48 results about "Goal modeling" patented technology

A Model Transformation Authoring Framework (MTAF)method and apparatus for authoring and providing model-to-model transformations from one domain to another domain is disclosed. Given a domain and a target domain, at least the given domain having a respective structured hierarchy, the invention system enables a user to specify a declarative mapping (transformation declarative) between a domain specific language modeling the given domain and a modeling language modeling the target domain. The declarative mapping models how the domain specific language modeling the given domain relates to the modeling language of the target domain. The system generates a transformation code implementation of a transformation from the given domain to the target domain. The MTAF provides to the user design decisions with respect to Specification, Transformation Rules, Rule Organization, Rule Application Control, Source-Target Relationship, Incrementality, and Directionality and Tracing. The generated transformation code is executed at runtime to perform the transformation of the domain specific language of the given domain to the modeling language of the target domain. Instances of models of the target domain resulting from the performed transformation at runtime may be output to other model transformations, to JET templates, or may be persisted, merged or chained among other post processing.

A weapon flyout simulation method, system, and computer program product, includes modeling a target as a plurality of ellipsoidal zones corresponding to a plurality of zones on the target, and performing hit / miss assessment on the target by determining if said trajectory of the weapon interferes with at least one of said plurality of ellipsoids.

Thermal images of an object are produced outdoors so as to be significantly differentiated temporally and / or geometrically and be inclusive of natural background such as would be illuminated by sunlight. A calibration of the thermal imager (e.g., radiometer) against theoretical blackbody radiation is effected using measured incident radiation, resulting in a calculated pixel intensity. A system transfer function calculation of the pixel intensity is effected, resulting in a radiance. A matrix inversion calculation of all of the radiances considered together is effected, resulting in a bi-directional reflectance distribution function (BRDF). Advantageously, the BRDF is determined from the target in a direct but mathematically-enhanced manner, thereby making economic use of natural illumination and obviating the need for modelling, simulating or reconstructing the target.

The invention relates to a cloud computing task scheduling method based on response time optimization. The method includes the steps that a cloud computing system based on response time optimization is constructed, the transmission time of task fragments is calculated, the average processing time of the task fragments at computational nodes is calculated, the total processing time of the task fragments is calculated, task response time is calculated, an objective function of the cloud computing task scheduling problem is constructed, and the task fragmentation scheme of schedulers is calculated. Starting from parallel processing of the task fragments, the cloud computing task scheduling method based on response time optimization uses the longest execution time of task fragmentation as the task response time, the minimum task response time of each scheduler is used for target modeling, and a novel task scheduling method is obtained. The cloud computing task scheduling method based on response time optimization can more reflect the characteristic of task parallel processing. Experiments show that whether the response time value of each scheduler or the objective function value, the cloud computing task scheduling method is superior to both a game playing algorithm and a balance scheduling algorithm, and when the scale of the system is increased or the load is increased, the cloud computing task scheduling method based on response time optimization has obvious advantages compared with the other two algorithms as well.

The invention provides a natural gas hydrate seismic data processing method and device. The method comprises the following steps: according to log data of a hydrate exploration area, performing fitting on sample data by utilizing a hydrate reservoir rock physical modeling method, and acquiring a target modeling method; constructing a wedge model of a hydrate by utilizing the target modeling method, performing forward modeling on seismic data according to the wedge model, and obtaining a synthetic seismic record; according to the synthetic seismic record, constructing free gas evaluation standards and saturation thickness evaluation standards of a natural gas hydrate; acquiring the actual seismic data of a target area in the hydrate exploration area, and acquiring the actual amplitude spectrum according to the actual seismic data; and according to the actual amplitude spectrum, the free gas evaluation standard and the saturation thickness evaluation standard, determining hydrate featureinformation of the target area. By utilizing each embodiment in the invention, the adaptability problem of the same hydrate reservoir rock physical model in different exploration areas is solved, andnatural gas hydrate seismic data processing accuracy is improved.

A business process (BP) adaptation system (400) includes a High-Variability enriched Goal Model (HVGM) (402) that captures and refines goals of a business process (BP) while modeling alternative options where the model captures non-functional or quality attributes used in an evaluation of a performance of the BP and an estimation of how various BP alternatives affect the quality attributes. The system further includes a High-Variability workflow-level / directly Executable Model (HVEM) (404), where the system is based on goal modeling and analysis for eliciting intentions behind a BP to achieve a desired goal and the HVGM explicitly models non-functional or quality concerns. The system can further include a semi-automatic generator (604) of BP metrics based on the quality attributes specified in the HVGM and a runtime infrastructure (610) where each deployed BP instance reflects a configuration selected for that instance in a corresponding portion of the HVGM.

The invention relates to a light scattering modeling method for a complex spatial object, belonging to the technical field of modeling aerospace spatial objects. The method of the invention comprises the following steps: firstly, establishing a light scattering empirical model of a material according to the comprehensive light scattering intensity of the material; obtaining incident sections of the spatial object by parallel projection transformation, and judging and selecting an effective incident section; according to the light scattering empirical model of the material, counting the mean value of the reflectivity of pixels on the drawn effective incident section to obtain the mean reflectivity; calculating the area of the effective incident section according to the proportional relation between the total number of the pixels on the effective incident section and the area of a drawn window; and establishing a light scattering model of the complex spatial object. In the invention, on the basis of considering the incoherent scattering characteristic and coherent scattering characteristic of the material, according to the practical propagation characteristic of rays, complex calculation of a three-dimensional space is projected to a two-dimensional surface, the computer graphics theory is utilized for realizing quick determination of the effective incident section and the mean reflectivity of the section, and the problem of light scattering calculation of the complex spatial object is better solved.

The invention provides a deep learning data sharing method based on a block chain mode, and a storage medium. The method comprises the steps of storing deep learning data and a target modeling model in all nodes of a block chain network, wherein the deep learning data is shared in the block chain network, and the target modeling model is protected; splitting the deep learning data into metadata with corresponding data attribute tags; when a deep learning request corresponding to the target modeling model is received, determining the corresponding data attribute tags according to the target modeling model, and obtaining the corresponding metadata; and according to the corresponding metadata, extracting the deep learning data corresponding to the target modeling model to perform learning. The method has the advantages that a deep learning mode is optimized, and one step of reinforcement learning can be achieved, so that the learning efficiency can be remarkably improved; and furthermore,the learning data can be reused, and the learning cost and time can be saved.

The invention discloses an account identification method and system. The account identification method comprises the steps of obtaining information uniformized chain related information of the same attributes of accounts to be identified; conducting first identification judgment on the accounts to be identified according to the information uniformized chain related information; judging whether the accounts to be identified are used by users corresponding to the accounts to be identified according to results of the information uniformized chain related information at least, wherein the information uniformized chain related information include one of the length of an information uniformized chains, the number of predetermined important indexes included in the information uniformized chains or the length information uniformized chains, the number of the predetermined important indexes included in the information uniformized chain and coincidence indexes corresponding to the information uniformized chains. By means of a target-free modeling method, the problem that an existing account identification method adopts a destination training model and accordingly needs to depend on a large amount of fact sample data is solved.

A Model Transformation Authoring Framework (MTAF) method and apparatus for authoring and providing model-to-model transformations from one domain to another domain is disclosed. Given a domain and a target domain, at least the given domain having a respective structured hierarchy, the invention system enables a user to specify a declarative mapping (transformation declarative) between a domain specific language modeling the given domain and a modeling language modeling the target domain. The declarative mapping models how the domain specific language modeling the given domain relates to the modeling language of the target domain. The system generates a transformation code implementation of a transformation from the given domain to the target domain. The MTAF provides to the user design decisions with respect to Specification, Transformation Rules, Rule Organization, Rule Application Control, Source-Target Relationship, Incrementality, and Directionality and Tracing. The generated transformation code is executed at runtime to perform the transformation of the domain specific language of the given domain to the modeling language of the target domain. Instances of models of the target domain resulting from the performed transformation at runtime may be output to other model transformations, to JET templates, or may be persisted, merged or chained among other post processing.

The invention discloses a parameter template-based corn leaf virtual simulation modeling method. The method comprises the following steps: establishing a parameterized template library for corn leaves by scanning real corn leaves, wherein parameters comprise leaf lengths, leaf widths and leaf vein curves; searching a parameter template matching characteristic parameters from the template library according to the characteristic parameters of a target modeling corn leaf, wherein the characteristic parameters comprise a target leaf length, a target leaf width and a target leaf vein curve; and modeling the target modeling corn leaf according to the matched parameter template by taking the characteristic parameters as constrains. The method of the invention effectively solves the problem of structural modeling which is not easy to be expressed parametrically on the corn leaves; and the established model has strong sense of reality, which can meet the requirement on a digitized design of corn leaves.

The invention provides an adaptive object feature tracking method, belongs to the technical field of computer graph and image processing and can effectively solve the problem of stably tracking rigid and non-rigid target objects in real time for a long time. The adaptive object feature tracking method comprises the following steps: constructing a pixel-based target grayscale table saving grayscale and pixel point information, wherein the grayscale is contributive to a predicted target center position; performing adaptive two-point feature selection based on the target grayscale table and mutual information so as to determine a feature combination which can perfectly describe a target and model the target. In a tracking process, according to the adaptive object feature tracking method, firstly a Hough table corresponding to images is constructed according to the target grayscale table, secondly a predicted target position is searched and determined according to a target model, thirdly a position corresponding to the maximum pixel point number in the Hough table is searched within a relatively small local range by taking the predicted target position as the center, and the position is taken as the final target position, so that the target positioning is completed. The adaptive object feature tracking method is mainly applied to tracking of object targets.

The invention relates to an assembly line multi-target modeling method, a particle swarm algorithm and an optimization scheduling method, wherein the optimization scheduling method comprises the steps of S1 constructing a assembly line multi-target optimization model; S2 performing multi-target optimization design to the assembly line multi-target optimization model by the particle swarm algorithm and screening the optimization result to reconstruct the assembly line. According to the reconstructible assembly line optimization scheduling method, a crowding distance calculation method and an elite strategy are referred to; diversity maintenance and global optimal value update are conducted on the basis of individual crowding distance ordering; the complex fitness calculating process is avoided; and a small probability random mutation mechanism is introduced, thereby enhancing the global searching optimization capability gratly.

This invention relates to the goal monitor technology field, specially a kind of natural goal examination method used in the robot vision navigation. The method includes: goal modeling, picture gathering, pretreatment, division parameter choice, robustness natural picture division, characteristic withdraw, goal authentication, and output result.

The invention discloses an electromagnetic analysis method on the basis of matrix Taylor series expansion. The electromagnetic analysis method comprises the following steps: 1) carrying out target modeling and dispersing, determining strong interaction and weak interaction matrix ranges according to grouping information, and constructing a preconditioned matrix according to near field information; 2) using a strong interaction matrix to normalize an equation, carrying out the Taylor series expansion on the normalized equation, decomposing scattering current to be solved into one series of current summation forms, and gradually approximating to the scattering current through a nested solving method until solving accuracy is achieved; and 3) utilizing the scattering current obtained in 2) to determine a target radar scattering cross section. In a solving process, a sparse approximate inverse precondition is used to quicken an iteration process, and meanwhile, a fast multi-pole algorithm is used to quicken moment vector multiplication. The method has the characteristics of being quick in convergence.

The invention discloses a joint routing and power distribution method for a wireless multi-hop network, which integrates the energy efficiency, the transmission reliability and the safety. By considering a relatively realistic scene that both the position and the channel state information of an eavesdropper are unknown, for the wireless multi-hop network in a decoding and forwarding mode, the invention provides a safety routing and power distribution method under end-to-end bit error rate constraint. According to the method disclosed by the invention, a power distribution and routing strategy,which can satisfy the maximum safety connection probability, can be obtained by physical layer safety capacity definition based on an information theory at first; then, modelling is carried out by taking the strategy and the end-to-end bit error rate as the constraint and the total power consumed by a minimum path as a target; and a power distribution algorithm and a safety routing algorithm based on a classical Bellman-Ford algorithm are obtained by deduction. By means of the joint design method, the information transmission safety is ensured; furthermore, the transmission accuracy is realized; simultaneously, energy utility of the network is improved; and thus, the method has a certain practical significance.

The invention discloses a multi-grid precondition method for fast acquiring complex target radar echoes. The method includes: modeling a target, and using coarse triangular grids to subdivide the surface of the target; subdividing the coarse grids to obtain nested grids, and building RWG basis functions on grid layers; using RWG basis function expansion relations of adjacent grid layers to build a first set of interpolation, limiting operator and coarse grid matrixes, building rotation basis functions at the edges of the grid layers, and using RWG basis function expansion relations of adjacent grid layers to build a second set of interpolation, limiting operator and coarse grid matrixes; and using the multi-grid precondition method to solve equation sets, and using the first set of interpolation, limiting operator and coarse grid matrixes and the second set of interpolation, limiting operator and coarse grid matrixes to accelerate convergence of matrix equation iteration solution. Due to the fact the basis functions are constructed at the edges of the grids, the basis functions are high in modeling precision, two sets of operators are used to accelerate convergence of matrix equation iteration solution, and matrix equation solution can be accelerated evidently.

The invention discloses a data processing method and device and a storage medium, and belongs to the field of data processing. The method comprises the following steps: selecting a sample data set corresponding to a target modeling type from a plurality of stored sample data sets; and then selecting at least one feature dimension from the plurality of feature dimensions, and selecting a target training model from a plurality of training models stored for the target modeling type; training the target training model according to the data of each sample in the selected sample data set on the atleast one feature dimension; and finally, according to a model obtained after training, determining an extended data set according to the target expansion multiple and the optimization target information. Due to the fact that when the target model needs to be trained, the target model can be directly selected from the multiple training models, an operator does not need to compile different codes for the multiple training models to achieve the target model, the training process is simplified, and then the process of determining the extended data set is more efficient.

The invention provides a software quality demand modeling and inference method which is used for providing software quality demand oriented formalized modeling, automatic inference and an analysis platform r sigma. The method includes the following steps: setting a related concept, performing graphical modeling, clicking an r sigma drawing area, selecting a corresponding modeling element, clicking a canvas to generate the corresponding modeling element, performing formalized modeling, designing according to semantics and grammar of a target modeling language sigma orienting to software quality demand, performing strategy assessment to determine whether an implementation strategy is an acceptable strategy or not, performing strategy exploration to check all possible combinations of design options and classify strategies according to satisfied states of root nodes, and performing model extension to capture incidence relations among different modeling results. By the method, quality demand modeling results in a text format can be generated; no man-machine interaction is needed; in addition, multi-objective decisions of a quality demand implementation strategy are supported, and mutual dependent relationships among models can be captured.

The invention discloses a federation modeling method and device based on a backward method and a readable storage medium. The federation modeling method based on the backward method comprises the steps of conducting federation modeling, obtaining complete model to be trained, receiving a tagging party characteristic data set fed back by third equipment coordinated and associated with the first equipment; determining each to-be-rejected label feature corresponding to the label party feature data set, based on the features of each to-be-rejected label, rejecting the to-be-rejected labels; performing longitudinal federation modeling through second equipment federated with the first equipment, and executing a preset cyclic training process on each to-be-trained complete model to select each cyclic target feature meeting a preset contribution degree standard from each to-be-eliminated label feature, and eliminating each cyclic target feature in the to-be-trained complete model to obtain a target modeling model. The technical problems that longitudinal federation modeling is low in efficiency and poor in effect are solved.

The invention discloses a scene segmentation and target extraction method based on concavo-convex and RSD features, and belongs to the technical field of computer vision. The method comprises the following steps: step 1, acquiring a scene point cloud by using Realsense and a PCL, and performing radius filtering; step 2, over-segmenting the scene point cloud based on hyper-body clustering; step 3,completing scene segmentation based on concave-convex re-clustering by using an LCCP method on the basis of the superbody clustering obtained in the step 2; and modeling the target object according tothe RSD geometrical characteristics of the segmented scene point cloud target object. Experimental results show that the target object modeling problem in any scene can be effectively solved. The method is a learning-free method, and has the advantages of low calculation time consumption and high occasion applicability.

The invention discloses a video smoke recognition method based on dynamic features. The method has the advantages of both real-time performance and accuracy. The method comprises steps: (1) continuouskey frames of an input video are firstly extracted, noisy points are filtered through a median filter, and the definition is improved through contour mending; (2) through foreground difference processing on the continuous frames, modeling is carried out on moving targets, a VR feature classifier is used to extract dynamic features, the moving targets are classified in combination of typical dynamic features, and a smoke target is recognized; and (3) finally, the smoke target and a typical smoke image are subjected to similarity comparison, thereby improving the recognition accuracy. The videosmoke recognition method based on dynamic features is characterized in that as the VR feature classifier is designed, the target dynamic features in a complicated scene can be effectively extracted,interference of people, cars, trees, flags and the like is removed through target dynamic feature differences, the smoke target is accurately recognized, and a smoke recognition method with strong adaptability, real-time performance and accuracy is provided and can be used for smoke early warning at an early stage of fire in a complicated environment.

A maneuvering random hypersurface expansion target modeling method under multi-scattering point measurement comprises the specific steps: 1, obtaining maneuvering random hypersurface expansion target description parameters from multi-scattering point measurement, and preprocessing the description parameters; 2, using the preprocessed description parameters to obtain the maneuvering random hypersurface expansion target form descriptions; 3, translating a maneuvering random hypersurface expansion target contour curve according to the form descriptions, thus obtaining a maneuvering random hypersurface expansion target maneuvering description; 4, using a maneuvering random hypersurface expansion target motion state transfer matrix to solve a system transfer matrix; 5, using the maneuvering description and the system transfer matrix to obtain a maneuvering random hypersurface expansion target system model. The method can solve the time evolution problems of the target motion states and expansion forms in a maneuvering process, thus precisely tracking the target.

The invention discloses a three-dimensional modeling method and system. The method comprises the steps that original data of a target modeling article is collected, the original data is shared and stored, and a corresponding modeling task list is added; load balancing is performed according to the modeling task list and the task processing condition of an initial model production terminal which iscurrently logged in, and an appropriate production terminal is selected to distribute an initial modeling task; an operator of the initial modeling task is received, the original data is read, initial model production is performed according to modeling requirements, and the initial modeling task is submitted after an initial modeling result is formed; after task distribution software receives theinitial modeling task, a fine modeling task is distributed according to the task processing condition of a fine model production terminal which is currently logged in; and after the fine modeling task is received, an operator of the fine modeling task acquires the stored initial modeling result, fine model production is performed according to the initial modeling result to form a fine modeling result, and the fine modeling task is submitted. Through the three-dimensional modeling method and system, the overall ability requirement on model data production personnel is lowered, and the production efficiency of three-dimensional modeling is improved.

The invention provides an object modeling method of random configuration of a collaborative detection network. According to random configuration and an object of the collaborative detection network, fluctuating characteristics of different channels of object RCS are modeled into different distribution functions which include different parameters or types of distribution functions, and the problem of modeling random fluctuating characteristic of the object is solved; and correlation of the different channels of object RCS is modeled into fractional-order exponent part correlation, thereby solving the problem in modeling object random correlation, and realizing accurate object modeling of random-configuration collaborative detection network. Further, objects with relatively wide distribution scope and long trailing characteristic suitable for fluctuation of different channels are considered, and a generalized new distribution model provided by the invention can be adapted to more complex objects.

The invention discloses a conductor target electromagnetic scattering simulation method based on NURBS (Non-Uniform Rational B-Splines) curved surface modeling, aiming at mainly solving the problems of an existing method that the simulation efficiency is low and the requirements on a computer memory are high. The conductor target electromagnetic scattering simulation method comprises the following steps: 1, modeling a scattering target with an NURBS curved surface through commercial software Rhino; 2, decomposing a parameter region of a scattering target model and deducing a coordinate formula of the NURBS curved surface, so as to obtain conductor target physical optical scattering integral and edge diffraction field integral formulas; 3, calculating values of physical optical scattering integral and edge diffraction field integral; and 4, obtaining a single-station radar scattering section RCS of a conductor target according to the values of the physical optical scattering integral and the edge diffraction field integral. Under the condition of guaranteeing the precision, the simulation efficiency is improved, the requirements of simulation experiments for the computer memory are reduced, and the method can be used for obtaining the single-station radar scattering section RCS of the conductor target.

The embodiment of the invention provides a model, a service processing method, a device and equipment. The basic idea is that the method comprises the steps: determining a joint modeling task for thebusiness model; sending the joint modeling task to at least two target modeling nodes, training the service model by at least two target modeling nodes by utilizing all service data samples of at least two target modeling nodes; obtaining the internal operation information of the business model obtained by training from at least two target modeling nodes, and processing the obtained internal operation information to obtain a decision rule, so as to make a decision for the business evaluation result of the business model of each of at least two target modeling nodes according to the decision rule.

A method for model random rough object features that the power spectrum function and Monte Carlo method are used to obtain the height distribution of two-dimensional random rough surface by two-dimensional inverse Fourier transform. Based on the smooth geometrical parameter surface model of complex object, the basic model parameters such as point, normal, surface and so on are obtained by adaptivemesh generation. Based on the principle of vector superposition, the height distribution vector of two-dimensional random rough surface is superposed on the mesh model of the original complex object,and the rough model of arbitrary complex object with random roughness fluctuation is obtained.

The invention discloses a VR animation figure three-dimensional modeling optimization method, which comprises the following steps of obtaining original data of a two-dimensional animation figure, andadding the original data to a corresponding modeling figure list; establishing a 3D working cluster, and setting mapping paths from other computers to the shared host; and loading the collected original data of the two-dimensional cartoon character into a 3D working cluster, and encrypting the image data loaded into the working cluster. The original data of a two-dimensional animation figure is read, the complexity of a target modeling object is obtained according to the original data, and proper parameters are selected to perform automatic modeling processing according to the complexity of the target modeling object; and preliminary repair is carried out according to model specifications to form a corresponding preliminary model result, and a target modeling type is determined according to the preliminary modeling result. According to the target modeling type, a corresponding technical route is selected for fine mold production to form a fine mold. The two modeling modes enable the whole modeling process to be more meticulous.

The invention provides a method and a device for establishing a three-dimensional model of a long-distance pipeline, belonging to the technical field of long-distance pipelines. The method comprises the steps that three-dimensional modeling data of the long-distance pipeline are obtained, wherein the three-dimensional modeling data are used for creating pipe section units of the three-dimensional model of the pipeline, the three-dimensional modeling data comprise model node coordinates, the model node coordinates comprise pipeline starting point coordinates, middle node coordinates and pipeline terminal point coordinates, the three-dimensional model of the pipeline is composed of a plurality of the pipe section units, and middle nodes are located on the pipe section units and located at the joints of the adjacent pipe section units; based on the three-dimensional modeling data of the long-distance pipeline, a model building file is generated, wherein the model building file comprises a plurality of modeling commands suitable for target modeling software, and the modeling commands are used for building the pipe section units according to the model node coordinates; and the target model software is called to execute the modeling commands in the model building file, and the three-dimensional model of the long-distance pipeline is generated.