1930 results about "Calculation technique" patented technology

The invention discloses a dynamic scheduling method for a cloud computing resource pool in the technical field of cloud computing. Comprising the following steps: S1, analyzing resource demands; s2, performing performance prediction on the resource pool through load prediction; s3, predicting task demand resources; s4, resource allocation decision making; s5, task scheduling; and S6, resource monitoring and feedback. According to the invention, load prediction is carried out through the long-short-term memory network, an adaptive mechanism is introduced, and a load prediction model is updated along with continuous change of data, so that the accuracy is improved; the resource demand of the task is predicted through a machine learning algorithm, a reasonable resource allocation decision is made according to the state of the current resource pool and the resource demand predicted by the task, the utilization rate of the resource is improved, stable operation calculation of the task is guaranteed, efficient task allocation and scheduling are achieved, resource utilization maximization is guaranteed, and the resource utilization rate is increased. And the change of the system load is effectively responded.

The invention discloses a major hazard source risk dynamic calculation system based on AI large model enabling, and relates to the technical field of risk calculation, and the system comprises a multi-modal data collection and preprocessing module which is used for achieving the synchronous collection of original data through the butt joint of an industrial protocol with a sensor; the heterogeneous data space-time fusion engine module is used for constructing a space-time diagram model and analyzing a nonlinear coupling relationship of multi-source data; the online incremental learning and model fine tuning module is used for triggering dynamic parameter adjustment based on the real-time data flow; a risk conduction probability calculation module; a multi-modal knowledge self-evolution module; and a self-adaptive threshold management and alarm module. According to the method, sliding window dynamic confidence interval calculation is combined with a time-varying confidence coefficient adjustment mechanism, self-adaptive fitting of a threshold value to real environment disturbance is achieved by embedding a periodic correction term, the bidirectional contradiction between detection sensitivity and false alarm suppression is effectively cracked, and a complete technical closed loop from dynamic sensing and cross-domain verification to rapid linkage is formed.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention relates to the technical field of high-pressure dredge pump simulation calculation, and discloses a construction and application method of a high-pressure efficient dredge pump simulation calculation model. A model is established based on dynamic fluid-solid coupling parameters (mud pulsating pressure, impeller dynamic stress and the like), soil multiphase rheological parameters (particle phase volume fraction, viscous resistance coefficient and the like) and pipeline transient resistance parameters (transient pressure drop gradient and the like), and factors such as rotating speed-power matching and the like are incorporated to calculate an operation envelope and a critical failure threshold. A multi-physics field coupling analysis system comprising parameter configuration, dynamic simulation (integrating a flow field and a structural vibration solver) and a safety evaluation module is developed, after actually measured data and self-defined parameters are input, dynamic flow velocity distribution, pressure pulsation characteristics and the like are obtained through fluid-solid bidirectional coupling calculation, and an optimal configuration scheme is generated. The system supports database management, multi-rheological model matching and three-dimensional visualization, the simulation precision and the engineering efficiency are improved, and support is provided for performance optimization and safety evaluation of the high-pressure working condition.

The invention relates to the technical field of electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

The invention relates to the technical field of PC (Personal Computer) terminal AI (Artificial Intelligence) computing, and discloses a method and a system for dynamically scheduling generative AI heterogeneous computing resources of a PC terminal. The system comprises a resource state acquisition module, a scheduling graph generation module, a resource fluctuation entropy analysis module and a scheduling decision engine module. The resource state acquisition module captures running state parameters of a GPU kernel, a CPU thread and a memory block in real time, and generates a resource state feature tensor through normalization processing; the scheduling atlas generation module analyzes and computes the node connection topology, extracts the correlation between the devices, and constructs a multi-dimensional scheduling atlas; the resource fluctuation entropy analysis module separates the load feature vectors, calculates the entropy of each calculation unit and generates a heterogeneous resource entropy matrix; and the scheduling decision engine module jointly analyzes the atlas and the matrix, identifies bottleneck node resource competition characteristics, generates a dynamic scheduling instruction set, adapts to generative AI task requirements, and ensures efficient and stable operation of the task.

The invention discloses an efficient auditing method and system based on an edge computing technology, and relates to the technical field of auditing methods and systems.The efficient auditing method comprises the following steps that S1, data collection and preprocessing are conducted, specifically, business data are collected in real time through various sensors and interfaces at edge nodes, and preprocessing is conducted; s2, edge side data analysis: performing real-time analysis on the preprocessed data by utilizing edge computing resources; s3, feature extraction and model training; s4, constructing a risk assessment and early warning model based on edge analysis and a model training result; s5, edge and cloud collaborative auditing; the edge node compresses and encrypts the processing result and the key feature data and then uploads the data to the cloud; and S6, visualizing an audit result and generating a report. According to the method, data acquisition and preprocessing, analysis and model training are realized through edge calculation, the auditing efficiency and accuracy are improved, a risk assessment and early warning model is constructed, the edge and the cloud are coordinated, and comprehensive, timely and reliable auditing support is provided for enterprises.

The invention discloses a computing task scheduling method and device in a multivariate heterogeneous environment and a medium, and relates to the technical field of parallel computing. The method comprises the following steps: monitoring resource states of various computing resources in a multivariate heterogeneous environment in real time, and quantifying the resource states to obtain corresponding resource information; under the condition that the to-be-processed calculation task is generated in the scheduling task list, performing demand analysis on the to-be-processed calculation task to determine a task demand report; calculating the task demand report and the resource information by using a preset scheduling strategy generation model to generate an intelligent scheduling strategy; according to the intelligent scheduling strategy, various computing resources are called to execute the to-be-processed computing task; and continuously monitoring the scheduling task list and the resource state so as to update the intelligent scheduling strategy corresponding to the to-be-processed calculation task based on the scheduling strategy strategy optimization logic.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention provides a multi-data fusion method and system based on edge computing. Belongs to the technical field of edge computing. The method comprises the following steps: receiving heterogeneous resource data from different sensors, equipment and sources through an edge task allocation unit; according to the encryption level corresponding to each task in the heterogeneous resource data, dividing each task data into data groups with identifiers according to a preset segmentation strategy; according to the operation condition of the edge task processor, performing distribution calculation on the data set according to a preset distribution strategy; after the distribution is completed, obtaining an identification sequence of the distribution mode; the distributed data set is calculated and processed through the edge task processor, and after data calculation and processing are completed, the result in the data set is sent back to the edge task distribution unit. Through intelligent scheduling of the edge task distribution unit, data from different sensors and equipment can be received and processed in real time, so that the data processing efficiency and response speed are improved.

The invention relates to the technical field of cloud side end computing, and discloses a cloud side end cooperative computing resource dynamic allocation method based on task-aware migration. The method comprises the following steps: acquiring real-time load characteristics and resource demand characteristics of calculation tasks in a cloud side end system, and dividing task priority queues in combination with task type identifiers; extracting historical execution records of tasks at cloud, edges and terminal nodes, constructing a task execution feature library, and generating a resource demand prediction model in combination with real-time load features; analyzing network transmission time delay characteristics of a cloud end and edge nodes, measuring real-time calculation capability fluctuation data of terminal equipment, and establishing an inter-node resource collaboration degree evaluation matrix; generating an initial migration strategy according to the prediction model and the evaluation matrix, monitoring actual resource occupancy deviation of the task, forming a final decision in combination with a node resource state correction strategy, triggering cross-node migration, and synchronously updating the priority queue and the evaluation matrix.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a method for improving the reasoning solution and training speed of a deep operator network model. According to the method, multi-level optimization is carried out on the PI-DeepONet model, and the reasoning solving speed and the training speed of the PI-DeepONet model are improved. According to the method, the SGEMM operator is customized, and compared with the best single-precision matrix multiplication in a basic linear algebraic library of a CUDA (Compute Unified Device Architecture) computing platform, the SGEMM operator can obtain a speed-up ratio of 1.5 times at most in a common model size range of PI-DeepONet. A fusion operator is designed according to the special structure of the modified MLP used in the model, the speed-up ratio of the end-to-end reasoning speed of the model is more than two times, and the calculation time of a graphics processor can be shortened by 5.7 times at most. According to the method, data parallel is used for carrying out distributed training on the model, a communication concealment strategy is used, and the expandability is kept at 83%-85%.

The invention discloses a distribution network low-voltage equipment state intelligent analysis method and system based on edge computing, and relates to the technical field of distribution network equipment state detection.The method comprises the steps that mesh network topology between edge computing nodes is constructed, and task allocation weights between the nodes are set; constructing a fault knowledge graph based on the multi-dimensional state features of the edge computing nodes; collaborative calculation is carried out based on the edge calculation nodes, calculation results of the edge calculation nodes are fused based on a weighted voting mechanism of a consistency algorithm, and a state evaluation result is obtained. According to the method, the multi-dimensional state features and the edge computing technology are combined, and intelligent analysis of the distribution network low-voltage equipment state is achieved. A mesh network topology is constructed based on electrical characteristics and environment characteristics, and efficient allocation of computing resources is realized through comprehensive evaluation of load complementation characteristics and state evaluation. The accuracy of fault propagation path identification is improved through double constraints of a feature association propagation chain and an equipment physical connection relationship.

The invention discloses a diastrophic stratum deformation analysis and calculation method for an active fault zone, and relates to the technical field of geological engineering calculation, and the method comprises the following steps: (1) constructing a three-field dynamic coupling model based on an energy density gradient, and unifying dimensions of physical parameters of a mechanical field, a seepage field and a temperature field as an energy density function; according to the diastrophic stratum deformation analysis and calculation method for the active fault zone, the problems of multi-physical field splitting and time scale solidification in a traditional method are solved by constructing an energy-mediated multi-field dynamic coupling model and a space-time decoupling algorithm. On the basis of an interaction path of energy density gradient tensor unified mechanics, seepage and temperature fields, instantaneous bidirectional feedback of three-field parameters is achieved, compared with a traditional model, the pore pressure prediction error is reduced, and the calculation efficiency is improved; and meanwhile, the joint simulation efficiency of the second-level seismic event and the ten-thousand-year tectonic motion is improved, the interface parameter continuity error is controlled, and the precision and stability of cross-scale modeling are improved.

The invention relates to a personalized federal learning method based on self-adaptive local model initialization and double knowledge distillation, and belongs to the technical field of edge computing. The method comprises the following steps: performing local model initialization on each client according to cosine similarity between a global model and a local model to obtain a self-adaptively initialized local model; respectively performing knowledge distillation on each client according to the global model and the historical local model, and updating parameters of the local model through double knowledge distillation; and the central server receives local models uploaded by all clients participating in training, and obtains a final global model based on dual dynamic aggregation weights of data quality and client contribution degrees. According to the method, the precision and convergence speed of the model can be remarkably improved when the data heterogeneity problem is processed, and good adaptability and robustness are shown in various edge calculation scenes.

The invention discloses a smart pipeline supervision system based on the Internet of Things, and relates to the technical field of smart pipeline supervision, and the system comprises the steps: deploying a plurality of sensors, and obtaining pipeline data; calculating a pipeline health value based on the pipeline data; based on the pipeline health value, performing preliminary analysis on pipeline health by using an edge computing technology, and performing preliminary processing; based on the preliminary analysis result, secondary analysis is carried out, potential fault points are identified, and response measures are executed; based on the response measures, an operation report is automatically generated. According to the method, multiple types of sensors are integrated, the pipeline state is comprehensively monitored in real time, health value abnormity is quickly responded through edge calculation, and fault diffusion is effectively prevented. The problem is accurately positioned through secondary analysis, and the maintenance strategy is optimized. On the whole, the intelligent monitoring level, the response speed and the maintenance efficiency of the pipeline system are remarkably improved, the operation risk and cost are greatly reduced, and the industry is promoted to be transformed to intelligent operation and maintenance.

The invention relates to the technical field of data linkage calculation, and provides a multistage document dependent freight trial calculation data linkage calculation method and system, and the method comprises the steps: expanding the attribute of a document node from a single business field to a composite vector containing a spatial-temporal feature through time information and geographic identification spatial-temporal dimension modeling; the dependency graph can capture the fine-grained dependency relationship of the freight service, and the space-time accuracy of data linkage is improved; when the basic information or the document information changes, the priority and the calculation path of the task queue are updated in real time through deep reinforcement learning, and calculation errors caused by change lag are avoided; through tensor synthesis operation, parallel subtask division, and a dual verification mechanism of a domain knowledge graph and a forced dependency edge, it is ensured that the whole process from dependency graph construction to calculation result output conforms to industry specifications, and the compliance and risk control ability of freight services are significantly improved.

The invention discloses a CXL-based training time consumption prediction method and device in heterogeneous computing power, and relates to the technical field of heterogeneous computing, and the method comprises the steps: obtaining task description information of a distributed training task; determining a neural layer calculation sequence of a neural network in the distributed training task and each heterogeneous computing power corresponding to the distributed training task according to the task description information; according to the neural layer calculation sequence, initial calculation process diagrams corresponding to the heterogeneous calculation power are constructed; adding a CXL-based parameter synchronization process to a backward transmission calculation node in the initial calculation process diagram to obtain each target calculation process diagram; respectively obtaining neural layer calculation time consumption and memory access time consumption of each node in the target calculation process graph; and according to the calculation time consumption of each neural layer and the access time consumption of each memory, predicting the total time consumption of training the distributed training task. The method solves the problems of long time consumption of parameter synchronization, low time consumption prediction accuracy and the like, reduces the time consumption of model parameter synchronization, and improves the accuracy of training time consumption prediction.

The invention discloses a quantitative scientific research project screening method based on patent situation analysis, and the method comprises the steps: obtaining patent data, and carrying out the vectorization of a patent text through a pre-trained Chine-BERT model; the method comprises the following steps: performing dimension reduction processing on patent text feature vectors by using a UMAP algorithm, performing clustering analysis on the feature vectors after dimension reduction by using a K-Means + + algorithm, identifying professional technical themes and technical effect themes, merging documents of the same clustering category into a text unit, calculating the importance of each word by using a TF-IDF algorithm, and extracting the feature vectors of the patent text by using the TF-IDF algorithm. Extracting a theme keyword of each cluster; and a technology-effect matrix is constructed based on a clustering result, and a quantitative screening result of the scientific research project is obtained through calculation of a technology life cycle, technology innovation evaluation and market competitiveness evaluation. The objective of the invention is to provide a comprehensive and efficient scientific research project screening method through an advanced semantic understanding patent clustering method, and help to rapidly screen scientific research projects.

The invention discloses an internal and external flow coupling numerical simulation method, device and equipment and a storage medium, and relates to the technical field of numerical calculation, a flow state at an initial moment is determined to obtain an external field wall surface temperature, an external field multi-component chemical unbalance N-S equation is solved according to external field fluid boundary conditions, and the internal and external flow coupling numerical simulation result is obtained. External fluid boundary heat flow at the next moment and external field pressure at the next moment are obtained as coupling interface boundary conditions for internal field solution, internal flow of the porous sweating structure is solved through an enthalpy method equation set of TPMM and LTNE to obtain parameters at the coupling boundary at the next moment, and finally high-enthalpy unbalanced external field parameters at the termination moment are obtained. An N-S equation under a multi-component chemical unbalance hypothesis is adopted to solve supersonic outflow, a chemical reaction between an active cooling working medium and supersonic incoming flow under a supersonic condition is simulated, and wall surface heat flow and structural thermal response of the porous sweating cooling system are obtained.

The invention belongs to the technical field of distributed computing, and relates to a distributed computing resource dynamic scheduling method and system suitable for a new energy scene. According to the method, the priorities of the calculation tasks are obtained by carrying out priority division on the calculation tasks according to the task characteristics. And a distributed computing node resource pool is established, so that centralized management and flexible scheduling of computing resources can be conveniently realized. Performance indexes of all nodes in the resource pool are collected and monitored, and reasonable allocation and efficient utilization of resources can be achieved. And scheduling the calculation tasks based on the priorities of the calculation tasks and the performance indexes of the nodes, generating a scheduling strategy, and ensuring that the tasks with high priorities preferentially obtain the calculation resources so as to meet the requirements on the task execution sequence in a new energy scene. And the scheduling strategy is adaptively optimized according to the performance index of each node, so that the utilization rate of resources and the stability of the system are improved. According to the method, the priority of the calculation task can be dynamically adjusted according to the characteristics of a new energy scene, and flexible scheduling of calculation resources is realized.

The invention relates to the technical field of data analysis and calculation, in particular to a distributed scheduling method based on tasks, nodes and link resources, which is applied to a solution for task scheduling and management in a large-scale distributed system. The method is particularly suitable for cloud computing, big data processing and distributed storage systems which need to process mass data and high-concurrency requests. And dynamically adjusting resource allocation by using real-time monitoring data and a prediction algorithm in combination with task priorities and resource requirements. Compared with traditional static resource allocation, a dynamic resource allocation mechanism is adopted, resource allocation can be intelligently adjusted in real time according to task requirements and node resource conditions, and therefore the resource utilization rate and the task execution efficiency are improved.

The invention discloses a multi-module-based analog simulation calculation method, and relates to the technical field of analog simulation calculation, and the method comprises the following steps: S1, system demand analysis and module division; s2, establishing a module model and setting parameters; s3, constructing a parallel computing environment; s4, module parallel computing task allocation; s5, data exchange and synchronization; s6, summarizing results; and S7, feedback optimization. According to the analog simulation calculation method based on the multiple modules, through modular design, a complex system is decomposed into the multiple relatively simple modules, each module can independently conduct simulation and calculation, and the maintainability and expandability of the system are improved; data and information exchange and cooperative work can be carried out among different modules through interfaces, so that comprehensive simulation and optimization of the whole system are realized; in addition, parallel computing resources are fully utilized, parallel computing is carried out on the multiple modules, the computing time is greatly shortened, and the computing efficiency is improved.

The invention discloses a GIS-based site vertical design earthwork volume calculation method and system, and relates to the technical field of earthwork volume calculation, and the method comprises the steps: constructing a site current situation DEM according to a topographic map; constructing a site design DEM according to the vertical design drawing; performing grid analysis on the site design DEM and the current situation DEM to obtain grid data of earth surface filling and excavation; in combination with control and regulation data, the earth surface filling and digging earth volume of each pattern spot of the planned land is calculated; calculating underground engineering excavation amounts of building underground spaces, subways and underground comprehensive pipe galleries of each pattern spot of the planned land; and calculating to obtain the total filling and digging amount of each pattern spot of the planned land, and generating a filling and digging thematic map. The system comprises a current situation DEM construction module, a design DEM construction module, an earth surface excavation and fill amount calculation module, an underground engineering excavation amount calculation module and a total excavation and fill amount calculation module. The method is simple, convenient, high in efficiency and precision, low in cost, wide in application range and more reasonable and accurate in calculation result by considering the unearthing amount of the underground engineering, and provides a more reliable basis for vertical design and construction of a site.

The invention provides a numerical calculation method for double-explosion-source underwater explosion impact cavitation loads, and belongs to the technical field of computer data processing-based explosion impact wave calculation, and the method comprises the following steps: solving a propagation process of far-field underwater double-explosion-source explosion impact waves in a two-dimensional acoustic flow field by using a local discontinuous Galerkin (LDG) method; and the cavitation characteristics of the flow field under the action of the double-explosion-source underwater explosion shock waves are researched. A flow field dynamic pressure result calculated by the method is compared with a result calculated by an acoustic finite element method. The result shows that the flow field dynamic pressure result calculated by the method is well matched with the calculation result data of the acoustic finite element method, and the effectiveness and the accuracy of the method are verified.

The invention discloses an efficient task scheduling method based on distributed collaboration, and belongs to the technical field of distributed computing. Aiming at the problems of non-uniform resource allocation, insufficient task type difference adaptation, lack of multi-task dependence global optimization and the like existing in the existing scheduling strategy, the invention provides the following technical scheme: firstly, classifying tasks based on calculation characteristics and establishing a multi-dimensional resource index; secondly, performing dynamic weight evaluation on the heterogeneous resources by adopting an optimal worst weighting method (BWM), and constructing a task-resource matching matrix; a task dependency relationship is modeled through a directed acyclic graph (DAG), and a global priority sequence is generated in combination with a list scheduling algorithm; and finally, single-task optimal node matching is realized by adopting an elimination selection method, and cooperative scheduling is performed on multiple tasks by applying an arithmetic optimization algorithm (AOA). According to the method, accurate resource matching of a calculation-intensive task and a data-intensive task is realized through three technical dimensions of task feature perception, resource dynamic adaptation and dependency relationship collaborative optimization.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a neural network large model efficient reasoning method based on multiple GPGPUs. The method aims to solve the problems of high communication overhead, non-uniform load, low resource utilization rate, high data transmission delay and the like among multiple processors. Dividing a calculation task into a plurality of sub-graphs through static analysis and mixed granularity partitioning of a model calculation graph; distributing the sub-graphs to the optimal GPGPU based on a weighted cost function in combination with heterogeneous resource perception and a dynamic mapping strategy; a global pipeline scheduling plan is constructed by using communication topology perception, and calculation and communication overlap are maximized; data are loaded in advance through a host side hierarchical caching and asynchronous prefetching mechanism, and transmission delay is hidden; multi-stream concurrent execution and event-based lightweight synchronization are adopted on each GPGPU, so that waiting overhead is reduced. According to the method, the reasoning delay can be remarkably reduced, the throughput and the hardware utilization rate are improved, and the method has good adaptivity and expandability.

The invention provides a parallel computing method and device, electronic equipment and a storage medium, and relates to the technical field of parallel computing, and the method comprises the steps: carrying out the first protocol operation of a target tensor based on each computing core in each stream processor cluster, and generating a data block containing the computing result of each computing core; writing a data block generated by each stream processor cluster into a shared cache; under the condition that each stream processor cluster completes the first protocol operation, reading a data block written by each stream processor cluster from the shared cache; and executing a second protocol operation on the data block read from the shared cache to generate a calculation result of the target tensor. According to the method and device provided by the invention, the parallel architecture and memory access characteristics of the artificial intelligence chip can be better matched, the unnecessary calculation delay and synchronization overhead of the cross-flow processor cluster in the parallel calculation process are reduced, the bandwidth utilization rate of the shared cache is improved, and the overall performance and calculation efficiency of parallel calculation are remarkably improved.

The invention discloses a visual setting calculation system and method, and relates to the technical field of power grid setting calculation, and the method comprises the following steps: obtaining operation data of a target system, extracting feature data, and constructing a knowledge graph of entities and relationships, the feature data including control data; constructing a system structure diagram, and generating a semantic enhancement diagram in combination with semantic information in the knowledge graph; inputting the semantic enhancement graph into a pre-trained graph neural network model, performing node feature aggregation and edge weight learning in combination with a prior rule of a knowledge graph, and outputting sensitivity scores of all control data nodes on performance indexes; according to the sensitivity score, constructing a boundary constraint condition, and generating a control data setting candidate solution based on a CSP solver; performing parameter response simulation verification on the control data setting candidate solution, and screening out an optimal control data setting candidate solution; according to the application, accurate sensitivity scoring can be realized by fusing the knowledge graph, the semantic enhancement graph and the graph neural network.

The invention discloses a power network parameter-based power flow system ill-conditioned evaluation method and system, and mainly relates to the technical field of data calculation of a power system. Comprising the following steps: generating a multi-dimensional evaluation index set comprising a network topology structure index, an admittance matrix characteristic index and branch impedance parameter statistics according to basic network parameters of a power system; through comprehensive analysis of the generated multi-dimensional evaluation index set, traditional Jacobian matrix singular value calculation is replaced, and the ill-conditioned condition number of power flow calculation of the power system is evaluated; according to the power grid type and the operation scene, dynamically adjusting the judgment threshold value of each index, and outputting the ill-conditioned risk level and the optimization suggestion. The method has the advantages that calculation independent of Jacobian matrix singular values is achieved, dynamic quantitative evaluation is carried out on bad operation conditions of the system, and meanwhile the load flow calculation stability evaluation precision of a large-scale power grid is remarkably improved.