive key updating mechanism; performing multi-layer defense linkage; and implementing anti-side channel protection, and constructing a security audit log. According to the method, the hardware data security of the intelligent controller is comprehensively guaranteed, various security threats are effectively resisted, and the data confidentiality, integrity and availability are improved.

The invention relates to the technical field of network security, in particular to a computer network information security monitoring method, system and device and a medium, and the method comprises the steps: obtaining encrypted traffic data and application log data in a network environment, and constructing a space-time associated original data set based on the encrypted traffic data and the application log data; performing protocol analysis on the original data set with the time-space association to generate a protocol fingerprint feature vector; inputting the protocol fingerprint feature vector and the application log data into a preset heterogeneous multi-modal analysis model, and obtaining a multi-dimensional security situation assessment result containing a threat level and an attack path; and based on the multi-dimensional security situation assessment result, generating a dynamic defense strategy instruction set through a reinforcement learning algorithm, and issuing a strategy instruction to a network execution node in real time. The method and the device have the effects of realizing real-time accurate detection of encryption threats and constructing a dynamic defense system with balanced security and efficiency.

The embodiment of the invention provides an unmanned aerial vehicle data anomaly detection method based on multi-modal time sequence modeling, and the method comprises the steps: carrying out the multi-stage data preprocessing of original data obtained in the working process of an unmanned aerial vehicle, and generating a standardized input data flow; extracting time sequence features of the standardized input data stream through continuous LSTM blocks in the multi-level feature extraction network, and generating a shallow time sequence feature set; performing depth time sequence modeling on the shallow time sequence feature set to generate a deep time sequence feature set; performing multi-stage information fusion and mask screening on the shallow time sequence feature set and the deep time sequence feature set to generate a fusion time-space correlation feature set; and generating an attitude parameter prediction result of the unmanned aerial vehicle according to the fusion time-space correlation feature set, and obtaining an anomaly detection decision result of the unmanned aerial vehicle by using a deviation comparison and multi-level anomaly judgment mechanism based on an adaptive threshold obtained based on the attitude parameter prediction result and prediction error statistical distribution. Therefore, the anomaly detection accuracy of the unmanned aerial vehicle data is improved.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention discloses a trusted data space cross-domain collaborative analysis method based on privacy computing, which comprises the following steps: step 1, trusted data space construction: edge computing nodes are deployed in a plurality of data owners participating in cross-domain collaborative analysis, and each edge computing node is interconnected through a block chain network to form a distributed trusted data space; a strategy engine is configured in the trusted data space, and the strategy engine is used for defining a data use permission, a privacy protection level and a calculation task life cycle management rule; and step 2, dynamically selecting privacy computing protocols, and selecting at least one privacy computing protocol from a pre-configured protocol library according to the data type, privacy protection requirement and computing complexity of the collaborative analysis task. When the scheme is implemented, the data participates in calculation in a ciphertext or desensitization form in the whole process, and the original data is not out of the domain, so that the privacy compliance requirement is met; and data use rules are automatically executed through block chain evidence storage and the intelligent contract, so that malicious node tampering is prevented.

The invention discloses an adaptive teaching real-time feedback method based on multi-modal fusion, and the method comprises the following steps: synchronously collecting text modal information, voice modal information and image modal information generated by students in a teaching process, forming multi-modal original data information, and extracting historical student interaction behavior data; preprocessing the multi-modal original data information, and respectively generating corresponding text, voice and image sequence features; a visual feature encoder and a sequence feature encoder are adopted to encode each modal sequence feature to obtain a high-dimensional feature; inputting the modal high-dimensional features into a cross-modal fusion network for deep fusion; parameters of the feedback model are optimized through a model-independent element learning feedback regulation and control algorithm, and a personalized feedback strategy is generated; generating comprehensive feature representation according to the fusion features, and outputting personalized teaching feedback; and the interaction information is updated based on the feedback behavior data to realize closed-loop optimization.

The invention discloses a data security and privacy management method and system based on a block chain technology, and the method comprises the following steps: S1, collecting original data in a target system, and carrying out the preprocessing of the original data; s2, constructing an access control strategy set; s3, converting into a structured strategy configuration file, and deploying a smart contract; s4, matching and judging the access request and a preset strategy by the intelligent contract; s5, judging whether the access request is legal or not; s6, performing structured processing on the access log, and writing the access log into a block chain account book; s7, intelligently selecting an encryption mechanism, and providing access return data to the access party; and S8, carrying out modeling prediction on the access mode by adopting a federal gradient lifting tree algorithm. The block chain intelligent contract and the federal gradient boosting tree algorithm are fused, differential privacy, zero-knowledge proof and homomorphic encryption technologies are combined, data intelligence and refined privacy protection are realized, and the method has the advantages of high credibility, high intelligence and high traceability.

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

The invention relates to the technical field of user behavior analysis, and discloses an activity matching method and system based on user behaviors, and the method comprises the steps: carrying out the full-dimension collection of behavior data of a user in a multi-channel marketing environment, and obtaining a structured user behavior data set; constructing dynamic user portrait data based on the structured user behavior data set; performing three-layer tagging processing of basic attributes, content features and experience values on the original activity data to obtain structured activity tag data; performing asynchronous iterative calculation and intelligent weighted matching according to the dynamic user portrait data and the structured activity tag data to obtain a personalized activity recommendation list; and executing a multi-brand cooperation strategy and global constraint processing on the personalized activity recommendation list to obtain a target matching scheme meeting cross-scene requirements. According to the method, coordinated activity recommendation in a multi-brand environment is realized, and the technical problem of mutual conflict of different brand activity recommendation is solved.