102 results about "Open source data" patented technology

A cooling system running method includes obtaining an open source data set of a cooling system including components each being configured with parameter(s). The open source data set includes a parameter value corresponding to each parameter, and the parameter(s) configured for one component include a controllable running parameter. The method further includes determining an initial model for predicting power consumption of the cooling system, selecting a target algorithm using an open toolbox, training the initial model using the open source data set and the target algorithm to obtain a target model, deploying the target model to a controller of the cooling system, and causing the controller to at least control operation of the one component according to a parameter value corresponding to the controllable running parameter that is defined in a power consumption optimization policy determined by the controller using the target model.

The invention relates to the technical field of power grids, in particular to a vertical domain distribution network planning method and system based on a large language model. The method comprises the following steps: collecting and arranging a distribution network structure and operation data, and constructing a'graph 'management system; extracting historical planning schemes to form a typical case library; fusing external open source data and source side data to establish a big database and an access interface; constructing a bilateral prediction model and a planning scheme generation model based on machine learning; carrying out secondary pre-training and practical packaging on the open source large language model; planning parameters are obtained in a guiding mode in a question-answer mode, and a scheme is generated. According to the method, the problems of high labor cost, long planning time, large error, unstable planning quality, poor interactivity, high learning threshold and the like of traditional distribution network planning are solved, the planning efficiency and precision are improved through an intelligent technology, the professional threshold is reduced, a brand new solution is provided for coping with the new situation of high permeability of new energy and electric energy replacement acceleration, and the method is worthy of popularization and application. And the practical value is remarkable.

The invention relates to a security alarm noise reduction method based on rules and a large model, which belongs to the field of network security and comprises the following steps of: collecting security alarm data and security logs and preprocessing the security alarm data and the security logs; constructing a dynamic comprehensive rule knowledge base comprising a screening rule base and a plug-in knowledge base, and periodically updating the dynamic comprehensive rule knowledge base; selecting an open-source large model of the security field, using the open-source data set of the security field and historical security alarm data, and utilizing a distillation technology to finely adjust a noise reduction small model; a knowledge base driven periodic fine tuning and dynamic knowledge injection double-path coordination mechanism is adopted to improve the capability of the noise reduction small model; and finally, realizing efficient and accurate alarm noise reduction based on the screening rule base and the noise reduction small model. According to the invention, the efficiency and accuracy of security alarm noise reduction are effectively improved, and an efficient, accurate and intelligent alarm noise reduction method is provided for the field of network security.

An instance segmentation model training method includes training the instance segmentation model firstly based on a first data set stored in a first database, and training the firstly-trained instance segmentation model secondly based on a second dataset stored in a second database, where the second dataset includes a large-scale open-source dataset and a segmentation target object-absent image acquired by capturing a working environment within an industrial site.

The invention discloses a boundary representation generation method based on graph diffusion. The method comprises the following steps: step 1, constructing an industrial part data set and preprocessing the industrial part data set; step 2, constructing and training a Brep-GD model based on the preprocessed self-built data set and the two open source data sets; the Brep-GD model comprises a variational auto-encoder, a graph diffusion model, a continuous topology decoupling model and a post-processing module; 3, reasoning and evaluating the trained Brep-GD model by using a test set of a data set so as to verify multiple types of indexes of distribution measurement, CAD measurement and efficiency measurement of the model; and step 4, applying the evaluated Brep-GD model to generate a simplified B-rep model, the method can significantly reduce redundancy calculation, improve generation efficiency, and ensure that the generated B-rep model has higher geometric accuracy and topology effectiveness.

The invention relates to a traffic facility attribute mining method and system based on multi-modal network open source data. The method comprises the following steps: extracting traffic facility information from a webpage to construct a knowledge graph; extracting positions and appearances of traffic facilities from the images, and analyzing streetscape images to obtain attributes such as road traffic; collecting map images, tiles and vector data to construct a road network topology and attribute database; associating webpage texts, pictures, streetscape images and network map multi-source data according to the spatial position of the traffic facility; through comprehensive and deep attribute mining, different modal data are integrated to improve the accuracy and reliability of attribute mining, the real-time and dynamic updating capability, the convenient visualization and interaction operation, the data sharing and integration convenience, and the traffic facility management efficiency and collaboration are improved. According to the method, comprehensive, accurate and real-time traffic facility attribute data support can be provided for urban traffic planning, traffic management and intelligent traffic system construction, so that the efficiency and the intelligent level of traffic facility management are remarkably improved.

The invention provides a domain reasoning model training method and device, a domain reasoning model using method and device, equipment and a medium, and relates to the artificial intelligence technology. The domain inference model training method comprises the steps that an original domain model is finely adjusted based on a preset inference data set, and an intermediate model with the inference capability is obtained; the preset reasoning data set is generated by reasoning general open source data through a reasoning model and comprises reasoning process data and reasoning conclusion data which conform to the output form of the target domain; based on the current inference data set, performing iterative training on the basis of the original domain model to obtain a domain inference model; wherein in each iteration process, the current reasoning data set is updated based on the field problem data set and a current intermediate model obtained based on the current reasoning data set in the current iteration process; the domain problem data set comprises task feature information of the target domain. By means of the method, the domain model reasoning effect can be effectively improved.

The invention discloses a mechanical drilling rate prediction method of a bidirectional LSTM model based on attention enhancement, and the method comprises the steps: selecting an open source data set from a Norway Volve oil field, carrying out the preprocessing, and enabling all data to be logging data based on a depth domain; establishing a mechanical drilling speed prediction model by using an attention-based bidirectional LSTM network, and training the model to obtain an optimized mechanical drilling speed prediction model; according to the invention, by introducing an attention mechanism, the model can automatically identify and preferentially process data which has great influence on a prediction result, so that the prediction accuracy and efficiency are remarkably improved. Compared with a traditional one-way LSTM, the two-way LSTM can consider past and future data information at the same time, and a more effective solution is provided for processing time series data with complex nonlinear characteristics.

The invention discloses a method for data mutual migration between relational databases, which is characterized by comprising the following steps that: an SQLite database is opened, a user sets an original password by himself / herself, and the SQLite database is an open source SQLite database; a structural body is self-defined in codes of the SQLite database and used for configuring parameters and achieving encryption of the SQLite database, and the structural body comprises a database original password, an original password byte length, a tree structure, an encryption password generated after the database original password is adopted for encoding and a decryption password generated after the database original password is adopted for encoding; generating an encrypted password and a decrypted password by adopting the original password; data writing of the SQLite data the encrypted password and to-be-written data are subjected to xor according to word bits, and the result serves as encrypted data to be written into the SQLite database; and reading the data of the SQLite database, wherein the decryption password and the data to be read are subjected to xor according to word bits, and a result is used as a read result of the decrypted data of the SQLite database.

The invention discloses a small language model fine tuning method based on an LLaMA Factory tool, and the method comprises the following steps: 1) carrying out the thinking chain distillation of an open source data set through a large model, and generating a training data set for the fine tuning of a small language model; 2) based on an LLaMA Factory tool, building a model fine tuning environment; 3) fine-tuning real-time monitoring and adjustment, in the fine-tuning process, monitoring loss of the verification set and the test set, and if the performance of the model is reduced or an over-fitting phenomenon occurs, adjusting training parameters and retraining; and 4) completing training and exporting the model: after the fine tuning process is completed, if the model reaches preset performance on the verification set, completing training, exporting the trained small language model, and deploying the small language model to practical application for reasoning. According to the method, targeted training is performed on the pre-trained small model through a fine tuning technology, specific task requirements can be efficiently met, and rapid migration of a new task can be realized only by using a small-scale task data set to adjust part of parameters of the model.

The application discloses a kind of open source data semi-automatic data labeling method and system based on multi-modal large model, belong to data labeling technical field.The method includes: enhancement processing;Determine image-text-category data pair training set and image-text-category data pair test set;Adjust SimCSE text encoder parameter;Adjust CLIP-SimCSE model parameter;Adjust CLIP-ViT model parameter;Test in multi-modal large model, to adjust multi-modal large model parameter;Classified labeling is carried out to determine the different image-text data pairs in the labeling result of CLIP-SimCSE model and CLIP-ViT model;Different image-text data pairs are corrected.The application can guarantee the accuracy of professional field data category labeling result, improve data labeling efficiency, significantly reduce the cost of artificial data verification.

The present invention discloses a data acquisition and processing method and system for cross-modal retrieval, comprising: performing distributed parallel acquisition of multi-modal data on a target open source data network; cleaning text modal data for special characters and invisible characters, and then storing the cleaned text modal data and image modal data in different message queues; utilizing a feature extraction model to extract features from each text and each image in the message queue to obtain text features and image features, matching and screening the text features and image features to obtain image-text combinations based on similarity, and storing the image features and text features of the image-text combinations as indexes in a database; during retrieval, screening and returning matching image-text combinations as retrieval results for the uploaded data based on the similarity between the uploaded data and the image-text combinations in the database. This method and system can realize high-quality cross-modal retrieval of image-text data.

The invention provides an ionosphere parameter and short wave frequency band prediction method and electronic equipment, and the method comprises the steps: obtaining the ionosphere parameter data in a corresponding IRI model above the midpoint of a great circle through obtaining the geographic coordinates of two places of a communication link and the midpoint of the great circle, obtaining the geomagnetic solar activity data of an open-source data website, and obtaining the geomagnetic solar activity data of the open-source data website; the method comprises the following steps: carrying out ionosphere oblique detection by using ionosphere detectors at two communication places, obtaining an ionosphere oblique detection ionogram, obtaining frequency band data available for short-wave communication from the oblique detection ionogram, carrying out data preprocessing, selecting different tasks, setting corresponding Transform-LSTM model hyper-parameters, carrying out model training, and storing optimal model parameters; and performing an ionosphere parameter prediction task or a short-wave communication available frequency band prediction task by using the corresponding model. The ionosphere detection data, the environment monitoring data and the like are fused in the deep learning field and the short-wave communication field, and the method has important significance and application prospects.

The application relates to a corner case-based deployment model construction method, which comprises the following steps: obtaining a first data set according to automatic driving images and preset open source data, obtaining a second data set according to the automatic driving images, the preset open source data and part of image in a corner case scene image, obtaining a third data set according to the remaining images in the corner case scene image, and merging the second data set and the third data set to obtain a fourth data set; training a preset multi-task detection model based on the fourth data set to obtain an automatic driving perception detection pre-training model, training the pre-training model based on the first data set and a preset period of cosine annealing learning strategy to obtain a plurality of perception detection models; and performing transfer training on the plurality of perception detection models based on the third data set, performing average weight and correction BN on the plurality of perception detection models after the transfer training to obtain a final deployment model.

The present application relates to the technical field of image retrieval, and particularly to an image retrieval method based on a combination of a deep convolutional neural network and a local sensitive hashing algorithm, which has the following steps: step S1: training set and validation set in an open source data set of image retrieval; step S2: input of the model during training; step S3: test retrieval ranking; step S4: the loss function of image retrieval adopts a contrast loss function, and in addition to mAP, the model evaluation index also newly adds mP@k; the present method is based on a combination of a deep convolutional neural network and a local sensitive hashing algorithm, the algorithm extracts image features of a gallery library and a query library in a deep convolutional manner, carries out LSH hash coding, greatly improves the retrieval performance, and uses contrast learning in a twin network, thereby greatly improving the retrieval precision.

The invention discloses a code change readability evaluation method based on fine-grained difference perception and enhanced context, and belongs to the crossing field of software engineering and deep learning. The invention constructs a core method for context attention enhancement based on multi-granularity difference representation and difference guidance. Through multi-granularity feature extraction fusing a token level (semantic grammar core change) and a character level (structure details and fine adjustment information), and in combination with a triple context enhancement mechanism of global attention, local attention and similarity attention, accurate judgment of readability change trends (increase / decrease) before and after code change is realized. Experiments on a GitHub open source data set show that the method is remarkably superior to an existing baseline model, readability quality control in a dynamic code evolution scene (such as GitHub warehouse iteration) is effectively supported, and technical support is provided for software maintenance and team cooperation.

The application relates to a case data statistical analysis method for the judicial field, and belongs to the technical field of computer big data. The application organizes judicial case data by using a wide table model in the field of data warehouses, finally forms a case data wide table model and a personnel data wide table model, adopts an open source data calculation and storage framework ClickHouse calculation engine, adopts a dynamic template method to define a statistical analysis model, when statistical analysis is performed, an analysis model template is combined with analysis parameters to dynamically and timely generate a corresponding analysis script, the analysis script is submitted to the calculation engine, calculation and analysis are completed by the calculation engine, and the calculation result is normalized and then transmitted back to a calling party. The overall design idea of the application greatly improves the flexibility of judicial case data in the analysis process.

The invention discloses an urban flood risk assessment method and device based on open source data, and belongs to the technical field of urban flood. The method comprises the following steps: obtaining open source road data, simplifying the open source road data into a drainage pipe network initial topological graph, determining a water outlet, and further carrying out iterative optimization to obtain a drainage pipe network model; and predicting the drainage effect of an actual urban drainage system according to the drainage pipe network model, and evaluating the urban flood risk. The method completely depends on open source data, and thoroughly gets rid of dependence on special drainage pipe network data which is difficult to obtain; according to the method, the modeling efficiency is improved, and through reasonable physical rules and optimization algorithms, the generated model shows good consistency with a real model in a system level (such as total overflow and water outlet flow) and a submerging range, and is sufficient to support planning-level risk assessment requirements.

The application provides a kind of urban drainage system river-entering water quantity calculation method and system based on GIS platform and open source data, which comprises obtaining open source data of drainage area to construct basic database and store it in GIS platform;Based on the spatial resolution of open source GIS data in the basic database, the drainage area is divided into multiple independent calculation units and the parameter value of each independent calculation unit is obtained;Construct the runoff model of each independent calculation unit and combine the parameter value of each independent calculation unit to obtain the runoff and sewage flow of each independent calculation unit;The concentration flow of the drainage area is obtained by using the transport time algorithm;Based on the concentration flow of the drainage area and combined with the treatment capacity of the sewage plant in the drainage area, the river-entering water quantity of the drainage area is obtained.The method of the application is easy to obtain, widely applicable, and has high reliability and high time accuracy of the calculation result, which is helpful for the control and management of urban runoff pollution.

The invention discloses a track fusion method based on open source data and AIS data, and relates to the technical field of Internet open source data processing and situation fusion. An acquired AIS original message is analyzed to generate a track of a ship target, open source text data acquired through the Internet also includes track information of the ship target, entity extraction is performed by adopting a deep neural network algorithm, the open source text data is processed and recognized, and the track information of the ship target is acquired. Information such as an IMO number, an MMSI number and a ship name of a ship in the data and information such as occurrence time and occurrence place of a related event are identified, ship information and event information are integrated to form a trace point of the ship, and the mark, time and space information of the ship correspond to records in an AIS original message, so that the information of the ship is obtained. And inserting the ship trace point obtained from the open source text data into the track of the ship in the AIS, and further smoothing the track through a Kalman filtering algorithm to form track information after ship target fusion, thereby realizing track fusion based on the open source data and the AIS data.

To provide an information processing system, method and program for comprehensively collecting researcher information.SOLUTION: A method includes a reception step for receiving a name of a first researcher, and an institution name of an institution to which the first researcher belongs. The name and the institution name are used as identification information of the first researcher for identifying the first researcher. The method further includes: an acquisition step for acquiring first research information corresponding to the first researcher identification information, on the basis of open source data in an external part and is related to the research information; an extraction step for extracting second research information which is similar to the first research information, and in which a researcher name matches the name of the first researcher, from the open source data, on the basis of the text information of the first research information; and an integration step for integrating the second researcher identification information corresponding to the second research information to the first researcher identification information, for creating or updating the researcher information database.SELECTED DRAWING: Figure 5

The embodiment of the invention provides a target detection model training method, a target detection method, devices and equipment, and relates to the technical field of deep learning. The training method comprises the following steps: obtaining a first target detection model obtained by pre-training a target detection model of an initial structure by using a first open source data set, and obtaining a third target detection model obtained by pre-training a second target detection model by using a second open source data set in combination with a large language model; the second target detection model is obtained by pre-training the target detection model of the initial structure by using a third open source data set; the third open source data set is a proper subset of the first open source data set; replacing the image feature extractor in the third target detection model with the image feature extractor in the first target detection model to obtain an intermediate target detection model; and training the intermediate target detection model based on the labeled sample data of the specified scene to obtain a trained model. And the model performance can also be improved when the labeled sample data is less.

The application provides a kind of unmanned vehicle global path planning method considering vehicle rollover stability, belong to unmanned vehicle path planning technical field, method includes: according to open source data obtains original map, establishes fitness map by extracting topographic information of original map by fusing various environmental information;According to the fitness map, obtain the subsampling factor, get the sub-fitness map through the subsampling factor;Global path planning is carried out to the sub-fitness map using improved A* algorithm, and the global path is mapped to the original map;Introduce vehicle basic constraint by using the method of quadratic optimization, complete the optimization of global path.Compared with the prior art, the vehicle rollover stability considered in the application can plan a more flat and safer road compared to the traditional three-dimensional A* algorithm.

The present application relates to a kind of Laos character transphoneme method based on transfer learning, to utilize the model pre-trained on other language, and by fine-tuning to limited Laos data, to realize efficient phoneme conversion.This method can not only alleviate the problem of data scarcity, but also accelerate the training process of model, improve the generalization ability of model on new task.Through transfer learning, a more robust Laos character transphoneme model can be built, laying the foundation for subsequent speech synthesis.Because Thai language, which has high language similarity with Laos, is also a low-resource language, it cannot be used as a pre-training language, so Chinese is chosen as the pre-training language.Based on the Transformer architecture, a Chinese G2P model is first trained using the Chinese open-source dataset, the decoding end parameters of the model are initialized to the student model, and the model is fine-tuned and trained using a 10,000-size Laos dataset to improve the accuracy of the Laos character transphoneme model.

This invention discloses an environmentally friendly insulating gas molecule and its high-throughput design method and related apparatus, belonging to the field of novel environmentally friendly insulating gas molecule design technology. By establishing a dedicated database containing microscopic discharge parameters and macroscopic physical property parameters of insulating gas molecules, a gas molecule performance evaluation and prediction model is constructed using this database. A molecular structure gene scoring system is established, dominant genes are extracted and recombinated, and chemical space for high-throughput screening is increased by adding groups to the backbone. The performance evaluation and prediction model is used to perform high-throughput screening on a massive number of molecules in an open-source database and molecules generated by adding groups to the backbone. Based on the target property range, a novel environmentally friendly insulating gas molecule is finally obtained. This invention can quickly and accurately evaluate and predict the performance of gases, improve high-throughput screening capabilities, save significant manpower, material resources, and time, and ultimately obtain a novel environmentally friendly insulating gas molecule.

The invention belongs to the technical field of database performance evaluation, and discloses a query performance evaluation method and system for a multi-model database. The method comprises the following steps of: constructing a test data set coexisting with four models, namely a relationship, a document, a graph and a vector, by an open-source data source and a native model, and performing global entity ID semantic association; four types of cross-model query workloads are defined, point query, aggregation, vector ANN and graph matching scenes are covered, and the method is used for systematically evaluating the comprehensive capacity of the multi-model database in the aspects of connection sequence selection, model data conversion cost estimation, aggregation operation optimization and the like. During testing, under the conditions of fixed hardware, a memory buffer area and concurrent parameters, the workload is automatically executed, the median execution time is collected, then the performance score of each load is calculated based on a logarithm normalization speed-up ratio model, and fair quantitative comparison among different multi-model databases is achieved. The method is reproduced in a common multi-model database system to verify the feasibility of the evaluation benchmark.

The embodiment of the invention provides a model training method and device and a related product, and relates to the technical field of machine learning. According to the model training method, the data highly related to the first sample data of the target task can be selected from the open source data set as the second sample data, so that the data redundancy is reduced, the data quality is improved, and the training effect of the model is enhanced; and the most suitable open source model is selected from the open source model library as a sub-model for constructing the hybrid expert model, so that the accuracy of the hybrid expert model is improved. And finally, inputting the first sample data and the second sample data into the hybrid expert model, so that the sub-model which is most matched with the first sample data and the second sample data can output the most accurate execution result, thereby improving the accuracy of the execution result output by the hybrid expert model, and improving the generalization ability of the hybrid expert model.

This invention relates to the field of 3D target detection technology, and in particular to a multi-sensor-based 3D target detection method. The specific steps are as follows: Step S1, data construction and preprocessing; Step S2, camera image feature extraction; Step S3, LiDAR-Image Fusion Module: Constructing a LiDAR-Image Fusion module to map the hierarchical semantic features generated from RGB images to LiDAR point cloud features, point-wise image features, and fusing them with LiDAR features to obtain the final enhanced features; Step S4, LiDAR point cloud feature extraction. This invention designs a 3D target detection method based on a LiDAR-Image Fusion module, using the LiDAR-Image Fusion module to refine and map LiDAR feature points point by point onto the image, ultimately obtaining a high-precision 3D detection network that ranks among the top on open-source datasets.

The invention belongs to the technical field of data processing, and particularly relates to an open source data information mining method and system. The open source data information mining method comprises the steps that multi-modal data of an open source scene are obtained, and the multi-modal data comprise texts, images and time sequence data; performing association confidence coefficient cleaning processing on the data of the open source scene to obtain multi-modal cleaning data; extracting each modal feature from the multi-modal cleaning data, and combining and embedding spatial mapping to obtain a multi-modal semantic vector; according to an attention weight mechanism, performing dynamic feature fusion on the multi-modal semantic vector to obtain a multi-modal fusion feature; and filtering conflict modal information in the multi-modal fusion features, and storing complementary modal information to obtain open source data information mining data.

This invention discloses a multidimensional intelligent evaluation method for architectural space visual comfort based on model knowledge transfer. This method forms a target dataset by combining existing open-source image evaluation datasets with newly added data based on annotation rules. Evaluation labels are standardized and normalized using a fitted normal distribution. A multi-gated hybrid expert model is employed, with pre-training and distillation learning performed via a teacher-student model. Through the transfer of model knowledge and the updating of normal distribution parameters, a comprehensive multidimensional evaluation of image color, lighting, and architectural layout is achieved. The advantages of this method include improved evaluation accuracy and interpretability, the integration of subjective and objective evaluations, and the efficient application of knowledge transfer, providing an intelligent and comprehensive evaluation tool for the design of architectural space visual comfort.