Abstract: A method for business processing based on an agent includes: recognizing an operation intent of a user for a host target of the agent and generating a target text indicative of the operation intent of the user for the host target based on a recognition result; generating a cue word based on the target text, inputting the cue word into an LLM model carried on the agent, performing, by the LLM model, logical reasoning based on the cue word, splitting the operation intent into at least one sub-task, and performing a task orchestration for the at least one sub-task to generate a target text sequence including a task description text corresponding to each sub-task; obtaining the target text sequence output by the LLM model, and performing each sub-task contained in the target text sequence, to perform an operation corresponding to the operation intent on the host target.

Abstract: A train data transmission system and method, an apparatus, and a medium are disclosed. The method includes: acquiring, by a train data acquisition device, train-related data; receiving the train-related data from an onboard central control device and forwarding the train-related data to a database server by a network address translation (NAT) server; and in response to a web server receiving access information of an access terminal accessing a web service port exposed by a public network, acquiring, by the web server, target data from the train-related data stored in the database server according to the access information; and receiving, by the access terminal, the target data from the web server.

Abstract: Disclosed is a multi-nested twisting loom, a method, a fabric weave, and an upper. The multi-nested twisting loom includes several nested twisting heald sets. The nested twisting heald sets are sequentially arranged along a weft direction on the loom. Each nested twisting heald set includes a second-level twisting heald at a front end of a warp advancing direction and a first-level twisting heald at a rear end. The second-level twisting heald is composed of a single twisting heald. The first-level twisting heald is composed of m twisting healds, where m is greater than 1. The leno weave in the present disclosure is composed of different levels of twisted areas, which increases the variety of leno weaves.

Abstract: This application provides a voice control method and apparatus, a wearable device, and a terminal. The method includes: obtaining voice information of a user; obtaining identity information of the user based on a first voiceprint recognition result of a first voice component of the voice information, a second voiceprint recognition result of a second voice component of the voice information, and a third voiceprint recognition result of a third voice component of the voice information, where the first voice component is captured by an in-ear voice sensor of a wearable device, the second voice component is captured by an out-of-ear voice sensor of the wearable device, and the third voice component is captured by a bone vibration sensor of the wearable device; and executing an operation instruction when the identity information of the user matches the preset identity information.

Abstract: A train data transmission system and method, an apparatus, and a medium are disclosed. The method includes: acquiring, by a train data acquisition device, train-related data; receiving the train-related data from an onboard central control device and forwarding the train-related data to a database server by a network address translation (NAT) server; and in response to a web server receiving access information of an access terminal accessing a web service port exposed by a public network, acquiring, by the web server, target data from the train-related data stored in the database server according to the access information; and receiving, by the access terminal, the target data from the web server.

Abstract: A speech separation method is provided, and relates to the field of speech. The method includes: obtaining, in a speaking process of a user, audio information including a user speech and video information including a user face; coding the audio information to obtain a mixed acoustic feature; extracting a visual semantic feature of the user from the video information; inputting the mixed acoustic feature and the visual semantic feature into a preset visual speech separation network to obtain an acoustic feature of the user; and decoding the acoustic feature of the user to obtain a speech signal of the user. An electronic device, a chip, and a computer-readable storage medium are provided.

Abstract: Disclosed are a target speaker separation system, an electronic device and a storage medium. The system includes: first, performing, jointly unified modeling on a plurality of cues based a masked pre-training strategy, to boost the inference capability of a model for missing cues and enhance the representation accuracy of disturbed cues; and second, constructing a hierarchical cue modulation module. A spatial cue is introduced into a primary cue modulation module for directional enhancement of a speech of a speaker; in an intermediate cue modulation module, the speech of the speaker is enhanced on the basis of temporal coherence of a dynamic cue and an auditory signal component; a steady-state cue is introduced into an advanced cue modulation module for selective filtering; and finally, the supervised learning capability of simulation data and the unsupervised learning effect of real mixed data are sufficiently utilized.

Abstract: This application provides an upgrade method including: An electronic device acquires a first verification voice entered by a user; processes the first verification voice by using a first model stored in the electronic device, to obtain a first voiceprint feature; verifies an identity of the user based on the first voiceprint feature and a first user feature template stored in the electronic device; after the identity of the user is verified, if the electronic device has received a second model, processes the first verification voice by using the second model, to obtain a second voiceprint feature; and updates the first user feature template based on the second voiceprint feature, and updates the first model by using the second model.

Abstract: This application provides a voice control method and apparatus, a wearable device, and a terminal. The method includes: obtaining voice information of a user; obtaining identity information of the user based on a first voiceprint recognition result of a first voice component of the voice information, a second voiceprint recognition result of a second voice component of the voice information, and a third voiceprint recognition result of a third voice component of the voice information, where the first voice component is captured by an in-ear voice sensor of a wearable device, the second voice component is captured by an out-of-ear voice sensor of the wearable device, and the third voice component is captured by a bone vibration sensor of the wearable device; and executing an operation instruction when the identity information of the user matches the preset identity information.

Abstract: Disclosed are a target speaker separation system, an electronic device and a storage medium. The system includes: first, performing, jointly unified modeling on a plurality of cues based a masked pre-training strategy, to boost the inference capability of a model for missing cues and enhance the representation accuracy of disturbed cues; and second, constructing a hierarchical cue modulation module. A spatial cue is introduced into a primary cue modulation module for directional enhancement of a speech of a speaker; in an intermediate cue modulation module, the speech of the speaker is enhanced on the basis of temporal coherence of a dynamic cue and an auditory signal component; a steady-state cue is introduced into an advanced cue modulation module for selective filtering; and finally, the supervised learning capability of simulation data and the unsupervised learning effect of real mixed data are sufficiently utilized.

Abstract: A speech separation method is provided, and relates to the field of speech. The method includes: obtaining, in a speaking process of a user, audio information including a user speech and video information including a user face; coding the audio information to obtain a mixed acoustic feature; extracting a visual semantic feature of the user from the video information; inputting the mixed acoustic feature and the visual semantic feature into a preset visual speech separation network to obtain an acoustic feature of the user; and decoding the acoustic feature of the user to obtain a speech signal of the user. An electronic device, a chip, and a computer-readable storage medium are provided.

Abstract: A global and local binary pattern image crack segmentation method based on robot vision comprises the following steps: enhancing a contrast of an acquired original image to obtain an enhanced map; using an improved local binary pattern detection algorithm to process the enhanced map and construct a saliency map; using the enhanced map and the saliency map to segment cracks and obtaining a global and local binary pattern automatic crack segmentation method; and evaluating performance of the obtained global and local binary pattern automatic crack segmentation method. The present application uses logarithmic transformation to enhance the contrast of a crack image, so that information of dark parts of the cracks is richer. Texture features of a rotation invariant local binary pattern are improved. Global information of four directions is integrated, and the law of universal gravitation and gray and roundness features are introduced to correct crack segmentation results, thereby improving segmentation accuracy.

Abstract: The method of the present disclosure includes: obtaining an image to be processed and a question text corresponding to the image; using an optimized dialogue model to encode the image into an image vector and encode the question text into a question vector; generating a state vector based on the image vector and the question vector; decoding the state vector to obtain and output an answer text. A discriminator needs to be introduced in an optimization process of the optimized dialogue model. The dialogue model and the discriminator are alternately optimized until a value of a hybrid loss function of the dialogue model and a value of a loss function of the discriminator do not decrease or fall below a preset value, thereby accomplishing the optimization process.

Abstract: Systems and methods are provided to perform operations on a packet of network traffic based on a routing rule of the packet. A stateful network routing service may include multiple network gateways for receiving packets of network traffic. The stateful network routing service may receive a packet and obtain or generate a routing rule based on the source and destination of the packet based on receiving the packet via a client-facing network gateway. The stateful network routing service may transmit the packet to a network appliance based on the routing rule. The stateful network routing service may further receive a packet via an appliance-facing network gateway. Based on receiving the packet via the appliance-facing network gateway, the stateful network routing service may decapsulate the packet and transmit the packet to a network destination. The stateful network routing service may further validate the packet.

Abstract: The method of the present disclosure includes: obtaining an image to be processed and a question text corresponding to the image; using an optimized dialogue model to encode the image into an image vector and encode the question text into a question vector; generating a state vector based on the image vector and the question vector; decoding the state vector to obtain and output an answer text. A discriminator needs to be introduced in an optimization process of the optimized dialogue model. The dialogue model and the discriminator are alternately optimized until a value of a hybrid loss function of the dialogue model and a value of a loss function of the discriminator do not decrease or fall below a preset value, thereby accomplishing the optimization process.

Abstract: A speech extraction method based on the supervised learning auditory attention includes: converting an original overlapping speech signal into a two-dimensional time-frequency signal representation by a short-time Fourier transform to obtain a first overlapping speech signal; performing a first sparsification on the first overlapping speech signal, mapping intensity information of a time-frequency unit of the first overlapping speech signal to preset D intensity levels, and performing a second sparsification on the first overlapping speech signal based on information of the preset D intensity levels to obtain a second overlapping speech signal; converting the second overlapping speech signal into a pulse signal by a time coding method; extracting a target pulse from the pulse signal by a trained target pulse extraction network; converting the target pulse into a time-frequency representation of the target speech to obtain the target speech by an inverse short-time Fourier transform.

Abstract: A speech extraction method based on the supervised learning auditory attention includes: converting an original overlapping speech signal into a two-dimensional time-frequency signal representation by a short-time Fourier transform to obtain a first overlapping speech signal; performing a first sparsification on the first overlapping speech signal, mapping intensity information of a time-frequency unit of the first overlapping speech signal to preset D intensity levels, and performing a second sparsification on the first overlapping speech signal based on information of the preset D intensity levels to obtain a second overlapping speech signal; converting the second overlapping speech signal into a pulse signal by a time coding method; extracting a target pulse from the pulse signal by a trained target pulse extraction network; converting the target pulse into a time-frequency representation of the target speech to obtain the target speech by an inverse short-time Fourier transform.

Abstract: An auditory selection method based on a memory and attention model, including: step S1, encoding an original speech signal into a time-frequency matrix; step S2, encoding and transforming the time-frequency matrix to convert the matrix into a speech vector; step S3, using a long-term memory unit to store a speaker and a speech vector corresponding to the speaker; step S4, obtaining a speech vector corresponding to a target speaker, and separating a target speech from the original speech signal through an attention selection model. A storage device includes a plurality of programs stored in the storage device. The plurality of programs are configured to be loaded by a processor and execute the auditory selection method based on the memory and attention model. A processing unit includes the processor and the storage device.

Abstract: An auditory selection method based on a memory and attention model, including: step S1, encoding an original speech signal into a time-frequency matrix; step S2, encoding and transforming the time-frequency matrix to convert the matrix into a speech vector; step S3, using a long-term memory unit to store a speaker and a speech vector corresponding to the speaker; step S4, obtaining a speech vector corresponding to a target speaker, and separating a target speech from the original speech signal through an attention selection model. A storage device includes a plurality of programs stored in the storage device. The plurality of programs are configured to be loaded by a processor and execute the auditory selection method based on the memory and attention model. A processing unit includes the processor and the storage device.