671 results about "Graph theoretic" patented technology

The invention relates to the technical field of intrusion detection, in particular to a network threat detection method and system, and the method comprises the following steps: building a threat path logic diagram through collecting field dependency items, action trigger timestamp items and action propagation hop count items of an attack behavior chain, and matching field dependency items among nodes based on a graph theory algorithm to obtain a threat path logic diagram; and detecting a mutual exclusion logic field combination, and generating a logic diagram structure with a connecting edge and a mutual exclusion mark. In the method, a threat path logic diagram is constructed by fusing field dependence, action timestamps and propagation hops, graph theory identification field mutual exclusion combination enhances cross-protocol attack chain analysis, and hidden Markov modeling state transition probability verifies time sequence continuity and path length. And performing dynamic time warping alignment on forward and reverse instruction sequences to extract semantic offset, overlapping rate and time sequence entropy, and performing non-linear score classification based on an isolated forest to detect an adversarial sample, topological structure analysis, time sequence verification, instruction alignment and non-linear classification to cooperatively identify a composite attack with field mutual exclusion and time sequence confusion.

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The invention relates to the field of clean energy, and provides a distributed secondary control method based on deep reinforcement learning, and the method comprises the steps: S1, deploying a DDPG intelligent agent in a distributed micro-grid, and enabling the DDPG intelligent agent to be used for secondary control; s2, establishing a communication graph model of the distributed micro-grid based on a graph theory, so that each distributed power supply in the distributed micro-grid only exchanges control signals and voltage information with the adjacent distributed power supplies; and S4, regulating and controlling the output power of the distributed power supply by using the intelligent agent, and dynamically regulating the frequency and voltage secondary control signal according to the decision of the intelligent agent. Therefore, the dynamic adaptability and the system-level reliability of the distributed power system are improved.

The invention provides a water network monitoring method and system based on intelligent water supply and drainage, and the method comprises the steps: obtaining multi-source heterogeneous monitoring data of a water network in real time, carrying out the unified format conversion and time-space synchronization processing of the multi-source heterogeneous monitoring data based on a protocol adaptive fusion mechanism, and obtaining a standardized multi-source time series data set; performing exception identification processing on the standardized multi-source time sequence data set based on the time-varying statistical characteristics and the dynamic control limit to obtain a candidate exception event set; performing multi-sensor cross validation processing on the candidate abnormal event set based on a pipe network topological space association knowledge graph to obtain a high-confidence abnormal event set; performing reverse scene matching analysis on the high-confidence abnormal event set based on a hydraulic numerical simulation model to obtain a preliminary positioning area; performing positioning optimization processing on the preliminary positioning area based on a graph theory pressure wave propagation algorithm to obtain an abnormal position; and performing multi-dimensional fusion display processing on the abnormal position to generate a monitoring report. According to the method, accurate positioning of the abnormal position is realized.

The invention discloses an urban healthy community space intelligent assessment method and system, and relates to the technical field of urban space intelligent assessment and healthy community planning, and the method introduces space grid division, street network modeling and graph theory analysis technologies, achieves the high-precision modeling and reachability simulation of an urban community space, and achieves the intelligent assessment of the urban community space. And the coverage capability and the service balance of the 15-minute walking life circle of residents can be accurately evaluated. According to the method, the reachable path network taking each grid unit as a starting point can be dynamically constructed, and the service accessibility level and the comprehensive service scoring index of each grid are quantitatively evaluated according to the quantity vector of the resident reachable service facilities. By setting dual thresholds of coverage rate and score, a'potential function blind area 'is intelligently identified, and a'cold space grid' is accurately discriminated by further combining a resident popularity activity sequence, so that scientific support and decision basis are provided for subsequent city updating, service facility layout optimization and community health space transformation.

The invention discloses a truck-mounted crane group operation safety monitoring system and method based on multi-source perception and collaborative decision, and particularly relates to the field of safety monitoring, the truck-mounted crane group operation safety monitoring system comprises a distributed perception module, an edge calculation module, a graded early warning and execution module, a digital twin platform module and a man-machine interaction module; collected multi-source data are fused through Kalman filtering and then transmitted to an edge computing node; generating a dynamic safety envelope region based on an adaptive kinematics model, predicting a group conflict risk through a graph theory algorithm, and calculating collision time; the digital twin platform fuses a BIM model and real-time data, hoisting path rehearsal, load swing prediction and foundation bearing capacity visualization are carried out, an improved RRT * algorithm is adopted to plan a collision-free path, and a Lagrange dynamics model is adopted to predict a swing track; the grading early warning module triggers differential response according to the risk grade; and risk warning and path guidance are provided through the AR-HUD and the three-dimensional situation billboard.

The invention provides a method for optimizing the mechanical property of a bio-based composite material based on nano modification, and belongs to the technical field of high-molecular compound compounds.The method includes the steps that wood flour is subjected to silane coupling agent modification pretreatment, a functionalized nano particle dispersion liquid is prepared, the dispersity is evaluated through a graph theory model, and the mechanical property of the bio-based composite material is optimized. The modified wood flour and polyethylene are subjected to melt blending in a twin-screw extruder in proportion, the functionalized nanoparticle dispersion liquid is synchronously added, real-time regulation and control are performed by using an intelligent dispersion optimization model, and the network structure integrity is evaluated through gridding performance indexes. The tensile strength, the bending strength and the impact strength are comprehensively optimized by adopting a multi-objective optimization game model, the process parameters such as the nano-particle addition amount, the mixing temperature and the mixing time are adjusted according to the optimization result, and the optimization is repeated until the preset target values are reached, so that the problems that the nano-particles are non-uniformly dispersed, local stress concentration points are formed, and the stability of the nano-particles is improved are solved. The overall mechanical property is influenced.

The invention provides a geological reservoir fracture segmentation method fusing a 3DU-Net graph attention mechanism and graph theory connectivity detection and a system thereof, and aims at solving the technical problems of multi-modal data registration, 3D U-Net graph attention feature extraction, graph theory connectivity detection and the like. The problem of insufficient geological reservoir fracture segmentation precision caused by data heterogeneity and connectivity abnormity is effectively solved, high-precision segmentation of a complex geological reservoir fracture network is realized through accurate alignment of multi-modal data and a graph attention feature extraction and graph theory connectivity detection technology based on 3D U-Net, and the method has the advantages of high precision and high efficiency. The problems of local misjudgment and abnormal connectivity caused by the fact that a traditional method only depends on amplitude or texture features are effectively solved, and therefore segmentation precision and robustness are remarkably improved.

The invention discloses a data communication method, system and device under a disaster condition, and belongs to the field of power systems, and the method comprises the steps: determining a graph structure model based on a current network topology structure and network topology change information under the disaster condition, carrying out the calculation through a graph theory algorithm, and determining a transmission node and a standby path; if the node connection is abnormal, performing multi-path planning calculation on the standby path to obtain a planning result; splitting the power grid information packet into a plurality of data packets, performing data transmission through a multi-path planning result, and monitoring a transmission progress and arrival confirmation information in real time; and if it is monitored that the transmission is overtime or lost, updating the planning result based on a preset path switching rule to obtain a target planning result, and performing rerouting processing on the remaining data packets according to the target planning result to realize data communication under the disaster condition. And efficient, reliable and stable operation of data communication of the power grid under disaster conditions can be realized.

The invention provides a key user identification method and system in a social network, and relates to the technical field of user identification, and the method comprises the steps: constructing a social network graph model according to social network data; calculating a basic centrality index through a graph theory algorithm; dividing the social network data into a plurality of communities, and identifying local roles of user nodes in the plurality of communities; calculating a local centrality index of the local role; generating a global score and a local score; and performing score ranking on the global scores and the local scores, and screening key user nodes. According to the method, through two-dimensional analysis fusing global centrality indexes and local role features, the problem that cross-community bridging nodes and local core users are insufficient in recognition in a traditional method is effectively solved, and the comprehensiveness and accuracy of key user recognition are remarkably improved; and through dual verification of time sequence feature analysis and rule engine verification, the abnormal state of the disguised high-influence user is effectively identified, and the result reliability is remarkably improved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention discloses a heterogeneous unmanned cluster collaborative hunting method, device and equipment and a storage medium, and relates to the technical field of agent automatic control, and the method comprises the steps: determining a heterogeneous unmanned cluster, environment information and constraint conditions of a multi-agent collaborative hunting system, and constructing a physical model of the multi-agent collaborative hunting system; at the beginning of each decision cycle, modeling a task allocation problem of multiple agents into a'maximum flow-minimum cost 'graph theory problem, constructing a task allocation directed graph, determining an individual capture target corresponding to each agent, and based on a PPO framework, taking the maximum return of the corresponding individual capture target completed by each agent as an individual target, and performing task allocation on each agent; and with the maximum return of all captured targets completed by the heterogeneous unmanned cluster as a global target, an intelligent agent is guided to learn an optimal hunting strategy. According to the method, the problem of obvious insufficiency in the aspect of processing heterogeneous capability constraints in the prior art is solved.

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention relates to a river network grading system and method based on a graph theory and confluence cumulant, and belongs to the technical field of water conservancy projects and geographic information. Comprising the following steps: acquiring original DEM data and water system data, and converting the water system data to a preset projection coordinate system; extracting a primary river network based on the DEM data, and screening the primary river network in combination with the length of the river network and the area of the drainage basin to obtain a simplified river network; river network flow calculation: carrying out flow assignment on each river network element in the simplified river network based on the confluence cumulant; graph theory modeling: abstracting the simplified river network into a directed graph model; and river network grading: reverse tracing is carried out by taking the water outlet as a starting point, and the hierarchical relationship between the trunk and the branch is determined by combining the in-degree / out-degree and the edge weight of the node, so that river network grading is realized. According to the method, river network grading is upgraded from morphological description to hydrological function description, and the method is suitable for high-precision river network refined analysis, intelligent water conservancy emergency management and other scenes.

The invention discloses a three-dimensional fracture network seepage and solute transport numerical simulation method based on topological pruning, which comprises the following steps of: analyzing a fracture network topological structure from global and local scales by applying a graph theory, establishing a quantitative relation between fracture connectivity and an underground flow field, and accurately describing an influence mechanism of topological characteristics on a seepage field; on the basis of network topology analysis, the key fracture sub-network is extracted through a pruning method, the key fracture sub-network is used for replacing a complete network for high-precision numerical simulation, and the calculation efficiency is remarkably improved while the calculation precision is guaranteed. The invention provides an efficient and reliable solution for the problems of seepage and solute transport in a hard rock fractured medium.

The invention discloses a coal rock instability precursor identification method and system based on fracture topological parameters, and the method comprises the steps: obtaining sound wave data through micro-seismic monitoring, and extracting coal rock fracture core parameters in combination with seismic source positioning and seismic source mechanism inversion; building a fracture network topology model based on a graph theory method by considering the geometric dimension and direction characteristics of the fracture, and obtaining fracture space-time topology evolution parameters to build a multivariable time sequence topology characteristic data set; introducing a multivariable time sequence graph model MTAD-GAT, dynamically capturing a structural dependency relationship among topological parameters by using a graph attention mechanism, mining collaborative anomaly characteristics of the structural dependency relationship, and capturing an evolution trend of the structural dependency relationship in combination with a time sequence modeling module; and fusing the prediction error and the reconstruction error to form an abnormal score as an instability precursor index, extracting covariant features and evolution trends of a plurality of key topological indexes in the precursor stage, and assisting in distinguishing an instability critical state, thereby realizing unsupervised automatic identification and intelligent early warning of the topological precursor.

The invention provides an unmanned aerial vehicle-unmanned vehicle combined formation cooperative control method and system, and relates to the technical field of vehicle-vehicle cooperation, and the method comprises the steps: taking a virtual unmanned aerial vehicle as a leader, taking the unmanned aerial vehicle and the unmanned vehicle as followers in a unified manner, setting a fixed formation offset for each follower, and defining a communication topological relation based on a graph theory; the method comprises the following steps: acquiring an actual measurement state vector of a sensor in real time, processing a current estimation state through a constructed radial basis function neural network observer, outputting a disturbance estimation value in combination with a weight matrix, updating the estimation state according to a core equation, and judging whether to update the weight matrix or not according to an observation error; calculating the expected state of the follower according to the reference trajectory of the leader and the fixed formation offset, calculating the formation error, constructing an event trigger communication condition, judging whether the condition is met or not, enabling the follower to interact the state and the error according to the communication topology only when the condition is met, or else, continuing to use the previous trigger data; and finally, current control input of the follower is calculated through a distributed control law.

The invention relates to the technical field of image processing, in particular to a vehicle-mounted battery gluing defect detection method based on machine vision, and the method comprises the steps: obtaining an original image of a vehicle-mounted battery gluing region, generating color smoothness through a dual-channel variance fusing brightness and saturation, constructing structure tensor analysis to determine local gradient disorder degree, and determining the gluing defect of a vehicle-mounted battery. The method comprises the following steps of: generating abnormal confidence of pixel points by synthesizing color smoothness and local gradient disorder, remodeling gray difference among the pixel points by using the abnormal confidence as a physical modulation factor to obtain a physical remodeling weight of an edge, and realizing pseudo gradient shielding in a graph theory segmentation framework by using the physical remodeling weight to obtain a pseudo gradient shielding result. And accurate segmentation is carried out on the original image so as to realize gluing defect detection. According to the method, the complete connectivity of the glue overflow defect area is improved, the situation that the chaos texture in the defect is misjudged as the boundary is avoided, and the gluing defect detection result is more accurate.

The invention discloses a highway super-large-scale road network management and control response method based on heterogeneous multi-agent reinforcement learning, and the method comprises the steps: dividing a super-large-scale road network based on a graph theory and a fuzzy C-means clustering algorithm to form different sub-road network agents, and constructing a multi-agent reinforcement learning method to search for an optimal strategy for each sub-road network. Traffic event parameters are abstracted, macroscopic, mesoscopic and microscopic traffic state input of each intelligently formulated sub-road network and a lane control strategy set of the sub-road network are determined, and a reward function form is comprehensively determined through average speed, accident punishment and vehicle emission. Furthermore, on the basis of an entropy function of a maximized joint strategy and meeting security constraints, a heterogeneous agent soft actor-commentator algorithm is constructed to construct a commentator and actor network, and action constraints are met during full exploration; and finally, asynchronously updating the network parameters of the actor, and carrying out soft updating on the reviewer network so as to iteratively obtain an optimal sub-road network lane strategy.

The invention discloses a low-voltage equipment management method and system for a power distribution network, and the method comprises the steps: S1, collecting the operation parameters of all low-voltage equipment in real time through an Internet of Things terminal node disposed at the low-voltage side of the power distribution network; s2, on the basis of the real-time data in the S1, combining dynamic change characteristics of the power distribution network topology, constructing an adaptive topology network model through edge computing nodes, and dynamically updating a connection relation and a load path between equipment by the model by adopting a graph theory algorithm; s3, inputting the real-time data in the S1 into a preset fault diagnosis model, identifying abnormal equipment and positioning a fault type by the model through fusion of a deep belief network and a random forest algorithm, and predicting a health degree attenuation trend of the equipment at the same time; and S4, according to the topology model in the S2 and the fault diagnosis result in the S3, generating a resource optimization allocation strategy based on a dynamic programming algorithm, including adjusting the switching priority of standby equipment, reconstructing a load allocation path, and sending a control instruction to a specified terminal through a communication module.

The invention relates to the technical field of intelligent substation fault positioning, and discloses a fault positioning method based on Sdd-Scd fusion, which comprises the following steps of: 1, analyzing an SDD file and an SCD file of an intelligent substation, extracting physical link information and logic link information, and establishing a mapping relationship between a physical port and logic signal subscription; 2, based on the mapping relation between the physical link and the logic link, a fault propagation matrix M is constructed, and elements of the matrix M represent the fault propagation probability between equipment nodes; the fault propagation model is constructed based on SDD-SCD fusion, the fault propagation probability is calculated through the fault propagation matrix M, a multi-fault scene under a complex topological structure can be effectively handled, the limitation that the prior art is only based on an expert rule or a simple graph theory algorithm is overcome, and the accuracy and reliability of fault positioning are improved.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention provides a multi-agent cooperative control method based on a graph theory, and the method comprises the steps: constructing a communication topology of each agent according to the graph theory, and generating a neighbor set; generating a weight matrix according to the neighbor set; generating a control signal according to the weight matrix and the state data of the neighbor agent; acquiring environment data and shared state data according to the control signal, and generating a state feature set; adjusting the motion trail of each agent according to the state feature set, and generating trail data; according to the trajectory data, all agents are coordinated to complete a collaborative task, and a consistency result is generated; and adjusting control parameters according to the consistency result, and generating a grouping consistency result.

The invention discloses a power system optimization method based on topology compression, and the method comprises the following steps: 1) constructing a power system topological graph: abstracting a power system into a weighted graph, representing power equipment by nodes, representing power transmission paths by edges, distributing electrical attributes and weights for the nodes and the edges, and providing a basis for subsequent topology compression and optimization; 2) analysis and implementation of topological compression: identifying redundant nodes and edges through cut point and bridge edge algorithms in a graph theory, and simplifying a topological structure by merging or removing redundant parts; 3) power flow analysis and optimal scheduling: performing power flow analysis on the compressed topological structure, and realizing power scheduling and load distribution by using an optimization algorithm to improve the operation efficiency of the system; and 4) dynamic adjustment and fault recovery: updating a topological structure and node attributes based on real-time data, and quickly responding to load fluctuation and equipment faults of the power system through an optimization algorithm. The method has the beneficial effects that the calculation complexity can be remarkably reduced, and the optimization effect and the scheduling efficiency are improved.

A honeycomb-shaped active power distribution network topological structure selection and optimization method based on a graph theory comprises the following steps: taking line impedance and rated apparent power as constraints, constructing a multi-target weight factor, adopting an improved spectral clustering algorithm to cluster and distribute power grid nodes, and transiting a micro-grid group after cluster division into a honeycomb-shaped active power distribution network; abstracting each micro-grid as a honeycomb distribution network single node, constructing a connection relation mathematical representation of the HSPH and the micro-grid, and realizing sparse matrix method modeling of the honeycomb active power distribution network; optimizing a site selection strategy of the HSPH by taking a maximized HSPH return on investment index and a system stability index as a target function; and optimizing the energy storage capacity of the HSPH based on the net load time sequence data of the micro-grid group, and finally generating an optimal topological structure of the honeycomb-shaped active power distribution network. And through honeycomb topology and HSPH addressing and sizing optimization, the new energy consumption capability of the power distribution network is improved, and the economy, reliability and flexibility of the power distribution network are improved.

The invention provides a structural engineering bearing capacity field detection method, and belongs to the technical field of structural engineering detection.The structural engineering bearing capacity field detection method comprises the steps that a multi-axial acceleration sensor and a strain gauge array are installed on the surface of a to-be-detected structural member, and an intelligent loading device is adopted to apply incremental loads; a mobile data acquisition terminal is utilized to acquire vibration response data and strain response data in real time, modal parameters are extracted based on a frequency domain analysis method to establish a load-modal parameter relation curve, the bearing capacity of a component is analyzed and calculated, a graph theory shortest path algorithm is adopted to optimize load transmission path analysis, and the load transmission path analysis is optimized. The bearing performance of the component is accurately evaluated through the structural response prediction model based on the Transform architecture, the technical integration of multi-dimensional detection data fusion, intelligent loading control, quantitative bearing capacity evaluation and accurate performance prediction is realized, and the technical problem of insufficient detection precision of the bearing capacity of the structural component is solved.

The invention provides a method for constructing an arch-shaped large-span shell steel roof, and belongs to the technical field of arch-shaped large-span shell steel roof construction. A temporary supporting system is built, and distributed optical fiber sensors are arranged to monitor the lifting process of a main arch; a cable force distribution scheme of a cable supporting system is calculated by adopting a double-layer game optimization model to realize cooperative control of stress uniformity and form precision, tensioning force is applied by using an intelligent tensioning system in stages, and a crane moving path is planned in combination with a path optimization algorithm based on a graph theory to realize graded unloading of a temporary supporting system. The technical problem that it is difficult to achieve structural stress uniform distribution and form accurate control of an arched large-span shell steel roof at the same time in the tensioning process of a cable supporting system is solved.