122 results about "Platform model" patented technology

The invention provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention relates to the technical field of computer vision, in particular to a station platform limit intelligent detection method and system based on computer vision, and the method comprises the following steps: obtaining platform region spectral data, extracting spectral features according to the reflectivity value of each pixel point in a spectral range, comparing the spectral features with the features of various known materials, and determining the position of the station platform limit. And identifying material types of the plurality of positions, detecting obstacles, and generating material classification feature values. According to the method, the spectral data is acquired, the reflectivity value is compared to identify the material type, the texture feature and the gradient change rate are utilized to analyze and identify the fuzzy area, misjudgment on the fuzzy identification is reduced, point cloud data and image information are adopted for time-space synchronous registration, and multi-source sensing information is fused to enhance the time-space consistency of three-dimensional reconstruction. The size accuracy of the platform model is improved, the space offset is calculated by comparing engineering design drawing data, a structure deviation quantitative evaluation system is established, a deformation area is positioned, and the foreseeability of safety early warning is improved.

The invention provides a cement production management and control platform PHM model system, and the system comprises the steps: obtaining the monitoring data and production management data of a cement production process, and enabling the monitoring data and production management data to form an original data set; performing data cleaning and preprocessing on the original data set, and removing noise and abnormal values to obtain a standardized data set; inputting the feature component into a preset deep learning model, and outputting a prediction result of the equipment state; the prediction result is compared with a set threshold range, and if the prediction result exceeds the threshold range, early warning information is generated; classifying the early warning information in combination with historical fault data and current production environment parameters, and distinguishing normal production fluctuation and potential faults; if the classification result is a potential fault, a fault mode is analyzed, a fault type is determined, a pre-training model is finely adjusted by using a fault sample, and the generalization ability of the model is improved; new data are continuously collected in the production process, the model is periodically updated, and the accuracy of model fault early warning is improved.

The present invention discloses a method for constructing an intelligent computation engine of an artificial intelligence cross-platform model based on knowledge self-evolution. The method comprises: determining source and target moments; dividing a discrete manufacturing system data set; initializing a dynamic discrete manufacturing system model; preprocessing data and constructing a task pool; constructing a meta learning framework; migrating a trained neural network to new tasks; iterating until convergence and storing model parameters; and testing in a new environment. This invention shortens the convergence time of model parameters, significantly benefiting the training of dynamic discrete manufacturing models subject to temporal disturbances in actual production.

The invention discloses an industrial mechanical arm tool changing operation monitoring system based on digital twinning, and relates to the technical field of digital twinning. The system establishes a complete digital twinning model by integrating a floating platform model, a mechanical arm model and a tool changing device model; and a sensing sensor group is arranged to acquire real physical world data of the platform, the mechanical arm and the tool changing device in real time. The data are transmitted to a digital twin model in real time for processing and simulation, so that the virtual world state and the physical world state can be bidirectionally synchronized. According to the highly integrated monitoring mode, the resonance risk between the platform and the mechanical arm and the change of the tool locking trend can be recognized in time. Through an effective risk early warning and adjusting mechanism, the system can ensure the accuracy and safety of tool changing operation, and tool changing failure or equipment damage caused by resonance or abnormal locking is avoided, so that the production efficiency is improved, and the downtime is shortened.

The invention discloses an intelligent data question answering and visualization system and method for leader decision making, and relates to the technical field of artificial intelligence, and the system comprises a knowledge base unit, an intelligent question answering module, a dynamic visualization unit, a quality control unit and a performance optimization unit. According to the method, the customized knowledge base is constructed through the knowledge base unit, cross-modal alignment and layered long context processing are weakly supervised during data fusion, the defects that cross-modal data labeling cost is high and long context processing is redundant can be overcome, and an interaction platform of the knowledge base and a data engine in the knowledge base unit is constructed through the intelligent question and answer module; the model mixing module mixes an NLP model, constructs a combined architecture of an LLM + field fine tuning model, ensures understanding of natural languages and intention analysis precision, introduces an online expert error correction mechanism, and reduces misjudgment during question answering, so that the processing capacity of long texts or complex logic can be ensured, missing of key information in question answering is avoided, and then the answering quality is ensured.

The invention belongs to the technical field of low-code development platforms, and particularly relates to a low-code platform model construction and dynamic execution method and system based on a hybrid DSL (Digital Subscriber Line), and the method comprises the steps: describing static model information in an application by using a structure DSL in a declarative grammar; defining a behavior DSL in a mounting mode under a corresponding element of the structure DSL; analyzing the structure DSL into a meta-model object tree in the platform, and converting the behavior DSL into an executable expression tree / intermediate representation; jointly driving the meta-model object tree and the expression tree / intermediate representation by utilizing a template engine, and automatically generating at least one application code in a front-end page, a back-end interface and a database script; a common structure DSL and a behavior DSL are combined and packaged into a scene template, and the scene template is stored in a template library so as to be reused by different service scenes, and modularization and templating of service modeling are realized. The complexity of the system is reduced, and the development efficiency and quality are improved.

Systems and methods are disclosed for managing the processing and execution of models that may have been developed on a variety of platforms. A multi-model execution module specifying a sequence of models to be executed may be determined. A multi-platform model processing and execution management engine may execute the multi-model execution module internally, or outsource the execution to a distributed model execution orchestration engine. A model data monitoring and analysis engine may monitor the internal and / or distributed execution of the multi-model execution module, and may further transmit notifications to various computing systems.

The invention discloses an information operation dimension intelligent management and control platform system and method, electronic equipment and a storage medium, and belongs to the field of intelligent operation and maintenance of an information communication technology. The system comprises an anomaly detection module, a multi-modal feature fusion module and the like, multi-source data is processed through an unstructured data preprocessing unit and a time sequence data alignment unit, model parameters are dynamically adjusted by utilizing a self-adaptive online learning strategy, and dynamic resource scheduling is realized by combining a reinforcement learning algorithm. The method comprises the steps of data receiving preprocessing, feature fusion alignment, model training analysis and the like. The electronic equipment adopts a heterogeneous computing architecture, and the storage medium stores corresponding program codes. According to the method, the problems of weak generalization ability, insufficient unstructured data processing and the like of a traditional platform model are solved, the anomaly detection precision and the response speed are improved, a closed-loop operation and maintenance system is formed, the operation and maintenance decision intelligent level is improved, and the generalization recognition ability and the environmental adaptability of the model to an unknown abnormal mode are remarkably enhanced.

The invention discloses a parallel six-degree-of-freedom platform pose control method based on a PSO-GA method, and the method comprises the following steps: S1, simplifying a six-degree-of-freedom platform model, and facilitating the mathematical modeling; s2, constructing a target function according to the six-degree-of-freedom platform structure parameters; s3, solving an objective function by using a PSO-GA method; s4, according to the solution of the objective function and the geometrical relationship of the six-degree-of-freedom platform, the platform pose is obtained; and S5, a position servo controller is designed, the difference between the pose obtained through the positive solution and the given pose is obtained and input to the position controller, and the rod length of each rod of the six-degree-of-freedom platform is controlled. The invention provides a kinematics algorithm capable of quickly solving the platform position and realizing stable control so as to realize quick and stable control on the parallel platform.

The invention belongs to the technical field of power systems, and particularly discloses a wind power plant controller hardware-in-the-loop simulation method for a wind turbine generator test platform, which comprises the following steps: establishing a platform simulation model comprising a wind turbine generator transmission chain test platform model and a wind power plant simulation model, and setting a control strategy; configuring parameters of the platform simulation model; establishing a controller hardware-in-the-loop simulation system; the platform simulation model, the control strategy and the model parameters are loaded into the system, and data interface matching is completed; a simulation test is carried out, and the operation state of the wind turbine generator is acquired and analyzed; and if the control effect does not meet the preset requirement, adjusting the control strategy or the model parameter and repeating the test until the requirement is met. According to the method, dynamic coupling simulation among a wind turbine generator transmission chain, wind power plant operation and power grid working conditions can be achieved, control strategy verification and parameter optimization under multiple working conditions are supported, and the problems that a traditional test platform is limited in test range, insufficient in strategy verification and high in real machine test risk are solved.

The invention provides a method for joint answering of a user knowledge base and a service system under an AI platform, and belongs to the technical field of AI platform models.According to the method, hierarchical semantic matching is achieved by constructing a three-level knowledge vector cache and a three-level question intention vector cache and adopting vector representation of different dimensions; after user query is received, a semantic similarity algorithm is used for performing parallel retrieval to generate an initial matching degree, multi-dimensional similarity information is fused through a joint matching degree calculation function, and the precision is improved by using an intention recognition optimization model; calculating an optimal sequence length through an adaptive parameter adjustment function according to the long and short memory matching length to obtain real-time data of the service system, fusing the static answer of the knowledge base and the real-time data of the service system to generate a final answer, and updating a vector cache and optimizing algorithm parameters according to user feedback. And high-precision joint answering of the knowledge base and the business system is completed.

The invention relates to the field of electric digital data processing, in particular to a micro-operator-based large-model cross-platform system, which comprises a hardware execution layer, a hardware access layer, a pre-compiling layer, a micro-operator layer, a rear-end layer, a framework layer and a model layer. The embodiment of the invention can be suitable for different hardware platforms, can quickly realize the reasoning and fine tuning of a large model, effectively simplifies the operator development, debugging and deployment process, and improves the cross-platform model development efficiency. The invention provides a micro-operator development standard for a large model scene. The micro-operator development standard comprises a device access management micro-operator, a tensor calculation micro-operator and a large model typical operator realized by combination. In addition, the invention provides a software architecture scheme, which defines a large model development process based on the micro operator, covers a micro operator definition and calling mode, adaptation methods of different back ends, calculation core scheduling and a calculation framework and model building scheme based on the micro operator, so as to realize high efficiency and cross-platform adaptation capability of large model reasoning and fine tuning.

The invention discloses a software radio unified modeling method and system, and belongs to the field of software radio, and the method comprises the steps: determining modeling objects as platform modeling and waveform modeling; wherein during platform modeling, abstract modeling is performed on each calculation unit, and during waveform modeling, waveform component modeling and waveform application modeling are included; in the waveform component modeling, at least one modeled waveform component runs on a calculation unit, and a plurality of waveform components can be deployed on the calculation unit at the same time; the waveform application modeling comprises the following steps: establishing a connection relation between waveform components, and assembling into waveform application; then executing a modeling process of creating a platform model, creating a waveform model and establishing a platform and waveform connection relationship, and performing modeling detection; establishing a basic model set after modeling detection; the basic model set comprises a platform model subset and a waveform model subset. The modeling efficiency and the standardization degree are improved.

The invention belongs to the technical field of multi-unmanned aerial vehicle collaborative planning, and particularly relates to a multi-unmanned aerial vehicle intelligent avoidance target searching method based on prior probability information guidance, which comprises the following steps: step 1, establishing an unmanned aerial vehicle platform model, a sensor model and a target motion model; 2, an environment information graph model is established, wherein the environment information graph model comprises a target search probability graph, an environment uncertain information graph and a digital pheromone graph; and step 3, performing path planning by combining particle swarm optimization (PSO) and a Dubins curve under a distributed model predictive control (DMPC) framework, tracking the target by the unmanned aerial vehicle according to avoidance dynamics of the target, and stopping moving once the target is captured by the unmanned aerial vehicle until all the targets are captured. According to the method, the motion prediction model is established, the probability map is updated in real time, efficient search of multiple targets is achieved under the condition that the number of the targets, the initial positions and the motion states are known, and the problems that a collaborative search algorithm is low in efficiency and resources of multiple unmanned aerial vehicles are wasted are solved.

The invention discloses a fire-fighting pipe network inspection management system based on BIM (Building Information Modeling) and CIM (Common Information Modeling), and aims to solve the technical problems that a current fire-fighting pipe network inspection management system cannot realize three-dimensional visualization of a fire-fighting pipe network and is low in management efficiency and fault positioning accuracy, and the fire-fighting pipe network inspection management system comprises a multi-dimensional model data integration unit which is used for building a BIM platform model and expanding a CIM platform; the real-time monitoring and dynamic updating unit is used for acquiring operation data in real time and transmitting the operation data to the BIM / CIM platform, and realizing synchronous updating of a physical pipe network state and a digital model and model parameter adjustment; the intelligent analysis and decision support unit is used for fire-fighting pipe network fault diagnosis and prediction; and the visualization and collaborative management unit is used for displaying the full view of the pipe network and supporting 360-degree view angle switching, attribute query and simulation drilling. Through integration of BIM and CIM technologies, the system realizes three-dimensional visualization of the fire-fighting pipe network, so that information such as pipe network layout and equipment installation positions is clear at a glance, and the management efficiency and the fault positioning accuracy are greatly improved.

The application discloses a kind of floating platform for offshore wind power generation and its design method, comprising: 1) theoretical numerical simulation analysis, establish floating platform model;2) manufacture platform structure and supporting assembly by three-dimensional printing technology;3) build test device in experimental pool;4) after each component is built, carry out equipment safety inspection;5) set initial environmental parameter, determine sampling frequency and sampling time;6) start experiment, adjust control system to obtain set sea state;7) carry out platform performance measurement, adjust parameter to obtain optimal state;8) collect the response data of platform under different environments;9) using orthogonal experimental design method, carry out multiple experiments to optimize platform design;10) collect and process all data.The application starts from platform structure design, and the optimization step is simple and easy to operate, suitable for floating platform design and optimization under different sea conditions, and provides reliable verification for offshore wind power generation.

The invention relates to the technical field of transformer substation design, in particular to a power transmission and transformation project intelligent autonomous design platform and a working method thereof. The method comprises the following steps: acquiring design standard information and design demand information, creating an initial power transformation platform model based on BIM in combination with the design standard information and the design demand information, then creating a platform optimization model, collecting platform peripheral information, and importing the platform peripheral information and the initial power transformation platform model into the platform optimization model. Fusing the initial power transformation platform model and the platform peripheral information through the platform optimization model to obtain an optimized power transformation platform model, finally performing construction based on the optimized power transformation platform model, acquiring a construction real-time image, and judging whether a construction progress plan is met or not by combining the construction real-time image. According to the method, the power transformation platform model is designed by combining the design standard information and the design demand information, and the construction progress is judged in real time by combining the construction real-time image in the construction process, so that the construction progress is supervised.

The invention relates to the technical field of intelligent jacking platform models, in particular to an integrated intelligent jacking platform dynamic teaching model which comprises a base platform and is characterized in that the base platform is detachably provided with a front panel, a jacking mechanism is arranged in the base platform, a building body is fixed to the top of the base platform, and supporting frames are arranged on the periphery of the building body; the top of the supporting frame is connected with a steel platform system, the periphery of the steel platform system is externally connected with a protection frame, a protection net is arranged outside the protection frame, and the tower crane is arranged in a building body. According to the integrated intelligent jacking platform, the jacking mechanism, the supporting frame, the protective net, the steel platform system, the tower crane, the ceiling and other components are arranged, operation of all the components is remotely controlled through the control computer, students can have a certain understanding of the structure and the operation mode of the integrated intelligent jacking platform through the model, and the teaching efficiency is improved. And how all the devices in the integrated intelligent jacking platform cooperate with one another to operate and complete building construction is known.

The invention provides a low-code platform model generation method and device, equipment, a medium and a product, and relates to the technical field of artificial intelligence and low-code platform development. The method comprises the following steps: taking an interface type keyword; obtaining an association matching result of the first demand description and the keyword, and obtaining a second demand description according to the matching result; key information is taken from the second demand description, and a third demand description is obtained from the information; describing a first query language according to the third demand; splitting the first query language to obtain each minimum grammar unit, and screening to obtain each effective grammar unit; establishing a syntax tree, and when the syntax tree conforms to a pre-stored syntax rule, performing basic semantic check on the syntax tree to obtain a semantic check result; obtaining a second query language according to the check result; verifying the second query language rule according to a preset rule constraint condition to obtain a third query language; and obtaining a visual data model through the third query language and the low-code platform interface. The seamless connection from the natural language requirement to the visual application is realized, and the technical threshold is reduced.

The invention discloses a pile head spring stiffness matrix iteration method for jacket platform seismic analysis, and the method comprises the steps: building a three-dimensional structure model of a jacket platform, simulating a seismic load in a manner of applying an acceleration field, carrying out the structural static analysis calculation, building a jacket structure analysis model copy, and judging the reasonability of a pile head spring stiffness matrix. After a reasonable pile head spring stiffness matrix is obtained, the model can be adopted to complete the subsequent structural strength check of the jacket platform under the action of the seismic load. According to the method, in the process of determining the pile head spring stiffness matrix through jacket platform earthquake analysis, designers do not need to determine the size and the application position of a concentrated point load according to experience, and the problems of calculation complexity, efficiency and the like are solved. According to the method, the seismic load is simulated by applying the acceleration field to the platform model, iterative calculation can be completed more conveniently, more accurately and more quickly, a reasonable and accurate pile head spring stiffness matrix is generated, and then seismic analysis calculation of the jacket platform is achieved.

The invention discloses a cross-platform model deployment framework, electronic equipment and a storage medium, and the framework is applied to the electronic equipment and specifically comprises a framework interface abstraction layer and a plurality of runtime libraries connected with the framework interface abstraction layer. The framework interface abstraction layer is used for being connected with an application framework layer and providing a uniform abstraction interface for the application framework layer; the runtime library is used for providing running support for the framework interface abstraction layer. Compared with a traditional model deployment scheme, the scheme provided by the invention can accelerate the deployment speed of the model, ensures the uniformity of evaluation standards of the model on different platforms, and improves the reasoning performance. If the running environment is inferred to be updated, deployment updating of the model on a new platform can be rapidly completed according to the framework, and manpower and material resources needed by deployment can be greatly saved.

The invention relates to the technical field of network security identity verification and game experience optimization, and discloses a self-adaptive user identity verification and game experience linkage optimization system based on federated learning. Comprising a federated learning cooperative training module, a user behavior feature baseline construction module, an adaptive identity verification module, a game experience dynamic adjustment module, a multi-dimensional data acquisition module, a security privacy protection module, a cross-platform model migration module and a system self-learning iteration module. And each module realizes data closed loop and function linkage through a distributed architecture. According to the method, a transverse federated learning architecture and an improved FedAvg + algorithm are adopted, cross-platform local node cooperative training is combined, only model parameter gradients instead of original data are uploaded, and a differential privacy disturbance mechanism is combined, so that the problem of data islands is solved, user privacy security is guaranteed, the privacy leakage risk of centralized training is avoided, and the user experience is improved. And the safety and generalization ability of model training are remarkably improved.

The invention provides a wind turbine generator interface hierarchical configuration method, which comprises the following steps of: acquiring and analyzing interface information of hardware equipment, and determining a registration ID (Identity) of the hardware equipment according to the interface information; calling a preset conversion function according to the registration ID, and converting the interface information into configuration information by using the conversion function; and retrieving the configuration directory table, calling the corresponding function module according to the configuration information, and connecting and controlling the hardware equipment. According to the invention, the problems of long development period and weak adaptability of new models under different platform models and different hardware configurations in the prior art are solved.

This invention provides a method, apparatus, and electronic device for modeling maritime rescue processes. The method includes: constructing a hierarchical model architecture driven by discrete event system specifications for the maritime rescue process, dividing the maritime rescue system model into a discrete event system layer and an object model layer; constructing a generalized rescue platform model and modular sub-models; the modular sub-models include a controller model, a maneuvering model, and a sensor model; constructing a complete rescue system simulation architecture based on the generalized rescue platform model; and performing repeated simulations and performance analysis on the complete rescue system simulation architecture to obtain a performance analysis report. This invention achieves accurate representation of the rescue system and solves the problems of poor accuracy and completeness in existing related technologies for rescue modeling.

The invention discloses a self-description southbound acquisition method based on a global Internet of Things platform, and the method comprises the steps: obtaining original physical feature parameters based on an interface protocol of an edge layer sensor, and constructing a feature vector of the sensor; converting the feature vector into initialization metadata which can be identified by a gateway terminal through a physical feature extraction module; the gateway terminal obtains an object model of the global Internet of Things platform, performs multi-modal binding on the feature vector and the object model, and updates the initialized metadata to obtain standardized metadata; a self-description coding and decoding rule is constructed, and the gateway terminal performs coding and decoding on the original data collected by the sensor based on the coding and decoding rule in combination with the standardized metadata; decoded original data is subjected to structured storage and protocol packaging, and is uploaded to a global Internet of Things platform through a gateway terminal, physical characteristic parameters are converted into metadata which is convenient to identify, and then dynamic adaptation from southbound original data to a global Internet of Things platform model is realized based on a constructed self-description coding and decoding rule.

The application relates to a power grid blockchain platform configuration method and device, computer equipment and a readable storage medium. The method is applied to a blockchain platform. In terms of hardware, node equipment is redundantly designed and cascaded. In terms of software, a plurality of different local aggregation coefficients are configured for the blockchain platform. According to the set local aggregation coefficients, corresponding blockchain nodes are deployed to obtain different blockchain platform models. Key data of a power grid is acquired, and the key data includes power grid equipment information, operation data and user data. In the case that communication between the blockchain nodes is normal, the performance of the blockchain platform models corresponding to the different local aggregation coefficients is evaluated and tested based on the key data of the power grid, and a normalized weighted score result is obtained. According to the normalized weighted score result, the blockchain platform model corresponding to the local aggregation coefficient with the highest score is selected as a target blockchain platform specially used for power grid book management.

The invention provides an intelligent inspection method and system for a booster station, and relates to the data processing technology, and the method comprises the steps: building a limited model of a limited area in the booster station based on the point cloud data collected by an unmanned aerial vehicle for the limited area, and the limited model comprises a collection platform model; according to a preset hovering height from the unmanned aerial vehicle to the acquisition platform model, constructing a swing space for suspending the acquisition device; and on the basis of comparison of the swing space and the model space of the limited model, a plurality of lowering stages and control parameters of the lowering stages are determined, and automatic inspection can be performed in combination with the actual condition of the limited area, so that the inspection data in the narrow space can be acquired on the premise of improving the operation efficiency.

The invention discloses a block chain-based federated learning cross-border trade service platform model verification method, and belongs to the technical field of block chain federated learning of cross-border trade, and the method comprises the steps: in the federated learning of the current round, each client node carries out the local learning of a global model through employing a first data set, and obtains a local model, submitting the data to all verifier nodes; each verifier node trains the global model by using the second data set to obtain a verification model, verifies the quality of each target local model based on the verification model to obtain each verification result, and submits each verification result and the corresponding target local model to an aggregation node; and the aggregation node aggregates each target local model based on all verification results to obtain a new global model, and adds the new global model to the block chain network. According to the method, the quality of each local model can be accurately verified, so that the quality of the aggregated global model is guaranteed.

This invention belongs to the field of electromagnetic field numerical calculation technology, specifically proposing a modular analysis method for the interfaced response of a platform antenna. By constructing a unified interface mode space, the platform's external domain response and antenna radiation behavior are mapped to finite-dimensional mode coefficients, respectively. The platform response operator can be formed with only one full-wave solution. The antenna is connected in the form of a surrogate operator, and the interaction relationship between the two is established through an interface coupling operator. This method can quickly predict the comprehensive performance after different antenna installations without repeatedly solving the platform model, and is suitable for the engineering needs of antenna loading analysis in complex equipment.