58 results about "Linear number" patented technology

The disclosure relates to the technical field of power system management, and in particular to a large-scale electric vehicle cluster coordinated charging optimization method based on greedy repair genetic algorithm. the method includes the following steps: Si, establishing a task model: establishing a task model of electric vehicle cluster coordinated charging in large charging stations; S2, constructing constraint conditions: constructing constraint conditions for the coordinated charging optimization problem; S3, constructing a constrained optimization problem: designing an objective function with the minimum charging cost, performing linear mathematical transformation on non-linear constraint conditions, and constructing a 0 / 1 integer linear programming problem; and S4, solving the constrained optimization problem with a custom genetic algorithm based method: carrying out genetic coding on a decision model of the charging station, and specifically designing a genetic algorithm with greedy repair operators to solve the constructed constrained optimization problem.

The invention relates to the technical field of artificial intelligence, can be applied to the digital medical field and the financial field, and discloses a causal graph model-based data processing method, device and equipment and a storage medium, the method comprises the steps of obtaining target data and a corresponding to-be-explained reasoning result, and constructing a causal graph according to the target data, obtaining a corresponding structured causal graph; performing causal constraint on the structured causal graph by adopting a causal constraint graph network learning mechanism to obtain an optimized causal graph; and performing anti-fact reasoning on the reasoning result based on the optimized causal diagram, and generating an interpretation result of the target data according to an anti-fact reasoning result. By fusing the causal graph model and the graph neural network technology and introducing the anti-fact reasoning module, accurate causal modeling and explanation of the high-dimensional nonlinear data are realized, the limitation of a traditional causal graph model in the aspect of processing the high-dimensional nonlinear data is solved, and the defect of a graph neural network output result in causal explanation is made up.

The invention provides a metro section parameterized linear digital model construction method based on Revit and Dynamo, and the method comprises the steps: building a three-dimensional space control reference through analyzing track line design data, and constructing a metro section model in a manner of combining parameterized modeling with an intelligent algorithm; the contact network and pipeline models are ensured to meet the standard requirements through real-time verification of the conductor height change rate and the turning radius-pipe diameter matching matrix; space coordinate unification and interface matching precision control are achieved through a multi-professional model cooperation system, and a parameter linkage updating mechanism is established to respond to design changes; and finally, outputting an LOD400 precision BIM model and associating construction data for dynamic correction, and supporting RVT / IFC format conversion and engineering quantity automatic statistics. The method is mainly used for efficient collaborative design and construction management and control of metro sections, the modeling precision and the multi-specialty coordination efficiency can be improved, and the design error rate is reduced.

The invention discloses a distributed electric power measurement anomaly detection method and system based on a graph neural network. The method comprises the steps of collecting distributed data; processing the distributed data, and recording a timestamp; converting the nonlinear distributed data into linear data; constructing a graph model by taking each metering device as a node, taking the processed distributed data as a node initial feature and taking a physical connection or communication interaction relationship between the devices as an edge; constructing a graph neural network model; inputting the graph model into a graph neural network model, extracting node space features through a graph convolutional neural network layer, constraining edge weights through a graph attention network layer, and updating node features; and inputting the output of the graph neural network into K-Means clustering, dividing a center node and other nodes, calculating the distance between the center node and the other nodes by using Euclidean distance, setting a threshold value, and if the distance is greater than the threshold value, corresponding metering equipment is abnormal. The method can accurately distinguish the abnormal power fluctuation, shortens the response time, and reduces the omission ratio.

The invention provides a calculation method for the optimal excavation slope ratio of a groove foundation pit. The calculation method comprises the steps that physical and mechanical parameters and geometric parameters of the groove foundation pit to be analyzed and the stress state of a sliding body of the groove foundation pit are obtained; establishing a safety stability objective function and an excavation volume objective function of the trench foundation pit, and a slip mass limit equilibrium constraint condition of the trench foundation pit; establishing a multi-objective optimization nonlinear mathematical programming model of the safety and stability of the trench foundation pit and the excavation workload; and solving the multi-objective optimization nonlinear mathematical programming model to obtain a safety stability coefficient and an optimal excavation slope rate when the excavation workload is minimum under the condition of guaranteeing safety. According to the method, an effective calculation method can be provided for the optimal slope ratio of slope excavation of the soil trench foundation pit, and the trench foundation pit slip mass limit equilibrium condition can be combined with the actual workload, so that the actual requirements of foundation pit engineering can be better met, the workload is as small as possible while the safety is ensured, and the construction efficiency is improved. And an effective basis is provided for stability evaluation and actual design construction of the trench foundation pit.

The application discloses a linear prediction method based on orthogonal compensation, relates to the technical field of data processing and machine learning, and calculates pixel offset between two adjacent images after initial linear compensation and corresponding offset data through an image matching algorithm; offset data is used as a difference value between an observation value and a prediction value, and a reference value of reference data is used as a state quantity; if the offset is normal, an interactive multiple model based on Bayesian theory is used to fuse real-time data, a prediction value of the interactive multiple model based on Bayesian theory is used as input, linear data error of a system linear data error model is used as output, and the system linear data error model is subjected to model training; if the offset is abnormal, a compensation data information is used to calculate a reference value, so that modified data information is obtained, and when an abnormal type that has not been sufficiently learned is encountered, the recognition capability can be greatly reduced, and even a missed detection or a false detection can occur.

The invention discloses a slow action server processing method, device and equipment and a storage medium, and the method comprises the steps: firstly, accessing a plurality of paths of video signals through video collection equipment, and carrying out the parallel double-code-stream real-time coding, and generating high and low code stream frame data; binding each frame of data with the PTP hardware timestamp to generate a bare frame data packet; constructing a channel-code stream-time sequence three-level collaborative index table, and performing efficient management on frame data; the bare frame data packets are written into an annular buffer area according to a timestamp sequence, the bare frame data packets are written into linear data files in batches through asynchronous threads, random writing is converted into sequential writing, and low-code-stream frame data are retrieved through an index table to be used for previewing and high-code-stream frame data are retrieved through an index table to be used for slow action processing; and finally, performing slow action processing on the high code stream frame data and outputting a slow action video sequence, and through combination of frame-level direct storage and three-level indexing, realizing millisecond-level accurate retrieval and efficient storage, and remarkably improving the response speed and the processing performance of a slow action server.

The invention discloses a high-speed train traction system fault detection method based on local linear embedding, and belongs to the technical field of fault diagnosis. The method comprises the steps that firstly, traction system sensor data are collected and preprocessed; secondly, mapping high-dimensional data to a low-dimensional space by utilizing a local linear embedding algorithm, and reserving a local geometric structure of the data; thirdly, reconstructing original data based on the data matrix of the low-dimensional space, calculating residual signals, and extracting fault features; and finally, judging whether the high-speed train traction system breaks down or not by comparing the evaluation function with a threshold value. According to the method, the limitation of processing high-dimensional and nonlinear data by a traditional method is effectively solved, the accuracy of fault detection is improved, fault early warning of the high-speed train traction system can be realized, and the method has a relatively high engineering application value.

This invention relates to a quantum-enhanced clustering method based on spheres, belonging to the field of quantum computing technology. This method addresses the problems of high computational complexity, difficulty in capturing intrinsic geometric features, and sensitivity to noise in traditional clustering algorithms when processing large-scale, high-dimensional, nonlinear data. The technical solution includes obtaining a compressed set of spheres from the original dataset and extracting the center vector; encoding the center vector into a quantum state through quantum feature mapping; constructing a quantum kernel matrix using quantum circuits to calculate fidelity; calculating cohesion based on the quantum kernel matrix to identify and remove noisy spheres; performing hierarchical clustering on the core sphere set; and mapping the results back to the original data points. This invention improves clustering accuracy, robustness, and the ability to handle complex data structures.

The application relates to the technical field of computational fluid dynamics, and discloses a transonic flow numerical simulation method, system, device and medium, the method comprising the following steps: determining whether a flow field has a region with a Mach number greater than a preset value; if not, the entire flow field is a subsonic region and belongs to a non-shock region; if yes, a supersonic region exists; determining whether a shock wave exists between adjacent grid points or non-structured grid elements; if a shock wave exists, a shock wave region and a non-shock wave region are divided; for the shock wave region, a TVD format is used to discretize the spatial term of a control equation to obtain a TVD format discrete result of the shock wave region; for the non-shock wave region, a linear numerical format is used to discretize the spatial term of the control equation to obtain a linear format discrete result of the non-shock wave region; a flow field is updated by using a time advancing method; and if a residual error converges, a simulation result of the transonic flow numerical simulation is output. The application can realize efficient, high-precision and robust simulation of transonic flow.

This invention relates to the field of image signal processing, specifically to a CCM matrix calculation method for the nonlinear effect of Gamma in a dual-segmented dynamic gain offset. The method includes the following steps: linear data acquisition (Raw data) → Demosaic to RGB data conversion → nonlinear Gamma parameter solution → initial CCM solution → Gamma calculation → DeltaE calculation → CCM matrix iterative optimization → final CCM matrix output. With dynamic changes in grayscale, it enhances dark area brightness, suppresses dark area noise, and avoids highlight clipping. Segmented debugging, with dynamic increases in k and adaptive adjustment in both segments, makes debugging more flexible and increases the algorithm's robustness. Improved color accuracy: By aligning calibration with the actual processing flow, the DeltaE target value is reduced, resulting in more natural and accurate color reproduction. High adaptability: Supports different Gamma coefficients and standard color spaces, suitable for various imaging devices such as mobile phones, cameras, and security monitoring systems. Strong robustness: Based on an iterative optimization framework, it avoids calibration failures caused by domain mismatch, resulting in stable and reliable results.

A memory block structure comprising: a plurality of memory blocks configured to store data of at least part of a graph or other non-linear data model as one or more linear data structures; a plurality of local controllers, each local controller associated with a corresponding memory block and configured to search and / or retrieve data of the graph or other non-linear data model by navigating the one or more linear data structures, wherein the linear data structure comprises a head node and a plurality of nodes each represented by at least a reference to a head node of the linear data and a reference to a further node of the plurality of nodes.

The application belongs to the technical field of artificial intelligence and cross, and discloses an artificial intelligence aided decision-making method of a digital engineering project management platform, which comprises the following steps: obtaining original engineering data and converting the original engineering data into one-dimensional text sequences to generate standardized linear data; processing new project data according to end-to-end optimization results to generate actual reorganization sequences and reorganization order sequences and compressed sub-block data, and obtaining sub-block position information of high-contribution sub-blocks in the reorganization sequences; based on the sub-block position information of the high-contribution sub-blocks in the reorganization sequences, the original sub-block sequence numbers are inversely inquired through the reorganization order sequences, the original sub-block sequence numbers are decompressed and the original engineering data is restored to obtain an aided decision-making report. The application generates learnable reorganization sequences and soft reorganization order sequences through a soft position mapping mechanism, realizes self-adaptive aggregation of similar data sub-blocks in a local area, enhances data semantic compactness, and lays a structural foundation for efficient compression and feature extraction.

The invention discloses a kernel norm-based robust flexible multi-view subspace clustering method and system. The method comprises the steps of mapping original image data to a high-dimensional regeneration kernel Hilbert space through a kernel technique; learning a consistency kernel through a self-weighted multi-kernel learning strategy; introducing low-rank constraint into self-expression learning of the Hilbert space to obtain a similar matrix; and calculating in the similar matrix according to a k-means algorithm to obtain an image clustering result. According to the method, original image data are mapped to a high-dimensional regeneration kernel Hilbert space by adopting a kernel technology, so that nonlinear data are effectively processed, weights are automatically distributed for different kernels, the importance of different views is effectively considered, the influence of noise is considered, low-rank constraints are added in a similarity matrix, and the image quality is improved. Therefore, an optimal similarity matrix with a clear block diagonal structure is constructed, and a better image clustering result can be obtained.

The invention belongs to the field of artificial intelligence cross technology, and discloses an artificial intelligence auxiliary decision-making method for a digital engineering project management platform, which comprises the following steps: acquiring original engineering data, converting the original engineering data into a one-dimensional text sequence, and generating standardized linear data; according to an end-to-end optimization result, processing the new project data, generating an actual recombination sequence, a recombination sequence sequence and compressed sub-block data, and obtaining sub-block position information of the high-contribution sub-blocks in the recombination sequence; based on the sub-block position information of the high-contribution sub-block in the recombination sequence, the original sub-block serial number is reversely searched through the recombination sequence, the original sub-block serial number is decompressed, original engineering data is restored, and an auxiliary decision report is obtained. According to the method, a learnable recombination sequence and a soft recombination sequence are generated through a soft position mapping mechanism, local adaptive aggregation of similar data sub-blocks is realized, data semantic compactness is enhanced, and a structural foundation is laid for efficient compression and feature extraction.

This electronic device comprises a power amplifier (PA), one or more processors connected to the PA, and a memory for storing instructions. When executed individually or collectively by the one or more processors, the instructions instruct the electronic device to: monitor input data of the PA and output data of the PA; identify a weighted value for a first digital pre-distortion (DPD); identify a hyperparameter for a second DPD on the basis of estimated input data of the PA estimated on the basis of the second DPD based on a neural network (NN) scheme, estimated input data of the PA estimated on the basis of a first DPD, and the input data of the PA; correct, on the basis of the weighted value and the first DPD, first nonlinear data included in the input data of the PA; and correct, on the basis of the hyperparameter and the second DPD, second nonlinear data included in the input data of the PA.

This invention provides a method and related apparatus for establishing a nonlinear mathematical model of a hybrid DC transmission system considering discontinuous characteristics. The method includes: representing the dynamic processes of discontinuous components in the main and control loops of the hybrid DC transmission system using a step function; approximating the step function as a continuous function using a linear combination of hyperbolic tangent functions to obtain a continuous expression; obtaining modular nonlinear differential equations for the rectifier, inverter, and AC / DC networks, and replacing the discontinuous dynamic parts involved in the equations with the continuous expression; and interconnecting the modules according to their input-output relationships to obtain a complete nonlinear mathematical model of the hybrid DC transmission system. This invention establishes a nonlinear mathematical model of a hybrid DC transmission system that accurately reflects discontinuous dynamic characteristics by employing the continuous approximation method using step functions and hyperbolic tangent functions, thereby improving the accuracy and practicality of system modeling.

The invention provides a target number statistical method, and belongs to the field of mathematical statistics. The problem of low statistical efficiency of multi-type data is solved; the method specifically comprises the following steps: acquiring a target data set A and a target data set B; performing time sequence analysis on the one-dimensional data in the target data set A, and constructing a one-dimensional mathematical model; analyzing main body attributes of the multi-dimensional data in the target data set A, determining important attribute basis, and constructing a regression model; if the multi-dimensional data does not have the main body attribute, dimension reduction is carried out on the multi-dimensional data, and then a linear mathematical model is constructed; counting the occurrence frequency of the target picture and the target audio in unit time in the data set B, and constructing a mathematical model; acquiring identity information of the data source, creating a storage structure according to the identity information, and storing data; according to the method, data mining and analysis are carried out on data provided by a user, potential rules of each group are found, and a mathematical model is constructed.

The invention discloses a selective revocation method of a document editor, which comprises the following steps of: recording a user operation of editing a document by a user, and recording in a linear data structure according to an execution sequence of the user operation; selecting the user operation in the record, and judging whether the user cancels or redoes according to the selection of the user; calculating an offset operation of the target operation; executing a counteracting operation of the target operation, and returning the current document state to a desired document state; and recording the revocation action of the user, and marking the revocation action of the user in the linear data structure recording the user operation. According to the method and the device, a user can cancel any editing operation executed in the editor at a certain moment, and is not limited to cancel the last operation every time, so that the loss caused by any misoperation is avoided.

A transformer on-load tap-changer defect identification method comprises the following steps: establishing a linear mathematical model of voltage, current, acceleration and temperature sensor output, and forming a multi-dimensional information fusion vector; carrying out low-pass filtering processing, constructing a multi-dimensional feature set comprising a mean value, a variance, a dominant frequency amplitude and a wavelet coefficient under an optimal scale, and carrying out feature extraction; based on defect identification and classification decision of multi-dimensional features, introducing a linear scoring function to evaluate an abnormal degree reflected by a current feature vector, performing nonlinear transformation on scores, calculating probabilities of various defects, calculating confidence, and setting a preset threshold to perform binary alarm judgment; and three levels of early warning states are divided through the defect probability, a comprehensive decision function fusing the early warning level and the recognition confidence is designed, and the reliability of the diagnosis result is evaluated. According to the invention, fusion perception and intelligent identification of electrical, mechanical and thermal multi-dimensional information are realized, and the defect identification accuracy and the early warning capability are improved.

The invention relates to the technical field of energy monitoring, in particular to an energy maximum tracking prediction system under vector product meaning, comprising: a data acquisition module for acquiring physical quantity data related to energy, sensors including but not limited to an electric field intensity sensor, a magnetic field intensity sensor, a power sensor and a flow sensor; the vector construction module is connected with the data acquisition module through a data transmission technology, and is used for converting the associated data group into a vector form according to the characteristics of the acquired physical quantity and a physical model preset by the system; and the vector operation module is connected with the vector construction module through a data transmission technology and is used for carrying out vector product operation on the vector from the vector construction module. According to the method, the characteristics of the optimal hyperplane are searched by using support vector regression # imgabs0 #, nonlinear data are accurately fitted, compared with a traditional linear prediction model, the advantages are remarkable, the capability of tracking the energy state in real time is achieved, and the operation cost is reduced.

Disclosed is a system (140) and a method (300) for identifying a coverage boundary of cluster of nodes in a network (100). The method (300) comprises retrieving input data including latitude and longitude data points of the cluster of nodes in two dimensional plane, and removing duplicate data points from the input data. Further, a pair of linear data points separated by a maximum Euclidean distance is determined from remaining data points, and one data point from the pair of linear data points is selected to sort the remaining data points in order of increasing polar angle of the remaining data points around the selected data point. Subsequently, a data processing operation is executed to process sorted data points iteratively until a bottom-left-corner data point is identified and as a result a convex hull of finite 15 data points representing the coverage boundary is determined.

The invention discloses a comprehensive sorting and clustering method and system for retired lithium ion batteries, and the method comprises the steps: obtaining the combination weight of battery evaluation indexes through a game theory method, combining a VIKOR algorithm of comprehensive sorting with a KSOM algorithm of clustering, achieving the two-stage sorting and clustering, and improving the sorting and clustering precision. According to the algorithm, subjective and objective weights are combined by using the thought of the game theory, weight distortion caused by human experience and data extreme values is avoided, comprehensive sorting is performed by using VIKOR, compromise coefficients are introduced on the basis of positive and negative ideal solutions, preferences can be manually controlled, and finally the Euclidean distance in a traditional SOM algorithm is replaced by using a kernel function, so that the algorithm has high robustness. And the method has higher clustering precision and capability of processing high-dimensional nonlinear data. The method is suitable for the field of echelon utilization of the retired lithium ion battery.

The present teaching relates to method, system, medium, and implementations for approximating a non-linear relationship between a numerical feature and an output of a model. A value of a numerical feature is received and is transformed, via a transform function, into a transformed value within a fixed range. With respect to each of a plurality of basis functions used for approximating the non-linear relationship, a respective basis function value of the basis function is computed based on the transformed value. An approximated value of the non-linear numeric feature is generated based on a sum of the plurality of basis function values weighted respectively by each corresponding one of a set of the weights, obtained via machine learning.

The present invention relates to the field of reactor thermal hydraulic technology, and specifically, to a method, system, and medium for constructing a non-uniformly distributed CHF table. The critical heat flux (CHF) is a key input parameter of the reactor thermal design criteria and is also the key to characterizing the core heat exchange limit. The prediction accuracy of CHF can be improved based on interpolation calculation of the table. Starting from the nonlinear influence of the main thermal parameters on CHF, the present invention proposes a nonlinear CHF table construction method for high-precision interpolation calculation based on the table. The core content is the connotation, form, and specific determination method of the non-uniform distribution of the table. It can support high-precision calculation of CHF based on the table, while reducing the total amount of data required for the table and saving experimental costs, thereby achieving the engineering purpose of helping to improve the calculation accuracy of CHF and releasing the core design margin.

The embodiment of the invention provides a map element topological relation judgment method and device, equipment, a medium and a product. The method comprises the following steps: acquiring geometric positions of a road line and a plurality of linear elements in a map, establishing a reference line based on the road line, projecting the linear elements, and judging a topological relation among different linear elements according to projection coordinates. Aiming at the problem of space calculation complexity of linear elements, a uniform reference line is projected, a multi-dimensional geometric position is mapped into a linear numerical value, and multi-dimensional geometric operation is replaced by one-dimensional numerical value comparison, so that the limitation of traditional geometric function superposition calculation can be broken through, the implementation complexity of topological relation judgment is remarkably reduced, and the calculation efficiency is improved; an operator can process map data more efficiently, and the map making and updating period is shortened.

Embodiments of the present disclosure include systems and methods for compilation of a data stream using granular version control and context associations, the system comprising: a processor and memory coupled to the processor, both coupled to one or more large language models, the memory having instructions that perform the steps of a method comprising: receiving user-defined global modules with global module data containers; receiving user-defined local modules with local module data containers; associating object data from the user-defined global modules with the user-defined local modules; and publishing the data stream. The publishing comprises: submitting the associated data to the large language models, the large language models generating prompts; submitting the prompts to the large language models to generate images; associating the images with object data contained in the local data containers; collating the images to form the linear data stream; and outputting the linear data stream in re-playable format.

The invention is suitable for the technical field of data processing, and provides a main curve optimization method and device based on feature constraint, and the method comprises the steps: obtaining to-be-analyzed high-dimensional nonlinear data, and mapping the to-be-analyzed high-dimensional nonlinear data to a target Riemannian manifold; the high-dimensional nonlinear data has specific attributes; constructing an initial main curve to be optimized through the data points of the target Riemannian manifold; determining a target projection point of the target data point projected to the target position; determining a joint contribution factor of the target data point according to the spatial constraint coefficient and the at least one data attribute feature constraint coefficient; and updating the to-be-optimized main curve by minimizing the target optimization error to obtain a final target main curve. According to the method, the data attribute feature constraint coefficient is introduced on the basis of the spatial constraint coefficient to jointly determine the joint contribution factor of the data points of the high-dimensional nonlinear data, so that the problem of fitting errors caused by neglecting data attribute features in a traditional main curve method is solved, and the main curve fitting precision is improved.

The invention discloses a crown porcupine optimization and deep learning-based short-term electricity price prediction method and device, and belongs to the technical field of electricity price prediction, and the method comprises the steps: S1, carrying out the optimization of a VMD model based on crown porcupine optimization, obtaining an optimized VMD model, and decomposing first historical electricity price data into modal components based on the optimized VMD model; s2, introducing an index gating mechanism and a memory hybrid mechanism into LSTM to obtain an sLSTM model, and performing feature extraction on the modal component based on the sLSTM model to obtain a first time sequence feature vector representing a long-term dependency relationship; and S3, based on the first time sequence feature vector and second historical electricity price data, using a Transform model to obtain a second time sequence feature vector representing a short-term dependency relationship, and based on the first time sequence feature vector and the second time sequence feature vector, obtaining a short-term electricity price prediction result. The technical problems that nonlinear data are difficult to process and the long-term dependency relationship existing in the data is difficult to capture are solved, and the accuracy of electricity price prediction is remarkably improved.

The invention discloses a video repeated clip detection method and device, computing equipment, a storage medium and a program product, and the method comprises the steps: carrying out the feature extraction of a to-be-detected video, and obtaining a first feature matrix and a second feature matrix of the to-be-detected video; obtaining a similarity matrix based on the first feature matrix and the second feature matrix; in the similarity matrix, elements with the similarity larger than a preset threshold value are screened out, and an element set is constructed; candidate segments are determined according to the element set, and each candidate segment corresponds to a linear array in a similarity matrix; for each candidate segment, obtaining a sub-array with the maximum sum in the linear array corresponding to the candidate segment; and determining whether the candidate fragment is a duplicate fragment based on the sub-array with the maximum sum.