252 results about "System model" patented technology

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention belongs to the technical field of mass concrete construction, and discloses a temperature control system model, a construction method and application. A water cooling technology is mostly adopted in traditional mass concrete construction, but water is difficult to take in special construction sites, a large number of water resources need to be wasted, temperature control measures of the traditional water cooling technology are lagged, and the crack probability is remarkably increased. According to the temperature control system model, the construction method and the application, a second-order response surface is used for fitting a multi-parameter temperature control system model, and the temperature control system model is corrected and optimized according to measured data acquired by a temperature control acquisition unit, so that a closed-loop control system is formed. The concrete grid units and the corresponding air guide pipes serve as minimum temperature control units, construction, temperature control and management are carried out, the management and control granularity is finer and more accurate, and a new research and development direction is provided for the temperature control technology of mass concrete construction.

The invention discloses an edge computing network task unloading and resource allocation method based on optimal service quality, which comprises the following steps of: constructing a system architecture comprising a cloud server, a plurality of edge servers and mobile equipment, and establishing a multi-dimensional system model covering task characteristics, service cache, communication transmission, computing resources, service cost and service quality; constructing an optimization problem aiming at maximizing the long-term service quality of all mobile equipment, and forming a mixed integer nonlinear programming model under the constraints of mobile equipment cost constraint, storage capacity limitation, bandwidth, computing resources and the like; a double-time-slot hierarchical decision-making mechanism is designed, and multi-dimensional joint optimization of service caching, task unloading and resource allocation is realized through a collaborative mechanism that short-term resource allocation is constrained through a long-term caching decision and short-term performance feedback optimizes long-term caching. According to the method, the overall quality of service (QoS) can be effectively improved and the task processing delay and energy consumption can be reduced under the condition that the cost of the mobile equipment and the system resource limitation are met.

The invention belongs to the technical field of power plant thermodynamic and control, and particularly relates to a fractional order model of an ultra-supercritical coal-fired unit coordination control system and an identification method. The fractional order model comprises a coal pulverizing system, a boiler steam-water system and a steam turbine power generation system; the modeling method comprises the following steps: S1, acquiring and storing data of an ultra-supercritical coal-fired unit coordinated control system DCS; s2, constructing a fractional order model of the coordination control system; and S3, fitting a to-be-solved function through acquired data, and identifying dynamic parameters of the fractional order model offline by adopting a segmented time-sharing adaptive particle swarm method. According to the method, fractional order modeling is carried out on the coordinated control system of the coal-fired unit, the dynamic parameters of the system are identified by adopting a segmented time-sharing adaptive particle swarm identification method, the optimal model parameters are obtained through iterative calculation, the precision of the unit coordinated control system model under the wide load is greatly improved, and the system performance is improved. And implementation of a control strategy based on a system model is facilitated.

The invention relates to a disturbance source positioning method based on dynamic mode decomposition, which belongs to the technical field of power system operation and analysis and comprises the following steps of: accurately extracting oscillation mode characteristics of a system by performing matrix decomposition on actually measured data; furthermore, a participation factor analysis model fusing time-space characteristics is constructed, and the spatial distribution characteristics and the time evolution characteristics of the modal are comprehensively considered, so that accurate positioning of the oscillation source is realized. Compared with a traditional method, the method has the advantages of no dependence on a detailed system model, high positioning precision and the like, is realized completely based on measurement data calculation, avoids complex system modeling, and has relatively high practical application value and popularization potential.

The invention relates to the technical field of building safety, and discloses a safety transformation monitoring system for building construction and a method thereof. The system comprises a data acquisition and standardization module, a dynamic security entity relationship map construction module, an organization capability and cognitive load modeling module, a hybrid risk identification and conduction calculation engine, a self-adaptive security transformation strategy generation engine and a closed-loop self-calibration module. According to the method, a dynamic security entity relation graph is constructed and updated in real time, the cognitive load of management personnel is quantified, and the cognitive load is used as a dynamic adjustment factor of the conduction probability of risks on the graph; when a safety transformation strategy is generated, comprehensively evaluating a risk reduction effect, resource cost and additional cognitive load cost to output an optimal adaptive strategy; and finally, performing closed-loop self-calibration on the system model by utilizing execution feedback. According to the method, dynamic prospective risk prediction, self-adaptive strategy generation and continuous self-optimization are realized.

The invention relates to the technical field of automatic control, in particular to a switching positive system identification method and system and a storage medium, and aims to solve the problems that in the prior art, a model violates non-negative physical constraints, hyper-parameter estimation is prone to local optimum, calculation efficiency is low, and switching path identification precision is insufficient. The method comprises the following steps: firstly, determining non-negative priori of a system model and pulse response truncated Gaussian distribution through a preparation module; constructing a Bayesian network, and iteratively optimizing a switching path by adopting Gibbs sampling; and finally, based on regularization optimization and Gibbs sampling in combination with Monte Carlo approximation, accurately estimating the pulse response of the subsystem. According to the method, the non-negative characteristics are embedded into the whole identification process, global parameter optimization is achieved, high-dimensional integral calculation is simplified, identification precision and stability are improved, and the method is suitable for multi-scene engineering application.

The invention discloses a ship pipeline system model generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a pipeline schematic diagram and a specification configuration file after receiving a modeling instruction for a pipeline system, determining an element model of a part forming the pipeline system from a model library according to the pipeline schematic diagram, according to the pipeline schematic diagram and the specification configuration file, the primitive model is adopted to generate an initial pipeline system model, and the initial pipeline system model is adjusted to obtain a target pipeline system model when a model adjustment instruction is received. The initial pipeline system model is generated by triggering the modeling instruction, and the target pipeline system model is obtained by triggering the adjustment instruction to adjust the initial pipeline system model, so that a designer does not need to manually model and adjust the model for multiple times according to a pipeline schematic diagram, and the modeling efficiency of the pipeline system model is improved; the design progress of the pipeline system is accelerated; and the design quality is improved.

The invention relates to the field of intelligent water affairs, and discloses a precise flow control system for intelligent water affairs, which comprises the steps of collecting local water flow change data in real time, extracting flow velocity disturbance characteristics and pipe network transient pressure response, and judging whether unexpected disturbance trigger exists or not; constructing a local disturbance label library based on disturbance trigger source features and a historical operation model, and identifying disturbance sources and flow direction tendentiousness by using fuzzy discrimination and variable weight filtering algorithms; according to the pipe section hydraulic characteristics corresponding to the mismatching behavior, the flow of the target pipe section is corrected through a high-resolution electric proportional valve set and a two-way feedback control unit; constructing a cross-section response chain through the intelligent edge control node, and generating a multivariable adjustment gradient according to the multi-period callback simulation data; flow state data and peripheral environment variables before and after the current round of regulation and control intervention are collected, and system model parameters are dynamically corrected through the integrated evolutionary prediction network. The stability of the intelligent water affair system is improved.

The invention relates to the technical field of comprehensive energy systems, and particularly provides a low-carbon building-oriented electro-hydrogen-heat comprehensive energy system modeling method, which comprises the following steps of determining different physical fields involved in a low-carbon building-oriented electro-hydrogen-heat comprehensive energy system and mechanism models under each physical field, determining a model interface of each mechanism model; establishing a dynamic model of each component device according to each mechanism model; based on the dynamic model of each component device, establishing each subsystem model; and combining the sub-system models according to the model interfaces in the physical fields to obtain the low-carbon building-oriented electro-hydrogen-heat comprehensive energy system model. Therefore, on the basis of a modularized modeling thought, the modeling models are divided according to different fields, the coupling relation and the dynamic characteristics in the electro-hydrogen-heat comprehensive energy system can be accurately reflected, then the maintainability and the simulation efficiency of the models can be more effectively improved, and pre-judgment of dynamic changes of the system under different load conditions is helped.

The invention provides a building power supply and distribution electrical system model construction method based on an analogy analysis method, and belongs to the technical field of power distribution electrical system models. And carrying out load identification and capacity prediction by using the load capacity adaptive matching model. And then constructing a high-voltage side capacity model block by adopting a graph theory algorithm, and realizing capacity adaptation through recursion degree calculation and model change gain degree evaluation. Establishing a multi-physics field coupling effect sub-model, and describing the stability characteristics of the system by using a current stability matrix; and analyzing and evaluating a nonlinear load and a distributed power supply through big data, and establishing a load prediction and power evaluation sub-model. And then a capacity gain matrix is calculated by adopting comprehensive dynamic model adaptation simulation, parameter backstepping optimization is realized, and finally a final power supply and distribution electrical system model is generated.

The invention discloses an intelligent optimization control method and system for a compressed air energy storage power station, and relates to the technical field of power grids, and the method comprises the steps: S1, building a system model corresponding to the compressed air energy storage power station, and collecting data; s2, training a reinforcement learning agent, and learning to obtain an optimal control strategy capable of adapting to the operating environment of the compressed air energy storage power station; s3, using the prediction model to predict the future operation state of the compressed air energy storage power station, and optimizing the control sequence; and S4, executing real-time control operation, adjusting a control strategy according to feedback data, and performing dynamic optimization control. According to the method, reinforcement learning and model prediction control are organically combined, a reinforcement learning agent is introduced, a control strategy is continuously learned and optimized, and adaptive adjustment is quickly made according to factors in multiple aspects such as the current generation power, the power load, the energy storage state and the environment in the face of real-time fluctuation of the new energy generation power.

The invention relates to the technical field of artificial intelligence and control, in particular to a non-linear multi-agent system fixed time consistency control method and device and a medium. According to the control method, a backstepping recursion method is used as a control design framework, a self-adaptive fixed time consistency controller based on an auxiliary compensation system is provided, and the problem of non-linear multi-self-body system consistency control under time-varying input delay is solved. By providing and changing a control input signal by a person, the motion trajectory of the multi-agent system can be modified as required. Time-varying input time delay and an unknown nonlinear function are considered in a system model, so that the system is more general. Wherein an unknown time-varying delay function is processed by constructing an auxiliary compensation system, and an unknown nonlinear function is processed by using radial basis function neural network approximation. According to a fixed time control correlation lemma, an actual fixed time adaptive consistency control method is provided to ensure that the consistency error of the multi-agent system converges to a neighborhood near an original point in fixed time.

The invention relates to the field of temperature acquisition systems, and discloses a high-precision sampling processing method based on multi-model temperature sensors, and the method comprises the steps: obtaining an original ADC data block of a temperature sensor, and extracting a dynamic feature fingerprint; analyzing the dynamic feature fingerprints to determine a model probability vector characterizing a known working mode and a novelty score characterizing an unknown working mode; according to the model probability vector, the estimation results of the multiple extended Kalman filters are fused through an interactive multi-model algorithm, a temperature value is obtained, and a residual sequence is generated; and updating the system model online according to the novelty score or the residual sequence. The system comprises a data acquisition and feature extraction module, a sensor state sensing module, a self-adaptive temperature estimation module and a system online evolution module. According to the method, a closed-loop processing flow with state sensing and online evolution capabilities is constructed, so that the problem that the precision of a traditional fixed model is reduced under a complex working condition is solved.

The invention provides a SysML model construction method, a SysML model driven design model construction method and a SysML model driven design model construction system. The SysML model driven design model construction method comprises the following steps: obtaining a first system model, a first equipment model and a process model; the first system model, the first device model and the process model are described based on a meta-model extended from SysML semantics; constructing a design model according to a conversion rule, the first system model, the first equipment model and the process model; wherein the meta model extended from SysML semantics can be used for identifying information used for constructing the design model; the first system model is used for describing a system for constructing the design model; the first equipment model is used for describing equipment for constructing the design model; and the flow model is used for constructing the design model comprising a pipeline and instrument flow chart.

The invention discloses a polar region island micro-grid optimal scheduling method considering frequency constraint, and relates to the technical field of island micro-grid optimal scheduling, and the micro-grid optimal scheduling method comprises the following steps: building a system model; constructing and solving a multi-time scale joint optimization architecture; embedding a frequency transient constraint as a minute layer decision constraint in a linearization or convex approximation form; and according to a solving result, issuing a hierarchical instruction to various distributed resources and implementing online monitoring and safety guarantee measures. According to the method, through explicit embedding and linear convex approximation of frequency transient constraint, the defect that only steady-state frequency is considered or an empirical threshold value is adopted in traditional scheduling is overcome, uncontrollable frequency transient caused by short-time high-power disturbance is avoided, minute-level frequency safety guarantee is achieved, the frequency peak value and the minimum point are controlled, and the scheduling efficiency is improved. And load removal or equipment protection actions caused by transient overrun are reduced, so that the power supply continuity and the system reliability are improved.

The invention discloses a dynamic anti-disturbance coordinated scheduling method for a hybrid energy micro-grid, and aims to solve the problems of poor stability, high cost, low consumption rate and the like of the hybrid energy micro-grid caused by disturbance such as renewable energy fluctuation and load abrupt change. The method comprises the following steps: firstly, constructing a system model containing multiple units, and collecting and preprocessing real-time operation data; establishing a multi-target scheduling objective function, and solving an initial scheduling instruction by adopting an improved predictive control algorithm; and finally, generating a coordinated scheduling strategy by combining the disturbance information dynamic correction instruction. Meanwhile, a security constraint and scheduling effect evaluation link is set, and mechanisms such as multi-time scale scheduling and layered response are adopted. The method can significantly improve the anti-disturbance capability and stability of the system, reduces the operation cost by 15%, improves the renewable energy consumption rate by more than 8%, is adaptive to multiple scenes, guarantees the safe operation, and is suitable for various hybrid energy microgrids.

The invention discloses a 6DMA-assisted high-speed railway communication resource collaborative optimization method based on reinforcement learning, and belongs to the technical field of high-speed railway movable antenna communication resource scheduling. The 6DMA-assisted high-speed railway communication resource collaborative optimization method comprises the main steps of constructing a system model, designing a joint optimization problem, solving the joint optimization problem and the like. According to the invention, through a six-degree-of-freedom reconstruction radiation mode, compared with traditional MA / FAs, the spatial degree of freedom is further released, and blind area formation and double near-far effect can be significantly reduced; meanwhile, static precoding and step-by-step search are replaced by an active-passive beam + array surface motion linkage decision through the TTD3, end-to-end control is achieved, and the time delay requirement of the high-speed rail is met; in addition, the collaborative optimization mechanism gives consideration to exploration efficiency and convergence stability, so that the system can keep a link reliable, and the method has relatively high practical application value.

The invention relates to the technical field of comprehensive energy transformation, in particular to a digital twin-body-based comprehensive energy system transformation optimization method, which comprises the following steps of: constructing a digital twin body comprising a building thermal model and an energy equipment system model on the basis of static data, dynamic operation data and external environment data of a target area; global optimization with a preset objective function as a guide is carried out, an optimal system capacity configuration and architecture scheme is determined, and then local equipment type selection and control strategy optimization are carried out; issuing the optimized control strategy to a physical energy system for execution, and continuously collecting feedback data to calibrate the digital twin to form closed-loop optimization; a sensing-analysis-decision-execution-learning closed-loop system is constructed, an energy system of a physical world is deeply coupled with a digital model of a virtual world, global and local layered collaborative optimization is realized through a digital twin, and dynamic adaptability is improved through closed-loop operation.

This invention relates to a method and system for optimizing the quenching of large-diameter bearings based on multi-model set membership estimation. The method includes: determining multiple state parameters of the target quenching measurement system; constructing a system model based on the fusion of multiple set membership estimation models using the state parameters and a fully symmetric multi-cell method, employing a Kalman filter; determining the predicted state equation and probability density function of each set membership estimation model; updating the probability density function of the predicted set of each set membership estimation model according to the dimension of the output matrix of the real-time quenching measurement system; calculating the predicted set of the system model using Bayes' theorem based on the probability density function of the predicted set of each set membership estimation model; and solving for the optimal gain matrix of the target quenching measurement system by minimizing the set radius based on the predicted set and error set of the system model. This invention improves the stability and robustness of the optimized control for quenching large-diameter bearings through multi-model set membership estimation and Bayes' theorem.

The invention discloses a block system construction method suitable for various structural plane models, and relates to the field of three-dimensional rock block recognition, and the method comprises the steps: firstly classifying cutting planes and establishing a database based on the geometric data of an existing structural plane system model (DFN) and the data of a calculation region, calculating the intersection lines and intersection points between the cutting planes, and calculating the intersection lines and intersection points between the cutting planes; a vertex matrix of the block system is constructed, then vertexes are connected to form directed edges, closed surfaces are constructed based on the directed edges, and a polyhedron is constructed through surface combination. And finally, the conversion from the structural plane system to the block system is completed by outputting a complete geometric element matrix. Theoretical algorithms are adopted in the method, and only structural plane and calculation area data are used as input, so that the method is objective and simple to operate and easy to program. Compared with the prior art, the method breaks through the limitation of a traditional structural plane model, and can be suitable for various structural plane models including a disc structural plane model, a polygonal structural plane model and a general elliptical disc structural plane model. The calculation method disclosed by the invention can provide a preprocessing general normal form for rock mass stability analysis. A proper structural plane model can be selected according to engineering requirements, a rock block system closer to a real rock mass is further provided, and the adaptability and reliability of block system modeling under complex geological conditions are improved.

The invention discloses a hybrid multi-agent adaptive cooperative control method and device and a medium, and the method comprises the following steps: constructing a hybrid multi-agent system model for intelligent building temperature adjustment, defining a central energy management system as a leader, the central air conditioner host and the regional temperature controller are respectively defined as continuous time followers and discrete time followers; for a continuous time dynamic central air conditioner host, defining a consistent error, and designing a distributed cooperative control law based on the Lyapunov theory; a consistent error is defined for a discrete time dynamic regional temperature controller, and a distributed cooperative control law is designed based on an equivalent feedback principle. According to the invention, unknown dynamic and uncertainty in the system can be overcome, the robustness of the control system is enhanced, and the method has anti-interference capability.

The invention relates to an intelligent networking planning system based on multi-objective dynamic optimization, which comprises a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module and a scheme output module, and is characterized in that the data input module is used for receiving geographic information, environmental conditions, equipment models and performance parameters input by a user; the data preprocessing module is used for carrying out standardization processing on the input geographic information and equipment parameters; the system modeling module is used for establishing a terrain and terrain model; the multi-target optimization module is used for carrying out multi-target dynamic optimization and generating an optimal deployment scheme; and the scheme output module is used for generating an equipment deployment scheme report. According to the invention, complementary coverage between devices is realized, the system cost is reduced, and the deployment efficiency is improved. The system has good expansibility and adaptability, can be flexibly adjusted according to different application scenes and requirements, improves the overall performance and adaptability of a low-altitude security system, and meets diversified low-altitude security requirements.

The invention provides a ship cooling water system modeling and debugging method, which comprises the following steps of: performing parametric modeling on a cooling water system which is the most complicated pipeline system in a ship building process, and pre-judging the opening degrees of each branch and each valve of the system according to limited input conditions in combination with a numerical simulation calculation platform and an iterative optimization strategy; and important reference is provided for field debugging. Meanwhile, according to the method, serial and parallel valve position-flow control is conducted on a control branch step by step, the calculation time is reasonably saved, and a result meeting the flow deviation requirement can be obtained as soon as possible. Besides, when some ship systems are relatively complex in composition and a result meeting the flow deviation is difficult to obtain through full-system simulation, the same modeling debugging effect can be achieved in a system partition mode. According to the method, the field debugging work of the ship cooling water system can be more efficiently completed, and a theoretical reference is provided for operation and maintenance of the cooling water system when the ship sails under multiple working conditions after being put into operation.

A method for determining system excitations for system identification is disclosed. The method includes (i) receiving a system model, a plurality of system excitations, and a parameter library that comprises at least one parameter to be identified, (ii) determining a plurality of output variables based on the plurality of system excitations and the at least one parameter to be identified using the system model, (iii) determining a plurality of grade measures of the determined plurality of output variables with respect to the at least one parameter to be identified, and (iv) selecting system excitations for system identification based on the determined plurality of grade measures.

The invention relates to a method for diagnosing a situation in a process automation system (1). A field device (2) is installed in the system (1) and data about the system (1) are provided. Based on the data and on the basis of a prototype associated with the field device (2), a system model is generated using artificial intelligence. On the basis of a measured value generated by the field device (2), a statement about a situation in the system (1) is generated using the system model.

The invention relates to a system development method and device, computer equipment, a computer readable storage medium and a computer program product. Obtaining a system model constructed based on a first language; the first language is a semi-formalized modeling language; analyzing the system model to obtain an abstract instantiated syntax tree corresponding to the system model, and performing system analysis at least including system resource budget analysis based on the abstract instantiated syntax tree to obtain an analysis result; under the condition that the analysis result meets a preset condition, generating a target code of a second language matched with the system model based on the abstract instantiation syntax tree; the second language is a programming language. By adopting the method, the model development efficiency and quality can be improved.

The invention discloses a transformer substation secondary system modeling method and system based on multi-dimensional coupling, and relates to the technical field of power system automation, and the method comprises the steps: decomposing transformer substation secondary equipment, and constructing an equipment connection topological structure; analyzing electric signal transmission in combination with a working target to obtain a signal transmission topology and a geometrical relationship between the signal transmission topology and an equipment structure; building a multi-level model architecture; and a secondary system model is constructed by utilizing coding rule matching mapping and data fitting. The technical problems that in an information physical system, an existing substation secondary system modeling method is difficult to achieve coupling and hierarchical coding mapping of multi-dimensional information, consequently, a physical entity and a digital model cannot be effectively associated, and accurate analysis and control are difficult to meet are solved. The technical effects of equipment connection, signal transmission, coupling and hierarchical coding mapping of multi-dimensional information such as three-dimensional space and the like of the secondary system of the transformer substation are achieved, the physical entity and the digital model are effectively associated, and the requirements of accurate analysis and management and control under an information physical system are met.