41 results about "Multi dimensional" patented technology

The invention discloses a time sequence marine ecological environment early warning monitoring data abnormal point detection method, and relates to the technical field of marine ecological environment analysis, and the method comprises the steps: collecting a plurality of types of marine monitoring indexes, constructing a multi-source and multi-dimensional time sequence data set, and carrying out the preprocessing and unification of the data into a standard input format. And based on the causal relationship and co-evolution characteristics between the factors, constructing a graph structure model, and depicting a coupling propagation path between the factors. Generating a local time sequence model by adopting a sliding window, constructing an anti-fact path, comparing the propagation difference of an original path in a map structure, and identifying a key disturbance moment; and in combination with historical event similarity and space consistency information, the early warning credibility of abnormal points is evaluated, and efficient and interpretable graded early warning response is realized. According to the method, accurate identification and credible early warning of abnormal points are realized, causal modeling, anti-factual reasoning and hierarchical response capabilities are realized, and the intelligence and interpretability of marine ecological monitoring are improved.

The invention relates to the technical field of incremental forming, in particular to an industrial robot double-point incremental forming method and system based on a multi-modal large model, and the method comprises the steps: obtaining multi-modal data of a main forming robot and an auxiliary forming robot in a double-point incremental forming process; based on the multi-modal data, a joint stiffness coefficient is obtained; obtaining registered point cloud data based on the source point cloud data and the target point cloud data; based on the six-dimensional forming force value data, the data source point cloud data and the text description data of the forming scene, obtaining a description text and a preliminary compensation value; obtaining a prediction compensation value based on the registered point cloud data, the joint stiffness coefficient, the description text and the initial compensation value; and according to the predicted compensation value, the forming process of the to-be-machined plate is completed. According to the method, multi-dimensional data input of vision and the like is introduced, and a multi-modal large model is introduced as a training model, so that the compensation precision of double-point incremental forming is improved.

The present invention relates to the technical field related to atomic gravity measurement and provides a steady-state identification method based on the multi-factor and multi-dimensional characteristics of a cold atom absolute gravimeter. The method comprises: S1, at each steady-state level, collecting historical data of hull operation based on the cold atom absolute gravimeter, the historical data including the acceleration and inclination of the hull, and the atomic number data of the cold atom absolute gravimeter; S2, dividing the acceleration, inclination, and atomic number data into feature groups to form a multi-factor and multi-dimensional hull steady-state feature matrix; S3, establishing a hull steady-state identification model based on the hull steady-state feature matrix and a machine learning algorithm, and cross-validating the hull steady-state identification model; S4, identifying the hull steady-state based on the cross-validated hull steady-state identification model and the currently collected hull acceleration, inclination, and atomic number data. The present invention further improves the accuracy of hull stability identification based on the multi-factor and multi-dimensional information of the ship-borne cold atom absolute gravimeter.

The invention relates to the technical field of intelligent talent evaluation, and discloses a multi-dimension-based talent team evaluation method and system, and the method comprises the steps: collecting original talent data, and obtaining a standardized multi-source fusion talent data set through a deep semantic fusion method of a domain ontology; adopting a five-dimensional evaluation framework design and a dynamic weight learning method to obtain a multi-dimensional evaluation index system with a comprehensive initial weight; adopting a multi-factor adaptive weight adjustment method to obtain personalized dynamic weight configuration; a progressive mixed evaluation calculation method is adopted to obtain a comprehensive evaluation score; a multi-level time sequence evolution analysis and development potential prediction method is adopted to obtain a talent comprehensive value evaluation result; and an intelligent visual analysis and multi-dimensional decision support method is adopted to obtain a complete talent evaluation output result. According to the method, multi-dimensional dynamic evaluation and development potential prediction of talent teams can be realized, and scientific support is provided for talent management decision.

This invention relates to the field of incremental forming technology, specifically to a method and system for two-point incremental forming of industrial robots based on a multimodal large model. The method includes: acquiring multimodal data of a main forming robot and an auxiliary forming robot during the two-point incremental forming process; obtaining joint stiffness coefficients based on the multimodal data; obtaining registered point cloud data based on source point cloud data and target point cloud data; obtaining descriptive text and preliminary compensation values ​​based on six-dimensional forming force data, data source point cloud data, and textual description data of the forming scene; obtaining predicted compensation values ​​based on the registered point cloud data, joint stiffness coefficients, descriptive text, and preliminary compensation values; and completing the forming process of the sheet material to be processed based on the predicted compensation values. This invention improves the compensation accuracy of two-point incremental forming by introducing multi-dimensional data input, such as visual data, and by using a multimodal large model as a training model.

The invention provides a visualization dynamic permission allocation method and device based on multiple dimensions. The method comprises the following steps: configuring a department compilation rule according to an association relationship among an institution, a department and a table sample and permission allocation logic; generating a dynamic multi-dimensional table according to the authority association among the department compiling rule dynamic display mechanism, the department, the table sample and the user; according to a specified collection table style, collecting authority data containing operation authorities of departments and table samples and / or membership relationships of the departments and users; and according to the dynamic multidimensional table and the authority data, dynamically filtering departments, table samples and operation authorities visible to the user in a system function execution process.

This application discloses a method, apparatus, and related products for reverse analysis of CAN signals. The method calculates the entropy value of each bit in the original CAN message data, the covariance matrix between every two bits, performs connected component analysis, and calculates the temporal variance and range of each bit, resulting in a multi-dimensional feature matrix. This multi-dimensional approach more accurately and completely identifies signal boundaries, reducing the inaccurate signal segmentation caused by segmenting signals using a single feature. The feature matrix is ​​input into a prediction model to obtain candidate parsing results represented in a multi-dimensional form of six-tuples, solving the problem of missing parameter dimensions. LLM (Logical Learning Model) is introduced for logical verification, effectively filtering out erroneous parsings that violate common sense, and obtaining a logical score for each candidate parsing result, further improving accuracy. Furthermore, the method requires no manual intervention, improving reverse analysis efficiency, and is transparent throughout the process, enhancing the credibility and interpretability of the parsing results.

The application belongs to the technical field of data processing, and discloses a power supplier multi-dimensional data null value assignment method, system, device and terminal. Basic data of the supplier multi-dimension is input. According to the null value rate of each data dimension, the data is divided into a basic data set and a multi-null value data set. Each column of the multi-null value data set and each supplier data null value are calculated by the basic data. Comparison and analysis are carried out by using an industry change factor, and the multi-null data set is assigned. The multi-null data set is taken as the basic data set, and the basic data set is taken as the multi-null data set, so that the assignment of the basic data set is completed. The basic data set and the multi-null data set are merged, and the assigned data set is output. The power supplier multi-dimensional data null value assignment method based on industry ranking selection provided by the application improves the scientificity of the null value assignment, reduces the influence of subjective assignment on data ranking, and has better objectivity.

Methods, apparatus, and processor-readable storage media for predicting resource-related failures using multi-dimensional-based machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one resource-related activity involving at least one resource and one or more users; predicting one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques; predicting one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques; and performing one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons.

The invention relates to the technical field of game playing accompanying services, in particular to a playing accompanying teacher matching and service quality guarantee system and method, and the system comprises a data collection module which is used for collecting multi-modal data in a playing accompanying service process in real time; the dynamic portrait construction module is connected to the data acquisition module and used for constructing and continuously updating the dynamic ability portraits of the accompanied players and the user demand portraits; and the intelligent matching engine is connected to the dynamic portrait construction module and used for matching the dynamic portrait according to the service request of the user. According to the method, the dynamic and multi-dimensional ability portrait and demand portrait are constructed, the matching precision is remarkably improved, the user selection cost is reduced, further, a reinforcement learning algorithm is introduced, long-term commercial targets such as user retention and re-purchase are directly used as optimization signals, the matching strategy can be continuously self-optimized according to the actual effect, and the user experience is improved. The limitation of traditional static weight configuration is broken through, and the crossing of a matching mechanism from a fixed rule to intelligent evolution is realized.

A method for accelerating the comparison of massive faces based on deep learning, which adds a new fully connected layer branch to the original output face feature extraction network, so that face data can simultaneously output two-dimensional feature values ​​through the feature extraction network and be visualized in a two-dimensional plane; select a suitable center point, and divide N features of the base library into m areas from the center point according to the angle; when comparing the feature values ​​of the face to be tested, first determine the feature area where it is located through the two-dimensional feature value, and then compare it one by one with the multi-dimensional feature value in the area, avoiding comparing the multi-dimensional face feature value with all the face feature values ​​in the base library, thereby achieving acceleration, and realizing the acceleration of face feature value comparison of massive images.

Methods and apparatus for processing data associated with a device. A method for processing data associated with a device configured to transmit, via a communication system, semantic information, e.g., multi-dimensional valid data, the method comprising providing pilot data, where the pilot data is of the same type as the valid data, the pilot data being transmitted via the communication system.

This invention discloses a method for controlling advanced 3D packaging parameters for thin-film chips, relating to the field of chip packaging technology. The method includes: constructing a multi-dimensional real-time process status dataset during the execution of a target key process; predicting key indicators under current and next process conditions; based on the prediction results, collaboratively optimizing heat dissipation performance and process cycle, and determining the optimal process parameter settings within preset equipment parameter constraints; distributing the optimal process parameter settings to the process equipment controller corresponding to the target key process for execution, and performing real-time compensation of the process parameters; and periodically or trigger-based updating of the multiphysics proxy model. In summary, the advantages of this invention are: by constructing an intelligent closed loop of "perception-modeling-decision-execution-learning," it achieves multi-parameter collaborative optimization and forward-looking control of advanced 3D packaging processes.

The present invention belongs to the technical field of knowledge embedding in natural language and discloses a method, system, medium, device, and data processing terminal for knowledge embedding. A Trie tree is constructed based on the words in all background knowledge bases. Given an input sentence, the background knowledge words present in the input sentence are searched using the AC tree algorithm; words of different dimensions are converted into word embedding vectors; a multi-dimensional background knowledge embedding vector is generated; and the input vector obtained by concatenating the word embedding vector and the multi-dimensional background knowledge embedding vector is input into an entity recognition model. The knowledge embedding method provided by the present invention converts knowledge of different types, methods, and lengths into the same vector representation and can be applied to the input of any neural network model. The present invention achieved F1 values ​​of 95.03 and 96.78 on the open source named entity recognition datasets MSRA and Resume, respectively. The knowledge embedding improved the model by 1 to 2 points. The F1 value was also improved on the CoNLL2003, MSRA, and OntoNotes 4.0 datasets.

The invention relates to a role evaluation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a drama text related to a target role in a script document; extracting a plurality of sub-plot texts corresponding to the target role in multiple dimensions from the plot text; inputting the plurality of sub-plot texts into a multi-dimensional analysis model, so that the multi-dimensional analysis model outputs an evaluation value corresponding to each dimension, and obtaining an evaluation value set of the target role; and based on the evaluation value set, determining a comprehensive evaluation score of the target role in the script document. According to the method, a multi-dimensional role shaping exploding potential analysis prediction model is established, a script role effect can be evaluated and analyzed to a certain extent in a project making preparation stage, and a more accurate direction is provided for optimizing a character shaping evaluation dimension in script evaluation to better meet market requirements and providing a potential commercial value of project broadcasting.

The present invention discloses a method for detecting anomalies in time-series marine ecological environment early warning monitoring data, which relates to the technical field of marine ecological environment analysis, including collecting multiple types of marine monitoring indicators, constructing a multi-source and multi-dimensional time-series data set, and unifying it into a standard input format through preprocessing. Based on the causal relationship and co-evolution characteristics between factors, a graph structure model is constructed to characterize the coupled propagation paths between factors. Subsequently, a sliding window is used to generate a local time series model, and a counterfactual path is constructed to compare the propagation differences of the original path in the graph structure to identify key disturbance moments. Combined with the similarity of historical events and spatial consistency information, the warning credibility of the anomaly is evaluated to achieve efficient and explainable hierarchical warning response. The present invention realizes accurate identification of anomalies and credible warning, has causal modeling, counterfactual reasoning and hierarchical response capabilities, and improves the intelligence and interpretability of marine ecological monitoring.

The invention provides a data recommendation method and device, electronic equipment, a medium and a program product, and relates to the technical field of computers. The service data recommendation method comprises the following steps: based on package components of a fused package and a service dependency relationship between the package components, constructing a multilayer knowledge graph ontology which takes a fused package entity as a top node for radiation; performing filling operation on the multilayer knowledge graph ontology based on the specification data of the fusion package to generate a knowledge graph instance; in response to the obtained service demand, analyzing the service demand based on the entity type of the multi-layer knowledge graph ontology, and generating multi-dimensional structured demand data; taking the structured demand data as a trigger condition, and querying a matching rule matched with the trigger condition in a preset business rule base as a query condition; and executing query operation on the knowledge graph instances based on the query conditions to generate a recommendation list of the service data. Through the technical scheme disclosed by the invention, the use experience of the user can be optimized.

A method of compressing data for transfer between a local storage of a processor and an external storage. The data is formed in an array of three or more dimensions and the method comprises sequentially reading data stored in the local storage to a compressor in units of data. Each unit has a predetermined unit size corresponding to an integer number of a tile size. At an extremity of the array, a partial unit of data is read in a case that the array size is not an integer multiple of the unit size. The partial unit of data is filled at the compressor and the filled data is compressed on a tile-by-tile basis to form compressed data. The compressed data associated with the unit of data is transferred to the external storage.

The invention relates to the related technical field of atomic gravity measurement, and provides a steady-state recognition method based on multi-element and multi-dimensional characteristics of a cold atom absolute gravimeter, and the method comprises the steps: S1, collecting the historical data of the operation of a ship body based on the cold atom absolute gravimeter at each steady-state level, the historical data comprising the acceleration and inclination angle of the ship body, and the acceleration and inclination angle of the ship body; the atomic number data of the cold atom absolute gravimeter are obtained; s2, dividing the acceleration, the inclination angle and the atomic number data into feature groups to form a multi-element multi-dimensional ship body steady-state feature matrix; s3, based on the ship body steady-state characteristic matrix and a machine learning algorithm, establishing a ship body steady-state identification model, and performing cross validation on the ship body steady-state identification model; and S4, according to the ship body steady state identification model after cross validation, identifying the ship body steady state based on the currently acquired acceleration, inclination angle and atomic number data of the ship body. According to the multi-element multi-dimensional information of the shipborne cold atom absolute gravimeter, the ship stability identification precision is further improved.

The present invention discloses an image segmentation method based on a multi-dimensional interactive attention mechanism, relates to the technical field of image segmentation, proposes a variable spatial attention method, applies deformable convolution to the spatial attention method, designs a multi-dimensional spatial attention method branch, performs three-dimensional transformation on the input features, obtains the dependency between three different dimensions, obtains multi-dimensional spatial attention weights, averages the three output feature maps to obtain a multi-dimensional fused spatial attention method output, then proposes a fused channel attention method branch, fuses the two branches in the channel attention method in advance, makes the channel attention method more lightweight, and is used to obtain the channel attention weights, further realizes the multi-dimensional interactive attention method, further fuses the multi-dimensional channel and spatial attention outputs, and takes the weighted average to obtain the final method output.

The invention discloses a city image generation method, device and equipment based on multi-dimensional LoRA and a storage medium, and relates to the field of model reasoning. Semantic information of the text cue word and city keywords contained in the text cue word are extracted, a corresponding target LoRA model is matched according to the semantic information and the city keywords, and a corresponding fusion weight is calculated; receiving constraint information corresponding to the text cue word, and extracting geometric constraint features used for constraining the image content according to the constraint information; and extracting target LoRA weight parameters corresponding to the target LoRA model, injecting the target LoRA weight parameters into the Flux model in a mixed manner according to the proportion of fusion weights, performing urban style image reasoning by taking geometric constraint features as constraint conditions, and outputting an urban image map. The scheme has the advantages that regional culture features can be accurately analyzed, multi-city style elements are intelligently fused, and planning constraints are strictly followed.

The present invention relates to a method for full-duplex UAV relay user scheduling, trajectory optimization and resource allocation, belonging to the field of wireless communication technology. The present invention is a method for maximizing the minimum rate of users by jointly optimizing user scheduling, the flight trajectory of the UAV relay, beamforming and the transmission power of each transmitting end. This method decomposes the multi-dimensional complex joint optimization problem into relatively simple sub-problems of low dimension, namely user scheduling, UAV trajectory, beamforming and transmission power sub-problems, by introducing auxiliary variables and the block coordinate descent method, and uses methods such as introducing slack variables, successive convex approximation, and alternating interference suppression to transform and solve the sub-problems to obtain the final result. Thus, the minimum user rate is maximized.

A method for rock identification includes the steps of receiving a rock slice image transmitted by an image acquisition device, generating a geometric feature, a mineral feature and a structural feature corresponding to a rock slice based on the rock slice image, and generating an identification result of the rock slice based on the geometric feature, the mineral feature and the structural feature. After the rock slice image is obtained, the rock slice image is subject to feature extraction based on three dimensions, i.e., the geometric feature, the mineral feature and the structural feature, the properties of rock are determined based on multiple dimensional features, and finally the identification result comprising a textual description is generated. After a microscopic visual image corresponding to the rock slice is obtained, feature extraction based on multiple dimensions is performed on the image, and the rock slice is identified with reference to multi-dimensional features.

The application discloses a drilling parameter determination method, device and equipment. The method comprises the following steps: acquiring current drilling data in a current drilling environment; inputting the current drilling data into a pre-trained agent, outputting drilling parameter adjustment information corresponding to the current drilling data, and adjusting the current drilling parameter based on the drilling parameter adjustment information. The state space in the agent comprises a multi-dimensional drilling state feature corresponding to the current drilling environment determined based on the current drilling data. The value network comprises a composite reward function, which comprises a first reward item representing a first drilling index corresponding to the drilling parameter adjustment information and a second reward item representing a second drilling index corresponding to a plurality of drilling parameter adjustment information in a preset drilling period. Through the above method, the drilling parameter optimization can be accurately and efficiently performed. Meanwhile, the method can adapt to the rapid change of the underground formation lithology and the downhole dynamic working condition, and has high adaptability and flexibility.

This application provides a method, system, equipment, and medium for quality assessment of robotic automated welding. It extracts the temporal statistical characteristics of current fluctuations, the standard deviation of arc voltage, and the two-dimensional morphological features of the molten pool image to obtain multi-dimensional process features. These multi-dimensional process features are compared with the current standard process parameter range to determine the cumulative deviation of each feature within a sliding time window, thereby determining the current welding quality risk level. Based on the original time-series data within the sliding time window, the evolution trend and coupling relationship of each abnormal process feature are determined, identifying the dominant abnormal factor combination and its temporal triggering relationship. Combining the quality risk level, the dominant abnormal factor combination and its temporal triggering relationship, and associating it with keyframe evidence from the corresponding molten pool image, a quality assessment report for robotic automated welding is output. Using the scheme of this application, dynamic risk assessment of robotic automated welding can be performed based on multi-source fusion information.

The present invention discloses a method and device for calculating relationship intimacy based on multidimensional data. The method comprises: determining multiple relationships that affect the trust between a first entity and a second entity; quantifying the trust of the multiple relationships to obtain the intimacy value of each relationship; calculating the trust probability between the first entity and the second entity corresponding to each relationship based on the intimacy value of each relationship; clustering the multiple relationships using a relationship clustering method; obtaining the trust probability of each type of relationship after clustering based on the trust probability between the first entity and the second entity corresponding to each relationship; obtaining the trust probability between the first entity and the second entity based on the trust probability of each type of relationship; and obtaining the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity. The present invention can effectively identify the strength of relationships between entities based on multidimensional entity relationships.

A streaming engine employed in a digital data processor may specify a fixed read-only data stream defined by plural nested loops. An address generator produces address of data elements for the nested loops. A steam head register stores data elements next to be supplied to functional units for use as operands. A stream template register independently specifies a linear address or a circular address mode for each of the nested loops.

The invention discloses a multi-parameter target classification and identification method based on a photon counting laser radar, and the method comprises the steps: collecting three-dimensional point cloud data through the photon counting laser radar, dividing a point cloud into a multi-scale grid neighborhood, and carrying out the classification and recognition of a multi-parameter target according to the divided multi-scale grid neighborhood, features such as a point cloud density feature, a three-dimensional coordinate variance feature, an upper and lower neighborhood density ratio feature and a horizontal neighborhood density ratio are extracted, all the extracted features are combined into a multi-dimensional feature vector, the length of the feature vector is the number of photon points, and each point has a plurality of corresponding features; inputting the obtained multi-dimensional feature vectors into a classifier to carry out target classification and identification to obtain an initial classification result; aiming at the initial classification result, correcting misclassification points by using a category self-adaptive grid optimization algorithm; and finally, outputting a final classification label result of each point. According to the invention, denoising and target classification can be realized at one time, and the calculation speed and precision are ensured.

The invention discloses a dual-robot shortest time trajectory planning method, and relates to the technical field of robot trajectory recovery control, and the method comprises the steps: S1, designing a dual-robot task space parameterization expected time-varying trajectory with a synchronous motion constraint embedding feature; s2, uniformly mapping given multi-dimensional constraints of the double robots to one-dimensional constraints of a parameter space by adopting a path speed decoupling strategy, wherein the multi-dimensional constraints comprise a task space, a joint space and synchronous motion; and S3, substituting the shortest time trajectory solved in the parameter space by using a nonlinear filter into the dual-robot parameterization path function to obtain a dual-robot task space time optimal trajectory conforming to multiple constraints. According to the invention, the expected time-varying trajectory of the dual robots is designed into a special parameterization form; a multi-dimensional double-robot motion trajectory planning problem is converted into a path parameter trajectory planning problem in a one-dimensional parameter space, the trajectory planning steps are simplified, and the planning efficiency is improved.

The invention discloses a vegetation water demand prediction method and device based on multi-dimensional data, equipment and a medium. The method comprises the following steps: constructing a time sequence data set based on all ecological environment indexes of target vegetation; inputting the time sequence data set into a water demand prediction model comprising a long short-term memory (LSTM) network layer and a prediction calculation layer; the LSTM network layer performs feature extraction processing on the time sequence data set to obtain an implicit state, and the implicit state can represent a nonlinear mapping relation which is extracted from the time sequence data set and has the strongest correlation with the water demand of the target vegetation; and the prediction calculation layer calculates the target water demand corresponding to the target vegetation through a nonlinear fusion algorithm and an implicit state. According to the scheme provided by the embodiment of the invention, for the target vegetation, the dynamic change of the water supply and demand of the target vegetation in various ecological system environments is comprehensively reflected based on the multi-dimensional ecological index data and the LSTM network, so that the prediction accuracy of the water demand is improved.