4263 results about "Feature set" patented technology

The invention provides a perception collaborative decision-making method and system based on multi-modal heterogeneous data fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a global environment situation perception graph into a pre-trained multi-target collaborative decision-making model; the multi-target collaborative decision-making model forms a multi-target decision-making feature set by analyzing the resource entities and the incidence relation in the graph; based on the multi-target decision feature set, decision optimization is carried out to obtain a comprehensive collaborative scheduling scheme; performing instruction analysis and packaging on the comprehensive collaborative scheduling scheme to obtain an executable instruction sequence; and issuing the executable instruction sequence to a corresponding decision node and a control terminal in parallel through a distributed communication architecture to complete real-time scheduling of resources and collaborative issuing of control instructions. According to the invention, by constructing a linkage mechanism of multi-modal data fusion, dynamic environment perception and collaborative decision execution, intelligent perception and quick response to a complex environment are realized.

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal hierarchical feature fusion and decision making method, device, equipment and medium, and the method comprises the steps: obtaining vision, language and motion data, and carrying out the hierarchical feature extraction to generate a multi-modal initial feature set; feature importance is analyzed, screening and dimension reduction are carried out, and screened multi-modal features are obtained; performing semantic enhancement on the screened multi-modal features to generate multi-modal semantic enhancement features; executing cross-modal attention fusion on the multi-modal semantic enhancement features to obtain cross-modal fusion features; and inputting the cross-modal fusion features into a semantic reasoning network to generate a decision result. According to the method, through multi-level screening dimension reduction, semantic enhancement and cross-modal attention fusion, the model can accurately utilize key feature relationships among multi-modal data, redundant interference is reduced, and semantic reasoning accuracy and decision-making efficiency are improved.

The invention relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The invention discloses an intelligent planning method and system for a weak current system in a smart park, and belongs to the technical field of weak current intelligent design. The method comprises the steps of performing feature extraction on the weak current multi-source data of the smart park to form a weak current feature set; a multi-dimensional semantic space is constructed, semantic association features are obtained, and node features, topological relations and constraint rules of the weak current system are determined; generating a weak current knowledge graph based on the information, and performing semantic alignment on the basic information of the park to obtain a final scene demand representation; performing graph reasoning and constraint calculation according to the representation to obtain a feasible region and constraint satisfaction condition, and generating a candidate construction scheme; and screening out an optimal construction scheme from the candidate schemes according to a preset comprehensive optimization strategy and sending the optimal construction scheme to a control center. According to the scheme, the weak current scheme is promoted from demand understanding to scheme optimization, and a coherent and verifiable automatic process is formed; therefore, the manual intervention is less, the design judgment is more accurate, and the finally output construction scheme has higher engineering reliability.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention discloses a steel-concrete hybrid girder bridge operation monitoring and static and dynamic analysis method based on multi-source data fusion, which comprises the steps of 1, structural response data acquisition, 2, multi-source data fusion processing, 3, building a plurality of operation state evaluation models based on parameters acquired by a sensor, and 4, carrying out static and dynamic analysis on the operation state evaluation models. 4, performing static analysis, dynamic analysis and static and dynamic collaborative analysis on the girder bridge based on the comprehensive feature set after multi-source data fusion, and 5, providing data support for the static and dynamic collaborative analysis by the operation state evaluation model through the comprehensive feature set, and meanwhile, evaluating the operation state of the girder bridge. Reversely supplementing the operation state evaluation model with the structural mechanical response characteristics excavated in the static analysis and dynamic analysis processes to form a data closed loop; the method has the advantages that through data sharing and feature interaction, evaluation result cross validation and multi-scale analysis linkage, cooperation of an operation state evaluation model and static and dynamic collaborative analysis is achieved, and a data closed-loop and multi-dimensional evaluation system is formed.

The invention provides a power chip packaging detection method and device, and the method comprises the steps: obtaining a structure image, an infrared thermal imaging frame sequence and a working electrical parameter data set of a target power chip after packaging, and carrying out the layering and regional peak analysis, so as to construct a morphology feature parameter set and a thermal anomaly feature set; inputting the morphology feature parameter set, the thermal anomaly feature set and the working electrical parameter data set into a preset defect identification model to obtain a chip packaging anomaly identification map; and performing hierarchical division and node aggregation on the chip packaging abnormity identification map to obtain a packaging defect type result. By constructing a multi-modal packaging detection system taking a structural image, an infrared thermal imaging frame sequence and a working electrical parameter data set as input, accurate identification of the power chip is realized, the accuracy of packaging defect detection is effectively improved, and the problems of low identification precision and information isolation of an existing detection method are improved.

The invention provides a multi-view-oriented three-dimensional scene image reconstruction registration and optimization method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining multi-frame three-dimensional scene image data, extracting a multi-level feature set, constructing a cross-view-angle semantic association graph, building a feature corresponding relation, and calculating a multi-view-angle spatial transformation relation parameter. Performing coordinate system alignment on the image data to generate an initial three-dimensional reconstruction result, and performing optimization in combination with a multi-target joint optimization function and a dynamic adaptive weight regulation and control mechanism. According to the method, the precision and robustness of three-dimensional scene reconstruction are improved, and the problem of registration errors caused by large view angle difference in a complex scene is solved.

The invention provides an urban water affair situation awareness management method and system, and relates to the technical field of urban water affair management, and the method comprises the steps: obtaining multi-source heterogeneous data; performing preprocessing, feature extraction and fusion on the multi-source heterogeneous data to obtain a comprehensive analysis feature set; inputting the comprehensive analysis feature set into a situation assessment model to determine the situation level of the urban water affair; inputting the comprehensive analysis feature set into a time sequence prediction model, and predicting to obtain a situation change trend of a future time period; based on the comprehensive analysis feature set, the situation level and the situation change trend, performing comprehensive assessment by using a preset risk assessment index system, and generating a risk assessment result; based on the situation level, the situation change trend and the risk assessment result, a preset expert knowledge base is called for matching and reasoning, and a candidate disposal strategy set is generated; and evaluating strategies in the candidate disposal strategy set, and outputting an optimal disposal strategy. According to the invention, the management level of urban water affairs is improved.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention discloses a quantum classical hybrid computing task dynamic scheduling system and method based on SLURM. The system comprises a quantum task feature extraction module, a dynamic priority evaluation module, a dependency analysis module, a quantum perception backfilling module and a uniform resource abstraction layer module. The method comprises the following steps: extracting quantum features of a to-be-processed task and a computing resource to form a quantum feature set and caching the quantum feature set; the feature set is obtained in real time, task priorities are output through multi-dimensional evaluation, and a real-time priority sequence is generated; based on the task type and the feature set, forming a dependency relationship between the classic task and the quantum task, and converting the dependency relationship into a dependency constraint and / or resource reservation instruction; according to the task priority, the resource reservation instruction and the real-time resource state, a future idle period is predicted, and a short-time quantum task is inserted for backfilling; computing resources are distributed according to the task priority and the dependency constraint, and the resource utilization state is fed back to the backfill and priority evaluation module in real time. According to the method, efficient scheduling of hybrid computing tasks can be realized, and the overall performance of the system is remarkably improved.

The invention discloses a multi-source heterogeneous medical data fusion and intelligent diagnosis method, and relates to the technical field of medical data processing and intelligent diagnosis, and the method comprises the specific steps: firstly, synchronously collecting medical images and clinical text data of a patient, and carrying out the correlation and integration to form a heterogeneous diagnosis data set; performing standardized feature extraction to obtain a feature set in a unified format; then constructing a parallel model, fusing features by using a cross-modal attention alignment technology, and guiding correction by means of a knowledge graph; and finally, the cross-modal diagnosis features are input into the reference model, automatic focus positioning is realized through processing, and a visual marker graph is output. Heterogeneous data of medical images and clinical texts are synchronously integrated, and the diagnosis feature reliability is improved through standardization processing, feature fusion and the like; a focus sensing mask is generated through comparison with a normal model, a multi-scale feature fusion technology is combined to realize automatic and accurate positioning of the focus, a large amount of labeled data is not needed, the process is simplified, and the diagnosis efficiency and accuracy are improved.

The invention provides a thermoelectric unit intelligent early warning method and system based on an artificial intelligence model. The method comprises the steps that firstly, multi-source operation monitoring information of different operation dimensions of core components such as a turbine, a boiler and a generator of a thermoelectric unit is obtained; performing time sequence correlation analysis on each modal monitoring data, constructing a dynamic correlation matrix, generating a modal conflict correction coefficient, and obtaining a time sequence dependence feature set; inputting the model into a hierarchical evolutionary early warning model, and obtaining an evolutionary fusion early warning feature vector through modal conflict correction and dynamic feature evolution modeling; generating a working condition self-adaptive dynamic early warning threshold value based on the current operation working condition; through comparison and abnormal traceability of the traceability verification module, an abnormal root component and a propagation path are determined, and a targeted traceability early warning instruction is output, so that the early warning accuracy and adaptability of the thermoelectric unit are improved, and safe and stable operation of the unit is guaranteed.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention relates to the technical field of precision manufacturing, and discloses a multi-mode laser galvanometer calibration method and device. The method comprises the steps of obtaining a smooth angle sequence, comparing the smooth angle sequence with a calibration model, extracting an abnormal component if a comparison deviation is abnormal, and performing smooth processing to obtain a deviation vector; fusing the statistical characteristics of the light beam position and the deviation vector, predicting the light beam position, and generating a calibration parameter set if the light beam position exceeds a deviation range; extracting a key compensation item and optimizing a driving sequence, fusing frequency domain interference to generate a simulation track, and determining a stability index; and comparing the environment feature set, adjusting parameters to obtain an enhanced path model, and determining a final calibration scheme in combination with real-time feedback. According to the method, accurate calibration of the galvanometer in a multi-mode dynamic scene can be realized.

The invention relates to a dynamic power regulation and control method and system for an electric vehicle charging pile, and the method comprises the following steps: S1, based on the real-time operation data of a charging pile cluster, employing a multi-source information fusion technology, collecting a power grid voltage fluctuation signal through a Hall sensor disposed at a charging pile end, and combining with the battery charge state data transmitted by a vehicle-mounted BMS, and eliminating noise interference by using an improved Kalman filtering algorithm, and realizing multi-dimensional data fusion by using a D-S evidence theory to generate a multi-source working condition feature set. The method has the advantages that the power grid voltage fluctuation signal and the battery charge state data are integrated through the multi-source information fusion technology, the improved Kalman filtering algorithm is adopted to eliminate noise interference, multi-dimensional data fusion is achieved in combination with the D-S evidence theory, the integrity and reliability of working condition feature extraction are remarkably improved, and the working condition feature extraction efficiency is improved. And the LSTM-ARIMA hybrid prediction model is utilized to synchronously process the nonlinear features and the periodic rules.

A video tag recognition method, executed by an electronic device and comprising: by means of a video tag recognition model, respectively encoding video frames in a video to be recognized, to construct a global feature set comprising global features of the video frames obtained by encoding, and a local feature set comprising local features of the video frames obtained by encoding; on the basis of a feature similarity between the global features in the global feature set, compressing the global feature set to obtain a sequence of global features of a preset storage quantity; and on the basis of a feature similarity between the local features in the local feature set, compressing the local feature set to obtain a sequence of local features of a preset storage quantity (S91); concatenating a global query feature and a local query feature obtained by pre-training, and then using a self-attention mechanism to extract a self-attention feature, wherein the global query feature and the local query feature are obtained by training learnable query features on the basis of a sample video, and the self-attention feature fuses key information in the global query feature and the local query feature (S92); using a cross-attention mechanism to respectively extract a first cross-attention feature between the self-attention feature and each global feature in the global feature sequence, and to respectively extract a second cross-attention feature between the self-attention feature and each local feature in the local feature sequence (S93); and on the basis of the obtained first cross-attention features and second cross-attention features, recognizing a video tag of the video to be recognized (S94).

The invention relates to the technical field of micro-nano motion control, and discloses a self-adaptive control method of a micro-nano high-precision motion platform. The method comprises the following steps: acquiring real-time pose feedback data and target trajectory data of a motion platform, and extracting dynamic response features; calling the trained motion feature analysis network to perform multi-modal feature separation, and generating a platform pose feature set; based on the set, performing space-time coupling analysis on the working environment parameters through an environment disturbance perception model to obtain a fusion result containing mechanical deformation characteristics and environment disturbance characteristics; inputting a fusion result into a dynamic compensation model to calculate a track correction amount, and outputting a driving compensation instruction; and calibrating the target trajectory data in real time according to the compensation instruction, and generating an actual control signal. According to the method, through multi-modal feature analysis, space-time coupling perception and dynamic compensation, the control precision and stability of the motion platform in a complex environment are improved, and the method is suitable for a micro-nano high-precision motion control scene.

The invention provides an intelligent pipe surface quality detection method and system based on machine vision, relates to the technical field of industrial automatic detection and machine vision, and aims to establish a pipe surface feature library and mark shape abnormal features. The method comprises the following steps: collecting an image of a pipe in a bright and dark composite light field, extracting gray and texture features after polarization filtering processing, reconstructing a three-dimensional point cloud covering a mortar layer and a concrete layer by matching a principal component analysis dimensionality reduction fusion feature set with a feature library, and converting the point cloud into a two-dimensional expansion graph through cylindrical projection; a mortar abnormal area and a concrete abnormal area are segmented, the hole volume is calculated through point cloud residual errors in the mortar area, and internal hollowing is detected in combination with acoustic vibration excitation and thermal response; the concrete area locates defects based on point cloud features; according to the sequence of the concrete covering process before the mortar covering process, a correlation model is constructed to match and coincide the defect sites, and the detection result is output, so that the detection automation degree and accuracy can be improved.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses a pole-mounted transformer state evaluation and fault early warning method fusing multi-source data, and relates to the technical field of intelligent power grid monitoring, and the method comprises the steps: executing dual state judgment according to a multi-source fusion feature set, extracting a statistical deviation degree of operation fluctuation abnormity, and calculating a physical deviation degree of multi-physical field coupling imbalance; the statistical deviation degree and the physical deviation degree are fused in a double-domain cooperation mode, and a real-time health index is obtained; the degradation rate and multi-scale energy characteristics of the real-time health index are extracted, a potential degradation aggregation mode is identified by referring to a historical slow-varying baseline, and a hidden danger trend factor is generated; performing composite attribution matching on the hidden danger trend factor and the multi-source fusion feature set, executing grading risk criteria according to a multi-physical field anomaly mechanism chain, and outputting an early warning grade and an early warning reason; according to the method, diagnosis is carried out by embedding a multi-physical field anomaly mechanism chain, and the physical root and evolution stage of the fault can be accurately positioned.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

A document information extraction method and apparatus based on image-text modal fusion, and a storage medium, relating to the technical field of artificial intelligence. The method comprises: acquiring a text block in a picture to be processed; extracting a semantic vector from the text block on the basis of a pre-trained NLP model, extracting a visual vector by using computer vision technology, and fusing the semantic vector and the visual vector by means of an attention mechanism to form an image-text fused feature representation; constructing a fully-connected directed graph on the basis of image-text fused feature; on the constructed graph network, performing graph attention convolution on "node-edge-node" triplet feature sets; dynamically weighting important "text-position-text" triplet information by means of a self-attention mechanism; and outputting a feature vector in which text, vision and position information are fused. By deep fusion of text, vision and position information, the present disclosure significantly improves the accuracy and robustness of text information matching, thus adapting to complex and diverse image-text environments.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention relates to the technical field of RFID tag signal optimization, and discloses an RFID tag signal optimization method and system based on deep learning. According to the method, feature extraction is carried out on a backscattering signal, core features of a multipath channel are quantized from a frequency domain and a time domain respectively, numerical representation of a channel state is further obtained, then obtained data information is used as input of a deep learning network, training and forward calculation are carried out, original emission parameters are adjusted, and a multi-path channel is obtained. Therefore, the conversion from blind extensive control to precise control of the transmitting end is realized, so that the problem of mismatching of parameter control and channel state caused by information splitting of the transmitting end and the receiving end in the prior art is solved; according to the signal optimization method for realizing joint adjustment of transmitting and receiving parameters through an end-to-end deep learning architecture, the optimization effect of an RFID tag signal in a complex environment is remarkably improved.

The invention provides a data analysis mapping method and system based on context awareness, and the method comprises the steps: carrying out the directional crawling of a target website through a crawler instance, so as to obtain multi-format webpage source data, then extracting the structural features, semantic features and source features of the multi-format webpage source data, and constructing a source data context feature set; dynamically matching an adaptive analysis strategy based on the source data context feature set to accurately extract target field data, performing semantic similarity matching on the target field data and a preset service scene standard field library, and performing data format unified conversion in combination with a context format rule to obtain standardized field data; and finally, performing multi-dimensional verification on the standardized field data, and generating an SQL statement after the verification is passed, so as to solve the problems that an existing data analysis mapping scheme depends on a fixed template and cannot dynamically adapt to page structure change of a target website, manual auxiliary field mapping consumes time, and errors are easy to introduce.

The invention discloses an industrial AI assistant cross-modal interaction method based on a dynamic knowledge graph, and the method comprises the following steps: collecting texts, voices, images and multi-source sensor data in an industrial system, carrying out the modal recognition, feature extraction and time alignment, and generating an event feature set and a state feature set with timestamps. Modeling a time dependency relationship between event types by constructing a multivariable Hawkes model, and outputting an event trigger sequence and trigger strength; and in combination with a neural controlled differential equation model, guiding the state to evolve along with time and jump at a specific moment to form a state evolution trajectory. Performing fusion coding on the event and the state, constructing a dynamic knowledge graph with a causal structure and semantic continuity, and generating interactive output based on context reasoning; and after system feedback is received, the triggering strength and the state track are updated, and continuous evolution of the knowledge graph and reverse optimization of model parameters are achieved.