57results about How to "Increase computing speed" patented technology

The invention relates to the technical field of knowledge questions and answers, in particular to a method and system for detecting and repairing large language model output illusion, which comprises the following steps of: dividing answer content into a plurality of sentences, calculating the confidence coefficient of each sentence, judging whether the confidence coefficient of each sentence exceeds a set threshold value or not, performing fact extraction on the sentences with the confidence coefficient lower than the set threshold value, obtaining triple data of each sentence; generating a subgraph A of each sentence based on the triple data of each sentence; extracting a sub-graph B from the local knowledge graph based on the subject in the triple data of each sentence; calculating the similarity between the sub-graph A and the sub-graph B, and if the similarity is smaller than a set threshold value, repairing the sub-graph A according to the sub-graph B to obtain a repaired sub-graph A; according to the triad data corresponding to the repaired subgraph A, obtaining a repaired sentence; and after all the sentences with the confidence lower than a set threshold value are repaired, final answer content is obtained. And the accuracy and reliability of model output are improved.

The invention discloses a structure parameter library construction method, a super-lens design method and an optical system. The super-structure grating primitive structure parameter library construction method comprises the steps of obtaining a first grating primitive structure and determining a first periodic distribution function according to the first grating primitive structure; according to the characteristics of the grating primitive structure, full-analytic Fourier transform expansion is carried out on the first periodic distribution function, and a first Fourier analytic function corresponding to the first grating primitive structure is obtained; performing strict coupling wave analysis on the first Fourier analytic function in a preset polarization direction to obtain first complex amplitude information; and according to the first complex amplitude information, determining first matrix information used for describing a polarization behavior of a first grating primitive structure, and associating the first matrix information and the first grating primitive structure as first design information in a parameter library. The structural parameter library construction method provided by the invention is suitable for an application scene for processing polarization characteristics and phase information, and avoids segmentation or discretization.

The invention discloses a stereo matching method based on context geometric features and distortion parallax optimization, which comprises the following steps of: firstly, shooting a calibrated left picture and a calibrated right picture through a binocular camera, extracting multi-scale context features of the two pictures, and calculating the similarity between feature points according to the feature points of the left feature graph and the right feature graph under different parallaxes; the matching cost is the matching cost, so that the constructed geometric cube contains rich geometric matching information. Meanwhile, context features are copied and expanded into context feature cubes with the same size according to parallax, and rich context information is contained. The geometric cube contains matching conditions of feature points of left and right views with different parallax errors, cost aggregation is guided through context features of the left view, and context feature information, feature point cost matching information under different parallax errors and geometric information of adjacent feature points are preliminarily fused to improve cost matching reliability. And generating a geometric attention weight cube. And filtering the contextual feature cube.

The invention discloses a sewage treatment high-density pool coagulation dosing prediction method, which accurately and quickly predicts the optimal time and the optimal dosage of coagulation dosing by relying on a resampling technology and an AO algorithm on the basis of collecting real-time and non-real-time sewage treatment data and further utilizing a data processing algorithm to carry out dimension reduction on the data. Important reference information is provided for real-time management and control of a sewage treatment plant, for example, application services such as resource management, water quality monitoring and sustainable water treatment can be realized.

The invention provides a loan farmer repayment capability assessment method, device and equipment, a medium and a product, and relates to the field of financial science and technology. Firstly, agricultural product data and a first image set of a planting field of a loan farmer are acquired; wherein the agricultural product data comprises at least one of planting area, planting variety, growth environment, climate condition and historical yield; inputting the first image set into a pre-trained diffusion model, and generating a prediction frame of agricultural product fruit detection and a category label of the agricultural product fruit; performing detection and counting according to the prediction frame and the category label to obtain the agricultural product yield of the borrowing farmer in the current time period; according to the agricultural product yield and the agricultural product data in the current time period, the agricultural product yield of the borrowing farmer in the target time period is predicted; and determining the repayment capability of the borrowing farmer according to the agricultural product yield in the target time period.

The invention relates to the technical field of voltage optimization control of a power system, in particular to a multi-agent self-discipline collaborative optimization method and system for multi-region reactive voltage control, and provides an optimal reactive voltage optimization decision method for establishing a data interaction model, setting a data interaction mode and researching a power distribution network. By optimizing distribution of reactive power, the active loss of the power distribution network is reduced, the economical efficiency of the power system is improved, and the service life of equipment is prolonged; by using a deep reinforcement learning algorithm, effective adjustment of voltage is realized, and node voltage is stabilized; a deep reinforcement learning method is adopted, so that the influence of model errors is reduced, the calculation speed and the real-time response capability are improved, and the calculation cost is reduced; through continuous interaction with actual power grid data, the intelligent agent can cope with the uncertainty of distributed new energy, energy storage and flexible load power generation and power utilization, and the stability and reliability of the system under various complex conditions are improved.

The invention discloses a method and equipment for replacing sea-gas coupling parameterization in an atmospheric waveguide forecast model based on physical constraint deep learning, and a storage medium. The method comprises the following steps: step 1, preparing training data; step 2, increasing physical constraint to improve generalization ability; step 3, constructing a network for calculation; according to the method disclosed by the invention, the occupancy rate of computing resources can be reduced, the computing speed and the model forecasting performance are improved at the same time, a new idea is provided for application of artificial intelligence in the meteorological field, and a new technical development direction is provided for innovation in the meteorological field.

The invention provides a power system intelligent fault diagnosis method and system based on data fusion, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring vibration signals and electric signals of rotating equipment of different fault types in real time by using a sensor, and correspondingly obtaining a vibration matrix and an electric matrix; respectively carrying out weighted combination on the vibration matrix and the electric matrix to obtain a vibration element confidence coefficient and an electric element confidence coefficient; performing normalization processing and fusion processing on the vibration element confidence coefficient and the electric element confidence coefficient to obtain a fusion matrix; performing dimension reduction processing on the fusion matrix by using a principal component analysis method to obtain fusion features; and inputting the fusion features into a back propagation neural network for training to obtain a diagnosis model used for diagnosing the fault type of the rotating equipment. According to the invention, the problems of single characteristic parameter, low mechanical fault diagnosis identifiability and large error of the fault diagnosis method of the rotating equipment are solved.

The invention provides a hypergraph-based weighted gene co-expression hypernetwork module identification method and system, and the method comprises the steps: obtaining a gene expression profile matrix, and integrating an effective gene set of each sample to construct a weighted hypergraph; calculating a Laplacian matrix of the weighted hypergraph, and obtaining a topological overlapping matrix based on the Laplacian matrix; and based on the topological overlapping matrix, performing module identification on the gene by adopting a hierarchical clustering method, and outputting a gene co-expression module. According to the method, the calculation efficiency is effectively improved by optimizing hypergraph construction and utilizing the hypergraph Laplacian matrix to carry out network analysis. Compared with a traditional WGCNA method, the WGCHNA shows higher calculation speed and processing capacity when processing a large-scale gene data set, so that the gene co-expression network can be analyzed more quickly, and the requirements of multiple samples and complex networks are met.

The present application relates to a kind of lake reservoir type chlorophyll concentration prediction method, device and medium, method includes the following steps: obtaining the water quality, water dynamics and weather of the online automatic monitoring data of lake reservoir including forecast point and upstream point;The online automatic monitoring data is preprocessed, and the data after processing is obtained;The data after processing is carried out feature extraction to obtain the water quality feature and water dynamics index feature of forecast point and upstream point, and construct cumulative illumination feature;Upstream water quality feature, water dynamics index feature and illumination feature are input into the fusion prediction model pre-trained, to obtain upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result, using error reciprocal method upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result are fused;The chlorophyll prediction result obtained by fusion is output.Compared with prior art, the present application has the advantages of high accuracy, strong stability and the like.

The present invention discloses a time-dependent continuous and discontinuous number method for non-global contact identification and a system, the method comprising the following steps: creating a geometric body numerical model, applying boundary conditions, calculating unit forces, identifying units that may generate contact in the entity units at the boundary and the discrete fracture network unit list, storing and updating the potential contact entity unit list, and then performing contact detection in the potential contact entity unit list, updating the state of each unit and repeating the steps S300-S600 in the attached figure until the simulation is completed. The time-dependent continuous and discontinuous number method for non-global contact identification provided by the present invention can reflect the gradual increase of potential contact pairs with crack extension in real time, can effectively inherit the unit forces of failed joint units, maintain the numerical stability of the numerical model, and has sufficient parallel capability, which is of great significance to improving the operational efficiency of continuous and discontinuous numerical models and improving simulation accuracy.

The invention discloses an equivalent mechanical performance analysis method of a dual-phase TPMS lattice structure and application of the equivalent mechanical performance analysis method, and belongs to the technical field of material structures. Two materials in the two-phase TPMS lattice structure are equivalent to one material through an equivalent theory method, a shell unit can be used for establishing a model for calculation when a single material is used for simulation, and only one layer of unit is arranged on the thickness of the curved surface wall of the shell model, so that the number of model units is greatly reduced, and the cost is reduced. The coupling effect between the two materials does not need to be considered, so that the calculation efficiency can be improved by performing simulation calculation, and the effectiveness of the simulation calculation is verified; according to the method, the equivalent calculation method of the composite material plate is creatively applied to the curved surface of the structure, the calculation speed and efficiency are greatly improved on the premise that the calculation and analysis accuracy is guaranteed, compared with a common analysis and calculation method using entity units for modeling, the calculation duration is shortened by 95.32%, and the method has important application prospects.

The invention discloses a new energy consumption scheduling method considering an electric vehicle. The method comprises the following steps: establishing a day-ahead optimization model; performing optimization solution on the day-ahead scheduling by adopting a distributed algorithm to obtain a day-ahead scheduling scheme; constructing an intra-day optimization model; and according to the load and the photovoltaic prediction error value, an intra-day optimization scheduling model strategy is implemented, and rapid response is carried out by adjusting regional power grid interaction, an electric vehicle and energy storage so as to eliminate the error to the maximum extent. By means of the energy storage system and full play of the flexibility of the electric vehicle cluster, the power between different connecting lines can be flexibly adjusted, and it is ensured that new energy can be stably consumed. Meanwhile, load prediction errors need to be considered, and in order to enhance the reliability and stability of the system, the operation of closely tracking the scheduling plan is executed. The rolling optimization strategy can effectively cope with the uncertainty of the day-ahead stage, ensure the smooth consumption of new energy, and reduce the influence of prediction errors on the operation of the system.

This invention discloses a partial discharge (PD) signal denoising method based on image information entropy and multivariate variational mode decomposition (MMD). The method involves converting the noisy signal into a grayscale image, calculating the image information entropy, and optimizing the number of modes K in the MMD by combining Pearson correlation coefficient and execution efficiency to determine the optimal value. The noisy PD signal is then decomposed. The kurtosis value of each intrinsic mode component is calculated, and the nature of the mode component is determined based on a threshold, classifying it as either a dominant PD component or a noise component. A mathematical statistical method using the 3σ criterion is employed to filter out normally distributed white noise. The reconstructed signal is then denoised using an improved wavelet thresholding method to obtain the denoised PD signal. This denoising method accurately reduces noise in noisy PD signals, achieving good noise suppression and restoring the waveform characteristics of the PD signal while maintaining high execution efficiency.

The embodiment of the invention provides a prediction model generation method and device for power grid dispatching future state early warning, and the method comprises the steps: collecting historical data which may face scenes in the dispatching process of a power grid enterprise and cost data of investment future state early warning; processing the historical data and the cost data through a z-score standardization method, and scaling the data of different orders of magnitude to the same order of magnitude; performing feature selection on different prediction targets by adopting Elastic Net to obtain respective high-correlation feature data sets, splitting the high-correlation feature data sets into training sets and test sets, and training a BP neural network model by using the training sets to obtain prediction models of different prediction targets, and performing performance test on the prediction model through the test set to obtain a final prediction model. The calculation speed and the decision-making efficiency can be improved.

The application provides a micro-service message scheduling method, device, electronic equipment and storage medium. The method comprises the following steps: based on request information corresponding to each logical operation of a monomer service, a packaged single message body in a single logical operation unit is determined, and the single message body is added to a RabbitMQ message queue; based on message bodies corresponding to different logical operations in the RabbitMQ message queue, a required micro-service cluster is determined; and based on pre-configuration information of a zookeeper center, an optimal micro-service provider in the micro-service cluster is determined as an optimal micro-service scheduling scheme of the single logical operation according to an optimal rule. The micro-service message scheduling method provided by the application greatly accelerates the program operation speed and response time by decoupling the business logic through RabbitMQ, and the introduction of the zookeeper can centrally register and manage multiple distributed services, so that the system resources can be used more.

The water-fat separation method, system and terminal for magnetic resonance imaging of the present application obtain the corresponding water-fat separation result according to the echo complex image data through the water-fat separation model based on the deep learning and the traditional model segmentation combination. The present application optimizes the precision of the field map extraction, improves the stability and calculation speed of the water-fat separation, at the same time, retains the constraint force of the traditional algorithm on the output result, and maximizes the consistency of the output result and the original signal.

The invention discloses a data set generation method and system for converter harmonic coupling feature learning, and the method comprises the steps: determining the access capacity and position of a power electronic converter in a power distribution network, randomly generating m groups of multi-frequency harmonic voltages under the amplitude limitation, and defining the distance between two groups of harmonic voltages; calculating to obtain a distance matrix among all harmonic voltage data, and according to a traversal rule, carrying out small signal superposition on m groups of data in sequence; obtaining a steady-state equation coefficient matrix; dividing the harmonic voltage at the converter as large disturbance into a plurality of stages, and calculating the small disturbance response of each state variable of the small signal equation under the harmonic voltage disturbance at each stage; superposing the small disturbance response of each state variable solved in the stage into a steady-state component of the state variable, and updating a small signal equation set of the system; and superposing the disturbance of the next stage, repeating until the amplitude of the harmonic voltage disturbance is the same as the set target, and finally obtaining the harmonic coupling characteristics of the converter under m groups of random harmonic voltage data.

The present application discloses a multi-die storage and computing architecture FPGA with high storage bandwidth, and relates to the field of FPGA technology. The multi-die storage and computing architecture FPGA encapsulates an FPGA die and a storage die together, and a storage and computing unit is formed inside the FPGA die. Each storage and computing unit performs calculations based on instructions and data of a local storage unit. The data read and write ports of the local storage unit in each storage and computing unit are connected to a silicon connection layer, and are connected to the data read and write ports of the storage die via cross-die connections in the silicon connection layer to form a storage and computing read and write channel. The storage die reads and writes data to the connected local storage unit through the storage and computing read and write channel, and a larger data bandwidth can be achieved, so that the waiting time for data can be reduced when multi-core parallel storage and computing operations are realized through multiple storage and computing units, and the storage and computing performance is excellent, the data processing efficiency is high, and the computing speed is fast.

The invention discloses a carbon emission calculation method and system based on building main material equivalent, belongs to the technical field of carbon emission calculation, and solves the problems that an existing carbon emission calculation method is high in data requirement, low in calculation speed and insufficient in applicability. By analyzing historical project data, screening out key principal materials and constructing an algorithm model between consumption and total carbon emission, 'less data and high precision 'rapid calculation is realized, indirect carbon emission generated by building material production, transportation and construction is mapped to the key principal materials through weights, a whole-process carbon emission equivalent evaluation system is established, and the evaluation accuracy is improved. And the whole-process carbon emission can be output in a one-stop manner by inputting the consumption. The model output is an interpretable regression equation with a clear coefficient weight, a transparent decision basis is provided, and in addition, the key main materials are pre-determined through an algorithm, so that a new project can directly utilize the use amount of the existing key main materials in the project cost process to carry out carbon emission quick calculation.

The invention discloses a piezoelectric driving optimal trajectory tracking control method based on adaptive step length, which comprises the following steps of: performing mathematical modeling on a piezoelectric ceramic driver by adopting an electromechanical coupling nonlinear power system model to obtain a piezoelectric ceramic driver model; converting the piezoelectric ceramic driver model into a linear controllable standard subsystem; taking the expected displacement x * (0) when the time t is equal to 0 as the initial actual tracking displacement x (0); feedback control u is solved through a linear controllable specification subsystem; the initial actual tracking displacement x (0) is substituted into the linear controllable specification subsystem, and the actual tracking displacement is solved through a Runge-Kutta-Filbert method with the self-adaptive step length; and determining a performance index function of the linear controllable specification subsystem, substituting the feedback control u and the actual tracking displacement into the performance index function, and solving an optimal performance index of the piezoelectric ceramic driver model. According to the method, the calculation efficiency of the piezoelectric ceramic driver can be improved while high precision is kept in the calculation process.

This invention discloses a method for detecting internal leakage in hydraulic cylinders and its application in TBM support shoe hydraulic cylinders, comprising the following steps: S1, acquiring stroke data of the hydraulic cylinder within a predetermined time t1; S2, cleaning and segmenting the acquired hydraulic cylinder stroke data; S3, acquiring and filtering pressure data of the hydraulic cylinder in each specified time period; S4, performing wavelet decomposition and reconstruction on each group of filtered pressure data; S5, calculating the proportion of the energy of each order of high-frequency reconstructed signal in the total energy of the original pressure signal to determine whether internal leakage has occurred in the hydraulic cylinder. This invention completes the detection of internal leakage in TBM support shoe hydraulic cylinders using only pressure and stroke data, without the need to establish a complex physical model based on the geometric parameters of the TBM support shoe hydraulic cylinder, thus improving calculation speed and detection efficiency.

The present invention discloses a three-dimensional shape measurement method of metal materials based on stereo vision and model recovery. By projecting colored RGB dot matrix structured light with geometric constraints onto the object to be measured to modulate the light field of the object to be measured, it is easier to perform stereo matching on the object to be measured without texture or with less texture. Projection points are extracted from the collected image by using a threshold segmentation method based on slope difference and regional constraint corrosion. The regional constraint corrosion can effectively solve the problem of projection point adhesion caused by insufficient projector resolution and camera imaging resolution. A line clustering stereo matching method is used for coarse stereo matching. Accurate stereo matching is performed based on the structured light dot matrix with geometric constraints. A model recovery method is used to restore and constrain the dot matrix that cannot be imaged. The three-dimensional world coordinates of the object to be measured are solved by a space vector method, and finally the measurement of a high-precision metal block is completed.

The present invention discloses a water quality detection method and system based on artificial intelligence, and the method includes data collection, data preprocessing, water quality pre-classification, water quality trend analysis, pollution source tracing prediction and water quality detection. The present invention relates to the field of water quality detection technology, and specifically refers to a water quality detection method and system based on artificial intelligence. The present scheme adopts an artificial intelligence water quality detection method of water quality pre-classification, trend analysis and pollution source tracing; a deep neural network method combining genetic algorithm and random forest model is used for water quality pre-classification; a deep neural network method combining genetic algorithm and random forest model is used for water quality pre-classification and water quality trend analysis; a water quality pre-classification task is used to first pre-classify the data, exclude unpolluted water sources, and then combine the causal-inspired graph neural network, and through the causal-inspired operator and the improved graph neural network, the calculation speed and calculation effect of pollution source tracing are improved.

The application discloses a substation equipment identification method and system based on an improved point pair feature matching algorithm, and comprises the following steps: obtaining original point cloud data of equipment in a substation, performing mean value processing on point clouds in a grid by using a grid downsampling method to obtain new point cloud data; wherein the size of the grid is automatically adjusted according to the number of the original point cloud data; performing key point screening on the new point cloud data, randomly selecting a reference point from the extracted key points, and respectively forming point pair features of the reference point and other points in the scene; matching the point pair features with pre-stored equipment model point pair features in a hash table to obtain matched point pair features, and identifying substation equipment based on the matched point pair features. By optimizing and screening the key points, the application reduces invalid points and redundant points, thereby improving the calculation speed and matching accuracy, and finally accelerating the substation equipment identification speed.

The present invention relates to the technical field of power systems, and in particular to a method, device, equipment and medium for evaluating the inertia of a power system based on a difference-by-difference method. The method comprises: using a difference-by-difference method to calculate the frequency change rate after a disturbance in the inertia evaluation of the power system; based on the frequency change rate, using a physical model derived from the rotor motion equation to calculate the estimated inertia value, and using the inertia theoretical value formula to calculate the actual inertia of the system; constructing a neural network, dividing a training set and a test set, using the output result of the physical model as the input of the neural network, and using the trained neural network to evaluate the difference between the calculated inertia value and the theoretical value; using the evaluated difference and the inertia estimated value calculated by the physical model to obtain the final power system inertia evaluation result. In the present invention, the combined method of physical and data models can quickly and effectively evaluate the inertia of the power system, solving the problem of insufficient evaluation accuracy of the physical model.

The invention relates to a rectangular coordinate-to-polar coordinate conversion method and device based on an LUT and CORDIC hybrid architecture. The method comprises the following steps: S1, sector folding preprocessing; s2, performing coarse rotation iteration; s3, generating a dynamic address; s4, a compression lookup table is queried; and S5, data compensation and reconstruction. According to the method, a small amount of CORDIC iteration is introduced to rotate the input vector to a very small angle range, so that the input space required to be covered by a subsequent lookup table is greatly compressed; meanwhile, the bit width and the address depth of a storage value in the LUT are remarkably reduced by combining module value residual storage and a dynamic truncation technology, so that the storage resources of the LUT are greatly reduced on the premise of ensuring the precision, and the hardware implementation cost is lower.

The invention discloses a network loss allocation method and system for a distribution network system of a distributed power supply, and belongs to the technical field of data processing. Performing equivalent division by taking the segmented region as a main body; enabling the total generator power and the total load power of each equivalent area to be equivalent to a generator and a load, determining a power flow path of each segmented area, and obtaining the loss borne by a lead on the power flow path of each segmented area and the loss ratio; using an average network loss coefficient method in which participants including a power supply main body, a load main body and an energy storage main body are considered; calculating the respective line loss coefficients of the participants to obtain the line loss borne by each type of participants, allocating the loss in the segmented region to each equivalent region, and determining the allocated loss and proportion of each equivalent region; and summarizing the shared loss and proportion of each equivalent area, and outputting a distribution line or transformer area network loss allocation result. According to the method, the calculation precision of network loss allocation can be improved.

The application provides an artificial intelligence-based molecular processing method and device, electronic equipment, computer readable storage medium and computer program product; the method comprises: performing state sampling processing on a first state random distribution corresponding to a first time and a second time to obtain a first state at the first time and a second state at the second time; determining a plurality of third times from a first time period composed of the first time and the second time, and performing encoding processing on the first state and the second state to obtain a first encoding of the first state and a second encoding of the second state; generating a second state random distribution of the third time, performing random state sampling processing on the second state random distribution to obtain a third encoding of the third time; and performing decoding processing on the third encoding of the third time and the third encoding of the historical third time to obtain a third state at the third time. Through the application, the calculation speed and accuracy of the molecular state can be improved.

The present invention discloses a coupled inductor and a method for manufacturing the same, comprising a base block box (10), a base block plug (20) inserted into the base block box (10), and a coil group (30) arranged between the base block plug (20) and the base block box (10); the coil group (30) is embedded in the base block box (10); the present invention prepares a magnet of a specific structure with a specific composite material, and prepares the coupled inductor in combination with a specific process, so that the obtained coupled inductor has excellent inductance performance. Specifically, by using a specific composite material in combination with a specific cold pressing forming process, a base block box and a base block plug of a specific structure are first prepared, and then the primary coil and the secondary coil are sequentially embedded into the base block box groove, the base block column is embedded in the base block box groove, and then a formed assembly is formed by a specific hot pressing forming process, which effectively solves the problem of only one coil in a traditional inductor, and effectively improves the performance and operation speed of the circuit board product within the limited space of the circuit board.