439results about "Virtual/augmented reality" patented technology

The invention provides an intelligent decision-making method based on multi-dimensional risk coupling modeling of new energy ships and related equipment. The method comprises the following steps: fusing multi-modal data of each new energy ship to obtain a target multi-modal feature vector; mining risk implicit association strength by using a large language model, and constructing a risk knowledge graph; performing risk time sequence evolution prediction by adopting a dynamic Bayesian network based on the atlas to obtain a single-ship risk prediction result; constructing a graph structure according to the single ship risk and the operation parameters, and carrying out space coupling modeling by adopting a graph convolutional network to obtain a cluster risk prediction result; and generating an optimal operation and maintenance strategy by adopting reinforcement learning based on a cluster risk prediction result and a reward function by taking a high-fidelity digital twinborn body as a virtual environment. Therefore, according to the method, the problem of closed-loop adaptive control from risk deduction to decision response in a complex navigation scene is effectively solved by constructing a unified modeling mechanism of multi-dimensional risk coupling and linking real-time control strategy generation.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

The invention discloses an urban planning design method based on a live-action three-dimensional simulation system, relates to the technical field of urban planning, and is used for solving the problem of insufficient technology fusion depth in an existing method. Firstly, an initial data set is formed by collecting multi-source city data; performing cross-modal feature extraction and alignment based on a deep learning model to generate a fusion feature vector; constructing a dynamic three-dimensional city model by using the fusion feature vector, and embedding a GIS spatial analysis module to perform preliminary layout simulation; vR interaction logic is injected into the model, user behavior feedback is simulated, and environment influence indexes are calculated; according to simulation results and index feedback optimization planning parameters, multi-technology collaborative decision making is achieved; outputting an optimization scheme and generating a visual report, and supporting real-time iterative updating; according to the method, the technology fusion depth is remarkably improved, the response delay is shortened to millisecond level, the planning precision is improved, the environmental influence evaluation deviation is reduced, and efficient and sustainable urban planning practice is realized.

The invention provides a multi-modal fusion virtual simulation experiment teaching system, and relates to the technical field of virtual simulation, and the system comprises an information collection module which is used for obtaining the interaction information of a learner; the information fusion module is used for constructing a multi-modal input data set; the instruction analysis module is used for analyzing and generating an operation instruction sequence; the teaching intervention strategy generation module is used for generating a self-adaptive teaching intervention strategy; the teaching intervention strategy execution module is used for executing a self-adaptive teaching intervention strategy; and the learning effect evaluation module is used for quantifying the learning effect evaluation score. The technical problems that in traditional virtual simulation experiment teaching, multi-modal operation behaviors of learners are difficult to accurately capture in real time, personalized teaching intervention is lacked and learning effect evaluation is incomplete can be solved, and virtual experiment simulation and intelligent teaching intervention driven by multi-modal information fusion are achieved. And the experimental interaction experience and the learning effect evaluation precision are improved.

The invention relates to the technical field of intelligent monitoring, in particular to an electronic guide vehicle steering system fault prediction method based on time sequence data analysis, which comprises the following steps: mapping a steering system into perception, decision and execution virtual computing nodes to construct a digital twin model; extracting inter-node time lag correlation through multivariable Granger causality operation, and constructing a virtual causality matrix; fault parameters such as current, angle and bias are injected into the model for numerical simulation, and a response sequence is generated through time domain iteration; using a dynamic time warping algorithm to calculate the morphological difference between the time sequence and the reference sequence so as to generate a time sequence feature; and constructing a digital twinning dynamic graph, inputting the dynamic graph attention network, and extracting spatio-temporal evolution characteristics to generate a fault prediction result. According to the method, accurate deduction of the full-life-cycle fault evolution process of the steering system is realized through digital twinning.

The invention relates to the technical field of electrical wiring. The invention discloses a method for generating a cable wiring path of a power distribution cabinet, and the method comprises the steps: firstly obtaining the multi-dimensional environment parameters of a to-be-wired power distribution cabinet, wherein the structural parameters comprise the three-dimensional layout in the cabinet, the positions of components and wire duct guide rails, the electrical parameters comprise the connection relation of the components, the cable specification and the like, and the physical constraint parameters are the allowable bending radius and the minimum electrical gap; constructing a digital twinborn model according to the parameters, and performing risk analysis on the model to obtain a safety risk level; and finally, a double-path scheme is generated and output in combination with the model and the grade, the first path is the optimal path of the prediction model, the second path is the standby path of the historical case, and the priority of the first scheme is higher. According to the method, the security risk is quantified in advance through multi-dimensional parameter acquisition, digital twin modeling and risk assessment. The current situation that traditional manual wiring depends on experience is changed through intelligent generation of an optimal path, reuse of a standard scheme of a standby path and real-time monitoring and early warning.

The invention relates to the technical field of information, and particularly provides a complete denture arrangement virtual simulation method and system, and the method comprises the steps: obtaining the medical record data of a user; the medical record data comprises edentulous jaw soft and hard tissue data, jaw bone data, alveolar ridge morphological parameters, jaw position relation data and facial feature data; determining an initial dental arch curve based on the medical record data; inputting the initial dental arch curve and preset aesthetic parameters into a preset deep learning model to obtain a plurality of initial tooth arrangement schemes; screening the plurality of initial tooth arrangement schemes according to tooth arrangement constraint conditions to obtain tooth arrangement schemes meeting constraints; the tooth position and angle of each tooth arrangement scheme meeting the constraint are adjusted until the preset occlusion balance condition is met, and the first tooth arrangement scheme is obtained, so that the tooth arrangement scheme efficiency of the complete dentures can be improved on the premise that the quality of the tooth arrangement scheme of the complete dentures is guaranteed.

The invention provides a BIM (Building Information Modeling)-based engineering electromechanical installation method. The method comprises the following steps: establishing an engineering electromechanical BIM model; carrying out model collision check and optimization; construction simulation and scheme making are carried out; field installation and implementation; and installation acceptance and model filing. According to the method, the geometric and attribute integrated BIM model is constructed through data collection and digital drawing review, full-dimensional collision detection is carried out, key pipeline specification requirements are preferably guaranteed, model data reliability is guaranteed, construction disjunction caused by drawing errors is avoided, the problem of pipeline collision is solved in advance, and the construction efficiency is improved. Material waste and construction period delay of dismantling and reassembling in the construction stage are reduced, meanwhile, the risk of illegal acceptance of key pipelines is reduced, and the limited space utilization rate is increased.

The invention provides a factory three-dimensional digital twin model reconstruction and incremental updating method for industrial space intelligence, and the method comprises the steps: obtaining a factory multi-view basic image for a factory panoramic scene, so as to train a bifurcated structure neural implicit field network containing a radiation field branch and an SDF branch, and generating an initial three-dimensional digital twin base; acquiring factory real-time inspection images acquired by the inspection robot under different inspection poses, rendering the initial three-dimensional digital twin base by using a radiation field branch to obtain a virtual reference view, and performing difference analysis on the virtual reference view and the factory real-time inspection images to position a structural change area in a factory; determining an axis alignment bounding box for changing the physical range of the entity in the area, taking the axis alignment bounding box as an effective boundary of the local space patch, and determining matched radiation field network weight and SDF network weight according to the effective boundary; and adjusting the radiation field network weight and the SDF network weight, and updating the radiation field network weight and the SDF network weight to the network in an incremental updating manner to generate a factory three-dimensional digital twinning model based on the implicit three-dimensional geometric field.

The invention provides a ship anti-mold-penetrating and collision-preventing method in combination with Beidou and virtual channel constraint in a digital twinborn scene, and the method solves a problem of ship position deviation caused by the installation position difference of a Beidou terminal through Beidou and AIS dual-mode fusion positioning correction. Constructing a refined collision detection adaptation mechanism based on the ship models associated with various goods types, and adapting to size differences of different ship models; and a'positioning-ship model-collision detection 'cooperative link is opened, so that multi-target priority collision avoidance is realized. According to the method, the ship position display precision and the anti-mold-penetrating and anti-collision accuracy in the digital twin scene are effectively improved, and the method is suitable for real-time supervision and simulation in a high-density ship traffic environment.

The invention relates to the technical field of aluminum alloy part casting processes, in particular to a multi-parameter collaborative optimization system and method for a new energy aluminum alloy part casting process. According to the technical scheme, the multi-parameter collaborative optimization system for the new energy aluminum alloy part casting process comprises a multivariate heterogeneous data acquisition module, a multi-physics field coupling optimization module, an intelligent execution control module, a virtual-real fusion verification module and a man-machine collaborative decision module; by deploying the melt state monitoring unit, the mold dynamic thermal field sensing unit and the equipment health monitoring unit, all-around and high-precision monitoring of melt, mold and equipment states is realized, and meanwhile, space-time alignment and noise elimination are performed on multi-source data through a Kalman filtering algorithm; and generating a characteristic matrix containing a melt fluidity index, a mold temperature gradient field and an equipment health state, and providing a high-quality data basis for subsequent optimization.

The invention discloses a BIM-based pipeline autonomous optimization identification visual construction method and system, relates to the technical field of building information models, and is used for solving the problem of delivery mismatch caused by electromechanical pipeline layout limitation, dynamic deformation and information splitting. The method comprises the following steps: firstly, centrally filing multiple professional drawings and establishing a building, structure and electromechanical unified three-dimensional model; then space conflict detection, linear coupling section identification driven by thermal-stress data and maintenance pane construction are executed, and a compensation coefficient is generated based on scene mapping to realize two-stage path optimization; then hole checking, support and hanger arrangement judgment, pipe section prefabricating and splitting and 4D construction simulation are completed; and finally, through point cloud calibration, quality two-dimensional code binding and debugging data injection, outputting a completion BIM model containing actual measurement data, and realizing a full-link digital closed loop of design, construction, operation and maintenance.

The invention discloses a transformer substation wiring construction guidance method and system based on GIM and AR, electronic equipment and a readable storage medium, and relates to the technical field of transformer substation wiring construction. The problem of low wiring accuracy and efficiency caused by lack of real-time verification of field real-time construction guidance and misconnection prevention and control in existing substation wiring construction is solved. According to the invention, a graphical interface module GIM and augmented reality AR equipment are fused, the graphical interface module GIM is taken as a data center, and a digital wiring scheme is established, so that the planning and verification basis of each cable laying path is realized; the AR equipment serves as a field guiding tool, a wiring scheme is superposed to a construction scene, and interactive operation navigation of a construction field is achieved; and in combination with verification mechanisms such as identity comparison of the cable and the terminal, wiring error alarm prompt, final on-off test and the like, wiring construction guidance is completed. The method is mainly used for transformer substation wiring construction.

The invention relates to the field of animal husbandry industry environment regulation and control system optimization, in particular to a lightweight digital twinborn breeding environment monitoring method and system, and the method comprises the following steps: S1, carrying out the construction of a computational fluid dynamics (CFD) model for a breeding farm environment; s2, environment data are collected; s3, comparing model prediction with real-time data, and monitoring a breeding environment according to a comparison result; and S4, data presentation. According to the invention, through cooperative work of the flow field modeling unit, the environment data acquisition unit, the model prediction and data calibration unit and the visualization unit, light-weight, full-range and high-precision breeding environment monitoring and regulation are realized.

The invention discloses an intelligent guiding method based on a 3D virtual garment customization process, and relates to the technical field of garment customization, and the method comprises the steps: obtaining the basic information of a user through a user interaction interface, and generating a 3D virtual human body model matched with the body type of the user through 3D scanning equipment; performing intelligent matching through a machine learning algorithm according to the basic information of the user and the style information in the costume design database, and displaying a matching recommendation result on a user interface in a visual form for the user to select and preview; dynamically adapting the 3D model of the clothes selected by the user to the 3D virtual human body model, and providing an intelligent adjustment option and an intelligent algorithm to generate an adjustment suggestion; according to the method and the device, fitting simulation, comfort evaluation and visual effect evaluation feedback are performed, user design is optimized, and a customization scheme containing a 3D model file and a production drawing is generated according to a design scheme finally confirmed by a user, so that the operation of a virtual garment customization process is simpler, the auxiliary decision-making experience degree is higher, and the customization efficiency is improved.

The invention relates to the technical field of garden landscape design, in particular to a garden landscape design method based on a virtual reality technology, which comprises the following steps: step 1, multi-source data acquisition and three-dimensional modeling: adopting a laser radar to scan a design site to obtain high-precision terrain point cloud data and generate a three-dimensional terrain model, the method comprises the following steps: performing three-dimensional scanning on garden plants to construct a plant resource library containing plant morphological parameters, growth characteristics and a growth cycle algorithm, acquiring ecological data of field illumination intensity, soil pH value, annual precipitation and wind direction and wind speed through a sensor array, and establishing a field ecological parameter database; and 2, constructing an immersive virtual scene: importing the terrain model into a VR engine, and automatically generating a basic landform based on an ecological parameter database, and through growth simulation and space-time rendering, realizing full-cycle presentation from short-term landscape to long-term ecological change, improving the customer scheme recognition degree to more than 90%, and reducing the modification rate by 60%.

The invention discloses an XR-based immersive three-dimensional modeling and straight-through 3D printing system and method, and the method comprises the steps: S1, collecting user input, and converting the user input into an interaction instruction; s2, generating three-dimensional model data; s3, superposing the three-dimensional model into a real physical environment according to a proportion of 1: 1, and outputting a virtual-real fusion display result; s4, performing printability analysis on the model, marking a problem area, guiding a user to modify and providing an automatic repair suggestion; s5, generating a support structure, and providing visual preview and interactive editing; s6, slicing and path planning are executed, and a printing instruction file meeting the control requirement of the 3D printer is generated; s7, communication connection with the 3D printer is established, and printing state information is fed back; and S8, providing storage and calling support of the three-dimensional model file, parameter configuration and environment map data. According to the invention, seamless integration of immersive XR modeling, MR real-time verification and one-key direct-through 3D printing is realized.

The invention discloses a new energy vehicle fault diagnosis training system based on digital twinning, and the system comprises a data collection and assimilation module which is used for collecting vehicle operation data, and constructing a digital twinning body; the mechanism map construction module is used for constructing a causal failure mechanism map and generating a failure scene; the sample generation and management module is used for generating a virtual domain sample set, a disturbance domain sample set and a real domain sample set, and combining to form a training data set; the residual fusion diagnosis model module is used for setting a physical residual branch and a disturbance residual branch and outputting a fault diagnosis result; the consistency alignment training module is used for constructing joint loss and optimizing the residual fusion diagnosis model; and the intelligent diagnosis and simulation module is used for executing real-time diagnosis and risk prediction, generating a simulation drilling script and training maintenance personnel. According to the invention, through digital twinning, residual fusion modeling and three-dimensional consistency training, accurate diagnosis and safe and efficient training of new energy vehicle faults are realized.

The invention discloses an urban multi-objective collaborative optimization method and system based on deep reinforcement learning, and the method comprises the steps: providing or constructing a building performance agent model, taking a group of urban form design variables as input, and taking a building performance evaluation index as output; constructing a multi-objective collaborative optimization problem into a Markov decision process, and defining the action, state and reward function of an intelligent agent; and training the intelligent agent by adopting a deep reinforcement learning algorithm, and generating an urban form design scheme by applying the optimal decision strategy after training convergence. According to the method, a static and high-dimensional optimization problem is converted into a dynamic decision learning process, and the multi-target and nonlinear complex optimization problem in urban form design is efficiently solved by utilizing the autonomous exploration and optimization capability of deep reinforcement learning; the optimal solution set considering energy efficiency, renewable energy utilization and human settlement environment quality can be quickly generated, and automatic and intelligent decision support is provided for planning and design of sustainable urban communities.

The invention provides an intelligent water conservancy monitoring method and system based on the Internet of Things. The method comprises the steps of obtaining heterogeneous water conservancy data of various sources; preprocessing and standardizing the heterogeneous water conservancy data from various sources; constructing a water conservancy domain knowledge graph, and performing multi-dimensional data fusion to generate comprehensive water conservancy situation data; based on the comprehensive water conservancy situation data, constructing a water conservancy digital twinborn body, and realizing real-time driving and hydrological and hydraulic process simulation of the water conservancy digital twinborn body; and multi-modal deep analysis is performed on the comprehensive water conservancy situation data and the water conservancy digital twin, and interpretable intelligent decision support information is generated by using an artificial intelligence technology. According to the method, the problems of scattered data sources, high isomerism, relatively low intelligent degree and insufficient information presentation modes of a water conservancy management system are solved by acquiring and preprocessing the multi-source heterogeneous water conservancy data, fusing and cognizing the water conservancy data, twinning and simulating the water conservancy system and performing analysis and decision intelligent support.

The invention discloses a high-permeability new energy power distribution network collaborative optimization method, system and device and a storage medium, and belongs to the technical field of information, and the method comprises the steps: obtaining wind power plant operation data and signal intensity, processing and fitting a signal attenuation trend, recognizing mist environment path loss, obtaining a wind power output probability distribution function, and determining an output fluctuation range. Predicting an output deviation scene, calculating a wireless signal fluctuation probability, and extracting signal fluctuation characteristics; establishing a channel fading model in combination with mist path loss, outputting wireless signal propagation delay, and determining a potential delay interval of a scheduling instruction of the energy storage system; simulating dispatch out-of-order and power regulation instruction conflicts, and generating dispatch out-of-order evaluation data; combining historical wind power output data to judge power flow fluctuation, adjust an energy storage unit response sequence, generate a power adjustment instruction sequence, calculate adjusted power flow distribution data, and generate energy storage and topology configuration optimization data, so that the operation reliability and economical efficiency of the wind power plant are remarkably improved.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

A system includes a processing circuitry and a memory, accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry cause the processing circuitry to perform operation including receiving one or more route criteria and identifying a number of pipes, wherein the number of pipes is based on the one or more route criteria. The piping also includes generating a vector route, storing the vector route, generating a limiting zone, and performing an iterative process including determining one or more vector routes for each of the identified number of pipes. Further, the piping system also includes optimizing a route solution based on the one or more vector routes based on an optimization parameter and outputting a three-dimensional pipe layout, wherein the three-dimensional pipe layout is transmitted to an external platform for display via a user interface.

The invention provides a temperature field reconstruction method and system based on digital twinning and application thereof, and the method comprises the steps: obtaining multi-moment temperature field data of target equipment, and carrying out the spatial dimension reduction processing, and obtaining K dominant modes; based on the multi-moment temperature field data and the K dominant modes, calculating modal coefficients of the temperature field at each moment in each dominant mode, and forming modal coefficient time sequences in one-to-one correspondence with the dominant modes; training a time sequence prediction model based on the modal coefficient time sequence and the operation condition physical parameters of the target equipment in time alignment with the modal coefficient time sequence, and predicting an initial modal coefficient at a future moment by using the time sequence prediction model; performing feedback correction on the initial modal coefficient based on a correction mechanism to obtain a corrected modal coefficient; and reconstructing the temperature field of the target equipment by using the corrected modal coefficient and the K dominant modals. According to the method, through a technical chain of space dimension reduction, working condition fusion, feedback correction and modal superposition, high calculation efficiency and high full-field reconstruction precision are both considered.

The application discloses a kind of based on the multi-view spine biomechanics dynamic analysis method and system of augmented reality, belong to man-machine interaction and biomechanics field.The system of the application includes central client and augmented reality client.Central client includes spine biomechanics analysis module and visual interactive module, respectively for central client user provides the dynamic evaluation of human lumbar and sacral part stress and visual real-time information feedback.Augmented reality client provides gesture interactive mode for augmented reality client user, realizes corresponding interactive action behavior in augmented reality scene, user is based on interactive system, selects model by gesture and downloads selected model to current environment, and interacts with downloaded model by gesture;By building motion capture environment, the real-time collection of human body posture of augmented reality client user is realized, and the real-time motion simulation of virtual human model is realized.The application can optimize working posture and reduce spinal skeletal muscle disease.

A method of controlling a manufacturing process includes: creating a digital twin model representing one of the manufacturing process or a manufactured article that is formed or modified by the manufacturing process; revising the digital twin model with real-time data regarding a physical instance of the one of the manufacturing process or the manufactured article; making a decision, based on the digital twin model, regarding the physical instance of the one of the manufacturing process or the manufactured article; and causing or modifying an action, based on the decision regarding the physical instance of the one of the manufacturing process or the manufactured article.

The invention provides a process parameter optimization method and system for a functionally graded material and a medium, which can be applied to the technical field of process parameter optimization. According to the method, after corresponding technological parameters and forming response data under different component gradients are obtained through an orthogonal test, a quadratic term polynomial regression model used for representing the mapping relation between the technological parameters and the forming response data in the different component gradients is constructed according to the technological parameters and the forming response data. A quadratic term polynomial regression model is used as a virtual simulation environment, meanwhile, after a multi-dimensional continuous strengthening model is constructed based on a double-delay depth deterministic strategy gradient algorithm, based on the multi-dimensional continuous strengthening model, through dynamic interaction of an intelligent agent and the virtual simulation environment, an optimal process parameter combination is searched in different component gradients; and thus, cross-component gradient process parameter optimization is realized. And finally, verifying the optimal process parameter combination through a physical experiment, and determining the accuracy of model prediction.

The invention relates to the field of operation and maintenance management of Internet of Things equipment, in particular to a periodic maintenance task automatic scheduling system and method, and the method comprises the steps: firstly collecting vibration, current and environment signals, and constructing a three-dimensional digital twin map; obtaining a health index and a remaining available life in a preset window through time sequence alignment convolution and an ordinary differential equation recursive network, generating a risk coefficient through index mapping, and driving a reaction-diffusion equation to form a continuous degradation field; the scheduling optimization unit obtains an initial solution through symplectic gradient Hamiltonian-Monte Carlo sampling, and maintenance scheduling data meeting resource constraints are generated through bose sampling, topological bose evolution and dual gaming; the mixed reality terminal displays a risk hotspot and an operation instruction on site, retest data is recorded after maintenance is completed, and reinforcement learning updates a risk assessment and scheduling model on line according to an execution result to form a closed loop; according to the method, accurate quantification of risks, global optimization of scheduling and adaptive feedback of execution are realized, and the fault rate and the maintenance cost can be remarkably reduced.

The invention relates to the technical field of traffic infrastructure operation and maintenance, in particular to a bridge dynamic load intelligent operation and maintenance system based on digital twinning, which comprises a digital twinning modeling module, a dynamic load monitoring module, a data processing and simulation analysis module, a health state evaluation and early warning module and an operation and maintenance decision support module. According to the invention, through multi-source heterogeneous data standardization processing, cross-scale co-simulation fused by a high-fidelity finite element and a reduced-order proxy model, and model dynamic calibration driven by measured data, high-precision and high-efficiency core analysis support is provided for the system; and on the basis of load history inversion, probability fatigue life prediction of a high-speed agent model and failure probability calculation and graded early warning under logarithmic normal distribution, the accuracy of structural performance analysis and the perspectiveness of risk pre-judgment under the dynamic load of the bridge are greatly improved, and a scientific and reliable core basis is provided for subsequent operation and maintenance decisions.

The invention provides a material constitutive parameter inversion method which can be applied to the technical field of artificial intelligence. The material constitutive parameter inversion method comprises the following steps: acquiring an elastic modulus and an actually measured load depth curve of a to-be-tested material in a single nanoindentation test; performing extrapolation translation preprocessing on the actually measured load depth curve to obtain an equivalent ideal curve; carrying out characterization processing on the equivalent ideal curve to extract input characteristic parameters which comprise press-in rigidity, unloading rigidity and plastic work proportion; the indentation rigidity, the unloading rigidity, the plastic work ratio and the elastic modulus are input into a pre-trained indentation constitutive mapping model to predict the yield strength and the strain hardening index of the to-be-tested material, and the indentation constitutive mapping model is a full-connection back-propagation neural network pre-trained through a finite element virtual test sample. The invention further provides a material constitutive parameter inversion system and device, a storage medium and a program product.