2013 results about "Digital data" patented technology

The invention relates to the technical field of electric digital data processing, in particular to an artificial intelligence algorithm-driven digital human language understanding and corresponding reaction interaction method. The method comprises the following steps: collecting historical language data, text data, action instruction data and physical environment information of a user to obtain structured training data; vectorizing the text data to obtain a text semantic vector; environment feature vectors are extracted from the physical environment information; splicing the text semantic vector and the environment feature vector to obtain an environment enhanced text vector; and constructing a pre-training language model based on the environment enhanced text vector, and performing secondary pre-training to generate an optimized language model parameter. According to the invention, by fusing the user language and the physical environment information and combining with the multi-module intelligent component, context perception, efficient understanding and multi-modal natural interaction of the digital human in a complex scene are realized, and the intelligence, adaptability and user experience of the system are remarkably improved.

The invention provides a high-temporal-spatial-resolution surface temperature reconstruction system for urban thermal environment refined monitoring, and relates to the field of electric digital data processing. Comprising a multi-source heterogeneous data intelligent acquisition and preprocessing module, an urban thermal environment multi-dimensional feature knowledge modeling module, a physical constraint deep learning surface temperature reconstruction module and an intelligent monitoring early warning and decision support module. The multi-source heterogeneous data intelligent acquisition and preprocessing module is responsible for collecting and preprocessing various remote sensing and ground observation data, and the urban thermal environment multi-dimensional feature knowledge modeling module constructs a knowledge system of an urban underlying surface, a three-dimensional form and a thermal process. The physical constraint deep learning surface temperature reconstruction module is used for realizing accurate reconstruction of high temporal-spatial resolution surface temperature, and the intelligent monitoring early warning and decision support module converts a reconstruction result into visual display, risk early warning and regulation and control decision suggestions; according to the system, high-precision, physically consistent and interpretable urban surface temperature reconstruction can be realized.

The invention relates to the technical field of electrical digital data processing, and discloses a statistical entropy and multi-factor fused text similarity data processing method, which comprises the following steps that: a processor extracts substring sets which do not contain maximum common values of a first data sequence and a second data sequence, and calculates the quadratic sum of the lengths of substrings to generate local statistical entropy; traversing the maximum common substring set to obtain storage address indexes of the maximum common substring set in the first data sequence memory space and the second data sequence memory space, and constructing a topological mapping vector of a mapping structure displacement relationship; calculating the total number of inverted pairs of the topology mapping vector by using a merge sorting algorithm, and generating a normalized topology dissipation index; and by taking the local statistical entropy as an information carrier and taking the topological dissipation index as a structural damping factor, executing nonlinear damping modulation operation to obtain a final similarity score, and solving the technical problem that the block-level displacement cannot be identified by linear scanning logic by quantizing topological entropy increase of data distributed in a storage space.

The invention relates to the technical field of electrical digital data processing, and discloses a source code security vulnerability semantic detection method based on a large language model, which comprises the following steps: analyzing a source code to be detected to extract execution path constraint features, and constructing an orthogonal feature base vector sequence through orthogonalization feature extraction; inputting the source code to be tested into the large language model to obtain an initial semantic tensor; orthogonal projection is carried out on the initial semantic tensor to a code security constraint subspace constructed by an orthogonal feature basis vector sequence, weighted aggregation is carried out in combination with attention weight distribution information entropy, and a refined semantic vector is generated; according to the hidden logic offset vulnerability recognition method, business semantic noise is eliminated by utilizing a logic subspace projection mechanism, the association between a detection conclusion and code execution logic is established, and the precision of recognizing hidden logic offset vulnerabilities is improved.

The invention relates to the technical field of electrical digital data processing, and discloses a system conflict point intelligent review system based on a knowledge graph and a large language model, which comprises the following steps: constructing a dual-mode storage space containing an unstructured index and a structured logic graph, analyzing target text extraction features and triggering graph-based generation logic; converting the topological structure of the associated sub-atlas into a natural language instruction sequence to construct a forced logic constraint template, filling the template with a text, and inputting a pre-training language model to generate a verification result; according to the method, the discrete atlas topology is mapped into the linear logic constraint, random divergence of the generative model is restrained on the calculation principle, and precise decoupling and dynamic evolution of unstructured semantics and structured logic are achieved.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention provides a blood pressure dynamic monitoring system integrating overall risk and local anomaly detection, and relates to the field of electric digital data processing. Comprising a multi-source blood pressure data fusion and acquisition module, a dual-path feature learning and abnormity pre-detection module, a local and overall interactive abnormity accurate identification module and a dynamic risk assessment and intelligent early warning decision module, and the multi-source blood pressure data fusion and acquisition module is used for acquiring blood pressure and context data; the dual-path feature learning and anomaly pre-detection module is used for extracting time sequence features and performing preliminary anomaly screening, and the local overall interactive anomaly accurate identification module is used for detecting and analyzing local anomaly. The dynamic risk assessment and intelligent early warning decision module is used for comprehensively assessing the risk, forming a feedback optimization mechanism and outputting a personalized early warning decision; the system can accurately identify abnormal blood pressure, realizes accurate assessment and prediction of risks, and provides effective support for clinical decision and personalized health management.

The invention provides a driving fatigue monitoring auxiliary system based on Beidou satellite positioning and a multi-modal data fusion technology, which relates to the field of electric digital data processing and comprises a multi-modal information sensing and acquisition module, a data preprocessing and quality assurance module, a fatigue state recognition and evaluation module and an intelligent early warning and adaptive optimization module. The multi-modal information perception and acquisition module is responsible for acquiring driver states, driving behaviors and environment information in real time, and the data preprocessing and quality assurance module is responsible for performing space-time alignment, quality evaluation and feature standardization on multi-source data. The fatigue state recognition and evaluation module is responsible for fusing multi-dimensional features and judging fatigue levels and risk trends, and the intelligent early warning and self-adaptive optimization module is responsible for implementing hierarchical intervention and continuously optimizing system performance; according to the system, the accurate space-time reference provided by Beidou satellite positioning is utilized, effective fusion of multi-source heterogeneous data is realized, and the accuracy, the real-time performance and the individuation level of fatigue monitoring are remarkably improved.

The invention relates to the technical field of electric digital data processing, and provides a secondary development code cross-product transplantation method and device, computer equipment and a medium. The method comprises the steps of loading a source project, screening a to-be-transplanted source code file, identifying an original API type, determining a mapping relation between the original API type and a target API type, and generating an adaptation layer code framework according to the mapping relation; copying the to-be-transplanted source code file into the target project; on the premise of keeping the project hierarchical structure of the source project, replacing the calling node of the original API type with the calling node of the adapter type, and replacing the namespace reference with the corresponding namespace in the target project to obtain a migrated source code file; and obtaining a secondary development project of the target project based on the second product API according to the adaptation layer code framework and the migrated source code file. According to the method and the device, the transplantation efficiency and accuracy of the transplantation of the cross-product secondary development codes are improved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention relates to the technical field of electrical digital data processing, and discloses a train ice melting simulation optimization method of an electromagnetic thermal coupling model fused with a deep learning method, which comprises the following steps of: inputting a geometric representation tensor and a physical working condition parameter vector containing an electromagnetic excitation frequency and a reference environment temperature into a feature mapping neural network; outputting a dual-channel space source item tensor containing basic heat source power density and a heat source to temperature change sensitivity distribution matrix through nonlinear convolution operation; constructing a heat conduction discrete numerical value evolution operator configured with an active item linear correction interface; time stepping operation is executed according to the heat conduction time scale, a basic heat source is corrected in real time through the Hadamard product of a sensitivity distribution matrix and temperature deviation, and an operator is substituted for solution. On the premise that electromagnetic-thermal nonlinear coupling characteristics are reserved, decoupling of the time scale is achieved, and the calculation efficiency in high-frequency physical field simulation is effectively improved.

The invention relates to the technical field of electrical digital data processing, and discloses a lightweight-based multi-modal data enhancement method, which comprises the following steps: acquiring a multi-modal feature tensor; extracting a vector orthogonal projection scalar and determining the novelty; updating the global covariance matrix when the novelty is greater than a redundancy threshold, and maintaining the global covariance matrix in a register state when the novelty is not greater than the redundancy threshold; extracting a main diagonal variance component to determine a differential modulation coefficient; calculating a second-order moment manifold projection operator according to the global covariance matrix, and calibrating the operator by using a differential modulation coefficient; the calibrated operator is used for carrying out orthogonal projection on random noise to generate a structured disturbance vector, the structured disturbance vector is superposed to a multi-modal feature tensor to output enhanced features, a novelty judgment mechanism is used for restraining statistical deviation caused by steady-state redundant data, computing resource occupation is reduced, and it is ensured that semantic alignment between modals is maintained in enhanced feature distribution.

The invention discloses a test data intelligent generation and privacy compliance desensitization method, which belongs to the technical field of electric digital data processing, and comprises the following steps: monitoring the structure change of a preset regulation update source and a target application system, and obtaining compliance dynamic information; analyzing the compliance dynamic information to generate a dynamic compliance rule base; based on the dynamic compliance rule base, generating an initial test data set through a preset intelligent data generator; desensitizing the initial test data set, and outputting a desensitized test data set; scanning the desensitized test data set in real time for compliance verification, and generating a compliance verification report; and dynamically optimizing the generation strategies of the dynamic compliance rule base and the intelligent data generator based on the compliance verification report. Dynamic rule base construction, intelligent separated data generation, closed-loop verification optimization and desensitization audit tracing technologies are adopted, laws and regulations and system changes can be automatically adapted, and the service validity, privacy security and law compliance of test data are synchronously guaranteed.

A charging pile group dynamic power distribution method and system based on load prediction relates to the field of electric digital data processing, and the method comprises the following steps: obtaining charging data of charging piles in real time, and when a new vehicle requests to be charged, calculating the difference value between the required power and the current available power to obtain a power gap; and if the power gap exists, calculating a power amount which can be reduced by each charging vehicle according to the charging curve parameters and a preset rule. And calculating the charging time extension amount of each vehicle after power reduction, and calculating the pile position occupation time sensitivity in combination with the original predicted completion time. And sorting the sensitivities from small to large to form a power recovery candidate queue. And selecting proper vehicles from the candidate queue to form a recovery scheme, calculating the total pile position occupation time of the system corresponding to the scheme, and selecting the scheme with the minimum total time as the optimal scheme. And reducing the charging power of the selected vehicle according to the optimal scheme, and distributing the recovered power to the new vehicle. By implementing the method, the pile position utilization rate can be increased.

The invention relates to the technical field of electric digital data processing, in particular to a dangerous rock instability monitoring and early warning method. The method comprises the following steps: collecting multi-modal original data related to the dangerous rock in real time, and preprocessing the multi-modal original data to obtain multi-dimensional core parameters; lithologic partitioning is carried out on the monitoring area based on the borehole acoustic logging data, and a lithologic partitioning result is obtained; carrying out data association and fusion by adopting a D-S evidence theory based on the lithologic partitioning result and the multi-dimensional core parameters, and generating a comprehensive support degree of each catastrophe state; multi-modal characteristic spectrum mathematical characterization is carried out based on the multi-dimensional core parameters, a precursor mode corresponding to the multi-modal characteristic spectrum subjected to mathematical characterization is identified, and space-time correlation verification is carried out on an identification result; and according to the comprehensive support degree, the identification result and the time-space correlation verification result, outputting a graded early warning instruction of dangerous rock instability. According to the invention, the problems of one-sided consideration, low precision and poor real-time performance of dangerous rock instability early warning in the prior art can be solved.

The invention discloses an electric energy meter abnormal phenomenon automatic detection and identification method and system, and relates to electric digital data processing, and the method comprises the steps: carrying out the digital signal processing of initial data, the voltage effective value, the total harmonic distortion rate and the analysis of communication message abnormal codes, and generating a structured feature matrix; on the basis of the structured feature matrix, threshold comparison, composite condition judgment and continuous anomaly detection are executed through multi-level rule triggering, and an anomaly event report is generated; comparing the abnormal event report with a historical case, obtaining the similarity between the current abnormal event report and the historical case, and obtaining a similarity TOP-N case set; generating an anomaly diagnosis report by using weighted evidence fusion in combination with rule triggering intensity quantification and case matching credibility conversion; and updating the case library according to the abnormality diagnosis report, and counting and adjusting a rule threshold based on a sliding window. According to the method, the abnormal judgment threshold values of key parameters such as voltage and harmonic waves can be adjusted in real time through dynamic threshold value optimization, and the adaptability is improved.

The invention relates to the technical field of electric digital data processing, in particular to a radiator working state monitoring method and system based on data analysis, and the method comprises the steps: collecting multi-dimensional parameter data of a radiator at different moments, and taking the multi-dimensional parameter data of the radiator at the same moment as a data point; clustering all the data points to obtain a plurality of clusters; the abnormal degree of any cluster is calculated, the abnormal degree is in inverse correlation with the number of the data points in each cluster and is in positive correlation with the standard deviation of the data points in each cluster, the data points in the cluster with the abnormal degree larger than a set threshold value are responded to be abnormal data points, and it is judged that the radiator is abnormal at the moment corresponding to the abnormal data points. The problem that the monitoring efficiency and accuracy are not high in the process of monitoring the abnormality of the radiator is solved.

The invention discloses a campus intelligent consumption analysis system and method based on multi-dimensional data fusion, and relates to the technical field of electric digital data processing. The system comprises a terminal feature fusion module, an anomaly identification analysis processing module and a classification verification and synchronization module. The terminal feature fusion module fuses terminal feature data and time feature data in the original multi-dimensional data to generate behavior association features, and data dispersion is reduced; the anomaly identification analysis processing module performs anomaly identification and processing on the behavior association features to form a feature data set and verify the validity, so that the detection accuracy is improved; and the classification verification and synchronization module uploads the verified classification result as a core data set to a specified service platform and synchronizes the core data set to a database of the specified service platform, so that the platform processing efficiency is improved. Through cooperation of multiple modules, efficient fusion, accurate anomaly detection and data synchronization of campus consumption data are realized, and comprehensiveness of campus consumption analysis and processing efficiency of a service platform are improved.

The invention relates to the technical field of electric digital data processing, in particular to a flood control plan generation method based on a knowledge graph and a large language model, which comprises the following steps: acquiring a flood control plan query problem; extracting water conservancy entities, relationships between the entities and keywords, and obtaining semantic vector representation of a flood prevention plan query problem; retrieving in a pre-constructed knowledge base to obtain a knowledge retrieval result; performing duplicate removal on the knowledge retrieval result to obtain a target knowledge set; and utilizing the large language model to generate a flood control plan text corresponding to the flood control plan query problem. According to the scheme, deep fusion of the structured knowledge and the semantic vector is realized, and the structured information of the knowledge graph is combined with the semantic vector, so that an information island is broken, efficient organization and accurate matching of a semantic space are realized, the breadth and depth of knowledge retrieval are improved, generation and reasoning of an intelligent plan are effectively supported, and the accuracy of knowledge retrieval is improved. And the generated plan is more comprehensive and accurate.

The invention provides a business hall service system supporting multi-user security data circulation, which relates to the field of electric digital data processing and comprises a multi-user identity and access control module, a security data circulation and isolation module, a data life cycle security management module and a security monitoring and compliance auditing module, the multi-user identity and access control module is responsible for establishing a credible user identity system and realizing fine-grained authority control, and the security data circulation and isolation module ensures encryption protection, integrity verification and effective isolation among multiple users in the transmission process of data; the data life cycle safety management module covers whole-process safety protection of sensitive data from identification, processing to destruction, and the safety monitoring and compliance auditing module ensures that the system continuously meets compliance requirements and timely responds to safety threats through intelligent anomaly detection and whole-link auditing; the system can solve the problems of data security circulation and privacy protection in a business hall service system in a multi-user environment.

The invention discloses an LED chip service life rapid estimation method and device and a medium, and relates to the technical field of electrical digital data processing, and the method comprises the steps: designing a current pulse stress sequence for a calibration sample based on photoelectric thermal characteristic data, synchronously collecting photoelectric thermal response data before and after a pulse, and carrying out verification completion and mapping storage; generating a photoelectric thermal pulse response data set; according to the photoelectric thermal pulse response data set, photoelectric thermal change characteristics of the LED chip sensitive to the service life are extracted, and a photoelectric thermal pulse service life fingerprint of the LED chip is constructed; constructing a service life rapid estimation model, and predicting the service life of the LED chip according to the photoelectric thermal pulse service life fingerprint and the working condition information to generate the predicted service life of the LED chip; according to the method, the photoelectric thermal pulse life fingerprint of the LED chip is constructed, the photoelectric thermal evolution process under the pulse stress is compressed into a unified life characterization carrier, and a degradation feature basis capable of being directly called is provided for life modeling.

System and method for monitoring vehicular traffic on a bridge, including receiving digital data representing the response of the bridge to a traffic event, where the digital data has been collected during the traffic event, from displacement sensors and accelerometers mounted on the superstructure of the bridge, wherein the digital data from the displacement sensors embody bending responses of the bridge, the digital data from the accelerometers embody acceleration responses of the bridge, and the digital data from the sensor are synchronised in the same time space, providing a parametric model which uses modal parameters to simulate generalized boundary conditions and two-dimensional behaviour of the bridge; using the parametric model to process the digital data to solve for deformation of the bridge and characteristics of the vehicle traffic, and generating an output that describes the deformation of the bridge and characteristics of the vehicle traffic.

The invention provides a 6D attitude rapid estimation method and device for a target object, and relates to the field of electrical digital data processing in the technical field of machine vision and perception, and the method comprises the steps: obtaining an RGB image and a depth image of the target object, inputting the RGB image into a segmentation model, and obtaining a binary mask of the target object; acquiring a camera internal reference matrix, and constructing a curved surface point cloud matrix of the target object according to the depth image, the binary mask and the camera internal reference matrix; constructing a cubic package of the target object, and constructing a fitting penalty function according to the curved surface point cloud matrix and the cubic package; according to the curved surface point cloud matrix, the cubic package and the fitting penalty function, gradient vectors of the center point and the Euler angle of the target object are obtained through calculation; and performing iterative solution on the gradient vectors of the central point and the Euler angle through a gradient descent algorithm to obtain optimal solutions of the central point and the Euler angle, and taking the optimal solutions of the central point and the Euler angle as the 6D attitude of the target object.

Method and system for improving the security of storing digital data in a memory or its delivery as a message over the Internet from a sender to a receiver using one or more hops is disclosed. The message is split at the sender into multiple overlapping or non-overlapping slices according to a slicing scheme, and the slices are encapsulated in packets each destined to a different relay server as an intermediate node according to a delivery scheme. The relay servers relay the received slices to another other relay server or to the receiver. Upon receiving all the packets containing all the slices, the receiver combines the slices reversing the slicing scheme, whereby reconstructing the message sent.

The invention relates to the technical field of electrical digital data processing, and discloses an AI semantic enhanced unstructured manufacturing document data structuring method, which comprises the following steps: acquiring a character recognition stream of a target document to extract a semantic anchor point and topological coordinates thereof, and vectorizing a to-be-structured field to generate a to-be-processed vector; querying an engineering logic mapping table to determine association intensity, and calculating a logic gravitational field intensity value of the to-be-processed vector relative to the semantic anchor point according to the association intensity; associating the to-be-processed vector to a target semantic anchor point according to the field intensity value, starting a logic polarization program when the to-be-processed vector is identified to be interfered by the homogeneous semantic anchor point in the association stage, extracting bias characteristics to generate a polarization vector, and modulating the native gravitational field intensity; according to the method, the attribution ambiguity of similar semantic entities in a dense distribution scene is solved by introducing the logic gravitation constraint, the structured conflict caused by spatial distribution and engineering logic decoupling is eliminated, and the topology consistency in the data conversion process is ensured.

The invention relates to the technical field of electric digital data processing, and discloses a multi-source data automatic processing and service integration system based on artificial intelligence driving. Comprising the steps that a multi-source data access module achieves concurrent data access and caching, an AI driving data preprocessing module completes field automatic mapping through a data isomerism adaptation algorithm, and an intelligent data fusion module calculates a multi-source data fusion weight through a dynamic weight distribution algorithm; the service integration module realizes standardization of fusion data, and the monitoring scheduling module monitors each module in real time. High-concurrency multi-protocol data access is achieved through multi-source data automatic processing and service integration and a distributed Kafka cluster, automatic data mapping and precise data cleaning are completed through an improved algorithm depending on an AI driving module, the data fusion precision is improved in combination with a dynamic weight algorithm, the service integration flexibility is enhanced through multi-protocol API packaging and load balancing, and the service integration efficiency is improved. And stable system operation and maintenance are guaranteed.

The invention relates to the technical field of electrical digital data processing, and discloses a health data processing and alarming method for a family monitoring group, which comprises the following steps of: establishing a dynamic baseline for individualized long-term stable habits and a short-term baseline for reflecting a recent mode for a multi-mode data stream of a monitored person; two processing paths are executed in parallel, one processing path generates a system steady-state entropy value based on double-baseline difference aggregation so as to identify a long-term system evolution trend, and the other processing path performs multi-dimensional resonance judgment based on a difference value between an original data point and a short-term baseline so as to perceive a transient system impact event; according to the invention, by constructing a double-pedigree collaborative monitoring framework of slow-varying risks and acute events, mutually independent trend analysis and transaction detection in a traditional data processing mode are integrated into an integral processing structure with internal data collaboration and function complementation, and the problem of inherent information omission of a single processing mode is solved.

The invention discloses an infrared image super-resolution reconstruction method and system based on a convolutional neural network, and relates to the field of electrical digital data processing, and the method comprises the steps: inputting a low-resolution infrared image into a preset convolutional neural network, and obtaining a multi-layer feature map of the low-resolution infrared image, performing zooming processing on different levels of feature maps in the multi-layer feature map according to a preset proportion, and then performing splicing fusion to obtain a fused feature map; generating a feature vector for the fusion feature map through global average pooling, transforming the feature vector by using a full connection layer to obtain an attention weight, and weighting the attention weight and the fusion feature map to obtain an enhanced feature map; and performing up-sampling reconstruction processing on the enhanced feature map by using an up-sampling structure, and introducing a residual connection structure in the up-sampling reconstruction processing process to generate a high-resolution infrared image. By implementing the method, a high-resolution infrared image with more details is generated.

The invention discloses a motion artifact elimination method and system based on PPG signals, and relates to the technical field of electronic digital data processing, in particular to a high-accuracy and high-robustness artifact elimination method oriented to the PPG signals in wearable equipment. Firstly, artifact detection is carried out through a multi-base learner fusion model based on meta-learning, and efficient identification of different individual motion artifacts is realized; afterwards, for the detected artifact signals, the system introduces an AVAE model to carry out artifact removal, through multi-loss function joint optimization, high-quality PPG signals with time sequence details reserved are recovered, an effective solution is provided for high-quality perception and robustness health monitoring of the PPG signals in the wearable device in a complex dynamic environment, and the method has the advantages of being high in accuracy and high in robustness. Good popularization and application prospects are realized.

The invention discloses a data filling automation system and method based on a large model, and relates to the field of electric digital data processing, and the method specifically comprises the steps: monitoring and collecting the input behaviors of a user in different platform forms in real time, constructing a semantic state vector, and carrying out the aggregation to form a semantic global vector; constructing a structural disturbance tensor model, modeling the stable position of each field, and obtaining a stable state representation vector of each target field under disturbance through a tensor projection mode; and constructing a path cost function according to a matching relationship between the semantic global vector and a structure mapping result, solving an optimal execution path for field filling and reporting, and driving a browser to perform automatic filling and reporting according to a path sequence, thereby realizing automatic operation of data filling and reporting under semantic reasoning and structure disturbance. Automatic and intelligent cross-platform data filling and reporting are achieved, filling and reporting efficiency is effectively improved, platform independence, semantic adaptability and structure fault-tolerant ability are achieved, and data consistency and stability of the filling and reporting process are ensured.