517 results about "Graph theoretic" patented technology

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 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 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 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 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 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.

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.

The invention discloses a carbon dioxide seepage dominant channel judgment and permeation enhancement effect evaluation method and system, and the method comprises the following steps: obtaining three-dimensional image data through the three-dimensional CT scanning of a rock sample before and after a CO2 mineralization storage experiment reaction, extracting a crack network as a topological structure center line skeleton, calculating the topological indexes of a network structure, and calculating the permeability enhancement effect of the network structure; a fracture network topological structure model constructed based on a graph theory is coupled with a discrete fracture network model for CO2 flow simulation, an equivalent permeability tensor is obtained, an improved Dijkstra shortest path algorithm is adopted, spatial distribution of CO2 seepage dominant channels is obtained, dominant channel characteristics before and after transformation are compared with the equivalent permeability tensor, the seepage enhancement effect is quantitatively evaluated, and the CO2 seepage enhancement effect is obtained. And changing injection conditions to perform multi-group simulation under different working conditions, calculating the seepage enhancement effect and the mineralization reaction rate under different injection conditions, establishing a quantitative evaluation model of the mineralization sealing permeation enhancement effect, and realizing sealing effect prediction and seepage enhancement effect evaluation.

The invention belongs to the field of construction of a protein structure on a cryoelectron microscope density map, and relates to a method for constructing a protein structure from a cryoelectron microscope density map by combining de novo modeling and structure prediction, a computer device, a readable storage medium and a program product. According to the method, the atomic probability and the amino acid type are predicted through the three deep neural networks respectively, full-atom optimization is carried out, the output result is combined with the graph theory, optimization and geometric algorithms to jointly assist protein structure modeling, and protein structure information can be mined to the maximum extent. According to the method, de novo modeling can be carried out in the absence of full-length structure data of the protein monomer, integrated modeling can also be carried out under the condition of inputting the full-length structure data of the protein monomer, the application scene is wide, the structure of the protein compound can be automatically constructed in a cryoelectron microscope density map with middle and high resolution, and the method is suitable for popularization and application. And for a region with poor local resolution in a traditional method, the modeling precision can be remarkably improved.

The invention discloses a cascade power station connectivity fish monitoring method and system, and the method comprises the steps: collecting the water flow speed, water temperature, dissolved oxygen and other hydrological parameters of a water area cross section, the fish activity frequency data, the gate dam opening and closing state, and the pump set operation parameters through a sensing assembly; dividing a potential migration area; abstracting a water area node as a graph theory vertex, abstracting a connected state as an edge weight, and constructing a model to calculate node connectivity; according to the pump set parameters and connectivity indexes, calculating water area node connectivity probabilities under different pump set operation conditions; the number, body type distribution and motion trail of fishes are obtained through an image and acoustic module and are associated and matched with a migration area and a connectivity probability; and integrating multi-link data to generate a connectivity and fish activity relevance monitoring result. Functional units are arranged in all links of the corresponding method of the system, all the units operate cooperatively, cascade power station water area connectivity and fish activity association monitoring precision are improved, and data support is provided for drainage basin ecological protection.

The invention relates to the technical field of network communication, in particular to a network resource entry method and system based on a topology self-discovery technology, and the method comprises the following steps: a detection source device generates a detection data packet containing a device identifier, a TTL value and an accessed device list, and sends the detection data packet to an adjacent device hop by hop; after the intermediate equipment receives the probe packet, if the intermediate equipment does not exist in the accessed list, adding equipment information and a connection relationship and continuing forwarding until the TTL value is exhausted or no available port exists; the method has the beneficial effects that hop-by-hop collection of network equipment information is realized by designing an intelligent detection data packet, a dynamic network topology is constructed in combination with a graph theory algorithm, deep resource attributes are extracted by utilizing a machine learning technology, and finally automatic input and real-time updating of network resources are realized. According to the method, through detection-construction-extraction-entry-visualization five-dimensional architecture, the problems of low efficiency and poor real-time performance of traditional manual entry are solved.

The invention discloses a cooperative control and disturbance suppression method for a multi-axis servo system, and relates to the technical field of cooperative and disturbance control of servo systems, position and speed signals of each servo axis are acquired in real time, a cooperative error vector is calculated based on a graph theory model, the vector reflects the deviation between the state of each axis and an expected cooperative trajectory, and the cooperative control and disturbance suppression of the multi-axis servo system are realized. And synchronous movement is ensured. The collaborative error is used for generating a preliminary control signal, and a proportional, differential and integral term combination strategy is adopted to quickly correct the error and eliminate the steady-state error. Meanwhile, disturbance vectors are estimated through an observer, and external interference or internal changes are compensated. And after the preliminary control signal and the disturbance compensation are added, amplitude limiting processing is carried out to prevent saturation of the driver. The control parameters are adjusted through an off-line optimization algorithm to minimize errors and disturbance influences, so that the tracking precision, the anti-interference capability and the adaptability of the multi-axis servo system are improved, and the method is suitable for the fields of high-precision manufacturing, robot control and the like.

The invention provides a liver tumor early diagnosis method and system based on artificial intelligence, and relates to the technical field of biomedical engineering, and the method comprises the steps: obtaining original information metadata related to the liver, the original information metadata comprises image data original information metadata and biomarker original information metadata, the image data comprises liver CT (computed tomography) and MRI (magnetic resonance imaging) image data, the biomarkers comprise serum tumor marker levels, and the physiological and environmental factors of the patients comprise age, gender, dietary habits and genetic backgrounds of the patients. According to the method, the problems that a tumor in a small and complex area is easily missed or misdiagnosed in the prior art are solved, the path density is predicted through denoising, segmentation and feature extraction of image data preprocessing in combination with a path simulation model based on a graph theory and a Monte Carlo simulation method, and a potential tumor area can be accurately judged.

The invention discloses a control valve drawing part intelligent identification system, and belongs to the technical field of intelligent manufacturing and drawing identification, the system obtains a direction control valve drawing sample, combines a QATM algorithm, a YOLO model, EAST and TesseractOCR, and U-Net and a graph theory algorithm to carry out multi-modal feature extraction and joint identification, constructs a three-level classification matching system, and outputs an initial identification result; through label data conversion, manual sampling auditing, initial recognition standard construction, extended label data generation and comprehensive precision label data fusion, multiple times of iterative optimization of an initial model is achieved, and finally a high-precision detection model is output. Automatic and intelligent recognition of part structures, parameters and topological relations in control valve drawings is achieved; the drawing recognition efficiency and accuracy are remarkably improved, and the method is suitable for industrial drawing digitization and intelligent assembly application.

The invention discloses an assembly type machine room sub-module design system based on a BIM model and a three-constant system, and high-precision modular design of a machine room is realized through BIM parametric modeling, three-constant system performance analysis, graph theory module division, prefabricated member design, assembly process planning and CFD simulation verification. The system integrates the block chain and the knowledge graph technology, supports the prefabrication rate being greater than or equal to 85%, reduces the energy consumption by 25%, solves the problems of low construction efficiency and poor precision of a traditional machine room, and provides technical support for a smart building.

The invention discloses a method for exploring a heterogeneous catalytic reaction network based on a deep potential energy surface model, and the method comprises the steps: carrying out the accurate prediction of the free energy change of all possible element reactions on a catalyst through the combination of transfer learning and a density functional theory, and constructing a reaction network diagram based on a graph theory method; an improved Dijkstra method is used for searching a reaction network diagram to determine a reaction mechanism, and the selectivity of the catalyst is explored based on a Markov chain method. According to the technical scheme provided by the invention, when the CO2 reduction reaction mechanism on the monatomic alloy is explored, the prediction error MAE of the electron energy of each intermediate is only 0.16 eV, the calculated amount is reduced by 90%, the dominant path of the CO2 reduction reaction is visually displayed, and a new normal form is provided for complex mechanism exploration and catalyst screening.

The invention provides a power distribution network fault self-healing control method based on artificial intelligence, and relates to the field of power system intelligent control, in particular to a power distribution network fault self-healing technology, which comprises the steps of acquiring real-time operation state data of a power distribution network, extracting features to establish a fault prediction rule base, monitoring faults in real time and determining positions, and constructing a topological structure based on a graph theory algorithm. And calculating the load transfer capacity of the selectable reconstruction path, solving the multi-objective optimization function by adopting an improved particle swarm algorithm to obtain an optimal reconstruction scheme, generating a control instruction sequence to isolate a fault area, and recovering power supply of a non-fault area. The intelligent level of power distribution network fault processing is improved, the fault recovery time is shortened, and the power supply reliability is improved.

The invention relates to a GPU scheduling optimization method for task and node bidirectional modeling, which comprises the following steps: step 1, task modeling and classification, step 2, node feature modeling and scoring mechanism, step 3, priority scheduling of strong dependency tasks, and step 4, optimal matching scheduling of weak dependency tasks. And constructing an adaptive scoring matrix between the task and the node, and realizing optimal matching between the task and the node by means of a graph theory matching model. The method comprises the following steps: constructing a multi-dimensional feature modeling system, and abstractly expressing resource demand features of a computing task and computing power features of a computing node; and then, based on a multi-index comprehensive weighting mechanism, evaluating a matching relationship between the task and the node, fusing factors such as a task dependence structure and a task emergency degree, and executing fine distribution by adopting a hierarchical scheduling mechanism, so that efficient utilization of GPU resources and interpretable optimization of a scheduling result are realized.

The invention relates to the technical field of electronic design automation, and discloses a standard cell layout legalization method and system based on a graph theory constraint graph, and the method comprises the steps: building a bidirectional directed constraint graph based on an existing layout and a design rule, and building a directed edge between adjacent graph edges according to the design rule; traversing the constraint graph, detecting whether a directed edge which does not meet a constraint condition exists, and if so, identifying the directed edge as a constraint violation edge; for two end points of each constraint violation edge, searching a key path based on a depth-first search algorithm, and determining a maximum movable distance in combination with a greedy strategy; moving the endpoint to a legal position according to the critical path and the maximum movable distance, and updating the position of a related node in the constraint graph; and mapping the position changes of the nodes in the constraint graph back to the corresponding graph edges in the layout, and outputting the legalized layout, so that efficient and automatic repair of design rule violation is realized through the method, and the layout compliance rate and the area optimization effect are remarkably improved.

The invention provides a grounding grid corrosion state diagnosis method based on plate reduction, and belongs to the technical field of grounding grid corrosion state diagnos.The grounding grid corrosion state diagnosis method comprises the steps that a self-adaptive clustering algorithm based on the graph theory is adopted to decompose a grounding grid into meta-plates with the same topological structure, and a meta-plate corrosion working condition database is established; the method comprises the following steps: carrying out sweep frequency impedance measurement in a frequency range of 1000Hz to 10000Hz to obtain a frequency response characteristic curve, calculating an impedance modulus increase rate as a characteristic vector, carrying out sample expansion by using an SMOTE technology to establish a training data set, constructing a multi-scale frequency response characteristic fusion model based on a ResNet architecture to carry out training, dynamically adjusting a hierarchical fusion weight through a frequency response parameter adaptive adjustment function, and carrying out multi-scale frequency response characteristic fusion based on the ResNet architecture. Accurate identification of the corrosion branch position and the corrosion degree of the grounding grid to be diagnosed is achieved, and the technical problems that the grounding grid corrosion state diagnosis precision is low and the calculation complexity is high are solved.

The invention provides a construction method for installing a large-span special-shaped beam curved template system, and belongs to the technical field of building construction. According to the construction method, accurate three-dimensional modeling is conducted on the shape of a special-shaped beam through a special-shaped curved surface parametric modeling algorithm, and material prefabrication is guided; eight oscillation excitation devices and twelve vibration monitoring devices are optimally arranged by using experimental design and a graph theory analysis method to form a dynamic monitoring network, vibration response data are analyzed in real time by using a wavelet transform monitoring algorithm, and the energy ratio of each frequency band is calculated as a quality evaluation index. A game optimization model is established, and according to different ranges of energy ratios, the support spacing is automatically adjusted, a back ridge fixing mode is combined or a supporting system is reinstalled, so that a comprehensive quality assurance system of accurate geometric modeling-dynamic state monitoring-intelligent parameter adjustment is constructed. The technical problem that the installation quality of a large-span special-shaped beam curved template system is difficult to guarantee is solved.

The invention relates to a motorcade consistency formation control method and device based on a dynamic event triggering mechanism, electronic equipment and a storage medium. The method comprises the steps of generating a communication topological graph of a multi-agent system composed of a preset number of unmanned vehicles according to the theoretical basis of a graph theory, and building a mathematical model of each unmanned vehicle; based on the mathematical model, designing a combined measurement function, a measurement error and a consistency controller of the multi-agent system; distributed event triggering conditions and self-triggering conditions are designed based on Lyapunov parameters; according to a dynamic event triggering condition and a self-triggering condition, a triggering time interval expression of the intelligent agent is obtained through calculation; by constructing a multi-agent system consistency controller and combining distributed event triggering conditions and self-triggering conditions, multi-vehicle formation consistency control is completed. According to the fleet consistency formation control method disclosed by the invention, accurate and efficient control of a fleet formed by multiple unmanned vehicles can be realized.