14results about How to "Achieve migration" patented technology

The invention relates to the technical field of non-standard automation equipment control, in particular to an industrial robot machining program migration method based on a configuration description file, and solves the problem that a non-standard industrial robot cannot migrate a machining program. The method comprises the steps that S1, a configuration description file of a prototype industrial robot is constructed, and based on object-oriented abstract modeling, kinematics, peripheral and communication modeling and a machining program are included; s2, the target robot is assembled according to the prototype physical structure, and it is ensured that the same configuration, component assembly, the axis movement direction and the origin coordinate are consistent; s3, loading the configuration file to the target robot; s4, correcting assembly errors through visual sensing, obtaining accurate coordinates among the parts, and then generating joint coordinates through inverse solution; and S5, the target robot executes the adaptive machining program, and program migration is achieved. According to the method, prototype machine information is packaged through the configuration description file, after the target machine is loaded, errors are corrected only through visual calibration, rapid adaptive machining can be achieved through kinematic chain inverse solution, and rapid copying of robots of the same configuration is achieved.

The embodiment of the invention provides an automatic electronic fence setting method and device for protection device detection, and the method comprises the steps: capturing a real-time image of a target region through a camera, judging whether the real-time image of the target region is a black-and-white image or not through a protection facility detection model, and judging whether a moving target exists or not, inputting the real-time image of the target area into a YoV11 deep learning detection network under the condition that the real-time image of the target area is not the real-time image of the target area; detecting a protection facility in the real-time image of the target area through the YoV11 deep learning detection network, and outputting a current protection facility detection result; and taking the current protection facility detection result as a basis for setting an electronic fence algorithm, calculating an electronic fence coordinate parameter, and performing electronic fence setting according to the electronic fence coordinate parameter.

The application belongs to the technical field of fault diagnosis, and particularly relates to a twin data multi-modal fusion transfer diagnosis method for gear fault, which comprises the following steps: obtaining three mode components of Hilbert envelope spectrum, autocorrelation time domain waveform and autocorrelation envelope spectrum of gear measured and simulated signals, and respectively constructing three source domain subsets and three target domain subsets; performing JMMD mapping alignment on the source domain subsets and the target domain subsets of the same mode; training three independent DBSCAN classifiers by using the JMMD mapping features of the three source domain subsets respectively, and performing pseudo-label labeling on the JMMD mapping features of the target domain same mode components; updating the target domain sample labels by using a Sugeno fuzzy integral decision fusion method; repeating the JMMD mapping and the Sugeno fuzzy integral decision fusion until the maximum iteration number is reached, and obtaining a fault recognition result; and the application is supported by simulation data, and can realize accurate recognition of gear fault without the guidance of measured label data, and has a good application prospect in the field of gear fault diagnosis.

This specification provides a training method, text processing method, and system for a large language model, comprising: obtaining a sample set, the sample set including multiple sample texts; obtaining a first large language model to be trained, the first large language model employing a linear attention mechanism; reusing at least some model parameters of the first large language model from pre-trained model parameters of a second large language model; the second large language model employing a non-linear attention mechanism; training the first large language model using the sample set; and obtaining a trained target large language model. This method can accelerate the training process, reduce computational and time costs, and improve the performance of the target large language model. Furthermore, it can optimize the computational complexity of attention to linear levels and optimize storage space.

The present disclosure relates to the technical field of speech synthesis, in particular to a speech synthesis method and device, electronic equipment and storage medium, the method comprising: obtaining text features of a target language and an identifier of an original language; performing style prediction of the original language based on the text features of the target language and the identifier of the original language to obtain style features, and querying a codebook corresponding to the target language based on the style features to obtain vectorized style features, the codebook corresponding to a language one by one and the codebook being used for vectorizing style features; and performing coding and decoding processing based on the vectorized style features to determine target speech of the target language. Different codebooks are used to vectorize style features for different languages, which can use the codebook of the target language when performing cross-language style transfer to alleviate the accent phenomenon.

The embodiment of the application discloses a data cross-database migration method and device, relates to the technical field of big data, and comprises the following steps: when a cross-database migration instruction for migrating target data in a first database to a second database is received, a currently idle process group is determined, wherein each process group comprises a first process and a second process, the first process is used for exporting data in the first database, and the second process is used for importing data into the second database; a first process in the currently idle process group is called to export the target data in the first database and store the target data into a preset transfer module; and a second process in the currently idle process group is called to import the target data stored in the transfer module into the second database. The application has the beneficial effect of simply and efficiently migrating data across databases.

The present application relates to the technical field of silicone key conductivity test, and discloses a silicone key conductivity reinforcement learning test system and method, wherein the silicone key conductivity reinforcement learning test method comprises the following steps: constructing a silicone key conductivity test environment; establishing a test knowledge base, extracting general feature representation of key conductivity performance through feature mapping and domain adaptation algorithm; constructing a test parameter optimization model, modeling the test process as a Markov decision process, and learning the optimal test strategy based on a reward function; adjusting the test parameters in real time during the test process, performing reliability analysis after obtaining the test results, and feeding back the new test experience to the knowledge base to realize the accumulation and reuse of test knowledge; the present application realizes the self-adaptive optimization of test parameters and the cross-key knowledge transfer by fusing reinforcement learning, transfer learning and meta-learning technology.

The application relates to the technical field of cloud computing, and particularly discloses a method for cross-cluster migration of a virtual machine, which comprises the following steps: connecting a source cluster where a to-be-migrated virtual machine exists and a target cluster where the to-be-migrated virtual machine is to be migrated to a same backup storage device, generating backup data of the source cluster in the backup storage device, suspending the service of the to-be-migrated virtual machine when the to-be-migrated virtual machine reaches a migration condition, calling a backup recovery interface of the target cluster to recover the backup data to a second back-end storage device mounted by the target cluster, and configuring the to-be-migrated virtual machine in the target cluster, so that the cross-cluster migration of the virtual machine is realized in the form of backup data import and export, the storage structure of the source cluster and the storage structure of the target cluster do not need to be changed, an agent virtual machine for the cross-cluster migration of the virtual machine does not need to be created, and the efficient cross-cluster migration of the virtual machine can be realized. The application also discloses a device and equipment for cross-cluster migration of a virtual machine and a storage medium, which have the above beneficial effects.

The application provides a data migration method and device, electronic equipment and readable storage medium. The application analyzes parameter data to be parsed sent by a business system to obtain data migration information required for migrating data to be migrated in a source database associated with the business system to a target database; creates a target data external table for writing the data to be migrated and a pipeline file for implementing field type conversion of the data to be migrated in the target database with reference to the data migration information, and associates the target data external table with the pipeline file; uses a database query statement of the source database to obtain the data to be migrated from the source database; and converts the data to be migrated into target migration data by using the pipeline file according to a data storage format of the target database, and records the target migration data in the target data external table. In this way, the conversion and migration of the data to be migrated can be implemented without exporting the data to be migrated, and the data migration efficiency between different databases is improved.

The invention belongs to the technical field of nuclear power, and particularly relates to a nuclear power production data processing system and method based on a cloud native technology. According to the system disclosed by the invention, related principles of the ETL-NIFI framework are deployed on the basis of Kubernetes and Docker containerization virtualization technologies, and installation and deployment are simplified. And convenient NIFI installation, deployment and maintenance are provided, and powerful, safe, reliable and data migration functions are provided through the NIFI. Therefore, the manpower consumption of different data migration, backup and other requirements is reduced, and the accuracy and real-time performance of the data are ensured. Data migration can be realized through a user-defined processor according to different requirements, and high expansion is supported.

The invention discloses a rapid detection and pressure relief system for an arc fault in a ring main unit. The rapid detection and pressure relief system comprises a multi-channel sensing signal acquisition unit, a characteristic function calculation unit and a parallel short-time average and long-time average recursive calculation unit, the multi-channel fusion triggering judgment unit is used for calculating the ratio of the short-time average value to the long-time average value of each channel, dividing the ratio of each channel by a respective preset triggering threshold value to obtain the normalized triggering intensity of each channel, and carrying out comprehensive judgment according to preset multi-channel fusion logic; outputting an arc fault trigger signal when the fusion logic judgment is established; and a graded pressure relief control unit. According to the system, through a mode of combining multi-channel sensing fusion and graded pressure relief control, the problem that a traditional protection device is insufficient in response speed is solved, and the pressure relief response intensity can be adjusted in a self-adaptive mode according to the severity degree of an arc fault.

This application relates to a method, apparatus, computer device, storage medium, and computer program product for training an object detection model. The method includes: acquiring a first object detection model based on a first sample image in a first scene; the first sample image is a labeled sample image; acquiring a second initial sample image in a second scene; the second initial sample image is an unlabeled sample image; performing label prediction processing on the second initial sample image based on the first object detection model to obtain a second object sample image with predicted labels; obtaining an object detection teacher model corresponding to the second scene based on the second object sample image with predicted labels and the first object detection model; and performing knowledge distillation on the object detection student model corresponding to the second scene based on the object detection teacher model to obtain a second object detection model in the second scene. This method can improve the generalization ability of the object detection model.

This invention relates to the field of machine translation technology, and particularly to a vertical domain translation model training method and storage medium. The translation model employs an encoder-decoder architecture. The training method involves the following steps: first, inputting bilingual text into the translation model and training sentence vectors; then, fine-tuning the trained translation model using high-resource vertical domain data, which consists of high-resource sentences; next, retrieving high-resource sentences using low-resource sentences, and inputting both the low-resource sentences and the retrieved high-resource sentences into the encoder for cross-language multi-instance fusion to obtain the decoder's output state; finally, performing contextual nearest neighbor retrieval based on the decoder's output state to obtain the translation output probability distribution. In essence, this translation model training method utilizes high-resource vertical domain data to enhance low-resource vertical domain data, thereby achieving better translation results.