50 results about "Fixed topology" patented technology

The invention relates to the technical field of bridge structure health monitoring, and discloses a bridge key position disease prediction method and system based on a graph convolutional neural network. According to a bridge design drawing and real-time sensor data, a dynamic topological graph reflecting an actual mechanical state of a bridge is constructed, and a real-time incremental graph convolutional network is designed to realize continuous optimization of a model. The mechanical relationship of the components is quantified into dynamically adjustable edge weights, so that the graph structure can reflect the real state of bridge load transmission in real time. The dynamic representation mode breaks through the limitation of a traditional fixed topological graph, so that the model can capture a disease propagation path more accurately. Meanwhile, an online gradient updating mechanism ensures that model parameters are continuously optimized along with new data, the inherent problem that a traditional offline training mode is difficult to adapt to data distribution drift and data lag is avoided, and prediction precision and timeliness are further improved.

The invention belongs to the technical field of geographic information data processing, and discloses a graph attention-based road slope surface displacement time sequence prediction method, which comprises the following steps of: obtaining surface displacement time sequence data of a plurality of monitoring stations and associated environmental influence data, and constructing a weighted adjacency matrix based on geographic positions of the monitoring stations, defining an initial spatial topological relation; carrying out feature fusion on the displacement data and the environment data, and constructing an attribute-enhanced feature matrix; respectively inputting the weighted adjacency matrix and the feature matrix into a graph convolutional network and a graph attention network for parallel processing; extracting structured spatial features by the GCN through a fixed topological structure, and generating a first feature representation; the GAT adaptively allocates a dynamic weight by using an attention mechanism, extracts a non-uniform spatial dependency feature, and generates a second feature representation; and fusing the two feature representations, inputting a time sequence modeling module to analyze time dependence, and finally outputting a surface displacement prediction result at a future moment. According to the method, the accuracy of surface displacement prediction is remarkably improved.

The invention belongs to the technical field of intelligent power distribution networks, distributed energy management and energy internet, and particularly relates to a transformer area micro-grid mutual aid regulation and control operation method and system considering a flexible interconnection device and topology flexibility. Acquiring basic data; constructing a node power flow balance model containing a flexible interconnection device, taking energy storage of each transformer area as an independent regulation and control unit, and establishing an energy storage operation model in combination with charge state constraint, capacity limitation, charge and discharge mutual exclusion and life constraint; virtual commodity flow constraints and spanning tree structure constraints are introduced, and a radiation type topology model capable of being dynamically switched is constructed; constructing a comprehensive optimization model, and solving by utilizing power flow-topology-energy storage coupling constraint to obtain a multi-transformer-area global optimal scheduling scheme; and outputting an optimal topological structure, a flexible interconnection device power instruction and an energy storage charging and discharging scheme. According to the invention, a traditional fixed topology and isolated energy storage scheduling mode is broken through, a unified optimization mechanism of power flow-topology-energy storage linkage is established, and active and flexible operation of the power distribution network is realized.

The invention discloses a vehicle power battery pack intelligent energy storage system and method based on dynamic topology reconstruction. Dynamic parameters of a power battery pack can be monitored in real time. According to the power grid control requirement and / or the real-time dynamic parameters of the power battery packs, the series / parallel topological structures of the power battery packs are dynamically adjusted; whether the power battery pack breaks down or not is judged according to the dynamic parameters of the power battery pack, and when the power battery pack breaks down, the faulty power battery pack is removed from the power battery topological structure, so that the electrical isolation of the faulty power battery pack and the non-faulty power battery pack is realized; and meanwhile, real-time dynamic parameter transmission of the fault power battery pack is blocked. Through cooperative control of the IPM and the EMS, online addition and deletion of the battery pack and dynamic adjustment of series / parallel topology are supported, the system capacity is linearly expanded, and the limitation of a traditional fixed topology is broken through.

The invention relates to a bidirectional power converter circuit, system, device and control method, a sampling circuit unit converts a sampling data signal corresponding to a bidirectional power converter unit into a digital quantity signal, and outputs sampling data information to a control unit based on the digital quantity signal, so that the control unit controls the bidirectional power converter according to the sampling data information. And the control circuit unit performs control processing in combination with module communication information transmitted by the communication circuit unit and outputs a control instruction, so that the driving circuit unit outputs a driving control signal to the bidirectional power converter unit according to the control instruction, and single module independent control of the bidirectional power converter system is realized. Therefore, each bidirectional power converter module in the bidirectional power converter system can be in communication connection with the bidirectional power converter modules connected in parallel through the communication circuit unit, and the problem that in the prior art, a topological structure is fixed due to the fact that the bidirectional power converter system adopts a single module is solved. And the expandability of the bidirectional power converter system is improved.

The invention discloses a lattice structure generation method and device and a medium, and the method comprises the steps: taking a designated point on a preset three-dimensional geometry as an original point, and building a three-dimensional space coordinate system; designated coordinate points on a preset three-dimensional geometry serve as basic nodes, and every two basic nodes are combined and connected to obtain basic line segments; constructing a topological structure through every any preset number of basic line segments; after the topological structures are screened, a target structure set is obtained; and reading the target topological structure from the target structure set for modeling to obtain a lattice structure. Therefore, compared with a fixed topology generation mode, rich lattice structures can be generated based on free combination between the nodes and between the line segments, and the requirements of complex application scenes for structural diversity and complexity are met. In addition, the coordinate system is automatically established, the topological structure is automatically combined and generated based on the specified coordinate points, node coordinates do not need to be manually input, and the lattice structure generation efficiency is improved.

The invention discloses a switching communication device, a switching communication method and a server, relates to the technical field of computers, and realizes equipment-level decoupling and resource pooling basis by actively managing a GPU (Graphics Processing Unit) through a second interface configured to be in a root complex mode. The on-chip management system and the switching logic module form a control core, a data switching strategy is dynamically executed, and conversion from passive forwarding to autonomous management is achieved. And the virtual port module performs software definition and dynamic allocation on network resources, so that the flexibility of resource utilization is improved. Meanwhile, MAC ports in two modes of direct connection and exchange are supported, so that the device can flexibly construct a high-performance direct connection network or a large-scale exchange network, and the limitation of fixed topology is broken through. And the data processing module ensures high efficiency of data conversion between different interface protocols. According to the device, the communication efficiency and schedulability between the GPUs are improved by integrating high-speed exchange, intelligent management, resource virtualization and flexible networking capabilities.

The invention discloses a server system and a computing cluster, and relates to the field of servers, in order to solve the problem of low adaptation efficiency caused by the fact that a current server adopts a fixed topology, the server system comprises a carrier plate, at least one computing device and at least one core plate provided with a processor, and the core plate is detachably connected with the carrier plate. The connection board is respectively connected with the core board and the computing equipment and is used for loading and running the corresponding configuration firmware according to the current computing scene corresponding to the form of the computing equipment. Different numbers of processor configurations can be provided, so that different numbers of processing cores and interface resources are provided, the carrier plate, the core plate, the connecting plate and the like are designed separately, connection of different topologies is achieved, the connecting plate can flexibly switch running configuration firmware according to a current computing scene corresponding to the form of the computing device, and the computing device configuration efficiency is improved. Therefore, different forms of computing equipment can be adapted, and the adaptation efficiency is improved.

The invention discloses a multi-scene linkage power grid simulation scheduling system and method, and relates to the technical field of power system simulation, and the system comprises a data unit which is used for obtaining power grid data of different regions and analyzing the power grid data to obtain heterogeneous data, and the heterogeneous data comprises a differential topological structure, standard equipment parameters and rule constraints; the processing unit is used for obtaining a region parameter set based on the heterogeneous data; the modeling unit is used for constructing a cross-regional interconnection topology model based on the regional parameter set; the event unit is used for acquiring a monitoring event based on the power grid data; and the scheduling unit is used for constructing a scene structural body, and obtaining a simulation result based on the cross-regional interconnection topology model, the monitoring event and the scene structural body, and solving the problems that a conventional power system simulation system cannot dynamically adapt to a multi-regional heterogeneous structure by adopting a unified standardized template or a fixed topology template, and the scheduling misjudgment rate is increased due to multi-scene independent analysis.

The invention discloses a discrete combination optimization control method of a switchable series-parallel current limiting circuit based on an IGBT and a contactor, and relates to the technical field of power electronic control and electrical testing, the control method comprises the following steps: S1, a real-time sensing step: collecting a target current limiting value and a bus voltage of a system, and real-time state data of each reconfigurable power module, wherein the real-time state data at least comprises a current value, a voltage value and a device temperature. According to the discrete combination optimization control method of the switchable series-parallel current limiting circuit based on the IGBT and the contactor, by dynamically sensing a real-time working condition and constructing a discrete optimization model containing multiple targets, a scheme meeting static and dynamic performance requirements can be automatically and accurately screened out from massive possible topologies; the problems that a traditional fixed topology circuit is limited in adjusting range and insufficient in precision are solved, and the response reliability and the control accuracy of the system under the wide-range working condition are ensured.

The invention discloses a reconfigurable topology-digital twin sodium-lithium hybrid battery collaborative management system and method, and relates to the technical field of electrochemical energy storage systems, the system comprises a sodium-lithium hybrid cell module, a temperature sensor and a voltage acquisition unit; the reconfigurable switch matrix module is composed of MOSFETs and supports seamless switching of four kinds of topologies. The BMS module integrates data processing, a decision engine and switch driving; the digital twin module constructs a virtual model for simulation and early warning of faults; the topology adaptive state estimation module is internally provided with multiple equivalent models to estimate SOC / SOH; the load adaptation module monitors a load and distributes power; and the fault isolation module is linked with the switch matrix to disconnect the fault cell and reconstruct the topology. According to the method, the full-working-condition performance is optimized through dynamic topology, active fault isolation is achieved through digital twinning, differential technical system avoiding patents are constructed, and the performance, safety and precision pain points of a traditional fixed topology system are comprehensively solved.

This invention discloses a two-stage recovery method and system for high-proportion wind power transmission networks that considers frequency security, belonging to the field of power system resilient construction. The method acquires all parameters of the transmission network topology, generating units, loads, wind power, and frequency to construct a flexible recovery model without defined zones. It employs a two-stage optimization: the first stage solves the dynamic topology of the transmission network without frequency constraints and obtains the zone results through BFS; the second stage constructs two types of frequency constraints—power fluctuation and load picking—based on a fixed topology, and solves to obtain a compliant recovery decision scheme. The system is equipped with modular functional units corresponding to the method to achieve automated decision-making. This invention solves the problems of difficulty in constructing time-varying topology frequency constraints for post-disaster recovery of high-proportion wind power transmission networks, frequency exceeding limits caused by wind power fluctuations, and poor recovery effects in fixed zones. It balances frequency security, load recovery efficiency, and solution speed, is adaptable to the complete blackout recovery of transmission networks after extreme disasters, and has strong engineering practicality.

The application provides a cold rural human settlement cross-period prediction and scenario deduction method. Continuous annual multi-source spatio-temporal observation data is acquired to construct dynamic nodes and cross-period samples; the DPSIR system rule is expanded and corrected according to the node process to generate a binary candidate relationship mask before fitting; non-zero paths are screened in the mask by L1 regularization, and stable paths are formed through regional clustering resampling, rolling inspection, regional leave-one-out inspection and spatial error transformation reestimation; the directional posterior probability is frozen to form a cross-period graph, and the node conditional distribution is updated under the fixed topology to output the next annual posterior prediction interval; the new annual evidence is updated to the final graph according to the same access chain, and the output is propagated under the conditions of equal total adjustment intensity and period-by-period recovery response intensity to output the downstream posterior change, credible interval, positive probability and scheme priority. The application connects process constraint preposition, path stable access and same graph continuous propagation to realize cross-period probability prediction and equal intensity scenario comparison.

The present invention discloses a multi-agent system control method, device and equipment, and relates to the technical field of multi-agent system control. The method comprises: constructing a dynamic model of a follower and a leader, and constructing a communication topology diagram of the multi-agent system using an undirected graph; determining the feedback gain matrix and the continuous gain matrix of each follower and the leader by solving the Riccati equation inequality, and determining the control law of each agent according to the feedback gain matrix and the continuous gain matrix; determining the adaptive weight and control parameters of the control law of each agent based on the relative error of the agent, and constructing a distributed adaptive tracking control; through the distributed adaptive tracking control, each follower uses local information to track the leader of an unknown input, so that the state of the follower can converge to the state of the leader under the fixed topology and the switching topology of the controller.

The invention provides a navigation control method, a mobile robot and a navigation control system.The method comprises the steps that the first position of a target point in a topological map is obtained, the last topological edge and the next topological edge in the topological map are connected through a topological point, each topological edge is provided with an identifier, the length of each topological edge is fixed, and the first position of the target point in the topological map is obtained; the topological edge is used for representing a strip arranged on the ground, the strip comprises a code strip and a color strip, and the moving direction of the mobile robot is controlled to be in the strip direction; determining a second position of the mobile robot in the topological map based on indication information collected when the mobile robot moves along the strip direction; according to the first position and the second position, the remaining distance of the mobile robot moving to the target point along the strip is determined; and based on the residual distance, controlling the moving speed of the mobile robot until the mobile robot moves to the target point. According to the scheme, autonomous navigation control of the mobile robot can be realized, dependence on a platform is avoided, and operation reliability of the mobile robot is improved.

The invention provides a highway tunnel emergency evacuation path planning method, system, device and medium, and the method comprises the steps: collecting disaster data at a fixed time interval, generating a plurality of selectable escape paths according to the disaster data, and calculating the real escape rates of all selectable escape paths; calculating a predicted escape rate of each optional escape path according to the disaster data; dynamically generating a correction weight according to the change degree of the poison gas concentration at the current moment and the last moment; and dynamically correcting the real escape rate of each optional path according to the correction weight, and selecting the optional escape path with the highest real escape rate as an evacuation path. According to the method and the device, the problems that in the prior art, the planning of the escape path depends on a fixed topology model and a preset rule, and only the shortest path is taken as an optimization target, so that the generated escape path is disjointed with disaster evolution, and the planned path does not accord with the actual condition are solved.

The present application relates to image processing technology, especially to a flexible tool calibration method and device based on CT image, electronic equipment and storage medium, accurate data basis is provided by obtaining sub-voxel level center coordinates of each marker ball; further, based on the pre-stored tool topology model, the first type of tool with fixed topology structure is distinguished and identified from the second type of tool with variable topology structure, and the dependence on the absolute rigidity of the tool is reduced; for the identified second type of tool, by rigid registration calculation between the actual center coordinate set and the standard model coordinate set, the compensation parameters for representing the current physical deformation can be directly calculated, not only the flexible tool with slight deformation can be stably identified, but also the deformation error can be actively quantified and compensated, and finally the calibration data output contains the tool identification result and the deformation compensation parameter, so as to provide more reliable, more adaptive real application scene high-precision positioning and registration basis for the navigation system.

The application discloses a kind of switching communication device and method, server, it is related to computer technical field, by being configured as the second interface of root complex mode actively manages GPU, equipment level decoupling and resource poolization foundation are realized.The on-chip management system and switching logic module constitute control core, dynamically execute data exchange strategy, realize from passive forwarding to the change of autonomous management.Virtual port module carries out software definition and dynamic allocation to network resource, improve the flexibility of resource utilization.Meanwhile, support the MAC port of two modes of direct connection and switching, so that device can flexibly build high-performance direct connection network or large-scale switching network, break the limit of fixed topology.Data processing module then ensures the efficiency of data conversion between different interface protocols.The device improves the communication efficiency and schedulability between GPUs by integrating high-speed switching, intelligent management, resource virtualization and flexible networking capabilities.

This invention belongs to the field of power semiconductor device design and artificial intelligence technology, specifically relating to a method and system for multimodal large-model proxy design of power semiconductor devices. This invention addresses the problem of relying on manual, repeated interpretation of electric field diagrams and structural modifications followed by re-simulation in TCAD design of power semiconductor device terminal structures, as well as the difficulty of existing methods in automatically proposing and verifying new terminal structure schemes while meeting process / reliability constraints. This invention unifies the scheduling of terminal structure simulation execution, simulation result acquisition, and structural file rewriting within the TCAD simulation environment. It utilizes a multimodal simulation evidence-driven design suggestion generation and candidate structure screening mechanism to achieve a closed-loop iteration of "simulation—analysis—structural update—re-simulation." After obtaining high-potential structures, it further performs numerical optimization of continuous parameters under a fixed topology, thereby improving the breakdown voltage of power devices under manufacturability and reliability constraints while ensuring the traceability and reproducibility of the optimization process.

This invention relates to the field of power control system safety detection technology, specifically disclosing an automated identification system for abnormal behavior in power control systems based on deep learning. The system collects normal telemetry data from various nodes of the power control system under multiple operating conditions. A common embedding function is obtained through training with a three-layer graph convolutional layer of a deep equilateral neural network, superimposed with node permutation invariant regularization terms and contrastive loss. This maps telemetry data of the same physical state under different topologies to topology-invariant manifold coordinates. Kernel density estimation is used to construct the region occupied by normal behavior. Real-time telemetry data is collected online, mapped to real-time manifold coordinates through the common embedding function, and the drift distance from the normal region is calculated. The drift distance is compared with a dynamic threshold; if it exceeds the threshold, it is judged as abnormal and an alarm is triggered. This invention does not rely on a fixed topology structure, can adapt to dynamic topology changes, avoids lag caused by retraining, and improves the accuracy and real-time performance of anomaly detection.

The invention discloses a power grid power flow prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining power grid operation data corresponding to a target region, and building a topological graph and an adjacent matrix corresponding to the target region according to the power grid operation data; determining a node feature code corresponding to each node in the topological graph based on the adjacent matrix; determining an enhanced time sequence feature corresponding to the target area based on the node feature code; and performing linear transformation on the enhanced time sequence characteristics to determine a power flow prediction value corresponding to the target area. Based on the technical scheme, the enhanced time sequence characteristics corresponding to the power grid are extracted by establishing the topological graph and the adjacent matrix, and then the power flow prediction value is determined according to the enhanced time sequence characteristics, so that the limitation of dependence on a fixed topological structure is eliminated through global space attention, and nonlinear characteristics are captured in combination with time attention; and more accurate power grid power flow prediction is realized.

A computer-implemented method is provided for generating equipment parameters of a circuit using a trained reinforcement learning (RL) agent composed of a graph neural network (GNN) and a fully connected neural network (FCNN). The method is performed by steps including obtaining inputs related to a set of desired specifications or one desired specification, equipment parameters, and a fixed topology of the circuit, and providing the inputs to the trained reinforcement learning (RL) agent. The description of the desired circuit includes a graph that models the topology of the circuit and the equipment parameters of the circuit, and the desired specifications include gain, bandwidth, phase margin, power consumption, output power, and power efficiency. The trained reinforcement learning (RL) agent performs steps including sending an action selected from a set of actions to an environment module, updating the equipment parameters of the circuit according to the selected action using a data processor of the environment module, obtaining the current specifications of the circuit by simulating the netlist of the circuit, earning a reward from the environment module, and generating updated equipment parameters of the circuit.

The invention discloses an AC / DC power distribution network control method and device based on graph reinforcement learning, and belongs to the technical field of power distribution network control. According to the invention, the topological structure diagram of the AC / DC power distribution network is constructed through the topological structure data of the AC / DC power distribution network, and various dynamic changes of the AC / DC power distribution network can be fully and accurately perceived in the form of the diagram, so that the diagram reinforcement learning intelligent agent obtained through Markov reinforcement learning operation environment modeling is enabled to be more accurate. The operation condition of the AC / DC power distribution network can be represented more accurately; a direct-current power distribution network control model is constructed through alternating-current and direct-current side operation information, cooperative and unified control over an alternating-current system and a direct-current system is achieved through the direct-current power distribution network control model, and the adaptability of a reward function and the operation condition of the alternating-current and direct-current power distribution network is improved; and then, an AC / DC power distribution network control strategy is generated according to the real-time topological structure data of the AC / DC power distribution network, so that the control limitation caused by fixed topology is avoided, and the control accuracy of the AC / DC power distribution network is improved.

The invention discloses a power distribution network operation state deduction method, a risk assessment method, equipment and a medium, and the state deduction method comprises the steps: obtaining the electrical parameters of each node of a power distribution network at a current moment and in a past preset time period, and the current network topology structure and the target candidate network topology structure of the power distribution network, and constructing model input; and inputting the model into a pre-trained power distribution network operation state deduction model, and deducing and outputting a predicted voltage amplitude sequence of each node in a future prediction time domain. Compared with existing power distribution network operation state deduction based on a fixed topological structure, the power distribution network operation state deduction method can realize cross-scene and cross-topology power distribution network operation state deduction, and can provide effective data support for power distribution network operation risk assessment after topology switching. A multi-step voltage track can be obtained through one-time forward deduction, and a more efficient analysis approach is provided for obtaining future operation voltage safety information.

The invention belongs to the technical field of wireless transmission, and discloses a large-bandwidth wireless transmission system and method for a visual inspection image of a main insulating layer of a cable. The system comprises a starting terminal module, an intermediate terminal module and a target terminal module which are connected through a tree network, and is provided with a transmission path generation module and an encryption module. The method comprises the following steps: generating a temporary transmission path, splitting image data, disrupting a sequence, encrypting a path generation rule, transmitting data through a plurality of paths, and finally recombining at a receiving end. According to the method, through dynamic path and transmission behavior encryption, path prediction and data packet capture of malicious programs are prevented, the problem that a traditional fixed topology network is poor in safety is solved, and meanwhile the requirement for real-time and high-bandwidth transmission of large-data-volume images in industrial detection is met through a parallel transmission mechanism.

A technique manages graphics processing units (GPUs), as well as associated algorithm advancements, to improve and effectively hide computational costs of adaptive mesh refinement (AMR). A cloud-based framework dynamically utilizes a distributed pool of the GPUs to parallelize computations of physical simulation software (solver) partitioned across multiple GPUs. A highly parallelized and distributed architecture of the framework provides a hybrid approach that operates partly on GPUs, e.g., to perform the solver computations, and partly on general-purpose processing units (CPUs) to accelerate portions of AMR and mesh processing of an AMR module. In an illustrative embodiment, the technique leverages the GPUs to perform as much of the AMR module processing (e.g., algorithms that rely on fixed topology) that can be naturally and efficiently implemented on the GPUs, while maintaining those portions of processing that do not naturally map onto the GPUs (e.g., algorithms with dynamic topology changes) on CPUs.

A computer-implemented method is provided for generating device parameters of circuits using a pretrained reinforcement learning (RL) agent composed of a graph neural network (GNN) and a fully connected neural network (FCNN). The method is performed by steps including acquiring inputs with respect to a set of desired specifications or one desired specification of a circuit, device parameters, a fixed topology of the circuit and providing the inputs to the RL agent. The desired circuit description includes a graph modeling the topology of the circuit and device parameters of the circuit, and the desired specifications include gain, bandwidth, phase margin, power consumption, output power and power efficiency. The pretrained RL agent performs steps including transmitting an action selected from a set of actions to an environment module, updating the device parameters of the circuit according to the selected action using a data processor of the environment module, obtaining a current specification of the circuit by simulating a netlist of the circuit, acquiring a reward from the environment module, and generating the updated device parameters of the circuit.

The application discloses a circulating gas lift pressurization parameter self-adaptive regulation and control method and system, and relates to the technical field of industrial control systems; its technical points are: obtaining circulating gas lift well operation data, establishing a candidate gas injection topology set and performing topology state constraints; in the reference recovery state, executing the positive sequence pressurization trajectory and the reverse sequence pressurization trajectory with the reverse pressurization order, compensating for the high-pressure gas path inventory mass, forming the reverse sequence differential response under the effective well cumulative mass coordinate, and performing continuous mass interval inspection with the reverse sequence differential reachable domain of each candidate gas injection topology; updating the topology conversion parameter set and the in-channel control parameter set according to conditional topology conversion evidence and fixed topology response, forming a topology control certificate after independent record verification; parallel propagation of the candidate annulus pressure reference sequence, screening of the common annulus pressure reference sequence and rolling execution, and application to the circulating gas lift control without continuous downhole valve position feedback.

The invention provides a heat exchange device of a mixed TPMS structure and a construction method of the heat exchange device, relates to the field of heat energy engineering and new energy automobiles, and solves the problems that an existing TPMS heat exchanger adopts a fixed topology, heat exchange and flowing properties are difficult to collaboratively optimize under the same porosity and appearance, the energy efficiency is limited, and the service life is prolonged. And adaptive thermal management requirements under variable working conditions cannot be met. The method comprises the steps that two different TPMSD structures are selected, a basic topological unit of the mixed heat exchange core is constructed, and implicit functions corresponding to the two TPMS structures are extracted; according to the target heat exchange performance requirement, the weighting coefficients of the two TPMS structures are determined; based on the weighting coefficient and the implicit functions of the two TPMS structures, constructing a composite implicit function representing the variable coefficient mixed TPMS structure; and constructing a three-dimensional geometric model of the hybrid heat exchanger core based on the composite implicit function. The method and the device are used in the construction process of the mixed TPMS structure.

The application provides an event-triggered multi-unmanned aerial vehicle system fixed time synchronization consistency control method, comprising the following steps: establishing a multi-unmanned aerial vehicle system model and a state equation under a fixed topology structure; designing a fixed time synchronization consistency control strategy based on an event trigger mechanism; segmentally designing an event trigger mechanism in the fixed time synchronization consistency control strategy, designing a trigger condition based on speed information when each unmanned aerial vehicle tracks a virtual speed state, and switching to a trigger condition based on position information when the speed of each unmanned aerial vehicle is consistent with the virtual speed; designing a constraint condition of the fixed time synchronization consistency control strategy and estimating the stop time of the unmanned aerial vehicle, so that all states of the multi-unmanned aerial vehicle system are simultaneously consistent within a fixed time. The application meets the urgent demand of system state synchronization convergence in different dimensions, and can effectively reduce the update frequency of the controller and the energy consumption of the system.