193 results about "Parametric simulation" patented technology

The invention discloses a heat supply intelligent decision-making system based on an artificial intelligence large model, and the system comprises a data layer which is used for storing multi-type data of a heat supply system source network load; the knowledge base establishment and large model training layer is used for establishing a heat supply intelligent decision diversified task knowledge base and establishing each task large model; the man-machine enhancement optimization large model layer is used for providing improvement suggestions for a large model, optimizing generated answers, evaluating the answers generated by the large model and performing large model parameter optimization training; the agent decision-making layer is used for integrating each task large model into each agent to carry out environment perception, collaborative interaction and autonomous adaptive decision-making; the analogue simulation layer is used for providing a simulation operation environment, a user interaction interface and scene dynamic parameter simulation; and the agent evaluation optimization layer is used for constructing performance indexes for evaluating the agents, and evaluating, optimizing and adjusting behaviors and decisions of the agents.

The invention relates to the technical field of intelligent bed structures, in particular to a three-dimensional parameterized simulation design method for an intelligent bed structure. The method comprises the following steps: constructing a three-dimensional structure model based on an intelligent bed design structure to obtain an intelligent bed three-dimensional structure model; performing body type dynamic structure support analysis on the intelligent bed three-dimensional structure model, and performing self-adaptive adjustment mechanism design to obtain an intelligent bed self-adaptive adjustment mechanism; error parameter balance optimization is carried out on the intelligent bed self-adaptive adjustment mechanism, optimization adjustment is carried out, and an intelligent bed self-adaptive adjustment optimization mechanism is obtained; performing adjustment simulation mechanism model construction on the intelligent bed adaptive adjustment optimization mechanism to obtain an intelligent bed adjustment simulation mechanism model; and sending the intelligent bed adjustment simulation mechanism model to an intelligent bed control unit to execute intelligent bed parameterized simulation design. The intelligent bed structure technology is optimized, so that the intelligent bed structure technology is more perfect.

The present invention relates to the technical field of building foundation reinforcement deviation correction, and provides a building reinforcement lifting method based on numerical simulation, comprising: establishing an initial building model representing a building experiencing subsidence, and acquiring an initial subsidence displacement of the building; on the basis of the initial building model, adjusting shallow foundation parameters under the building foundation, simulating reinforcement of the shallow foundation, and forming a reinforced shallow foundation model; on the basis of the reinforced shallow foundation model, adjusting deep foundation parameters under the building foundation, simulating reinforcement of the deep foundation, and forming a reinforced deep foundation model; and, on the basis of the reinforced deep foundation model, dividing an intermediate lifting layer located between the deep foundation and the shallow foundation into a plurality of lifting areas and, on the basis of the subsidence displacement of each area, performing corresponding simulated lifting to implement simulated lifting of the building. The present method uses a numerical simulation approach to perform simulated optimisation on actual grouting deviation correction engineering cases, achieving both engineering economy and safety.

The invention discloses a power transmission line lightning arrester fault defect analysis method and system, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the following steps: obtaining a lightning arrester simulation model; simulating a preset defect working condition by adjusting parameters of the lightning arrester simulation model, executing simulation for a plurality of times, and constructing a defect state feature set; acquiring multi-dimensional time sequence monitoring data during a single discharge action period of the lightning arrester, acquiring equivalent strength data based on the deep learning model, and accumulating the equivalent strength data to obtain accumulated equivalent action strength; performing feature similarity matching on the defect state feature set and the action feature vector of the multi-dimensional time sequence monitoring data, and identifying the defect type of the lightning arrester; and carrying out proportion calculation on the accumulated equivalent action intensity and preset tolerable action intensity, and if a preset alarm condition is met, outputting alarm information. The method is used for solving the problems that a traditional lightning arrester monitoring pair lacks structural modeling, and accurate defect evaluation and dynamic early warning are difficult to achieve.

The invention provides an insulating material surface arc ablation resistance simulation method based on magnetohydrodynamics, and belongs to the field of insulating material performance detection. The method comprises the following steps: establishing an insulating material surface arc ablation resistance model; constructing a magnetohydrodynamic control equation set; setting boundary conditions of the model; and iteratively solving the magnetohydrodynamic control equation set through finite element simulation software to obtain a temperature distribution diagram of the insulating material under different arc ablation times, and comparing the temperature distribution diagram with a TGA curve of the insulating material to judge the arc ablation resistance of the insulating material. According to the invention, the power-frequency alternating-current high-voltage arc and the insulating material are used as simulation objects, engineering practice is better met, the surface arc ablation resistance process of any insulating material can be simulated by changing the physical characteristic parameters of the insulating material, and the TGA curve of the material is introduced to judge the arc ablation resistance of the insulating material. And technical support is provided for high-voltage switch equipment insulating material type selection.

The invention relates to the technical field of hemodialysis teaching systems, and discloses a hemodialysis teaching system based on AI intelligent pushing. The system comprises an operation feature extraction module, an instrument positioning analysis module, an operation step analysis module and a teaching strategy generation module. Operating a feature extraction module to obtain a hemodialysis teaching video stream, and extracting multi-modal features containing an instrument movement track sequence and physiological parameter simulation data; an instrument positioning analysis module calls a pre-training network for positioning and generates a coordinate data set containing instrument types and three-dimensional position coding mapping; the operation step analysis module analyzes the time sequence characteristics by means of an action segmentation model, and generates a result containing step integrity and an error operation mark; and the teaching strategy generation module inputs a result into the intelligent push model, plans a personalized teaching path, outputs a knowledge point sequence containing a student ability model and dynamic teaching elements of operation difficulty parameters, and assists the hemodialysis teaching to be precise and personalized.

The invention discloses a joint simulation method for researching the influence of an urban wind environment on urban building energy consumption. The method comprises the following steps: automatically generating a block model and calculating parameters; simulating selected block scale urban heat island intensity, and updating temperature and humidity data; simulating wind speed data of eight wind directions of the selected block for 8760h in the whole year; the average wind speed around each building is automatically obtained, and a new EPW file is generated for each building; building energy consumption of the selected block under three time scales is simulated and calculated; using the updated EPW file to simulate and calculate the building energy consumption of the selected block under three time scales; the influence of the wind environment on the urban building energy consumption is quantified according to the two energy consumption calculation results; and obtaining an influence rule of the stroke environment of the case city (climate zone) on the urban building energy consumption according to a result. According to the method, the speed and accuracy of urban wind environment calculation are both considered, and quantitative analysis of simulation results is simply achieved.

The invention discloses an oil reservoir balanced injection and production parameter determination method based on multi-algorithm analysis. The method comprises the steps that S1, a balanced injection and production influence parameter simulation model is established through oil reservoir numerical simulation software; s2, obtaining a prediction model result through numerical simulation operation, screening out balanced injection and production influence parameters, and summarizing various factor rules of different reservoir conditions; s3, correlation analysis is conducted on the oil reservoir data; and S4, determining balanced injection and production main control factors and weights thereof under different reservoir conditions. The invention further discloses a system. The method comprises the following steps: performing correlation analysis on oil reservoir data, and determining main control factors and weights of balanced injection and production under different reservoir conditions, thereby comprehensively analyzing numerous influence factors, considering the influence factors under different reservoir conditions, accurately screening out core parameters influencing balanced injection and production, and then determining the weights of the core parameters. A key basis is provided for formulating a scientific exploitation scheme, the oil reservoir exploitation process can be effectively optimized, the recovery efficiency is improved, energy waste is reduced, and therefore the economic benefits of oil reservoir exploitation are improved.

The invention discloses an asphalt mixture high-temperature performance prediction and design optimization method based on data driving. The method comprises the steps of sorting and preprocessing asphalt mixture high-temperature performance data, adopting a grey correlation analysis method, screening a data set based on a grey analysis result, and determining input and output of a high-temperature performance prediction model. Building three machine learning models, namely ANN, SVM and GPR, predicting the high-temperature performance of the asphalt mixture, and carrying out visualization processing on the models; based on the high-temperature performance optimization prediction model, a Bayesian optimization algorithm is utilized to perform targeted design optimization on the mix proportion of the asphalt mixture, and application requirements under different actual working conditions are simulated by limiting part of parameters in the mix proportion. According to the method, reliable prediction information is provided, the test cost of the asphalt mixture in high-temperature performance test and mix proportion design is reduced, the material design and research and development period is shortened, and the digital and intelligent development of asphalt mixture performance prediction and optimization is promoted.

The invention provides a carbon dioxide injection well perforation parameter optimization method and device and electronic equipment, and the method comprises the steps: carrying out the standardization processing of geological and logging data, and constructing a multi-dimensional data set comprising lithology, porosity, permeability, crack parameters and a ground stress field; a three-dimensional digital twinborn model is constructed based on the data set, and reservoir heterogeneity and fracture network distribution are restored; on the basis of the digital twinborn model, performing multi-physics field coupling simulation including carbon dioxide migration simulation, shaft stress distribution simulation and long-term corrosion prediction; based on a multi-objective optimization algorithm, with the effective wave and volume of the carbon dioxide, the integrity coefficient of the shaft and the anti-channeling safety coefficient as objectives, an optimal perforation parameter combination is screened; and outputting a visual report including perforation parameters, simulation results and risk assessment. Digital twinning, multi-physics field simulation and multi-objective optimization are deeply fused, and a set of complete, accurate and efficient perforation parameter optimization solution from data to decision is formed.

The invention discloses an ice rink re-paving ice layer thickness prediction method, system and equipment based on digital twinning, and relates to the technical field of ice rink ice surface thickness. The method comprises the following steps: step 1, constructing a digital twinborn model of the re-paved ice surface of the ice pouring vehicle through COMSOL numerical simulation software, and mapping and fusing the digital twinborn model of the re-paved ice surface of the ice pouring vehicle and real-time environmental data to generate a dynamic digital twinborn body; 2, real-time operation parameters of the ice pouring vehicle are input into the dynamic digital twin body, operation is simulated, and the simulated icing thickness is output; and 3, inputting the simulated icing thickness, the real-time operation parameters of the ice pouring vehicle and the real-time environment data into the trained machine learning model to obtain the thickness of the re-laid ice layer. According to the method, the icing thickness of the re-paved ice surface of the ice pouring vehicle corresponds to the twin model through the digital twin technology, so that the re-paved ice layer thickness prediction is more accurate and comprehensive, the re-paved ice layer thickness prediction is realized, the risk of different ice layer thicknesses is reduced, the waste of water resources is avoided, and the energy consumption is saved.

The invention belongs to the technical field of induction heating, and discloses a general induction heating system based on parametric simulation and an implementation method thereof, and the general induction heating system comprises an induction heating furnace system model, a simulation platform, a sensor and monitoring module and a control unit; the induction heating furnace system model comprises an induction furnace system and a crucible system, modeling is carried out on the induction furnace system through the simulation platform, and an alternating magnetic field is generated; modeling the alternating magnetic field and the crucible system through a simulation platform, and simulating heat energy generation and temperature change caused by eddy current; the sensor and monitoring module is used for detecting heat energy and temperature and transmitting data to the control unit; and the control unit is used for debugging parameters of the induction heating furnace system model, and then operating the induction heating furnace system model through the simulation platform to obtain optimal parameters and corresponding optimal heat energy generation and temperature change.

The invention discloses a multilayer wave-absorbing material design method, system, medium and equipment, and the method comprises the steps: building a multilayer wave-absorbing material model through electromagnetic simulation software, taking a radar cross-sectional area RCS as an optimization target, and controlling FEKO to be within a specified target frequency band; sampling electromagnetic parameters in a value range by adopting a Latin hypercube sampling algorithm, performing parameterized simulation on the multilayer wave-absorbing material model, extracting radar cross-sectional area (RCS) values to form a parameter-RCS data set, generating an electromagnetic parameter-RCS data set, training a convolutional neural network, and obtaining a multi-layer wave-absorbing material model; establishing a nonlinear mapping model from the electromagnetic parameters to a radar cross-sectional area (RCS); embedding a proxy model obtained by training into a Bayesian optimization framework, applying an expected improved function to guide a search space, automatically optimizing an electromagnetic parameter combination, and performing FEKO full-wave simulation verification; if the RCS value of the optimization result is lower than the preset threshold value, iteration continues until the performance requirement is met, and the optimal electromagnetic parameter combination is output.

The invention relates to the technical field of roller press main roller simulation, in particular to a parameterized simulation method and system for the radian of a roller press main roller and a storage medium. The method comprises the following steps that surface space scanning is conducted on a main roller of the roller press, and main roller space parameters are obtained; main roller radian distribution evaluation is conducted according to the main roller space parameters, and a main roller radian distribution diagram is obtained; carrying out regional division on the main roller radian distribution diagram, and importing mechanical analysis software to construct a roller press main roller initial state model; rolling material data are obtained; performing material-main roller stress parameterization simulation according to the rolled material data and the roller press main roller initial state model to obtain simulated main roller stress state data; and intelligent main roller bending compensation is carried out based on the simulated main roller stress state data, and main roller bending compensation parameters are obtained. Intelligent compensation of the radian state of the main roller of the roller press is achieved through main roller stress parameterization simulation, the product quality is effectively improved, energy consumption is reduced, and the service life of equipment is prolonged.

The invention relates to a simulation analysis method and device for bottle performance. The simulation analysis device comprises a bottle blank wall thickness obtaining module, a bottle blank heating simulation module, a blow molding simulation module and a structure simulation module, and the bottle blank wall thickness obtaining module, the bottle blank heating simulation module and the structure simulation module are all associated with the blow molding simulation module; a bottle blank wall thickness acquisition module acquires continuous and non-uniform bottle blank wall thickness data of the bottle blank three-dimensional solid model; the bottle blank heating simulation module receives the heating simulation parameters and simulates the heating process of the bottle blank before blow molding to obtain continuous and non-uniform bottle blank temperature distribution data; the blow molding simulation module simulates a bottle blank blow molding process according to the bottle blank wall thickness data, the bottle blank temperature distribution data and the received blow molding simulation parameters to obtain a bottle simulation model; and the structure simulation module performs bottle performance simulation analysis on the bottle simulation model to obtain performance simulation data of the bottle simulation model. And more real bottle body wall thickness distribution data and bottle blank temperature distribution data can be obtained, and the performance of the bottle can be predicted and evaluated more accurately.

The invention discloses a dynamic simulation method, a dynamic simulation device, dynamic simulation equipment, a dynamic simulation medium and a dynamic simulation product for a network construction type fuel cell power generation system, and relates to the field of physical object simulation dynamic modeling and performance analysis. The method comprises the following steps: firstly, determining the overall structure and configuration of the network-constructing type fuel cell power generation system, carrying out comprehensive modeling by utilizing a thermoelectric comparison method on the basis, and establishing an overall standard circuit model of the network-constructing type fuel cell power generation system; constructing a dynamic simulation model of the network-constructing type fuel cell power generation system based on the overall standard circuit model; by adjusting input parameters of the dynamic simulation model, simulating dynamic responses of the network-forming type fuel cell power generation system in different distributed power generation scenes; and performing cluster analysis on simulation results under different distributed power generation scenes, and formulating an intelligent control strategy of the distributed power generation system. According to the method, the dynamic response of the network-forming type fuel cell power generation system to the power grid in the distributed power generation system can be accurately simulated under the condition that large-scale on-site deployment is not needed.

The invention discloses a simulation charging pile test method, system and device based on a charging pile simulator, and relates to the technical field of simulation charging piles, and the method comprises the steps: obtaining parameters of a simulation charging pile and a platform server; tCP long connection is established between the simulation charging pile and the platform server; selecting different charging modes to test each charging flow to obtain charging process data; and obtaining fault data and log message data. According to the main technical scheme and effects, the charging pile simulator simulates data interaction of various business test scenes of a charging APP, an applet and a management platform, including charging pile charging and discharging, fault warning and the like, the test development process is separated from an actual charging pile and a new energy automobile, and the material preparation cost is greatly saved; according to the simulation charging pile, the multi-thread design is adopted, it is ensured that the process runs smoothly, the multiple charging pile simulators can be started to work at the same time, and the data interaction efficiency of the simulation charging pile and the platform server side is greatly improved.

The present invention discloses a heating intelligent decision-making system based on an artificial intelligence large model, including: a data layer for storing various types of data of the source-network-load-storage of the heating system; a knowledge base establishment and large model training layer for establishing a diversified task knowledge base for heating intelligent decision-making and establishing large models for each task; a human-machine enhanced optimization large model layer for putting forward improvement suggestions to the large model, optimizing the generated answers, evaluating multiple answers generated by the large model, and performing parameter optimization training on the large model; an intelligent agent decision-making layer for integrating the large models of each task into each intelligent agent for environmental perception, collaborative interaction, and autonomous adaptive decision-making; a simulation layer for providing a simulation operation environment, a user interaction interface, and dynamic parameter simulation of scenarios; an intelligent agent evaluation and optimization layer for constructing performance indicators for evaluating each intelligent agent, and evaluating and optimizing the behavior and decision-making of each intelligent agent.

The invention discloses a simulation parameter optimization method, device and equipment based on an APDL command stream, and belongs to the technical field of electrical digital data processing, and the method comprises the following steps: obtaining a design variable, a constraint condition and a target function defined by the APDL command stream through an interaction port, and carrying out parametric modeling to generate a parametric simulation model; a design variable is used as an optimization target object, a global optimization algorithm is integrated, the design variable is updated, a new parameterized simulation model is automatically generated, simulation and automatic iterative optimization are executed, and an iterative optimization result is obtained; if the iterative optimization result is convergent, ending the iterative optimization to generate a simulation result, and if the iterative optimization result is divergent, performing loop iterative optimization until the iterative optimization result is convergent; key performance index data in the simulation result are extracted, and a multi-dimensional analysis report is generated and displayed. The problems of low automation degree, low calculation efficiency and lack of flexibility in the prior art are solved.

The invention relates to an AMR task scene equipment parameter simulation generation method and device and a storage medium, and is applied to the technical field of digital twinning, and the method comprises the steps: determining equipment participating in a task scene through determining task processes of different AMR task scenes, and obtaining an interactive relationship between every two pieces of equipment (including AMR) participating in the task scene through obtaining the interactive relationship between every two pieces of equipment (including AMR) participating in the task scene; determining a coupling relationship between any two devices, screening N devices having strong coupling relationships with the AMR, and generating a sampling parameter combination of the AMR and the N devices according to historical operation data of the devices and the coupling relationships; a digital twinborn model of an AMR task scene is constructed through a digital twinborn technology, in the digital twinborn model, simulation operation of a task process is performed on different parameter combinations, and a reliability score corresponding to each parameter combination is obtained, so that an optimal parameter combination is screened out, and the reliability of the AMR task scene is improved. And updating the real AMR and the parameters of the equipment according to the optimal parameter combination.

The invention provides a deep foundation pit monitoring method based on BIM-physical model hybrid simulation. The method comprises the steps that real-time monitoring data and BIM model parameters of a deep foundation pit are collected; inputting a first model parameter in the BIM model parameters as an initial geometric condition into the initial physical model for simulation calculation to obtain first simulation data, and verifying the first simulation data based on the real-time monitoring data to obtain a verification result index; optimizing the soil mass mechanical parameters in the initial physical model based on the verification result index until the verification result index output in the optimization process meets a preset requirement, and obtaining an optimized physical model; simulating the construction process of the deep foundation pit on the basis of the optimized physical model in combination with a second model parameter in the BIM model parameters, and outputting second simulation data; and performing monitoring and early warning on the deep foundation pit based on the deep foundation pit knowledge graph in combination with the real-time monitoring data and the second simulation data to obtain a monitoring and early warning result. According to the invention, comprehensive monitoring of the deep foundation pit is realized.

The invention relates to the technical field of soil data processing, and discloses a soil data simulation method integrating machine learning and spatial analysis cooperation. The method comprises the following steps: acquiring multi-source soil data of a target area, determining spatial characteristic parameters of each soil data unit, calculating machine learning characteristic indexes and generating an analog input set; then, dynamic evolution parameters are determined by using a pre-trained machine learning model, and a collaborative simulation map is marked and generated in the spatial analysis model; and then performing data consistency detection, and simulating and generating soil data based on a detection result. And if the parameter conflict is detected, fusing the parameters according to the multi-source data priority. After the simulation result is generated, monitoring the attribute difference value of the soil data unit and the adjacent unit in real time, and dynamically correcting the parameter when the threshold value is exceeded. According to the method, multi-source data are integrated, machine learning and spatial analysis technologies are utilized, the precision and reliability of soil data simulation are improved, and powerful support is provided for soil research and related fields.

The invention relates to the technical field of vehicle brakes, in particular to a parameterized simulation design method for a vehicle brake structure. The method comprises the following steps: firstly, obtaining vehicle brake structure data and constructing a three-dimensional virtual model; then, multi-frequency rapid braking response simulation is carried out based on the model, the softening gradient of the brake disc is recognized, and displacement deformation of the brake disc caused by external force impact is analyzed; thirdly, defining the bearing boundary of the brake by combining the external force impact data and the structural data, and optimizing the structural performance; and finally, designing a parameterized simulation architecture based on the optimized data, and uploading the parameterized simulation architecture to a cloud platform to execute parameterized simulation of the brake. According to the method, the vehicle brake technology is optimized, so that the vehicle brake technology is more perfect.

The invention discloses a thermal-mechanical coupling behavior numerical simulation system for a reinforced concrete structure, and particularly relates to the field of constructional engineering, and the system comprises a heat conduction analysis module, a mechanical behavior analysis module, a bond slip behavior analysis module and a simulation calibration module. The heat conduction analysis module simulates the temperature field change of the concrete in the fire disaster process by defining temperature related parameters of the concrete; the mechanical behavior analysis module is used for simulating nonlinear mechanical behaviors of concrete and analyzing strength degradation, crack propagation and plastic deformation of the concrete at high temperature in combination with a temperature-dependent material model; and the bond slip behavior analysis module is used for setting a non-linear contact behavior between the reinforcing steel bar and the concrete. According to the invention, comprehensive simulation of reinforced concrete under high-temperature conditions such as fire disasters and the like is realized, and the strength degradation and crack propagation of the concrete and the bond slip behavior of the reinforcing steel bars and the concrete can be accurately predicted in combination with a temperature field and a mechanical field.

The present invention relates to the field of data processing, and discloses a test method, device and storage medium for a body controller compatible with multiple models. The method includes: when a function test instruction is received, generating function requirement information according to the function test instruction; selecting a target test item set from a preset test item set according to the function requirement information; performing parameter simulation operations for multiple models according to the target test item set and a preset parameter simulation pattern to obtain a test parameter set; generating test cases according to the test parameter set; and controlling the body controller to perform a test operation according to the test cases to obtain a test result. In the embodiment of the present invention, compatible testing of body controllers of multiple models can be realized, and the compatibility of the body controller test method is improved.

The invention belongs to the technical field of microstructure simulation in the solidification process, and particularly relates to a nickel-based superalloy laser welding solidification structure evolution law prediction method and system.The method comprises the steps that simulation parameters are determined, a macroscopic temperature field is simulated to obtain molten pool temperature field data, a cellular automaton model is constructed, and grids are divided; initializing state variables such as component concentration and solid-phase fraction; coupling temperature field data, solving an optimized solute diffusion and redistribution equation to obtain a concentration field, updating a solid phase fraction according to local supercooling drive, and marking grain growth; counting a solid-phase fraction, and updating a solid-phase position; and judging whether the temperature of the cladding layer is lower than the solidus temperature and the solid fraction reaches a complete solidification threshold value or not, and if yes, outputting weld microstructure and target element concentration data. According to the method, the evolution law of the welding macroscopic temperature field and the structure and solute concentration field can be effectively predicted, experiments are replaced with numerical simulation, the cost is reduced, the technical blank is filled up, and reliable support is provided for precise regulation and control of the nickel-based high-temperature alloy laser welding process.

The invention discloses an oil and gas development favorable area prediction method based on Winsored particle size mean curve simulation, and the method comprises the following steps: based on laser particle size experiment data, employing a Winsored algorithm to process rock particle size data, removing abnormal interference values, and simulating a well hole particle size mean longitudinal curve through inputting logging response parameters by means of an XLSTM-Transform hybrid neural network model; selecting a shear modulus density and a longitudinal and transverse wave velocity ratio as sensitive attribute parameters of a granularity mean value in combination with seismic data, and forming two sets of three-dimensional attribute data volumes in combination with waveform structure inversion; according to the method, model training is carried out on the nonlinear relation between the particle size mean value and the seismic sensitive attribute parameters based on a machine learning method, and finally a Winsored particle size mean value inversion body is formed, so that the spatial distribution rule of a relatively large particle size reservoir can be defined, and effective prediction of a development favorable area is realized.

The invention discloses a parameterized power transmission line distraction field site arrangement simulation method and equipment, structural characterization is carried out on extracted nodes by combining an interpolation method and a shape finding analysis method, parameterized modeling is carried out according to a characterization structure, a distraction field site model is further obtained, the distraction field site model accurately feeds back the shape of a distraction field, and the distraction field site arrangement simulation method and equipment have the advantages that the distraction field site arrangement simulation efficiency is improved. Meanwhile, the interaction relation between each machine and each structure is accurately fed back, and an accurate and stable distraction field model consistent with a construction field is provided for subsequent distraction field simulation; the arrangement simulation parameters subjected to construction verification are input into the distraction field migration model again to be circularly processed until the optimal arrangement simulation parameters are generated, the optimal arrangement simulation parameters serve as the optimal distraction field construction parameters, construction is conducted, and the problem that local engineering calculation is conducted on the parameters manually is solved; the calculation result is not standard, and meanwhile, the position of a dangerous point in the layout of the distraction field cannot be accurately estimated.

The invention relates to an on-orbit micro-thrust measurement simulation system of an electrostatic suspension accelerometer, and belongs to the technical field of high-precision acceleration measurement simulation. The system comprises an on-orbit parameter simulation device and an electrostatic suspension accelerometer output measurement device. Wherein the on-orbit parameter simulation device is used for simulating and generating an on-orbit simulation environment of the electrostatic suspension accelerometer, and the on-orbit simulation environment comprises acceleration simulation, noise simulation and satellite attitude simulation; the electrostatic suspension accelerometer output measuring device is used for processing the output of an electrostatic suspension accelerometer, the processing result is divided into two paths, one path serves as primary non-conservative force acceleration data measured by the accelerometer and is fed back to the electrostatic suspension accelerometer, and the other path serves as secondary non-conservative force acceleration data. And the other path is used as the electrostatic force acceleration after the mass block is subjected to the electrostatic force effect and is used for measuring the electrostatic suspension accelerometer.

The invention discloses an algorithm feasibility test method, system and device and a storage medium, and the method comprises the steps: obtaining a to-be-tested algorithm code and a test data set of the to-be-tested algorithm code, determining the parameters of a running environment, simulating an actual running environment according to the parameters of the running environment, compiling the to-be-tested algorithm code into an executable code, and storing the executable code in a database; in an actual operation environment, a plurality of groups of input data of a test data set are respectively input into an executable code to obtain execution result data, the data volumes of each group of input data are different, performance scores of a plurality of dimensions are calculated according to the execution result data, weighted summation is performed on the performance scores of all the dimensions to obtain a feasibility test result, and the feasibility test result is obtained. The performance scores of the multiple dimensions at least comprise two dimensions of a time evaluation score, a memory evaluation score, an energy consumption evaluation score, a delay evaluation score and a stability evaluation score. The method can meet diversified tests and improve the test accuracy, and can be applied to the technical field of algorithm test.