12 results about "Process classification" patented technology

The application discloses a kind of based on marketing text data processing method and device, the method includes: obtaining marketing text information to be processed;Utilize preset label processing rule to the marketing text information to be processed classification processing, obtain label information;Label information is related to the industry and text style of marketing text information to be processed;Utilize preset attribute processing rule to the marketing text information to be processed identification processing, obtain attribute information;Label information and attribute information are processed, and obtain first triplet information;First triplet information includes M element information;M is greater than or equal to 3 positive integer;Element information is related to label information and attribute information;First triplet information is used to construct the knowledge graph of guiding generation marketing text. Visible, the application is favorable to improve the labelization processing efficiency of marketing text, and then improve key information extraction quality, to construct effective knowledge graph.

The application belongs to the field of intelligent construction and robot technology, and relates to a robot operation description automatic generation method, an intelligent operation method and a hardware platform, which comprises the following steps: constructing a semantic analysis engine containing a process classification dictionary and a construction process knowledge base, reading a BIM model file and screening to-be-operated components; based on the geometric topological features and volume attributes of the components, deducing and establishing a local operation space containing a grabbing coordinate system, an approaching coordinate system and an installation coordinate system; using semantic mapping technology, converting static component attributes into dynamic robot atomic action sequences, and automatically injecting dynamic constraint parameters according to material physical attributes; and finally generating a standardized robot operation description file containing timing logic and spatial information. The application can bridge the semantic gap between BIM design data and robot control data, realize precast component installation process teaching-free autonomous operation, and significantly improve construction efficiency and safety.

The invention relates to an electric power facility disaster damage monitoring method and an identification device, and belongs to the technical field of electric power facility disaster damage monitoring. The method comprises the steps of constructing a multi-dimensional feature portrait of a disaster damage identification device by collecting multi-source data, and dynamically scheduling and matching a pre-layering model for concurrent tasks based on the multi-dimensional feature portrait; performing decoupling analysis on the multi-dimensional feature portrait through the pre-layering model, and completing classification and positioning in parallel; data uploaded by all disaster damage identification devices are aggregated to train and generate a new model, a multi-dimensional feature weight is set based on a release target to calculate a comprehensive score, and a gray release target is screened. According to the method, dynamic scheduling of model resources is realized through the multi-dimensional feature portraits, and the utilization efficiency and response real-time performance of edge-side resources are improved; classification and positioning tasks are processed in parallel by adopting a decoupling architecture, so that the recognition precision is improved; a closed-loop self-evolution system is constructed by means of an intelligent gray release mechanism based on cloud edge cooperation, and the monitoring accuracy, predictability and continuous optimization capability are comprehensively improved.

The present application relates to the emerging software technology field about processing classification, and especially relates to a hull part intelligent classification method and system based on internal and external contour features. Based on the hull part drawing, a structured part object list is generated, the internal structure features and the external contour features of the part are extracted, and a multi-dimensional feature vector set is constructed; based on the preset weight configuration of each dimension, the multi-dimensional feature vector is processed by the K-means clustering algorithm based on weighted Euclidean distance, and the clustering allocation result is output; the contour coefficient of the part is calculated, the CH index is calculated, and the classification effect is output according to the contour coefficient and the CH index. By extracting the internal and external contour features of the part, constructing the multi-dimensional feature vector, and classifying based on the K-means clustering algorithm based on weighted Euclidean distance and evaluating the classification by the contour coefficient and the CH index, high-precision and high-process-fitting-degree classification is realized, and the classification efficiency and consistency are improved.

The application discloses a large-scale multi-label text classification method based on label adaptive text representation. The application firstly explores label adaptive representation of the text to effectively process classification performance of head labels and tail labels under large-scale multi-label classification; a pre-trained language model is used to learn a representation pool for the text, so that different labels can focus on different representations to complete correlation discrimination. Considering the characteristics of deep models and long texts, text representation enhancement is proposed to ensure the difference and comprehensiveness of the representations in the pool. Therefore, the application can provide effective discriminative text features for large-scale labels to improve the prediction performance. Compared with current large-scale multi-label algorithms, the application can guarantee the overall classification performance of large-scale multi-labels on the one hand, and guarantee that tail labels can better focus on detailed text features on the other hand, and the performance is better than that of the current most advanced large-scale multi-label algorithm.

The invention provides an intelligent analysis method and system for operating room clinical nursing data of brain patients, and relates to the technical field of data processing. The method comprises the following steps: collecting clinical nursing record data of a plurality of patients, constructing task item structure data of each group of clinical nursing record data, obtaining operation process structure data, performing process classification on each group of task item structure data, and constructing a task item sample data set of each operation process stage; performing nursing state analysis to determine a nursing state label of each task item, and performing nursing deviation detection on a plurality of patients to generate individual deviation characteristics of each patient; stage nursing intensity analysis is carried out in combination with the nursing state labels and the individual deviation features, nursing intensity features of each operation process stage are generated, and group clinical nursing state portraits about the multiple patients are constructed. According to the invention, fine management and optimization of clinical nursing work are realized.

The application relates to a reimbursement form filling method and device based on an agent, equipment and a medium, and relates to the technical field of reimbursement processing. The method comprises the following steps: obtaining a reimbursement request input by a user through an agent assistant interface; inputting the reimbursement request into a pre-trained large language model to perform intent recognition, obtaining intent information of the reimbursement request, and determining a process classification result of the reimbursement request based on the intent information, wherein the process classification result comprises querying a business trip task sheet, generating a reimbursement form according to the business trip task sheet, and writing invoice information into the reimbursement form details; based on the process classification result, triggering a corresponding workflow branch to perform corresponding operations, obtaining an execution result of the reimbursement request, and feeding back the execution result of the reimbursement request based on the agent assistant interface, wherein the operations comprise calling a database query interface, a business plug-in interface or an external tool interface. The application realizes an intelligent reimbursement form filling process, and improves the efficiency and accuracy of the reimbursement processing.

To provide a machine learning device, method, and program capable of improving the reliability of labels used to train deep learning models that process classification problems. [Solution] The machine learning device according to the embodiment includes a learning unit, a determination unit, and an update unit. The learning unit obtains a first model output including inference probabilities and / or feature vectors output from a first deep learning model by applying a first sample to a first deep learning model that processes a classification problem. The determination unit determines whether or not it is necessary to update the labels used as teachers for training the first deep learning model, based on the first model output and / or labels. If it is determined that a label update is necessary, the update unit updates the labels based on the first model output and the current update count of the labels; if it is determined that a label update is not necessary, it terminates the label update.

The invention provides a wind speed early warning method and system based on weather forecast and topographic features, and relates to the field of meteorological disaster early warning technologies. The method comprises the following steps: S1, carrying out fusion and gridding preprocessing operation on meteorological data; s2, performing dynamic correction operation on the wind speed according to the terrain effect; s3, based on historical disaster data analysis of a specific area, establishing a typical weather process classification system, and realizing weather adaptation optimization; and S4, dividing risk levels and carrying out partition response. The system provided by the invention comprises a data fusion module, a dynamic calculation module, a weather process adaptation optimization module and an early warning output module. The method has the beneficial effects that the spatial scale limitation of weather forecast data is broken through, and refined wind speed distribution mapping of a complex terrain region is realized; the wind speed modulation effect of the typical terrain under different meteorological conditions is quantified, and a dynamically adaptive calculation model is established; and resource configuration is optimized through a space partition response mechanism, and the protection efficiency is improved.

The invention discloses a data index customized display system and method based on a graph editing engine, and relates to the technical field of data visualization and management, and the system comprises a business map module which is used for macroscopically displaying the process classification of company data assets and providing an entrance for jumping to a data map; the data map module is used for displaying a data asset structure and an association relationship in a specific business process in detail and supporting deep navigation and data viewing; and a graph editing engine module. According to the data index customized display system and method based on the graph editing engine, the problems that in an existing data asset visualization technology, a data asset panoramic structure cannot be visually displayed, data navigation and retrieval are inconvenient, interaction experience is poor, data dynamic updating is not timely and the like are solved, asset panoramic display is achieved, and the user experience is improved. The user can conveniently and quickly carry out data navigation and retrieval, the interaction experience of the user is improved, and deep data management and operation work of the system user is supported.

The invention relates to the technical field of process management, and discloses a production process station arrangement method and system based on big data, and the method comprises the steps: obtaining process information, an employee expectation salary and an employee historical production record, mapping a process name into a process vector through a text embedding model, and generating a process classification name according to a similarity threshold; the method comprises the following steps: calculating employee production efficiency and employee production frequency according to employee historical production records and process classification names, forming an employee skill grade evaluation table, enabling skill evaluation to be stable under double calibers of category and statistics, dividing in-line process information and out-line process information according to in-line marks, and generating an in-line arrangement result; and calculating an off-line station threshold value according to standard working hours and employee expected salaries, comparing off-line station total working hours, executing process migration and employee adjustment in an over-limit manner, directly outputting an off-line arrangement result in a non-over-limit manner, and finally writing an in-line arrangement result and an off-line arrangement result into an MES database, thereby reducing arrangement iteration and improving scheme performability.

The application belongs to the field of intelligent construction and robot technology, and relates to a robot operation description automatic generation method, an intelligent operation method and a hardware platform, which comprises the following steps: constructing a semantic analysis engine containing a process classification dictionary and a construction process knowledge base, reading a BIM model file and screening a component to be operated; deducing and establishing a local operation space containing a grabbing coordinate system, an approaching coordinate system and an installation coordinate system based on the geometric topological features and volume attributes of the component; converting the static component attributes into dynamic robot atomic action sequences by using a semantic mapping technology, and automatically injecting dynamic constraint parameters according to the material physical attributes; and finally generating a standardized robot operation description file containing timing logic and spatial information. The application can bridge the semantic gap between BIM design data and robot control data, realize precast component installation process teaching-free autonomous operation, and significantly improve construction efficiency and safety.