198 results about "Topology mapping" patented technology

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

The invention belongs to the field of parallel computing architecture optimization, and relates to a matrix multiplication acceleration method and system based on dynamic computing resource mapping, and the method comprises the steps: constructing a dynamic topology model driven by tensor dimension features, and generating a thread block distribution mode according to matrix parameters and GPU hardware information; constructing a multi-dimensional resource scheduling strategy library, dynamically selecting an optimal thread block distribution strategy from the multi-dimensional resource scheduling strategy library, and generating a binding relationship between the thread blocks and the data blocks; calculating collaborative access logic of thread blocks and storage hierarchies based on block parameters and dynamic mapping function optimization; distributed calculation is carried out, calculation and data transmission are parallelized through pipelining and a double-buffering mechanism, and result aggregation across calculation units is completed synchronously through atomic operation and a barrier. According to the method, discontinuous memory access conflicts can be effectively reduced, the execution efficiency of the calculation instruction and the utilization rate of the cache space are improved, the parallel calculation process of accelerating and optimizing the general matrix multiplication is realized, and the data processing efficiency is improved.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

The invention discloses an intelligent water affair monitoring management method and system based on the Internet of Things, particularly relates to the technical field of water affair management, and is used for solving the problems of control instruction mismatching, redundant execution and equipment overload caused by the fact that a static topology model cannot sense the dynamic change of a pipe network in real time in the prior art. Through real-time collection of pipe network operation data and analysis of time-space correlation characteristics of water flow propagation delay parameters and pressure mutation, a pipe network topology change event is dynamically perceived; the flow direction sudden change reasonability is verified in combination with fluid mechanics conservation constraint, and a corrected topological mapping table is generated through reverse calculation; candidate paths are screened based on water flow inertial parameters and pressure gradient threshold values, a safety control instruction set is generated through a multi-stage verification rule, dynamic matching of a pipe network regulation and control instruction and a real topological structure is achieved, the matching degree of the control instruction and the physical state of a pipe network is effectively improved, the leakage risk and energy waste are reduced, the manual maintenance requirement is reduced, and the safety of the pipe network is improved. The service life of equipment is prolonged.

The invention relates to the technical field of underground pipe gallery asset management, and discloses a BIM-driven underground pipe gallery asset dynamic management and emergency decision-making platform, and the platform comprises a dynamic topology mapping module which achieves the dynamic grid density adjustment of a BIM model through monitoring the change rate of environment parameters, and generates a logic topology network synchronized with a physical pipe gallery structure in real time; the asset state reverse derivation unit is used for reversely deducing asset degradation based on environmental data by using a physical rule library dynamically loaded in a time-sharing domain; the logic rule self-triggering engine constructs a multi-level response rule chain containing space-time prediction and optimizes an emergency plan; and the edge-cloud collaborative architecture synchronously realizes real-time decision-making of low bandwidth dependence through intelligent cache and asynchronous increment. When dynamic topological mapping is driven by the change rate of environmental parameters, the real-time state of the underground pipe gallery can be represented more finely, and a high-reliability data basis is provided for subsequent accurate analysis and decision making.

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 AC / DC module control, and discloses an intelligent control method and system for multi-mode switching of an AC / DC module. According to the method, a dynamic topology mapping model is constructed according to an impedance spectrum matrix, and a physical connection relation is converted into a node admittance relation network; generating an energy distribution state sequence based on the power fluctuation vector, and analyzing migration constraint conditions among different operation modes by adopting an association rule mining algorithm; when an operation mode switching instruction is detected, activating a node admittance relation network in the dynamic topology mapping model, and inputting a current energy distribution state sequence to an association rule mining algorithm; according to a real-time matching result of the node admittance relation network and the migration constraint condition, generating a switching control instruction containing an impedance adjustment gradient and a power distribution weight; and the power semiconductor execution unit transmits the switching control instruction to the AC / DC module. According to the method, multi-mode intelligent switching control of the alternating-current and direct-current module can be realized.

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 discloses a method for preventing a PCIe switch internal link state from being recognized to be abnormal by an OS, and relates to the field of link state recognition anomalies, a high-risk link is recognized by obtaining a historical data set, configuration processing and algorithm optimization are carried out, an injection verification mirror image is obtained, and accurate link training control analysis is carried out based on the injection verification mirror image, so that the reliability of the PCIe switch internal link state is improved. Carrying out intelligent topology mapping management on the final link stability score to obtain an optimized topology; carrying out anomaly detection and hierarchical response control on the optimized topology to obtain a response action set; and carrying out repair or degradation processing on the link based on the response action set. And finally, the link state and the topological structure are updated according to the processing result to obtain a repair result and the updated topological structure, so that the high-risk link can be identified, the OS link state can be accurately judged, the system availability is improved, the misjudgment risk is reduced, and a dynamic degradation and active repair mechanism based on the actual risk levels (high, medium and low stability) of the link is provided.

The invention discloses a heat dissipation control method and device for a single-phase immersed liquid cooling charging pile and a medium, and relates to the technical field of battery thermal management, and the method comprises the steps: carrying out the high-precision scanning and dynamic feature extraction of a built-in fiber grating array of a cold plate according to a partition flow control signal, and obtaining a temperature field matrix and a dynamic thermal feature set; performing multi-dimensional fusion analysis and electrochemical coupling analysis on the temperature field matrix and the dynamic thermal feature set to generate a thermal coupling parameter set; performing real-time risk quantitative evaluation and risk topological mapping on the thermal coupling parameter set, and outputting a risk topological data set; and performing cooling intensity dynamic modulation according to the risk topology data set, and obtaining a partition flow redistribution instruction. According to the invention, fine monitoring of the cold plate-battery interface contact thermal resistance is realized through a high-precision pressure field reconstruction technology, the accuracy and reliability of pressure distribution detection are significantly improved, and the local overheating risk caused by an assembly gap is effectively eliminated.

The invention discloses a dynamic topology mapping and GPU heterogeneous flood coupling model real-time early warning method, and solves the problems of inflexible coupling of a one-dimensional model and a two-dimensional model, low calculation efficiency, poor dynamic response capability and the like in traditional flood simulation. A dynamic topology mapping mechanism is introduced to respond to emergencies such as dike burst and overflow in real time, the calculation efficiency is remarkably improved by utilizing a GPU heterogeneous parallel architecture, rapid and accurate simulation of the whole process of flood formation, evolution and submerging is achieved, reliable technical support is provided for flood control dispatching and emergency decision making, and the method is suitable for large-scale popularization and application. The method comprises the following steps: constructing a two-dimensional hydrodynamic model based on an unstructured grid, establishing a dynamic topology mapping mechanism and realizing GPU parallel numerical calculation; a two-dimensional model lateral connection coupling mechanism is constructed, dam overtopping flow is calculated in real time, and breach flow dynamic calculation of an improved DAMBRK method is carried out; multi-target parameter calibration, multi-dimensional model verification and one-two-dimensional model GPU collaborative parallel computing are realized.

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault traveling wave positioning method and system, and the method comprises the steps: synchronously collecting a transient signal through a distributed traveling wave sensing unit, and extracting an initial traveling wave arrival time; constructing an adaptive wave velocity model based on line physical parameters to calculate an equivalent propagation velocity; dynamically generating a traveling wave propagation tree according to the real-time switch state to determine an effective monitoring path; based on the propagation path and the actual arrival time, an optimization algorithm is adopted to determine a preliminary fault point; and calculating a consistency index of the polarity of the traveling waves of the adjacent nodes, starting a multi-hypothesis tracking mechanism in a preliminary point neighborhood when the consistency index is lower than a threshold value, performing confidence coefficient weighted evaluation by combining a traveling wave attenuation model, and outputting an accurate positioning result. According to the method, through cooperation of dynamic topology mapping, adaptive wave velocity correction and multi-source closed-loop verification, the precision, reliability and adaptive capability of fault positioning in a complex power distribution network environment are remarkably improved, and the operation and maintenance requirements of rapid self-healing are met.

The invention relates to a computer vision technology, in particular to an event camera target detection method based on Euclidean-hyperbolic motion perception, which comprises the following steps: down-sampling original event data, constructing an event graph by using the down-sampled data, and inputting the event graph into a double-space network for target detection; performing local perception feature extraction on the event graph by using a graph convolutional network based on a B-spline kernel function, and projecting the extracted features to a hyperbolic space; extracting global event dependency features from the local sensing features mapped to the hyperbolic space through a learnable curvature hyperbolic graph convolutional network; and decoding the global event dependency feature and inputting the decoded global event dependency feature into a sensing head, and outputting a target detection result by the sensing head. The method solves the problems of noise suppression, topological mapping and hierarchical expression in event data, has good application prospects and popularization value, and is particularly suitable for real-time target sensing tasks in complex dynamic environments such as automatic driving and robot navigation.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

The invention provides an electric power settlement visual configuration method and system based on node dragging. According to the method, a data type set and a computing node of power settlement are provided in a visual interface, and a node dragging operation is responded to analyze an operation rule expression and bind power transaction data; a unidirectional node connecting line is generated by dragging a connecting line between nodes by a user, a settlement dependence topology with a calculation node as a vertex and the connecting line as a directed edge is constructed, and a data reference path between the nodes is mapped; according to a node initialization calculation sequence of which the in-degree is zero in the topology, converting an expression of the binding relation into an executable script through a variable binding device; and driving the distributed framework to schedule tasks according to a topological sequence based on the calculation sequence, determining affected downstream nodes and data items in combination with topology when monitoring data change, performing incremental re-calculation according to the topological sequence, and outputting a settlement result. According to the invention, visual configuration and dynamic updating automation of power settlement are realized.

The invention relates to the technical field of display, in particular to a Mini LED backlight module intelligent control system and method of a distributed driving architecture, and the system comprises a processor unit, an LED driving chip, a Mini LED module and a sensor. The processor unit converts an RGB color space into a backlight brightness signal in an XYZ tristimulus value form based on a topological mapping theory; the LED driving chip receives the signal, controls the light intensity of the Mini LED module, and executes verification and compensation based on a Bayesian inference framework; the Mini LED modules are arranged in a distributed architecture, receive the brightness signal and output backlight brightness; a sensor collects backlight brightness signals of an RGB color space to form closed-loop feedback, a calibration coefficient matrix is generated through a polynomial fitting calibration algorithm optimized through matrix decomposition, and high-precision and high-reliability backlight control is achieved in combination with adaptive parameter adjustment and a dynamic compensation mechanism.

The invention relates to the technical field of digital twinborn management, and discloses a full-life-cycle digital twinborn management system and a modeling method. A model driving unit; a decision optimization unit; and a credible evidence storage unit. The modeling method comprises the following steps: S1, analyzing a product design bill of materials; s2, establishing a topological mapping relation between the sensor nodes and the virtual model area; s3, running Monte Carlo simulation to predict the defect probability; and S4, binding and uploading the new virtual model version and the physical product serial number. Aiming at the problem of data dispersion in the prior art, the system automatically converges three types of heterogeneous data of geometric dimension design, working condition manufacturing and maintenance monitoring into a shared cache pool through a main data table of a unified metadata structure, so that cross-stage semantic alignment and time sequence synchronization are realized, data format difference is eliminated, and data consistency is remarkably improved.

The invention relates to the technical field of fault positioning, in particular to a power distribution network line fault positioning method and system based on a fault indicator, and the method comprises the following steps: obtaining node current data, calculating a jump rate, screening an early warning node, recognizing an intersection section, determining a fault position, and generating a recognition result. According to the invention, through collecting periodic current data of each node fault indicator, combining node numbers and topological structure mapping, systematically establishing a current sequence table and calculating a current increment difference value in unit time, and through grade division and periodic stability identification of a jump rate, key nodes with early warning characteristics can be accurately screened. The method comprises the following steps of: acquiring a path intersection area, positioning a fault centralized distribution section by virtue of jump intensity trend change analysis of the path intersection area, and determining a fault initial position and a grade identifier in combination with a comparison relationship between a sampling time point and trigger delay between nodes, so that accurate identification and quick response of a multi-path fault are realized under a complex topological structure of a power distribution network.

The invention provides a data quality evaluation method and system. The method comprises the following steps: firstly, collecting three types of data sets of three-phase unbalance degree, free charge density distribution and line impedance decomposition; and then, carrying out space topology mapping on the charge density gradient extreme point of the cable joint area and the voltage phase angle, considering charge migration effect compensation, and calculating a corrected three-phase voltage unbalance parameter. Secondly, establishing a coupling factor matrix based on the separated line inherent impedance component and the load fluctuation impedance component, and performing time domain convolution operation on the coupling factor matrix and a voltage unbalance parameter to generate a distortion feature vector; and finally, calculating data credibility weight distribution of the global monitoring nodes according to the nonlinear correlation degree between the three-phase unbalance degree data and the distortion feature vector, and using the data credibility weight distribution to evaluate the quality of the low-voltage treatment data. According to the technical scheme provided by the invention, the accuracy and reliability of data quality evaluation can be improved.

The invention discloses a network attack path automatic generation and defense strategy optimization method, system and device and a medium, and relates to the technical field of network security, and the method comprises the steps: constructing a topology mapping model, obtaining a network architecture, an asset list and a dependency relationship, constructing a visual topology chart, building a vulnerability association analysis model, and combining a vulnerability scanning result. The method comprises the steps of obtaining a vulnerability knowledge graph, optimizing a path calculation process based on the vulnerability knowledge graph, establishing a path derivation model, generating an attack chain by applying a path derivation algorithm, optimizing defense logic, formulating an optimization step model, and establishing a real-time simulation feedback model based on the generated attack chain. And performing simulation implementation on the defense strategy through a multi-level simulation training scheme, and dynamically optimizing the defense strategy according to feedback data. According to the method, the crossing from passive protection to active prediction and from single-point defense to global optimization is realized, and the accuracy and adaptive capacity of network defense are remarkably improved.

The invention relates to the technical field of computer data processing and network security, in particular to an illegal character automatic cleaning system, which comprises a rule compiling module for monitoring rule change, performing semantic fusion and topological mapping on a rule set, constructing a deterministic finite automaton and mapping the deterministic finite automaton into a state transition table; the state switching module is used for constructing a double-buffer context and operating lock-free switching through an atomic pointer to realize hot updating; the speculation execution module is used for carrying out vectorization pre-scanning based on a state transition table by utilizing single-instruction multi-data stream parallel loading, identifying a walk path, falling into a safe state for releasing and falling into a trap state for triggering external verification; the self-adaptive feedback module is used for counting trap state triggering frequency, generating a rule allergy report and dynamically adjusting the size of a read fragment; according to the method, the contradiction between rule flexibility and execution efficiency is solved, and high-performance cleaning based on speculative execution is realized.

The invention relates to the technical field of data processing, and discloses a gateway service integrated management method and system based on an FTTR demand. The method comprises the following steps: carrying out topology scanning on a test signal of an optical time domain reflectometer to obtain a room-level FTTR topology mapping table, carrying out feature extraction on service traffic of each room according to deep packet inspection to obtain a room function identification tag library, and carrying out attenuation compensation on a corresponding room optical port by an optical power meter to obtain an FTTR link quality evaluation matrix, and bandwidth allocation is carried out through a reinforcement learning algorithm to obtain a room-level QoS strategy configuration set, and building data interaction is carried out based on an MQTT protocol to obtain an FTTR gateway service integrated management scheme. According to the invention, the intelligent level of FTTR network room-level management and the resource allocation efficiency are improved. According to the invention, the real-time performance of optical fiber link quality monitoring and the collaboration of building network management are improved.

The invention relates to the technical field of remote collaborative embedded development, and discloses a remote collaborative embedded development system and method.The remote collaborative embedded development method comprises the steps that developer editing behavior data is received, a multi-dimensional intention vector field is constructed, and intention vector representation is obtained; embedded source codes are read, code structure topological mapping is executed to form code structure topological representation, the embedded source codes and the code structure topological representation are fused to create a unified intention-code space model, conflict entropy distribution is calculated to generate a distribution diagram, a self-organizing optimization method is adopted to generate an optimal code merging scheme, and an intention visualization interface is constructed to form a collaborative environment; the core technical problem of remote collaborative development is effectively solved, the development efficiency and the code quality are improved, the conflict management capability is enhanced, collective intelligence formation is promoted, and new member integration is accelerated.

The invention discloses an intelligent tea mat tasting system based on multispectrum and deep learning, and belongs to the technical field of artificial intelligence, the system comprises a tea mat body, a multispectral sensor, a PLC and a terminal device, the multispectral sensor is arranged at the bottom of the tea mat body and comprises two light sources and two light sensing elements, and the PLC is connected with the terminal device; the PLC receives light signals, converts the light signals into electric signals and transmits the electric signals to the terminal device, and the terminal device comprises a multi-channel signal processing module, a peak value feature extraction module, a chaotic dynamics analysis module, a quality mapping module, a multi-standard comparison module, a probabilistic reasoning module, a recommendation generation module and a display module. A tea soup quality model is established through nonlinear feature extraction of multi-channel spectral signals in combination with chaos dynamics analysis, and tea quality evaluation is realized by utilizing three-dimensional flavor topological mapping and multi-standard library comparison, so that objective quantitative evaluation of tea quality is realized.

The invention discloses a rapid modeling method based on topological mapping, and the method comprises the following steps: setting a plurality of measurement sites at an equipment site, and collecting the geometric position information and point cloud data of equipment through a three-dimensional laser scanner; preprocessing the collected point cloud data, including noise point removal, point cloud segmentation, equipment point cloud clustering and data simplification; based on the point cloud data, the topological relation of the devices is automatically identified, and a topological relation graph between the devices is generated; mapping the topological relation graph into a three-dimensional digital model of the equipment, and automatically generating a three-dimensional model of the equipment by using template matching and component matching technologies; and according to the identified topological relation graph, integrating the equipment model and the connection relation thereof to form a complete digital model of the transformer substation or the equipment system. Through automatic processing of point cloud data, intelligent identification of a topological relation and a component matching technology, limitation of traditional modeling is broken through, and rapid, accurate and automatic equipment modeling is achieved.

The invention discloses a data stream processing method and device, electronic equipment and a storage medium, and relates to the technical field of distributed storage. According to the data stream processing method and device, the data stream transmission mode is pre-judged to be intra-node transmission or cross-node transmission based on a hardware topology mapping table, and read-write caches are dynamically allocated according to the pre-judged data stream transmission mode; data received by the front-end network card is directly written into a controller memory buffer area of the target equipment through point-to-point direct memory access for data streams transmitted in the nodes, and is read and forwarded from the target equipment by the rear-end network card; the sending cache and the receiving cache of the data stream transmitted across the nodes are locked to the non-uniform memory access node to which the target network card belongs, and the data are sent to the target node through the remote direct memory access register memory, so that the problem that the data stream processing is not optimized in combination with hardware topology characteristics can be solved; the technical effects of reducing data replication overhead, avoiding redundant access across hardware nodes, improving data stream transmission efficiency and realizing end-to-end zero replication transmission are achieved.

The invention relates to a multi-fault real-time early warning system for a power distribution cabinet, and relates to the technical field of power distribution cabinet detection. Partial discharge, mechanical vibration, insulation dielectric loss and environmental parameters are synchronously acquired through a distributed sensor array; performing electromagnetic interference filtering and wavelet noise reduction preprocessing on the original data, and extracting the characteristics of pulse frequency, vibration kurtosis, dielectric loss change rate and the like; a'rule base + random forest 'mixed model is adopted to identify fault types and confidence; fault positioning with the precision of + / -3cm is realized based on sensor delay inequality and a triangulation positioning algorithm, and fault components are determined in combination with a component topology mapping table; early warning levels are divided according to fault types, confidence coefficients and component importance, and customized reports are dynamically pushed; a standard signal is injected every 24 hours to generate a compensation coefficient, and the drift error of the sensor is corrected in real time.

The invention relates to a gradient-based tactile topology mapping model construction method. The method comprises the steps of obtaining functional imaging data and structural imaging data of magnetic resonance scanning of a testee; constructing a voxel-level functional connection matrix based on a voxel space in the functional imaging data; constructing a vertex-level structure connection matrix based on a cortex space in the structure imaging data; based on a gradient dimension reduction analysis processing method, matrix similarity calculation and dimension reduction analysis are carried out on the function connection matrix and the structure connection matrix, and a plurality of gradient components and variance interpretation quantities are obtained; and on the basis of the variance interpretation quantity, determining a functional main gradient and a second structural gradient, and constructing a tactile topology mapping model in combination with a tactile topology distribution rule. According to the method, the problem that the current tactile perception topological mapping is not accurate is solved, and more accurate mapping of tactile perception and brain activation is realized.

The invention relates to the field of automation of computer-aided design, and discloses an automatic document generation system, which comprises a data input analysis module used for reading a structured data file and extracting a project parameter and an operation instruction; the trigger rule analysis module is connected with the data input analysis module and is used for analyzing a special symbol defined by a user based on the operation instruction and constructing a multi-stage trigger rule tree; the dynamic topology mapping engine is connected with the trigger rule analysis module and used for modeling a template document into a directed acyclic graph and generating an optimized block operation instruction sequence through matrix operation based on the operation weight of the trigger rule tree; and the multi-version adaptive engine is connected with the dynamic topology mapping engine. According to the method, through a version perception decomposition and containerization isolation mechanism of the multi-version self-adaptive engine, the compatibility problem caused by object structure difference of different versions of AutoCAD files is solved.