792 results about "Intelligent computing" patented technology

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.

The invention discloses an intelligent computing power integration service management method and platform based on cloud side-end cooperation, and relates to the technical field of computing power integration management, and the method comprises the steps: sensing the computing power resource state of a side-end cooperation port and terminal equipment in real time at a cloud controller, and building a resource topological graph; a dynamic task demand is introduced, and a computing power scheduling vector is generated; carrying out lightweight segmentation on the cloud training model, and determining adjacent edge nodes under hierarchical limitation to form a regional elastic cluster; and deploying a digital twin simulation engine rehearsal computing power distribution scene, establishing a heterogeneous resource pooling mechanism, and carrying out computing power integration service management. The technical problems of low management efficiency and insufficient utilization rate of heterogeneous computing power resources in the prior art are solved, and the technical effects of realizing efficient management of intelligent computing power integration services and improving the utilization rate of the heterogeneous computing power resources are achieved.

The invention discloses a distributed model training-oriented routing reconstruction method in a hybrid photoelectric network, and belongs to the field of network communication optimization. The method comprises the following steps: constructing a collaborative routing model based on a current network flow state and topological information, and introducing an adaptive weight mechanism to dynamically adjust the priority of optical channels and electric packet forwarding, thereby realizing efficient collaborative utilization of multi-layer forwarding resources. According to the method, path reconstruction can be rapidly completed according to task characteristics and a network dynamic state, so that the system resource utilization rate is improved, and the maximum task completion time delay is reduced. Compared with a traditional fixed routing and semi-dynamic routing method, the method has the advantages that the average completion time delay and the maximum time delay are improved by more than 45% in the large-flow environment generated by large-scale model training, and the method has good expandability and real-time performance and is particularly suitable for burst flow intensive intelligent computing cluster scenes.

The invention relates to a mineral reserve dynamic monitoring method and system based on satellite remote sensing and a block chain. The method comprises the following steps: acquiring original remote sensing data and metadata of a mining area, executing hash operation on the data by utilizing a satellite-borne trusted execution environment to generate integrity proof, and performing digital signature to construct a trusted data packet; transmitting the data packet to a ground station to verify the validity of the signature, extracting verified data, and submitting the verified data to a block chain for evidence storage; based on the on-chain evidence storage data, using a deep learning model to extract earth surface features and calculating reserves changes; and finally, a reserve change result is returned to the block chain, and dynamic supervision and early warning are realized through an intelligent contract, so that a technical system from data source credible guarantee, transmission process tampering prevention, reserve intelligent calculation to supervision automatic execution is constructed. The problems that in traditional mineral product monitoring, the authenticity of a data source is lack, the processing process is not traceable, and supervision response is lagged are solved, and the reliability, auditing performance and supervision timeliness of a mineral product reserve dynamic monitoring result are improved.

The invention provides an intelligent computing fusion network routing system and a routing method thereof. The system comprises an entrance gateway, a routing forwarding node, a computing node and a computing network controller, the computing network controller is used for carrying out path selection and computing resource allocation in combination with a real-time network state and load information of the computing node, dynamically generating and updating a computing network routing table through a computing network fusion routing algorithm, and issuing the computing network routing table to the entry gateway and the routing forwarding node; and the entry gateway receives and stores the computing network routing table issued by the computing network controller as a data stream access point, receives a computing power data stream which is input from the outside and carries a computing power service request, executes computing power routing table matching according to the computing power service request, and forwards the computing power data stream to a proper routing forwarding node according to a matching result. According to the invention, by optimizing the joint scheduling of the computing power service paths and the computing nodes, a plurality of paths of the anycast computing power routing and the weights of the paths are generated, the service flow level load balancing is realized, and the end-to-end time delay is reduced.

The invention provides a super-node system which comprises an intelligent computing resource pool and a general computing resource pool, the intelligent computing resource pool comprises a first in-pool switching module, a first inter-pool switching module and a plurality of GPUs, the general computing resource pool comprises a second in-pool switching module, a second inter-pool switching module and a plurality of CPUs, and decoupling of heterogeneous computing resources is achieved. The GPUs in the same intelligent computing resource pool and the different intelligent computing resource pools can communicate through the first in-pool switching module, the CPUs in the same general computing resource pool and the different general computing resource pools can communicate through the second in-pool switching module, and the system can provide intelligent computing and general computing resources at the same time. And the intelligent computing resource pool and the general computing resource pool can be expanded respectively, so that elastic matching of resources is realized, and the utilization rate is improved. Intelligent computing resources and general computing resources are pooled and deployed in different cabinets, deployment decoupling of heterogeneous resources is achieved, the single-cabinet GPU density of the intelligent computing resource cabinets is improved, the influence range of single-point faults is reduced, and maintenance is easy.

The invention relates to a cross-modal document information extraction method based on space-semantic alignment, and belongs to the field of artificial intelligence, computer vision and natural language processing. According to the method, the spatial feature and semantic information bidirectional alignment model is designed, by constructing the spatial feature and semantic feature bidirectional alignment model, the document layout information can dynamically adjust attention distribution of text semantic features, meanwhile, semantic information reversely optimizes the spatial features, collaborative modeling of spatial layout and semantic information is achieved, and the document layout efficiency is improved. Therefore, the accuracy and robustness of complex document information extraction are improved. According to the method, a hierarchical cross-modal information extraction model is designed, through the hierarchical cross-modal information extraction model, the overall structure of a document is recognized on the global level, local key content is focused on the regional level, fine modeling is conducted on fine-grained texts and visual elements on the entity level, and accurate recognition of a cross-modal entity and the semantic relation of the cross-modal entity is achieved; and the generalization ability and applicability of information extraction are enhanced.

The invention discloses an intelligent computing center automatic operation and maintenance management method based on computing power resource allocation, and relates to the technical field of resource management, and the method comprises the steps: obtaining real-time computing power load data and a to-be-processed task queue of an intelligent computing center; constructing a resource state matrix according to the real-time computing power load data, performing historical data backtracking analysis on the resource state matrix by using a sliding window algorithm, and calculating a resource load predicted value and a resource availability score of each computing node; based on the resource availability score, an improved genetic algorithm is adopted to carry out optimal allocation solution on the constructed task-resource matching matrix, and an optimal task allocation scheme is generated; and converting the optimal task allocation scheme into a resource allocation instruction set, sending the resource allocation instruction set to each target computing node to execute task scheduling, and updating the resource occupation state of the corresponding node in the task-resource matching matrix. According to the invention, the technical jump from passive response type management to active prediction type management is realized, and the resource configuration efficiency of an intelligent computing center is improved.

The invention discloses a multi-data center multi-computing power collaborative optimization method and system based on computing power and refrigeration system comprehensive energy consumption cost, and a storage medium. The method comprises the following steps: S1, uniformly dividing a total conventional computing power resource and a total intelligent computing power resource which need to be scheduled into a plurality of sub-computing power resources; s2, sequentially allocating and starting a data center for each conventional sub-computing power resource; S2.1, calculating the cost of each data center after the conventional sub-computing power resource is started, and selecting to start the data center with the minimum cost; s2.2, repeating the step S2.1 until the total conventional computing power resource needing to be scheduled is reached; s3, sequentially allocating and starting a data center for each intelligent sub-computing power resource: S3.1, solving an optimal operation period deployment scheme of the intelligent sub-computing power resource in each data center through a genetic algorithm, calculating the cost after starting the intelligent sub-computing power resource according to the optimal operation period deployment scheme, and selecting to start the intelligent sub-computing power resource with the minimum cost; s3.2, repeating the step S3.1 until the total intelligent computing power resource needing to be scheduled is reached; according to the method, the computing power resource operation cost can be minimized.

A full-automatic movie and television video production system based on digital actors belongs to the field of artificial intelligence, computer graphics and movie and television production and comprises a creative engine unit, a production unit, an intelligent shooting unit, a post-processing unit and a quality inspection and rendering unit. The method has the advantages that 1, the manufacturing efficiency is greatly improved, the traditional film and television manufacturing period is usually 6-12 months, the manufacturing period can be shortened to 4 hours by the method, and the efficiency is improved by about 1000 times; 2, the manufacturing cost is obviously reduced: the manufacturing cost of traditional movies and other movies is usually 5 million to 20 million dollars, but the method only needs the computing power cost, and the cost is less than one percent of that of the traditional movies; 3, the style consistency is guaranteed, traditional film and television production depends on personal ability, the style consistency is difficult to guarantee, and the style consistency is strictly guaranteed through an algorithm and is improved by 100%.

The application discloses a student intelligent psychological state evaluation system, and relates to the technical field of data communication. First, the quality of student published data is checked and automatically corrected to ensure the reliability of subsequent processing input. Then, data sequence clusters are divided through clustering analysis, and resource scheduling strategies are dynamically adjusted based on the similarity confidence of each cluster to realize the optimization of analysis frequency and sampling rate of different clusters. Meanwhile, the resource scheduling strategy in the cluster is dynamically adjusted, and the optimal task allocation plan is intelligently calculated and issued according to the scheduling exposure index and resource proportion index of each sequence cluster to realize the fine matching of computing power and manpower. Finally, the real-time monitoring of key performance indicators improves the real-time performance and accuracy of student psychological state evaluation, and significantly optimizes the resource utilization efficiency and stability of the system in a high-concurrency scenario.

The invention discloses a heterogeneous intelligent computing power optimization management scheduling system for accelerating a large model reasoning task, and relates to the technical field of computing power optimization management scheduling. The video memory fragmentation problem in a long sequence scene is converted into a controllable block migration task, and the performance bottleneck of a traditional video memory exchange mechanism is broken through; based on an operator-level scheduling strategy of a hardware capability fingerprint database, position coding and other compute-intensive tasks are accurately matched with vector instruction set hardware, and resource mismatch loss caused by black-box scheduling is eliminated; an expert selection process is reconstructed by an integer routing and counting sorting algorithm, near-lossless reasoning is realized at a limited node of an instruction set, and the potential value of an old computing power pool is activated.

The invention discloses a reinforcement learning calculation simulation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence computing, and the method comprises the steps: inputting the determined current model parameter configuration, current hardware configuration and current working load into a target simulation system to obtain a plurality of parallel grouping combinations, determining a target simulation system according to the current hardware configuration, determining an effective parallel packet combination from the plurality of parallel packet combinations based on a preset Monte Carlo method, inputting the effective parallel packet combination into a simulator of a preset neural network model, and performing delay time calculation according to the effective parallel packet combination through the simulator to obtain a delay time sequence; and the combination corresponding to the shortest delay time is used as a target parallel grouping combination, so that the technical problems of mismatching of simulation scenes, insufficient precision and lack of effective support for heterogeneous clusters are solved, reliable performance prediction and optimal parallel strategy suggestions are provided through high-precision performance modeling and automatic exploration, and the method is suitable for large-scale popularization and application. Therefore, the resource consumption of large-scale GRPO training is reduced.

The invention relates to the technical field of load balancing optimization, in particular to an intelligent computing center load balancing and energy-saving optimization method and system, and the method comprises the following steps: obtaining task execution duration and resource state data, calculating a multi-dimensional index, carrying out the weighted analysis, dynamically outputting a regulation and control scheme, and achieving the load balancing and energy-saving optimization of an intelligent computing center. According to the method, response time comparison based on a service level protocol is introduced, a task execution duration distribution structure is clearly recognized, the abnormal task recognition precision is improved, and the judgment accuracy of an overload trend is enhanced by comparing resource utilization rate changes of adjacent periods in the aspect of node state monitoring; in the task allocation process, the available memory of the node and the resource adaptation degree are combined, real performance load performance is obtained through energy efficiency and throughput data cross analysis, task allocation better conforms to the actual energy consumption level, the resource waste risk is effectively reduced, the response flexibility of a scheduling strategy to system load changes is enhanced, and the task allocation efficiency is improved. And the task execution efficiency and the energy efficiency distribution accuracy are improved.

The invention relates to a power grid project multi-dimensional digital supervision method based on big data, and belongs to the technical field of power grid project digital supervision, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-dimensional data, and constructing a supervision system through four supervision layers and a digital twinborn body; s2, multi-dimensional supervision indexes are intelligently calculated, and a supervision dimension index system is constructed; s3, risk dynamic early warning and root cause tracing; and S4, decision optimization and closed-loop control. According to the method, multi-source heterogeneous data integration is carried out by fusing multi-dimensional data sources of BIM model, GIS positioning, Internet of Things sensing data, environment monitoring and business system data, so that single data source deviation is avoided, information islands are broken, multi-target collaborative optimization is realized, a'supervision-decision-execution 'closed loop is realized, an optimal regulation and control strategy is convenient to calculate, and the system performance is improved. An optimal regulation and control strategy can be generated based on the reinforcement learning model, and an early warning instruction is directly connected to a field terminal, so that the power grid project monitoring and early warning efficiency can be effectively improved.

The invention provides a method and device for an intelligent computing center cloud platform to adjust model training parameters according to computing power operation states, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. The first working component is a component deployed in a node in the intelligent computing center cloud platform, the target link comprises a data preparation link, a model training link, a model evaluation link and / or a model test link in the first working component, and the target link performs data processing or model training based on preset hyper-parameters; s2, calculating target correction parameters corresponding to the operation related parameters in a preset period based on a preset regulation and control model; and S3, adjusting the preset hyper-parameter based on the target correction parameter to obtain a target hyper-parameter. The computing power utilization rate of the intelligent computing center cloud platform can be greatly improved, and then the model training efficiency is greatly improved.

The invention provides a cross-device real-time dynamic adjustment grouping system which comprises an identity authentication module, a distributed synchronization module, a rule engine module, a trigger engine module and an algorithm engine module, and all the modules are coupled and interacted through standardized data interfaces. Unified management and authentication of user identities in a multi-device environment are realized by establishing a user-device-grouping ternary association relationship, static distribution and dynamic adjustment of user permissions are realized by adopting a dynamic adjustment strategy, a multi-dimensional grouping rule is formulated to screen out users meeting conditions as the same group, and grouping is dynamically adjusted by adopting a multi-dimensional triggering strategy. Based on a K-means clustering algorithm in combination with a greedy strategy, intelligent calculation and optimization of dynamic grouping are performed, data synchronization among devices is realized based on a distributed architecture, and real-time dynamic adjustment and efficient cooperation of grouping in a cross-device environment are realized.

The invention provides a complex formula intelligent calculation method for the ship field, and the method comprises the steps: obtaining a complex formula calculation file in a ship design document, carrying out the multi-modal analysis of a text, an image, a formula and a table in the document, and converting the text, the image, the formula and the table into structural data; based on the structured data, calling a large language model to fill a standardized formula calculation document template, and generating a standardized formula calculation document including formula expression, parameter definition, calculation steps and unit expression; when a formula is incomplete, variables are missing or conditions are not explained, missing fragments and conditions are automatically complemented by utilizing a knowledge graph and a large model reasoning result; and performing cross-modal error correction and physical consistency verification on the document, and if logic conflicts are found, outputting correction suggestions and automatically updating formula expressions. According to the method, automatic analysis, complementation, inspection and calculation of complex formulas in the ship design field can be realized, and the standardization and reliability of engineering calculation are improved.

The invention discloses a blasting parameter intelligent computing platform, which belongs to the field of blasting engineering, and comprises a distributed computing resource scheduling module, which adopts a distributed computing architecture to decompose a computing task into a plurality of sub-tasks, and distributes the sub-tasks to different computing nodes for parallel computing; through an intelligent scheduling algorithm, according to factors such as the load condition and the computing power of each computing node, computing tasks are dynamically allocated, dispersed computing resources in a network are fully utilized, the utilization rate of the computing resources is improved, and dependence on single high-performance computing equipment is reduced; data are processed in real time through the edge calculation preprocessing module, and the data transmission time is shortened; the lightweight calculation model library can quickly complete blasting parameter calculation; and the real-time feedback and dynamic optimization module realizes real-time adjustment and optimization of blasting parameters, so that accurate blasting parameters can be provided in time according to actual conditions in the tunnel construction process, and the construction progress is improved.

The invention discloses an unmanned aerial vehicle dynamic projection map construction method and system based on spatio-temporal data fusion, and belongs to the technical field of unmanned aerial vehicle video monitoring and geographic scene fusion, and the method comprises the steps: obtaining multi-modal flight data in real time based on an unmanned aerial vehicle, and the multi-modal flight data comprise video image data and spatio-temporal reference data; and performing data preprocessing on the multi-modal flight data to obtain route data. And constructing an unmanned aerial vehicle dynamic projection model, and performing space-time synchronous projection rendering on the route data to obtain an unmanned aerial vehicle dynamic projection map. And constructing a dynamic calibration model, and correcting the dynamic projection map of the unmanned aerial vehicle. According to the method, an integrated dynamic geographic information sensing system is constructed through deep cooperation of a spatio-temporal data fusion framework and an intelligent calculation engine, accurate mapping of the geographic space driven by spatio-temporal reference fusion is realized, millimeter-level space registration capability is constructed through multi-modal data intelligent solution and terrain adaptive rendering, and the real-time dynamic geographic information sensing system is constructed. And a technical breakthrough is formed in the dimensions of accurate positioning, data fusion, real-time response and the like.

The invention discloses a performance prediction method for an intelligent computing system, and the method achieves the efficient and explainable performance prediction through mixed analysis modeling. The method mainly comprises the steps of performing tracking and programmed synthesis on a target deep learning model, and generating a final operator graph containing a complete calculation and communication operation sequence; a dynamic overlap analysis model based on available parallelism is adopted, independent calculation and memory access efficiency is predicted in combination with a machine learning method, and GPU operator execution delay is accurately estimated; based on a configurable network topology, applying a queuing theory network model, and carrying out white box type modeling on queuing and transmission delay of communication operation in each node in a network path; and the calculation and communication prediction delays of the operators are aggregated according to the scheduling characteristics of the distributed strategy to obtain the final system end-to-end delay. The method provided by the invention can ensure the precision while remarkably shortening the prediction time, and has the advantages of high efficiency, high interpretability and the like.

The invention discloses a reference crop evapotranspiration prediction method and device, a medium and equipment, and relates to the field of agrometeorology, water resource management and intelligent computation.The reference crop evapotranspiration prediction method comprises the steps that a historical reference crop evapotranspiration ET0 sequence and historical day-by-day meteorological factor data are obtained, and day-by-day meteorological data are determined according to the day-by-day meteorological factor data; empirical mode EMD decomposition is carried out on the historical ET0 sequence, and historical ET0 multi-scale time-frequency features are determined; serializing the day-by-day meteorological data, and carrying out weight dynamic allocation and fusion on the day-by-day meteorological data processed by adopting a multi-head self-attention mechanism to generate meteorological factor data dynamic characteristics; performing time sequence feature analysis and fusion on the meteorological factor data dynamic features and the historical ET0 multi-scale time-frequency features to generate meteorological factor features and historical ET0 features; and carrying out dynamic weighted fusion on the meteorological factor features and the historical ET0 features, determining fused features, carrying out time sequence feature analysis on the fused features, and obtaining a reference crop evapotranspiration ET0 prediction result.

The invention discloses a task hierarchical scheduling method for an intelligent computing fusion network, belongs to the technical field of the intelligent computing fusion network, and designs a double-layer task scheduling mechanism (ICCN-DSF) for the intelligent computing fusion network to effectively cope with task scheduling challenges in a large-scale heterogeneous resource environment. Wherein the global task scheduling layer is based on a CIEK-Means algorithm, integrates geographic position constraint and group intelligent optimization, and realizes efficient clustering and dynamic matching of resource ethnic groups; and the local task scheduling layer constructs a three-dimensional state action space and a multi-target reward function through a reinforcement learning algorithm to complete fine-grained resource allocation. According to the method, the scheduling complexity is remarkably reduced, meanwhile, the cross-domain connection requirement is reduced through a layering mechanism, global resource coordination can be achieved only through a small number of WAN links, the operation and maintenance cost and the safety risk are reduced, meanwhile, the cooperation potential of the computing power and the network is fully excavated, and efficient and robust scheduling support is provided for intelligent services in complex scenes.

The invention provides an electric energy metering device adaptive calibration system based on artificial intelligence, and belongs to the technical field of electric energy metering calibration. The system comprises a data acquisition module used for acquiring electric energy information, a data transmission module used for transmitting acquired power consumption data; the preprocessing module is used for receiving and preprocessing the transmitted power consumption data; the intelligent calculation module is used for generating a calculation result; the execution module is used for calibrating the electric energy metering device based on the regulation and control strategy; and the feedback module is used for verifying the accuracy of the metering result of the electric energy metering device. Calibration parameters of the electric energy metering device are automatically adjusted through the intelligent calculation module according to power utilization environment changes, factors such as power grid fluctuation and equipment aging are responded in real time based on the optimal control theory, the calibration process is optimized, and metering precision is guaranteed. According to the design, the adaptability and stability of the device are remarkably improved, manual intervention is reduced, the calibration cost is reduced, and continuous high-precision operation is achieved in a complex electric power environment.

The invention discloses a multi-dimensional dynamic sensing and intelligent computing power scheduling method and device oriented to a computing power network. The method comprises the following steps: deploying a plurality of target components forming multi-dimensional dynamic perception at a terminal, an edge node and a regional computing power center, and collecting computing power indexes and network indexes of each node; analyzing indexes in a preset period through a time sequence prediction model, and obtaining computing power bottleneck risk and time delay attenuation information; according to the type of a to-be-executed task, dynamically obtaining a computing power dimension weight and a time delay dimension weight, and combining a computing power bottleneck risk and time delay attenuation information to construct a cost function; determining a node corresponding to the minimum cost function and taking the node as a target node; and obtaining path costs corresponding to all feasible migration paths according to the target node, determining the migration path corresponding to the minimum path cost and taking the migration path as a target migration path, and triggering intelligent scheduling of computing power according to the migration path. According to the method, collaborative perception, intelligent decision and closed-loop control of computing power and network resources can be realized.

The invention discloses an intelligent computing cluster parallel training performance optimization method oriented to large model training, and relates to the technical field of artificial intelligence computing. Hardware feature indexes of heterogeneous computing nodes in an intelligent computing cluster are collected in real time, a hardware topological graph is constructed, and an optimal computing power combination suitable for the hardware topological graph is calculated; the method comprises the following steps: selecting an optimal communication path by adopting a self-adaptive routing algorithm, coding and decoding transmission data in combination with a mixed precision compression technology to realize efficient transmission of the transmission data, constructing a fault prediction model based on a deep learning algorithm, and inputting real-time data in a hardware monitoring log into the fault prediction model to obtain a prediction result. Prediction results are classified and stored according to a preset result classification standard, check points are trained based on the prediction results to achieve rapid recovery, the super-node computing power utilization rate is improved, meanwhile, a communication path can be dynamically adjusted according to the network congestion state, and the fault recovery speed is improved.

The invention discloses an embedded AI intelligent computing power architecture method, and particularly relates to the technical field of artificial intelligence processing architecture. Collecting a resource state parameter set R of the embedded device; obtaining a to-be-executed AI task set T, wherein each task comprises model complexity, real-time requirements, expected response duration and priority; constructing a computing power resource allocation evaluation function F based on R and T, and outputting a task scheduling priority score; allocating tasks to the embedded AI module according to the allocation scheme and executing reasoning; the resource state is dynamically monitored in the task running process, and if it is predicted that resources are about to be overloaded, a scheduling function F is triggered to reconstruct a resource allocation scheme; performing iterative optimization on weight parameters in the function F based on task history feedback; by means of the method and device, optimal adaptation of multi-task concurrent scheduling can be achieved under the condition that resources are limited, the computing power resource utilization rate, the response efficiency and the system stability are improved, and the method and device are suitable for various edge side AI scenes.