30 results about "Multidimensional model" patented technology

A decoupling control method for a six-axis vibration table and a system for implementing the decoupling control method for a six-axis vibration table, capable of performing real-time adjustment on the vibration table according to specific conditions, thereby enhancing the robustness of the system, and achieving high reliability and good accuracy. The method comprises: acquiring data information of a target six-axis vibration table; representing a dynamic coupling model of the target six-axis vibration table in a global linearization manner to construct a koopman predictor; using a deep neural network to obtain a feature function and an operator matrix of the predictor; performing training to obtain a deep koopman estimator, and obtaining state information of the target six-axis vibration table; using the deep koopman estimator as a prediction model to design a multi-dimensional model prediction controller; using a fuzzy neural network to perform online tuning; and using the tuned multi-dimensional model prediction controller to control the target six-axis vibration table, so as to complete decoupling control over the target six-axis vibration table.

This invention relates to the technical field of power government administration and proposes a cross-departmental government big data collaborative processing method based on computing power platforms and data fusion. The method comprises the following steps: S1. Deploying a heterogeneous computing power cluster to establish a data processing platform for dynamic scheduling of computing resources, including CPUs, GPUs, FPGAs, and edge computing nodes; S2. Acquiring multidimensional data on power grid equipment, loads, industry electricity consumption, and government policies; and S3. Designing a cross-departmental data access system based on a zero-trust architecture to develop multidimensional models for power grid risk prediction, industry energy consumption analysis, and carbon emission accounting in power management. By applying a spatiotemporal joint probability prediction and conflict priority sorting algorithm to power-government emergency response, this method achieves intelligent and precise cross-departmental collaboration, allowing for the rapid resolution of critical power outages based on priority, thereby reducing the harmfulness and unpredictability of power outages.

The invention relates to the technical field of data processing, in particular to a threat recognition system and method.The method comprises the steps that a self-adaptive collection module obtains network flow data and operation log output feature vectors; the multi-dimensional rule module generates rule confidence; the statistical modeling module outputs an abnormal probability score; the machine learning module outputs a classification probability in response to the abnormal probability score dynamic selection model combination; the simulation decision module is fused with the multi-source data to execute threat diffusion simulation and output hazard indexes; the grading execution module triggers grading response according to the hazard index; and the feedback optimization module adjusts model parameters in a cross-layer manner. According to the method, noise interference is eliminated through feature optimization, a multi-dimensional model is fused to calibrate an output result, a threat grading mechanism preferentially responds to key threats, and closed-loop feedback is continuously injected into a false alarm data training model, so that the problem of high false alarm rate caused by insufficient training data coverage of a machine learning model in a complex dynamic network environment is solved; security resource allocation is optimized and key threat response delay is shortened.

A system and method for detecting and counting objects in a warehouse environment is disclosed. The system may receive sensor data indicative of a warehouse environment. The method includes generating, based on the sensor data, a reconstruction of the warehouse environment, The reconstruction can include one or more slots comprising a plurality of objects. The method includes determining, based on the reconstruction, a number of occluded objects. The method includes determining, based on the reconstruction and the number of occluded objects, a total number of objects within each slot of the one or more slots.

The invention provides a multi-dimensional model processing method, device, system and equipment. The method comprises the steps that a query command sent by a client side is received, the query command comprises an index and is used for querying dimension members in a multi-dimensional model, the multi-dimensional model comprises multiple dimensions, each dimension comprises one or more dimension members, each dimension member comprises an index field, and the index field is used for searching the dimension members in the multi-dimensional model; the length of the index field of the same dimension is determined according to the length obtained by compressing the original index field of each dimension member of the dimension, and matching the index fields of the plurality of dimension members of the multi-dimensional model according to the index to obtain a first dimension member with the index field the same as the index. According to the scheme, the dimension members of the multi-dimensional model can be compressed, so that the query efficiency of the dimension members in the multi-dimensional model is improved.

The embodiment of the present application provides a data storage method, device and storage medium. In the embodiment of the present application, the multi-dimensional model data of a spatial object is divided into model data of multiple layers of entity objects from the constituent elements of the spatial object, and for the model data of each layer of entity objects, a reference object is introduced to compressively store a plurality of target entity objects that can be stored in combination in each layer of entity objects. On the one hand, the plurality of target entity objects can share one copy of model data, the compressive storage of the model data of each layer of entity objects is realized, the storage amount of the model data of the spatial object can be reduced, and storage resources can be saved. On the other hand, the upper layer entity objects in the spatial object can also share one copy of the model data of the lower layer entity objects, the storage amount of the model data of the spatial object can be further reduced, storage resources can be saved, and double compression of the multi-dimensional model data of the spatial object is realized.

The invention discloses an intelligent network proxy access system, which comprises a proxy resource layer used for constructing various types of proxy IP pools, storing IP resources in the corresponding proxy IP pools in a classified manner, and performing quality rating processing on the IP resources at the same time; the intelligent scheduling layer is used for synchronizing tasks with an intelligent marketing multi-dimensional model operation platform and a global data acquisition and analysis system in real time and realizing task linkage scheduling; the compliance control layer is used for simulating and avoiding platform rule risks according to the multi-platform agent compliance library in combination with human behaviors; the adaptive docking layer is used for providing a multi-type standardized interface to be docked with the intelligent marketing multi-dimensional model operation platform and the global data acquisition and analysis system; and the monitoring operation and maintenance layer is used for providing a multi-dimensional monitoring board and intelligent alarm and supporting full-link operation and maintenance management. The method has the advantages of high adaptability, high stability, high concurrent support, high compliance, service value increment and the like.

The present disclosure relates to the technical field of data processing, and specifically discloses a multi-dimensional statistical data processing method and device and computer equipment. The method comprises the following steps: acquiring transaction data of a business system in real time, and storing the transaction data in a message queue; performing initial processing on the transaction data in the message queue to obtain transaction detail data and initial processing data; acquiring metadata configuration information of a multi-dimensional model, and acquiring a current result of multi-dimensional statistical data to be processed according to the metadata configuration information; and calculating the multi-dimensional statistical data according to the transaction detail data, the initial processing data and the current result to obtain an update result of the multi-dimensional statistical data. The present disclosure adopts a set of multi-dimensional models to uniformly plan data models, realizes real-time collection and processing of transaction data, and improves data processing timeliness.

This invention discloses a distributed, block-based, multi-dimensional storage method and apparatus for seismic data volumes. The method includes: separating the header information from the seismic traces in the acquired seismic volume file and storing the header information in a preset storage space; determining multiple dimensions based on the keywords of the seismic traces to construct a multi-dimensional model; dividing the multi-dimensional model into multiple multi-dimensional data cubes and determining the name of each multi-dimensional data cube, as well as the extreme values ​​of operational values, the total number of location numbers, and the total number of multi-dimensional data cubes in each dimension; storing the multi-dimensional data cubes on a distributed storage system using a mapping algorithm; calculating the physical storage location of the target data based on the acquired target data information, and extracting the target data from the distributed storage system and / or the preset storage space. This method improves data storage flexibility and query efficiency by optimizing the seismic data storage structure and index calculation.

The invention provides a partner training strategy engine optimization method, system and device and a medium, and the method comprises the steps: obtaining a first partner training strategy engine and a to-be-partner training model in a current partner training optimization process, and business training data; the first partner training strategy engine is an original partner training strategy engine or a second partner training strategy engine obtained in a previous partner training optimization process; constructing a digital twin environment according to the service training data; according to the digital twin environment and the first partner training strategy engine, performing simulated environment partner training on the to-be-partner training model to obtain multi-dimensional model index data of the to-be-partner training model; and according to the multi-dimensional model index data, performing reward optimization on the first partner training strategy engine to obtain a second partner training strategy engine. According to the method, a partner training strategy engine can be provided, the partner training effect of the model can be effectively improved, and then the performance of the partner training model facing complex unknown challenges can be improved. The invention relates to the technical field of artificial intelligence.

The invention discloses a data processing method and device, and relates to the field of data processing, and the method comprises the steps: obtaining input data of a multi-dimensional model, the input data indicating a to-be-updated value of a specified node in the multi-dimensional model; the multi-dimensional model comprises at least one dimension, each dimension comprises one or more levels, each level comprises one or more nodes, and the specified nodes are nodes in all nodes included in the multi-dimensional model; determining an increment value of the specified node, wherein the increment value of the specified node indicates a numerical value variation between a to-be-updated value of the specified node and a storage value of the specified node in the multi-dimensional model; and updating a storage value of a relative node of the specified node based on the increment value of the specified node, wherein the relative node of the specified node comprises nodes of other levels influenced by the specified node in the multi-dimensional model. The storage value of the affected relative node is updated through the increment value of the specified node, the calculation amount of multi-dimensional aggregation calculation is reduced, the calculation performance is improved, and the existing calculation bottleneck problem is solved.

The invention discloses a data verification and early warning system based on an OLAP engine, and relates to the technical field of data processing. The system comprises a data processing module, a modeling optimization module and a data verification module: the data processing module is used for accessing enterprise original data, verifying data access accuracy based on a preset access standard, generating access abnormity prompt information if the data access accuracy does not reach the standard, and forwarding the data to a data cleaning module; if the data access accuracy reaches the standard, directly entering a modeling optimization module; the modeling optimization module is used for executing dynamic modeling optimization processing on the cleaned data by utilizing an OLAP engine, and the dynamic modeling optimization processing comprises a data storage unit and a multi-dimensional model construction unit; and the data verification module is used for performing output verification on the data processed by the OLAP engine based on the multi-dimensional verification parameters, and generating alarm information when the data meets an alarm triggering condition.

The embodiment of the invention provides a session category identification method, electronic equipment and a program product. The session category identification method comprises the following steps: acquiring multi-dimensional session data; determining multi-dimensional model input data according to the multi-dimensional session data; and performing session category identification according to a preset session category identification model and the multi-dimensional model input data, and determining a session category. Through the scheme of the embodiment, the session category can be intelligently and comprehensively recognized in real time without increasing network burden, and recognition is performed based on the multi-dimensional session data, so that multi-party information combination is realized, and the recognition accuracy of the session category is improved.

The present application relates to a kind of based on LASSO regression determination new energy consumption model method and system, the method includes: constructing new energy consumption model;LASSO regression is calculated using to the new energy consumption model, and it is judged whether there is mathematical relationship between each factor: if yes, then the multi-dimensional model is represented by factor function relationship formula;If no, then the multi-dimensional model is represented by Peaks function.The multi-dimensional new energy response surface model based on LASSO regression of the present application can be graphically depicted that new energy utilization rate changes with factor, and it is of great significance to study the influence of multiple uncertain factors on new energy utilization rate.

A method comprising receiving, by a computer system, a parameter value for each of multiple parameters for an application for an enterprise and determining, by the computer system based on a first database table lookup, a recommended authentication vector for the application based on the parameter values. The recommended authentication vector comprises assurance level values for authenticator strength assurance level (ASAL), Post Authentication Assurance Level (PAAL), and Origin Point Assurance Level (OPAL). The method further comprises determining, by the computer system based on a second database table lookup, one or more enterprise-certified authentication infrastructures for the application based on the recommended authentication vector and implementing the one or more enterprise-certified authentication infrastructures in the application for a production deployment of the application by the enterprise.

The invention provides a project cost data management system, and relates to the technical field of project cost, the project cost data management system comprises a dynamic association engine, a cost index multi-dimensional model module and a distributed block chain storage module, the dynamic association engine is used for establishing an association relationship among a project quantity list, a quota library, a real-time market price and design change data; a cost result is automatically generated and a cost deviation is updated in real time based on the association relationship; the cost index multi-dimensional model module is used for constructing a cost index multi-dimensional model based on a data warehouse and an online analysis processing technology; according to the method, a four-dimensional binding model of the bill of quantity-quota library-real-time market price-design change is constructed through the dynamic association engine, automatic data association is realized by presetting association rules, and manual matching and input are not needed; the cost is updated in real time and the deviation is calculated when any data changes, compared with traditional manual operation, the efficiency is higher, the cost deviation rate is smaller, and the cost risk caused by manual errors is reduced.

The invention provides a digital twinning modeling and intelligent visualization system for a discrete manufacturing workshop, and aims to solve the problems of high cost, difficulty in maintenance, poor flexibility and the like of digital twinning modeling of the discrete manufacturing workshop at present, and a modeling method of four dimensions including three-dimensional modeling, logic modeling, behavior modeling and connection modeling is constructed. According to the invention, a modularized digital twinning modeling process is provided, and information such as equipment, a production line and a process in a workshop is fused; the method comprises the following steps: carrying out content such as operation flow, man-machine interaction and multi-device linkage in production, and establishing data connection by utilizing an industrial Ethernet. The multi-dimensional model is subjected to fusion matching based on a Unity 3d platform, information, data and model fusion is carried out by utilizing a unified modeling language (UML), and the information, the data and the models are integrated into the Unity 3d platform, so that model building is realized. A digital twinborn model is utilized to build an intelligent visualization system, a three-dimensional model and a connection model are combined to realize a production process visualization function, and a logic model and a behavior model are combined to realize a production operation flow simulation rehearsal function.

Disclosed are systems and method for determining information related to building materials based on determined measurements from a multi-dimensional building model comprising features and elements embodying such materials and measurements. The multi-dimensional model may be based on a plurality of received images, such as ground-based images of a building. The multi-dimensional model may be scaled, or a scale extracted based on data of the model. The multi-dimensional model may comprise architectural elements, and the scale used to determine measurements of such architectural elements. With the scaled measurements of the architectural elements in the model, product information related to multi-dimensional model and its elements may be derived and combined in alternative means.

The invention provides an electric power prediction method and system based on a multi-source and multi-dimensional CIM model, and relates to the field of electric power system automation and communication. Comprising the steps of expanding an existing information model standard to construct a multi-source and multi-dimensional information model, integrating data of multiple energy types, including power generation power data, meteorological data and environmental data, and forming a standardized model by analyzing an existing class and inheriting or creating a new class to add attributes and relationships; historical data in the multi-source multi-dimensional information model are preprocessed, and abnormal data are cleaned by adopting a multi-dimensional verification mechanism and are intelligently corrected; constructing and training a neural network model, and processing a time sequence data capture dependency relationship; the neural network model after deployment training interacts with the multi-source multi-dimensional information model through an interface, real-time prediction of the generated power is realized, the long-term dependency relationship and complex nonlinear characteristics of the water-wind-light generated power are effectively captured, the prediction precision and reliability of the generated power are improved, and the safety and stability of power grid operation are improved.

The application provides a resistive device and a manufacturing method thereof, comprising: predicting an electrical resistance value of a phase change process caused by a phase change of a resistive material of the resistive device based on a tensor-based multi-dimensional model; and manufacturing the resistive device according to the prediction result. The method of the application can greatly reduce the calculation amount required for prediction by introducing a tensor to abstractly describe the material; meanwhile, the method can solve the problem that the channel behavior of the resistive device depending on the phase change material is complex, nonlinear and difficult to predict by sequentially changing the value of the tensor element to simulate the gradual phase change process of the material and tracking the change of the overall resistance of the material in the process to predict the behavior. The method is simple and easy to implement, the prediction is relatively effective, and the method has a wide application prospect.

Methods and systems for estimating values of parameters of interest from optical measurements of a sample early in a production flow based on a measurement condition dependent, multidimensional optical dispersion (MCD-MDOD) model are presented herein. A MCD-MDOD model includes a multi-dimensional parametric model characterizing modelled parameters of interest as dependent on one or more measurement condition parameters. In this manner, the MCD-MDOD model captures the dependency of dispersion properties of the measured material on one or more measurement conditions, e.g., temperature, humidity, pressure, nitrogen purge condition, deformation, material processing conditions, radial location on wafer, coordinate location on wafer, etc. In some measurement applications, measurement accuracy is improved and the computational effort required to develop the MCD-MDOD model is less than alternative techniques. In some embodiments, a MCD-MDOD based film thickness measurement model includes a multi-dimensional parametric model having one or more parameters of interest dependent on wafer location.

The application discloses an environment modeling method and device based on a hyperspectral laser radar, and the method comprises the following steps: acquiring point cloud information of a to-be-detected scene and original spectrum of a detection target in the to-be-detected scene through the hyperspectral laser radar; positioning and mapping are performed by using the point cloud information of the to-be-detected scene, and an initial environment three-dimensional model is constructed; surface attribute parameters of the detection target are determined; the original spectrum of the detection target is inversely processed according to the surface attribute parameters of the detection target and an output response model of the hyperspectral laser radar, so that reflectivity spectrum of the detection target is obtained; and the initial environment three-dimensional model is mapped again by using the reflectivity spectrum of the detection target, so that a target environment multi-dimensional model is obtained. According to the scheme, the original spectrum output by the hyperspectral laser radar is finely inversely processed, so that the real spectrum of the detection target is obtained, the accuracy of the spectrum is effectively improved, and the spatial geometric structure information and the spectrum information are fully matched, so that accurate environment modeling is realized.

The invention discloses a simulation method and device, which are suitable for the technical field of electronic equipment and are used for efficiently identifying potential condensation risks and reducing the influence of condensation on hardware infrastructures. The method comprises the steps that a multi-dimensional model of a target component is obtained, and the target component is associated with a heat dissipation assembly; obtaining target simulation parameters, wherein the target simulation parameters indicate at least one of the following items: material parameters of different components of a target component, a simulation working medium and an internal heat source of the target component; based on the target simulation parameters, simulation calculation is conducted on the condensation condition of wet air around the heat dissipation assembly in the multi-dimensional model through simulation software, and a simulation result of the condensation distribution condition is obtained. In a composite scene, the condensation risk can be efficiently identified and an improvement scheme can be provided by simulating and analyzing the condensation distribution condition.

The invention provides a distributed network semantic model query method and device based on a multi-dimensional demand, and the method comprises the steps: a demand obtaining step: if a user semantic model query request message sent by an end-side device or a target node of an edge service node belonging to the same distributed network as the end-side device is received, sending the user semantic model query request message to an end-side device; if yes, obtaining multi-dimensional demand information including the target scene and a plurality of demand vectors; setting a weight for each demand vector of the target scene according to a mapping relationship between the demand vector of the scene type and the weight to obtain a weighted demand vector; a matching step: searching whether a semantic model matched with the weighted demand vector is contained in each locally cached semantic model or not by adopting an approximate matching algorithm; and if yes, sending the searched semantic model as a target semantic model to the target node, and forwarding the target semantic model to the end side equipment. The problems that multi-dimensional model requirements cannot be accurately matched, and retrieval time delay is large can be effectively solved.

Methods and systems for estimating values of parameters of interest from optical measurements of a sample early in a production flow based on a measurement condition dependent, multidimensional optical dispersion (MCD-MDOD) model are presented herein. A MCD-MDOD model includes a multi-dimensional parametric model characterizing modelled parameters of interest as dependent on one or more measurement condition parameters. In this manner, the MCD-MDOD model captures the dependency of dispersion properties of the measured material on one or more measurement conditions, e.g., temperature, humidity, pressure, nitrogen purge condition, deformation, material processing conditions, radial location on wafer, coordinate location on wafer, etc. In some measurement applications, measurement accuracy is improved and the computational effort required to develop the MCD-MDOD model is less than alternative techniques. In some embodiments, a MCD-MDOD based film thickness measurement model includes a multi-dimensional parametric model having one or more parameters of interest dependent on wafer location.

The present invention discloses a method for automatically extracting functional dependencies based on a power grid data warehouse. The method includes the following main steps: constructing a power grid DV data warehouse; detecting the establishment pattern of functional dependency candidates based on power grid data characteristics; calculating the data confidence of the business key and the data confidence of the business entity association record based on the data semantics of the power grid business key itself and the evidence attribute; calculating the data confidence of the functional dependency candidate based on a set confidence threshold; and finally outputting all established functional dependency candidates. The present invention overcomes data noise interference caused by inconsistent multi-source data, solves the technical problem of automatically extracting functional dependencies from many-to-many link tables, improves the accuracy of calculating different entity attribute data and their record confidence, and realizes efficient and high-quality data semantic relationship extraction, thereby laying the foundation for automatically building multidimensional models.

This invention provides a method and system for early warning, flame retardancy, and explosion suppression in production workshops, relating to the field of safety production technology. The method includes: collecting equipment parameters, material properties, and environmental parameters of the production workshop; preprocessing these parameters using Kalman filtering technology; constructing a geometric model of the workshop equipment based on this technology; dynamically calibrating the geometric model to obtain a dynamic geometric model of the workshop equipment; constructing a multidimensional physical field coupling model based on multidimensional model technology; simulating the risk evolution of the dynamic geometric model of the workshop equipment based on the multidimensional physical field coupling model and the preprocessed material properties and environmental parameters to obtain corresponding workshop risk factors, and then executing corresponding solutions; constructing an early warning model of the production workshop based on a neural convolutional network and the workshop risk factors, thereby generating a production workshop early warning score; and generating a graded response early warning based on the production workshop early warning score. This significantly improves the accuracy of early warnings.

The invention relates to the technical field of engineering data sharing, in particular to an engineering project data security sharing collaboration method and system based on federated learning. The method comprises the following steps: acquiring an initial model gradient update parameter of each participant, and constructing an initial global model parameter data set; according to a multi-dimensional model security enhancement strategy, performing multi-dimensional security enhancement processing on the initial global model parameter data set, and determining an enhanced gradient update data set; the multi-dimensional security enhancement processing comprises anti-poisoning processing and anti-sensitive role inference processing; and performing aggregation processing on the enhanced gradient update data set, embedding a feature trap verification mechanism in the aggregation processing process, and generating and distributing a security enhancement global model. According to the method, the security of federated learning in the engineering data security sharing process can be comprehensively improved while the efficiency of the model is ensured, and the balance and unification of the security and the efficiency of the federated learning model are realized.

The invention provides a multi-dimensional model intelligent modeling method based on intention analysis and knowledge driving, and relates to the technical field of simulation calculation, and the method comprises the steps: receiving multi-source heterogeneous input data of a target; analyzing the multi-source heterogeneous input data based on a first AI model to obtain a modeling intention and a feature information set; according to the modeling intention and the feature information set, generating an entity hierarchical semantic ontology model instance based on a predefined entity hierarchical semantic ontology model, a modeling feature knowledge base and a second AI model; and based on the entity hierarchical semantic ontology model instance, generating an executable simulation construction script of the target through a third AI model. According to the method, agile, extensible and standardized entity intelligent construction is realized, and the simulation deduction efficiency is improved.

The invention discloses a multi-dimensional model and data driven photovoltaic power station aggregation output attenuation evaluation method. Comprising the following steps: constructing a multi-dimensional spatial-temporal feature matrix; establishing an output attenuation nonlinear dynamic model; outputting a reliability state sequence through a data-physics dual check mechanism; and carrying out attenuation rate aggregation and trend prediction based on a verification result. According to the method, the problems of incomplete evaluation, poor dynamic nature and low reliability in the prior art are solved, and accurate, dynamic and reliable evaluation of the output attenuation characteristics of the photovoltaic power station is realized.