220 results about "Metacomputing" patented technology

The invention discloses a power distribution cabinet bus temperature change analysis and early warning method and system, and relates to the technical field of power distribution cabinets, and the method comprises the steps: collecting the temperature data of a three-phase bus of a power distribution cabinet, dividing a temperature sequence into time-space units, and calculating the spatial characteristics and time characteristics of each time-space unit; performing frequency domain analysis on the temperature data of the space-time unit, decomposing the temperature data to obtain a low-frequency component, an intermediate-frequency component and a high-frequency component, extracting sub-components of different time scales, and calculating a time coupling feature, a space coupling feature and a physical coupling feature; fault-free data is collected to establish a normal mode library, historical fault data is collected to establish a fault mode library, the deviation degree of features and a baseline is calculated in real time, and marking and feedback updating are carried out on potential new faults; on the basis of distributed monitoring, temperature abnormal points are positioned, reconstruction errors of all scales are calculated, the abnormal origin scale is determined, and graded early warning is triggered, so that the problem that a traditional monitoring method is insufficient in fault recognition capability is solved.

Provided in the embodiments of the present application are a server system, a resource scheduling method for a server system, and a chip and a chiplet. The server system comprises: a multivariate computing resource pool, a data storage resource pool, a switching module and a control module, wherein the multivariate computing resource pool comprises a general computing resource pool and a heterogeneous computing resource pool, the general computing resource pool comprising a group of general computing units, and the heterogeneous computing resource pool comprising a group of heterogeneous computing units, and the multivariate computing resource pool is connected to the data storage resource pool via the switching module by means of a cache consistency bus inside the server system; the data storage resource pool comprises data storage resources allowed to be shared by multivariate computing resources in the multivariate computing resource pool; and the control module is configured to perform computing power scheduling on the multivariate computing resources in the multivariate computing resource pool, and dynamically allocate the data storage resources in the data storage resource pool to the multivariate computing resources in the multivariate computing resource pool.

The invention relates to the technical field of intelligent supply chain management and multi-modal artificial intelligence crossing, and discloses a multi-modal large model-based supply chain toughness evaluation system, which comprises a data acquisition module, a multi-modal processing module, a toughness evaluation module, a cloud edge collaboration module and a decision support module, the data acquisition module is used for acquiring supply chain texts, images and time sequence heterogeneous data; the multi-modal processing module is used for realizing dynamic feature fusion through term matrix enhanced Transform; the toughness evaluation module quantifies toughness scores based on a three-level index system and a fuzzy matter element calculation engine; the cloud edge collaboration module realizes edge end real-time reasoning through a lightweight model; the decision support module optimizes node reinforcement and path planning based on network topology and real-time weather data. According to the method, the problems of insufficient data isomerism processing, single evaluation dimension and lagging real-time response in the prior art are solved.

The embodiment of the invention provides a BIM-based bridge model intelligent construction method and system, and belongs to the field of BIM-based bridge model intelligent construction. The method comprises the steps that bridge model construction related data are collected and preprocessed, and a component library is established according to the preprocessed bridge model construction related data; defining geometric linkage rules and topological connection logic of components in the component library, and generating an initial BIM bridge model through a generative adversarial network; performing lightweight finite element calculation on the initial BIM bridge model to obtain an optimized BIM bridge model; and performing standard conflict detection and conflict parameter adjustment on the optimized BIM bridge model to obtain a final bridge model. According to the method, the parameter relation matrix and the dynamic linkage mechanism are constructed, the generative adversarial network is introduced to generate the complex bridge type, and the graph convolutional network is introduced into the generative adversarial network, so that reasonable bridge type topology and dynamic parameter linkage are guaranteed.

The invention discloses a hydrodynamic analysis method for a floating structure with multi-body and multi-coupling characteristics, which comprises the following steps of: acquiring geometric characteristics of each floating body, and dividing boundary element computational grids; wave excitation force, additional mass and radiation damping of each floating body are calculated, and a mass matrix and a rigidity matrix of each floating body are obtained; determining a connection mode and a coupling factor of the floating body; according to a connection mode and coupling characteristics of the system, establishing displacement connection conditions among the floating bodies; constructing a motion constraint matrix according to the displacement continuity condition and the motion constraint relation; determining a frequency domain motion equation of the multi-floating-body system according to a Lagrange multiplier method; and solving the motion equation to obtain the motion response of the multi-floating-body system, and further systematically analyzing the wave energy capture power according to the coupling characteristics of the system. According to the analysis method, the problem that the motion response of each degree of freedom cannot be accurately described when the hydrodynamic response characteristics of the complex multi-body system are obtained through an existing simulation method is solved.

The invention relates to the technical field of building energy consumption analysis, and discloses a building energy consumption and carbon neutralization analysis system based on big data analysis, which comprises an acquisition unit, an analysis unit, a calculation unit, a deficit unit, a judgment unit and a carbon neutralization management unit, and is used for generating a carbon neutralization management instruction of a target object according to a carbon neutralization urgency index, performance degradation of a target object along with increase of service life is captured, an analysis unit combines real-time carbon emission data of a power grid, generates a dynamic carbon track slope and reflects a carbon emission change trend in real time, a calculation unit performs segment processing on operation duration based on big data, and a complete-cycle carbon track vector is constructed. A healthy deficit degree and a stepped urgency index are generated through big data analysis of the deficit unit and the judgment unit, and finally a management instruction is output by the management unit, so that the problem of insufficient tracking of full-life-cycle energy consumption attenuation and carbon emission increment of a target object by traditional analysis is effectively solved, and the accuracy of long-term carbon neutralization planning is greatly improved.

The invention, which relates to the technical field of mine geology and urban safety, discloses a refined collaborative prediction method and system for subsidence of a global goaf, comprising the steps of establishing a hierarchical storage system, and correcting a working face calculation boundary; extracting the maximum influence radius of all the working faces and four-dimensional coordinates of the mining boundary of each working face, and calculating to obtain a global prediction range to generate a global surface prediction grid; point set insertion and Delaunay triangulation are adopted to obtain an effective triangular unit, a local influence domain range of the effective triangular unit is calculated, and an influenced sub-grid is screened from the global surface prediction grid; and calculating a predicted point movement deformation value based on a parallel computing architecture, accumulating the predicted point movement deformation value to a global result array through a coordinate index mechanism, synthesizing a deformation component in a specific direction, generating a visual result, and performing statistical analysis. According to the method, the problems of poor adaptability and low efficiency of a traditional scheme are effectively solved, and the prediction precision and efficiency are remarkably improved.

The invention relates to the technical field of rolling mill automatic control, in particular to a special rolling mill controller without a PLC framework and an integrated control system, and aims to solve the problems that in rolling of a rolling mill, the sampling rate difference of pressure and thickness sensors and material plastic deformation accumulation cause the time sequence dislocation of a rolling force peak value and a thickness valley value, the collaborative utilization of data by a thickness control algorithm is influenced, and the cost is reduced. In order to solve the problems that rolling force, thickness data and roller pulse signals are collected through a special controller, and a rolling length observation benchmark is converted; the dynamic phase compensation unit calculates a plastic deformation cumulant, after triggering compensation, a basic phase difference is established based on a sampling rate difference, a compensation coefficient is dynamically increased, a compensation data segment is generated through linear interpolation, and a time sequence is forcibly aligned; and the integrated control unit inputs the alignment data into a thickness control algorithm, the rolling force serves as feedforward, the thickness data serves as feedback, a precise roller gap adjusting instruction is generated, and the material thickness rolling precision is improved.

The invention discloses a 3D modeling calculation auxiliary system based on natural language driving, in the system, a basic information input unit is used for receiving a modeling request input by a user, and the modeling request comprises description of specific scene modeling requirements of the user in a natural language form; the semantic recognition unit performs semantic recognition on natural language input information to extract structural modeling calculation parameters, and performs standardized output; a corresponding modeling tool flow is called through a 3D modeling unit according to the semantic recognition result of the large model, and parametric modeling is carried out; the finite element calculation unit can perform data interaction with the 3D modeling unit, geometric information established by the 3D modeling unit is input into finite element calculation software, calculation parameter structures serve as other parameters to be input into the finite element calculation software, corresponding finite element models are established according to the calculation parameter structures, and solving calculation is performed. According to the system, a hybrid architecture of natural language semantic link parameterized workflow is adopted, so that the learning cost of parameterized modeling, finite element calculation and special calculation requirements can be effectively reduced, and the efficiency of modeling and calculation is improved.

A method may include: receiving a prompt and generated text from the LLM; computing an original token probability distribution for each token in the prompt and in the generated text; receiving a token position probability distribution for each token position in the generated text from the LLM; identifying keywords in the prompt; perturbing embedding vectors for the keywords used by the LLM by adding noise to the embedding vectors; computing a perturbed probability distribution for the perturbed embedding vectors by providing the perturbed embedding vectors as an input to a neural network used by the LLM, wherein the neural network returns a perturbed token probability distribution; evaluating a divergence between the original token probability distribution and the perturbed token probability distribution; identifying semantically meaningful tokens in the generated text; calculating a mean of divergences for the semantically meaningful tokens; and classifying the LLM based on the mean of divergences.

The invention relates to a real-time path planning method and system based on conflict resolution, equipment and a medium. The method comprises the following steps: preprocessing original observation data to obtain an evidence unit of each sensor; calculating a total conflict amount and a sensor pair contribution matrix based on an evidence unit, updating the credibility of each sensor and selecting a fusion operator to obtain fusion evidence and a conflict degree after fusion; a short-term time sequence is generated according to the fusion evidence and the conflict degree after fusion, the expected occupation probability and the expected conflict trend of each grid unit are predicted, and a time sequence conflict map is obtained; according to the time sequence conflict map, conflict-level adjacent grid unit aggregation fuzziness and area-level conflict characteristics are carried out; and inputting the fusion evidence, the conflict fluctuation degree, the expected stable time and the region-level conflict feature as cost and constraint into a real-time local path planner to obtain a control decision. By adopting the method, the problem of path decision oscillation caused by multi-source sensor conflicts can be relieved.

The invention discloses a seismic simulation method and system fusing an implicit iterative graph network and a spectral element method. The method comprises the following steps: acquiring acceleration data of a small time step node; performing grid sparse division and interpolation mapping processing, and projecting to a low-dimensional manifold to obtain large-time-step node acceleration data and a low-order unit calculation force matrix of the current frame; obtaining a physical relation sparse matrix I, a physical relation sparse matrix II and sparse matrix topological relation information of the physical relation sparse matrix I and the physical relation sparse matrix II through a processing method of Jacobian iteration and projection to a low-dimensional manifold; obtaining a seismic simulated diagram neural network model; and predicting by using the seismic simulation diagram neural network model, and predicting the acceleration by using the large time step of the sparse unit nodes projected back to the target area by using the projection matrix. According to the method provided by the invention, the mapping between the high-order unit small time step and the low-order unit large time step is established through the fitting capability of the neural network, so that the calculation time is greatly shortened, and the earthquake simulation is quickly carried out.

The invention is suitable for the technical field of flood monitoring, and provides a flood inundation area dynamic monitoring method based on time sequence feature enhancement, and the method comprises the steps: fusing optical images, synthetic aperture radar images and surface temperature time sequence data before and after a flood event; calculating the water existence probability of each time point pixel by pixel through a random forest model by using the fused time sequence features; pixel volatility is calculated based on the water body probability time sequence, and the flood process is divided into a dramatic change period, a slow change period and a stationary period; a three-dimensional convolutional neural network is used for mining a spatio-temporal evolution law of submerging and water recession in a dramatic change period, a time sequence and spectrum collaborative interpretation model is used for separating progressive water body and vegetation interference in a slow change period, dimensionality reduction is performed on a water body probability sequence in a stationary period, and a lightweight time sequence convolutional network is used for simulating a water recession rate; and optimizing the identification result of each stage, and finally generating a dynamic submerging map. The accuracy of monitoring the submerged area is effectively improved, and the real-time response efficiency of monitoring is improved.

The invention discloses an offset automatic calibration method for defect source analysis, and relates to the technical field of wafer detection and defect source analysis, and the method comprises the steps: determining the geometric center position of a wafer according to a theoretical coordinate matrix and an actual coordinate matrix of a chip unit in a wafer detection image, and extracting a candidate chip unit; calculating comprehensive stability scores of the candidate chip units, screening out an optimal reference chip unit, and calculating coordinate deviation values of the optimal reference chip unit in the actual coordinate matrix and the theoretical coordinate matrix; performing weighted average on the coordinate deviation value through a symmetry constraint condition to obtain a local calibration offset, and expanding the local calibration offset into a global offset calibration parameter; and applying the global offset calibration parameters to defect positioning calculation in an AOI detection process, and comparing a detection result with a preset verification chip unit to analyze calibration precision so as to complete offset automatic calibration. According to the invention, the consistency and reproduction precision of defect positioning are improved.

The invention relates to the technical field of electrical equipment manufacturing, and discloses a high-low voltage electrical cabinet continuous processing abnormity rapid response system. The system comprises a processing data acquisition module for acquiring sensor signals, process parameters and other elements and establishing a time sequence processing data unit; the edge calculation preprocessing module retrieves preprocessing rule terms according to the time sequence data unit and calculates a preprocessing complexity factor to judge data quality; the abnormal feature extraction module is used for calculating a cross-time-period feature association closeness index based on the multi-time-series data unit and constructing a multi-time-period feature association map; and the response strategy generation module is used for judging and generating a comprehensive abnormal response prompt in combination with the map and the data preprocessing state. In addition, the system can also be provided with an abnormal data storage module and an edge node cooperation module, abnormal data storage and cross-node cooperation analysis are realized, processing abnormity can be quickly responded, and the processing efficiency and accuracy are improved.

The invention relates to a system for determining the compaction of a flowable, solidifiable building material, in particular concrete, during the production of a structural element with the building material, wherein the flowable building material is introduced into a formwork system (10), comprising: at least three sensors (S1, S2, S3) to determine an amplitude (A S1 , A S2 , A S3 ) a vibration introduced into the flowable building material by a vibration unit (VE), wherein the sensors (S1, S2, S3) are arranged on the formwork system (10), at least one computing unit that is set up to perform calculations based on the amplitudes (A) determined by the at least three sensors (S1, S2, S3). S1 , A S2 , A S3) to calculate possible positions (D1, D2, D3) of the vibration unit (VE) relative to the respective sensors (S1, S2, S3) and, based on this, to triangulate the current position of the vibration unit (VE) relative to the formwork system (10), a documentation unit for documenting the dwell time of the vibration unit (VE) in the respective current positions of the vibration unit (VE) relative to the formwork system (10), the computing unit determines a calculated compaction value in the respective current positions based on the respective current positions of the vibration unit (VE) and the documented dwell time in the respective current positions.

Embodiments described herein provide a training framework for generative NLP models that operate on previously learnt knowledge from pretrained large language models. Specifically, to train an NLP model to generate a response to a user utterance (e.g., “resolve login issue”), document embeddings of support IT documents encoded by a pretrained LLM are fed to an NLP decoder together with a training dialogue (e.g., a dialogue between the chat agent on how to “resolve login issue”). The NLP decoder can thus be trained by a causal language modeling loss computed based on the predicted next token and the ground-truth token from the training dialogue.

The invention discloses a user demand insight positioning method and system based on AI driving, relates to the field of artificial intelligence and big data analysis, and solves the technical problem that it is difficult to accurately recognize user demands and respond to the user demands in real time through dynamic analysis of multi-source text data and construction of a knowledge graph. Comprising the steps that text data are collected, structured data are generated through preprocessing, and a structured data unit is generated; calculating text vocabulary diversity and syntactic complexity indexes, inputting the text vocabulary diversity and syntactic complexity indexes into a pre-training regression model to obtain a dynamic character threshold value, encoding short / long texts by adopting a lightweight graph neural network and a hierarchical Transform, and extracting core demand elements; a dynamic time sequence knowledge graph is constructed, a time sequence edge weight updating algorithm is introduced to adjust node association strength, and cross-domain association is mined; and calculating a user demand intensity evaluation value based on an entropy weight method in combination with the inventory pressure factor and the group influence factor, thereby realizing inventory scheduling and personalized recommendation.

To provide a technique capable of quickly correcting discharge characteristics while reducing an environmental load.SOLUTION: A regression model construction unit 919 constructs a regression model Y, by utilizing machine learning that uses training data which uses as input a feature difference dF, which is a difference between a reference feature F0 of a reference waveform Wst measured with a reference parameter P0 and the peripheral feature F1 to Fm of the peripheral waveforms W1 to Wm measured with peripheral parameters P1 to Pm obtained by changing some of the values of the reference parameter P0, and which uses as output the parameter difference dP, which is the difference between the reference parameter P0 and the peripheral parameters P1 to Pm. A correction unit 917 inputs a feature difference F0'-F0, which is the difference between the reference feature F0 of the reference waveform Wst and a target feature F0' of a target waveform Wst' measured with the reference parameter P0 after the reference waveform Wst was measured, into the regression model Y, and corrects the reference parameter P0 according to the correction amount output from the regression model Y.SELECTED DRAWING: Figure 10

The present invention discloses a method for constructing a Golomb Costas sequence based on a high-order spread domain, which belongs to the field of wireless communication technology. The method comprises the following steps: determining the order N of the Golomb Costas sequence to be constructed; and determining the order N of the Golomb Costas sequence to be constructed according to N=p. n ‑2, determine p, n, where p is a prime number and n is a positive integer, and explicitly state the extended field GF (p n ); select an irreducible polynomial of degree n with a leading coefficient of 1 on the prime field GF(p) as f(x), and clearly expand the field GF(p n ) in the form of elements, define the extended field GF(p n ) two binary operations on elements in the GF(p n ) primitive elements and their powers, and list the powers of primitive elements; in the extended field GF(p n ) selects two primitive elements; calculates the placement coordinates and completes the construction of the Golomb Costas sequence. The present invention calculates the primitive elements of the extended domain and their powers by calling functions such as get_combins(p,n), modBKY(p,n,BY,BKY,res) and calc(p,n,BKY,res_product), and according to the construction formula of the Golomb Costas sequence and by searching the extended domain GF(p n ) list of powers of primitive elements, quickly calculate the placement coordinates of the Golomb Costas sequence, reduce the workload of manual calculations, and improve the efficiency of constructing high-order Golomb Costas sequences based on extended domains. At the same time, it ensures the correctness of the calculation of the placement coordinates of the Golomb Costas sequence, solving the difficult problem of constructing Golomb Costas sequences based on high-order extended domains.

The invention provides a high-performance mixed-order tetrahedron analysis method based on a proportional boundary finite element, and belongs to the technical field of numerical analysis methods. The method comprises the following steps: 1) constructing a mixed-order tetrahedron unit triangular boundary surface shape function by adopting a triangular area coordinate method; 2) constructing a mixed-order tetrahedron unit solving system; (3) calculating all units in the grid model by adopting second-order tetrahedron units; 4) giving an allowable threshold value A of a bending angle index theta, judging all unit boundary orders according to an order conversion criterion, and realizing unit boundary high-order and low-order reasonable configuration; and 5) carrying out numerical simulation analysis by adopting the mixed-order grid model. According to the method, unit boundary high-order and low-order reasonable configuration is achieved for the complex grid model through the order conversion criterion, the contradiction that in traditional numerical simulation analysis, the precision of a first-order unit is insufficient, and calculation of a second-order unit is redundant is effectively solved, and an innovative solution with high precision and high efficiency is provided for refined numerical simulation of a complex engineering structure.

The application discloses a kind of direct identification method of low-order linear system order of different real pole, comprising the following steps: initialization to be measured system to zero state, constant excitation is applied to the input end of to-be-measured system and simultaneously acquires the response output signal of to-be-measured system, calculates sample adjacent different elements, discriminant factor is calculated using sample adjacent different elements, calculate first discriminant element, secondary discriminant element, tertiary discriminant element and its average value and standard deviation, identify, eliminate singular discriminant element and update, the order N of to-be-measured system is analyzed out;The application gives a solution to the key technical pre-problem in the control and operation level for different real pole low-order linear system, and builds a core technical foundation for subsequent realization of safe and controllable, efficient and low-consumption and high-performance stable operation.

The invention discloses a service traffic prediction method and device, a network element, a computer storage medium and a program product. The method comprises the steps of obtaining traffic data of a plurality of applications, dividing application categories to which the applications belong based on the traffic data of each application, and generating a classification list according to a division result; obtaining user information and application use data of the user, dividing user groups to which the user belongs based on the user information and the application use data, and determining a portrait label corresponding to each user group; determining first traffic consumption of the first user in the historical specified time, determining second traffic consumption of a first user group to which the first user belongs in the historical specified time, and determining a first coefficient based on the first traffic consumption and the second traffic consumption; and predicting the service traffic of the first user according to the first coefficient, the classification list and a first portrait label corresponding to a first user group to which the first user belongs, and generating service traffic prediction information.

The application belongs to the technical field of additive manufacturing, and particularly relates to an intelligent 3D printing system, which comprises a rack and a controller, the inside of the rack is provided with a laser printing head and a powder laying mechanism, the powder laying mechanism lays powder, and then the laser printing head melts the powder by laser to complete the action of cladding. The intelligent 3D printing system can utilize forward mapping to output the trained network in seconds or even milliseconds, greatly accelerating the real-time simulation effect of design iteration. Meanwhile, the neural network can replace time-consuming finite element calculation in the optimization process of genetic algorithm, and quickly find the optimal design parameters, thereby achieving the effect of optimization design. On the other hand, the backward mapping is used to quickly and accurately identify the complex material model parameters, which is several times faster than the traditional trial-and-error method, and the input or physical characteristics of the system can be back calculated according to the dynamic response of the structure.

A method, an electronic device, a computer readable medium, and a computer program product are provided. The flow management and control system comprises a request processing unit which is used for periodically obtaining an exception list from a strategy center unit and performing local caching, performing legality check on a received request according to the exception list, and reporting request related information to a computing center unit; the computing center unit is used for receiving the requests reported by the request processing unit, distributing the requests received in a preset time period to the same processing equipment according to a timestamp partitioning algorithm, and aggregating and computing the traffic indexes corresponding to the preset time period in the memory of the processing equipment; and the strategy center unit is used for storing a global exception list, receiving the traffic index exception list reported by the calculation center unit to update the global exception list, and synchronizing the updated exception list to each request processing unit.

The invention provides a cluster system comprising a computing unit and a switching unit and a task processing method and device, and relates to the technical field of computers. The cluster system comprises N computing platforms, and each computing platform comprises a switching unit, a computing unit, a power supply unit, a monitoring management unit and a controller. The computing unit comprises at least one of a PC, a CPU, a GPU and a neural network computing module; the switching unit comprises a first type switching module which is used for connecting a hardware or software interface of a network, is responsible for sending, receiving and processing data, and comprises but is not limited to a gigabit Ethernet interface; the second type of switching module provides support for high-speed serial data transmission, adopts high-speed data interfaces, and comprises but is not limited to a Serdes interface and an LVDS (Low Voltage Differential Signaling) interface; the third type of switching module is a high-bandwidth and low-delay data interface which comprises but is not limited to an MCIO interface and a PCIe interface; and the fourth type of switching module is a high-bandwidth and high-density data transmission interface which comprises but is not limited to a QSFP interface and an OSFP interface.

The embodiment of the application provides a kind of based on BIM bridge model intelligent construction method and system, belong to based on BIM bridge model intelligent construction field;The method includes collecting bridge model construction related data and pre-processing, according to the bridge model construction related data after pre-processing, component library is established;The geometric linkage rule and topological connection logic of component in the component library are defined, and the initial BIM bridge model is generated through the generation of adversarial network;Lightweight finite element calculation is carried out on the initial BIM bridge model, and the optimized BIM bridge model is obtained;After the specification conflict detection and conflict parameter adjustment of the optimized BIM bridge model, the final bridge model is obtained.The application constructs parameter relation matrix and dynamic linkage mechanism, introduces the generation of complex bridge type in adversarial network, and introduces graph convolution network in adversarial network, to ensure that bridge topology is reasonable and parameters are dynamically linked.

The application discloses a neuron computing core and system. The computing core comprises a storage module, a computing module, a pulse generation module and a core control module. The computing module is used for performing operation on coarse-grained data, pulse data and weight data previously set in the storage module, and the operation result is sent to the pulse generation module. The pulse generation module is used for sending the operation result to a target address. The computing core can support operation with different precisions without two repeated computing resources, and can reuse the same hardware resources, thereby ensuring high operation efficiency and saving hardware resources. The application can be widely applied in the field of artificial neural network computing technology.

The present invention provides a quantum system error detection device, the device comprising: a control register for receiving information representing a quantum state of a control qubit and outputting an operation result; an auxiliary register for receiving, on a one-to-one basis, information representing a quantum state of a target qubit; a CNOT gate connected to the control register and the auxiliary register; a first detector connected to the control register and configured to measure a quantum state of the control qubit to which the CNOT gate is applied; a second detector connected to the auxiliary register and configured to measure a quantum state of the auxiliary qubit to which the CNOT gate is applied; a probability calculation unit for calculating probabilities of quantum states of the control qubit and the target qubit from measurement results of the first detector and the second detector; and an error rate calculation unit for calculating error rates of quantum state initialization, the CNOT gate, and quantum measurement from the probabilities calculated by the probability calculation unit.

The invention provides an equivalent materialization modeling and efficient simulation method for an additive manufacturing lattice structure, and belongs to the technical field of additive manufacturing and structure simulation. The method comprises the following steps: firstly, designing and preparing a standard test sample consistent with a real sample piece in process according to dot matrix geometric characteristics of the real sample piece, and obtaining equivalent material parameters through mechanical test; secondly, extracting the outer contour of a dot matrix area from the original CAD model, constructing a continuum entity to replace an original dot matrix structure, and fusing the continuum entity with an entity area to form a simplified model; and endowing equivalent material parameters to the entity area, and performing thermal-mechanical coupling finite element calculation in simulation software to obtain stress and deformation distribution in the forming process. According to the method, huge calculation overhead caused by full-dot-matrix fine modeling is avoided, the simulation efficiency is remarkably improved, meanwhile, the prediction precision is guaranteed through experiment-driven equivalent material parameters, the method is suitable for various dot matrix configurations and metal material systems, and efficient and high-fidelity simulation of additive manufacturing complex dot matrix components is achieved.