37results about How to "Reduce the number of labels" patented technology

The invention relates to a digital circuit, and discloses a memory optimization type static time sequence analysis method and system. According to the method, on the premise that the subgraphs are notexpanded, in the traversing and scanning process, false paths are identified and discarded in real time, the influence of the false paths on downstream nodes is reduced, redundant calculation is reduced, meanwhile, multiple time sequence constraints on each node are dynamically and differentially processed in the traversing process, dichotomy coverage is correspondingly carried out, and the number of labels on each node is reduced. According to the application, occupation of system memory space can be greatly reduced, the system operation efficiency is improved, and the system performance isimproved.

The embodiment of the application discloses a method for realizing ring-network Steering protection. The method includes: establishing work channels and protection channels, which are corresponding to different nodes of a ring network; and according to the state of links of the nodes of the ring network, switching to a transmission network service of the work channels or the protection channels so as to realize the ring-network Steering protection. The embodiment of the application also provides a device for realizing the ring-network Steering protection and a method and device for realizing the ring-network Steering protection on the basis of an LSP service. The scheme of the embodiment of the invention reduces the number of the work channels and the protection channels so that the configuration workload and configuration complexity of the nodes of the ring network are reduced and hardware demands on network ring node devices are reduced.

The invention provides a multi-label anti-collision algorithm, which comprises the following steps: resetting, transmitting inquiry commands of labels by a reader writer; judging the inquiry commands received by a responder, inquiring, responding and judging the labels received and signed by the reader writer; carrying out data processing to the responder by the reader writer; setting a pd value to 1, and setting Q value to Q; transmitting inquiry variation commands by the reader writer and back transmitting the inquiry variation commands by the responder; inquiring, responding and judging the labels received and signed by the reader writer; and carrying out data processing to the responder by the reader writer. Therefore, the algorithm can circulate in a quick exclusive manner, has the higher practical recognition efficiency to the collision labels and can rapidly reduce the quantity of responders in the working range of the reader writer; moreover, on one hand, the recognition efficiency is improved through reducing the quantity of the responders at a high speed, on the other hand, the disturbance of the electromagnetic environment in the working range of the reader writer can be reduced so as to further improve the recognition quantity.

The invention discloses a robot data collection iterative training method and system based on an active learning technology and a storage medium. The method comprises the following steps: S1, taking labeled picture data in a preset proportion as training data, and taking picture data in a remaining proportion as test data; s2, establishing a supervised deep learning model, and training the deep learning model by using the training data to optimize the supervised deep learning model; s3, detecting the confidence coefficient of the detection result of the supervised deep learning model by using the test data; s4, formulating a strategy for collecting a rough service data set by the robot, and collecting the rough service data set; and S5, introducing an active learning course to guide active learning process mining to carry out manual annotation on low-confidence samples in the collected rough service data, and by means of the active learning technology, a semi-supervised robot data collection iteration system is realized, the effectiveness of data collection is greatly improved, and the cost of manual annotation is reduced.

The embodiment of the invention provides a text processing method and device, electronic equipment and a readable storage medium, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining a plurality of initial texts, and constructing a training data set based on the plurality of initial texts; training the initial neural network model based on the training data set until a value of a target function of the model meets a training ending condition; using the feature extraction module after training is finished as a text feature extraction module of a text similarity judgment model, training the text similarity judgment model again, obtaining a final text similarity judgment model, and the text similarity judgment model comprises a text feature extraction module and a second text similarity prediction module which are cascaded. The training data set constructed in the embodiment of the invention is weak supervision text similar corpora, and label data does not need to be manually labeled, so that the manpower and time cost can be effectively saved.

The invention provides a scheduling method to prevent conflicts in intelligent RFID read system. The method utilize the mechanism that reader controls multiple antennas, which is: all the antennas are controlled by the reader in order; the reader polls the antennas connected to the reader, and control the interactive between the single antenna and label; each time only one antenna can be activated to respond instructions from the reader; every two antenna effective covered regions are connected to reduce the overlap. The invention uses multiple antennas, at any time only one antenna takes effect, and then labels are divided into groups to recognize. The number of labels in the coverage covered by one antenna is reduced, thus the probability of conflicts is effectively reduced.

The invention discloses a corpus acquisition method and device. The method comprises the following steps: acquiring a voice sample; filtering truncated voice and invalid voice in the voice sample to obtain a qualified voice sample; performing multiple voice recognition on the qualified voice sample to obtain a plurality of corresponding voice texts; comparing the plurality of voice texts to obtain a similarity score; if the similarity score is greater than a preset similarity threshold value, taking the voice sample greater than the similarity threshold value as a voice sample to be labeled, and taking the voice text with the longest text content as a voice text to be labeled; and performing manual annotation on the to-be-annotated voice text to obtain an annotated sample. The invention solves the technical problems of long time period and high cost of obtaining voice samples for training in the prior art.

The invention relates to a digital circuit, and discloses a memory optimization type static time sequence analysis method and system. According to the method, on the premise that the subgraphs are notexpanded, in the traversing and scanning process, false paths are identified and discarded in real time, the influence of the false paths on downstream nodes is reduced, redundant calculation is reduced, meanwhile, multiple time sequence constraints on each node are dynamically and differentially processed in the traversing process, dichotomy coverage is correspondingly carried out, and the number of labels on each node is reduced. According to the application, occupation of system memory space can be greatly reduced, the system operation efficiency is improved, and the system performance isimproved.

The present invention provides a path establishment method, device, and network node, wherein the method includes: on the first node on the path from the entry node to the exit node, forwarding the incoming label and The / or outgoing label is used as the incoming label and / or outgoing label of the parasitic FEC of the egress node, and an MPLS-based label switching path LSP corresponding to the parasitic FEC is established. The present invention solves the problem that label resources cannot meet requirements in related technologies, achieves the effects of saving label resources and improving the efficiency of MPLS LSP creation.

The invention provides a VPN route label distribution method, ASBR, PE and a distribution system, the method comprising: receiving a VPN route update message; judging whether to assign a label for a new VPN route according to the VPN route update message; if necessary, assigning a new VPN route Label. Through the implementation of the present invention, after receiving the VPN route update message, it is first judged whether a label needs to be allocated for the new VPN route, and only when necessary, the label is allocated, which is different from the prior art after receiving the VPN route update message. Compared with assigning labels for VPN routes, the number of labels for VPN routes can be reduced, which relieves the pressure on ASBRs.

The present application discloses an active learning event extraction method based on memory loss prediction and delayed training. The scheme screens unlabeled samples by predicting their losses, and obtains high-quality unlabeled samples for labeling. Firstly, two memory modules are constructed, which are the learned memory module and the selected memory module. During the training process of the supervised learning model, the learned memory module stores the learned information to the learned memory module. In the process of sample selection , the selected memory module stores the information of the selected sample along with the selection of the sample, and predicts the loss of the new sample by combining the information of the two memory modules. After obtaining the sample loss, heuristically selects the valuable At the same time, a delayed training strategy is proposed to simulate the sample selection scene to supervise the sample loss model, and finally a high-quality event extraction model with low labeling cost is obtained.

This application proposes a sample tagging resource allocation method, device and application. The number of taggers is preset. For any transaction sample in the sample set, multiple taggers with the preset number of taggers are selected from the tagger list to generate taggers. Collect and assign to each annotator in the annotator set to annotate to obtain the annotating result; record the annotating completion time of all annotators on the transaction samples, and take the latest annotating completion time as the audit start time when the annotating result enters the audit node; Calculate the time interval between two adjacent audit start times and the historical audit interval to obtain the idleness of audit resources; adjust the number of markers according to the idleness of audit resources, and re-generate markers for the remaining transaction samples based on the adjusted number of markers member assembly. Through the present invention, the allocation of sample labeling resources can be automatically performed based on various risk control rules, which replaces the existing manual screening method, so the accuracy and efficiency are higher.

Embodiments of the present application provide a model training method, device, and server based on active learning, which can automatically retrieve training samples for each application scenario indicated by the started training task, without the need for developers to manually export data for model training. After the initial deep learning network model pre-matched with each application scenario labels the training samples, the active learning service is dispatched to actively screen the labeled training samples, without the need for developers to participate in labeling, thereby effectively reducing the number of sample labels. After the active screening samples are calibrated according to the user's calibration instructions, the training service associated with each application scenario can be flexibly scheduled to perform model training based on the calibrated active screening samples and released to the corresponding software application for each application scenario. Furthermore, training services of different application scenarios can be flexibly connected, and automatic labeling, training and business updates of the training process can be realized.

The invention provides a data processing method and device, and a storage medium. The method includes: performing feature recognition processing on the data to be processed according to preset dimensions to obtain the dimensional features of the data to be processed; wherein, the preset dimensions include: behavior dimension, field dimension and purpose dimension; thus, according to the identification Label the data to be processed based on the obtained dimensional features. The technical scheme provided by the invention can reduce the labeling cost and improve the processing capacity.

The embodiment of the present application discloses a method for implementing ring network Steering protection. The method includes: establishing a working channel and a protection channel corresponding to each node of the ring network; switching to the working channel or the protection channel to transmit network services according to the state of the links of the ring network nodes to realize the ring network Steering protection. The embodiment of the present application also provides a device for realizing ring network Steering protection, a method and a device for realizing ring network Steering protection based on LSP services. The solutions of the embodiments of the present application reduce the number of working channels and protection channels, thereby reducing the configuration workload and complexity of each node of the ring network, and reducing the hardware requirements for ring network node devices.

The embodiment of the present application provides a message transmission method and device, the method includes: obtaining the first message, and determining the forwarding entry corresponding to the first message; the forwarding table includes the main path forwarding entry and the standby path Publication item, the outgoing interface included in the standby path forwarding table item is the tunnel identifier of the TE tunnel; if the main path corresponding to the main path forwarding table item fails, and the second node is not the destination node corresponding to the destination address included in the first message, Then obtain the section routing label pointing to the destination node on the second node and the tunnel label corresponding to the TE tunnel from the standby path forwarding table item; add the section routing label and the tunnel label in the first message to obtain the first Second message: sending the second message to the second node through the TE tunnel corresponding to the tunnel identifier, so that the second node uses the segment routing label to send the second message to the destination node. Through the technical solution of the embodiment of the present application, the message can be correctly forwarded through the standby path.

The invention provides a weakly supervised in-depth station logo detection method, the steps of which are: preprocessing massive network video data files to obtain a large data set that only marks the station logo category and a small data set that only marks the station logo position ; Input the above-mentioned small data set into the station logo positioning network for training, and obtain a station logo positioning network capable of predicting the station logo area; input the above-mentioned large data set into the above-mentioned trained station logo positioning network, and obtain each piece in the large data set Some predicted station logo regions of the picture, and several predicted station logo regions of each picture are input into the station logo classification network for training, and the station logo classification network that can be classified as the station logo is obtained; the video to be detected is carried out with the same part as above Preprocessing, and inputting the image obtained after preprocessing into the trained station logo positioning network to obtain the predicted station logo area of ​​the picture; inputting the predicted station logo area of ​​the above picture into the trained station logo classification network to obtain the image's predicted station logo area The location and category of the station logo.

The invention provides a cloud storage data auditing method for preventing a secret key from being revealed. The method comprises the first step of system parameter generating, the second step of secret key updating and the third step of file uploading and auditing. According to the method, a physically safe safety device is introduced to help a user to periodically update the secret key, and therefore data auditing in other time periods is still safe even when an attacker attacks the user at one time period and obtains the user secret key at the time period.