Abstract: A computer-implemented method is provided. The computer-implemented method includes obtaining one or more attributes of one or more entities; performing a vectorized representation on a respective attribute of the one or more attributes, the vectorized representation being encoded as a vector of d dimensions; and performing computation on vectorized representation of the one or more attributes based on attention mechanism to construct a graph neural network model. Vectors of m number of dimensions are used to represent an individual entity in the graph neural network model, (m?1) dimensions out of m dimensions represent (m?1) number of attributes, and one out of the m dimensions represents a flag bit. Constructing the graph neural network model includes performing self-attention on the individual entity, during which the flag bit is converted into a fused flag bit by fusing the individual entity's own attributes.

Abstract: A kitchen waste wastewater treatment equipment includes a reaction kettle, a discharging mechanism and a pressurizing mechanism. In the process of high-temperature and high-pressure thermal hydrolysis of wastewater in the present disclosure, the pressurizing disk in the pressurizing mechanism continuously moves downwards in a vacuum state in the reaction kettle, reducing the volume in the reaction kettle and thus increasing the pressure. The secondary pressurization of wastewater is achieved while using heating for high-temperature and high-pressure, and the high-temperature and high-pressure environment greatly improves the reaction rate of thermal hydrolysis. The thermal hydrolysis reaction also generates gas for further pressurization, which greatly improves the treatment efficiency of wastewater. Moreover, the pressurizing mechanism and the exhaust annular groove can separate the gas generated after thermal hydrolysis.

Abstract: The present disclosure provides a training method and apparatus for an image-text matching model, a device and a storage medium. The method includes: acquiring a positive sample and a negative sample; where the positive sample includes text and an image, the text in the positive sample is used to describe content of the image in the positive sample; the negative sample includes text and an image, the text in the negative sample describes content that is inconsistent with content of the image in the negative sample; training the image-text matching model by using the acquired positive sample and the acquired negative sample based on a manner of contrastive learning; where the image-text matching model is used to predict, for an input image and input text, whether the input text is used to describe content of the input image.

Abstract: A model training method includes: acquiring a sample set including a plurality of sample groups; the sample group includes an original image sample and original text samples; performing mask processing on the original image sample and the original text samples to generate a mask image sample and mask text samples; using the mask image sample and the mask text samples to perform adversarial training on a generator and a discriminator to obtain a target model; the generator includes a feature extraction network and an output network, the feature extraction network is used to perform feature extraction after information fusion of an input image and an input text of the generator.

Abstract: The present invention discloses a method and a system for improving the alarm reliability of a smoke fire detector. The method comprises the following steps: S1, Set an updated background threshold, an alarm threshold and an aerosol type threshold. S2, Initialize the background value of the smoke detector. S3, Regularly update the detection value. S4, Regularly update the long-period background value. S5, Update the short-period background value according to the threshold method. S6, Identify the aerosol type. S7, Give an alarm of fire smoke alarm. S8, Give an alarm of interfering substances.

Abstract: A circuit for a multi phase bridge inverter having a plurality of high and low side switches arranged to be coupled, respectively, to first and second power source busbars. The multi phase inverter is arranged to control current flow in coil windings of an electric motor, and a DC link capacitor is coupled between the first and second power source busbars. The circuit comprises first and second impedances coupled in series between the first and second power source busbars and in parallel to the DC link capacitor, and means coupled between the first and second impedances for providing a voltage from the coupling between the first and second impedances to the low side switches upon the occurrence of a first predetermined condition to place the low side switches in a close circuit configuration to allow coil windings of an electric motor to be placed in a short circuit configuration.

Abstract: The present disclosure provides a chassis collision structure of a new energy vehicle, comprising a front lower collision beam assembly, a front sub-frame assembly, a front battery pack bottom fender, a rear battery pack bottom fender, and a rear sub-frame assembly which are arranged sequentially along a direction from a head to a tail of a vehicle, wherein the front lower collision beam assembly is connected to a front end of the front sub-frame assembly; the front battery pack bottom fender is connected to a bottom of the front sub-frame assembly; the rear battery pack bottom fender is connected to a bottom of the rear sub-frame assembly; and connecting parts which are connected with a battery pack are arranged at one end of the front battery pack bottom fender and one end of the rear battery pack bottom fender, which are close to each other, respectively.

Abstract: A method and device estimate life consumption for an electronic component for use with an electric motor. The method and device allow maximal/minimal temperature values of semiconductor devices to be continuously monitored to allow on-cycle thermal damage and post-cycle damage to be determined in a parallel manner.

Abstract: Provided are an inspection method, and an inspection system including: at least one ray source; a detector assembly and a conveying device. At least one ray source and the detector assembly may move in a traveling direction relative to the conveying device, so that the to-be-inspected object may enter an inspection region. When viewed along a central axis of the inspection region, at least one ray source may translate between scanning positions, and a translation distance of at least one ray source between two adjacent scanning positions is greater than a spacing between adjacent target spots of each ray source. When at least one ray source is located at one scanning position, at least one ray source and the detector assembly move in the traveling direction and at least one ray source emits X-rays. After moving a predetermined distance, at least one ray source translates to another scanning position.

Abstract: Disclosed are an asset value evaluation method and apparatus, a model training method and apparatus, and a readable storage medium. The asset value evaluation method includes: acquiring input asset value query information for a user; when it is determined that there is historical asset interaction information of the user, determining an asset set obtained by means of making a query using the asset value query information, the asset set includes at least one asset; performing embedding representation on each asset, so as to determine an asset embedding vector of each asset, the asset embedding vector is obtained by means of training based on the relationship between each asset and an attribute, and the attribute is used for representing an inherent parameter of the asset; and inputting the asset embedding vector of each asset into a graph convolutional network model to obtain the value of each asset for the user.

Abstract: The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.

Abstract: Provided in the present disclosure are a text recommendation method and apparatus, a model training method and apparatus, and a readable storage medium. The text recommendation method includes: acquiring text retrieval information from a user; when it is determined that there is historical text retrieval information for the user, determining text information of each text in a text set retrieved by using the text retrieval information; performing embedded representation on the text information of each text based on a self-attention model, and determining a text embedding vector of each text; inputting the text embedding vector of each text into a trained graph convolutional network model, to obtain the probability of interaction between the user and each text in the text set; and screening out, from the text set, target text which meets a preset interaction probability, and recommending the target text to the user.

Abstract: The present disclosure relates to a method for natural language processing, a method of training a natural language processing model, an electronic device, and a non-transitory computer-readable storage medium, and relates to the technical field of natural language processing. The method for natural language processing includes: acquiring text data; and processing the text data by using a natural language processing model to obtain output information, wherein the natural language processing model comprises a first attention model, the first attention model comprising a sequential coding matrix for adding, on the basis of the text data, sequential relation information between at least one word and other words in the text data.

Abstract: The present disclosure relates to a meter recognition method, which includes: determining embedded features of pixels in a target image of a meter, and encoding position information of the pixels to obtain encoded position features; inputting superimposed features obtained by superimposing the encoded position features and the embedded features into an encoder of a target model; wherein an input of the target model includes the labels, and an output of the target model includes coordinates of key points in a sample image of the meter. According to the present disclosure, the image of the meter can be processed by the trained target model, the coordinates of key points in the target image of the meter are outputted. It can reduce manual operations and improve efficiency, and on the other hand, it can avoid possible misoperations during manual operations, which is beneficial for improving accuracy.

Abstract: Provided in the present disclosure are a text recognition method, and a model and an electronic device, which are applied to a mode in which primary classification is first performed from different dimensions, and secondary classification is then performed, such that the meaning of text is analyzed from different dimensions, thereby improving the accuracy of text recognition. The method includes: acquiring text to be recognized, and performing primary classification on the text to obtain a plurality of text features, wherein the primary classification is used for performing feature extraction on the text from different dimensions, and there are differences between features extracted from the different dimensions (100); splicing the plurality of text features, so as to obtain spliced features (101); and performing secondary classification on the spliced features to obtain a text category corresponding to the text, wherein the secondary classification is used for classifying the spliced features (102).

Abstract: An inspection system and method, and the system includes: a ray source; a detector assembly; and a conveying device for carrying an aviation pallet cargo. The ray source and the detector assembly are movable in a traveling direction parallel to the central axis relative to the conveying device so that the aviation pallet cargo enters an inspection region, the ray source is translatable between a plurality of scanning positions, and a translation distance of the ray source between two adjacent scanning positions is greater than a spacing between adjacent target spots of the ray source. When the ray source is located at one of the scanning positions, the ray source and the detector assembly move in the traveling direction and the ray source emits X-rays; and when the ray source and the detector assembly move a predetermined distance in the traveling direction, the ray source translates to another one of the plurality of scanning positions.

Abstract: An inspection system and method, the system includes: a ray source rotatable between at least two scanning positions around a rotation axis, a rotation angle of the ray source between two adjacent scanning positions being greater than an angle of adjacent target spots of the ray source relative to the rotation axis; a detector assembly; and a conveying device for carrying an object. The ray source and the detector assembly are movable in a traveling direction relative to the conveying device so that the object enters an inspection region. When the ray source is at one of the scanning positions, the ray source and the detector assembly move in the traveling direction and the ray source emits X-rays; and when the ray source and the detector assembly move a predetermined distance in the traveling direction, the ray source rotates around the rotation axis to another one of the scanning positions.

Abstract: The present disclosure relates to an image recognition method and system for a display panel, a training method, and an electronic device and a non-volatile computer-readable storage medium. The image recognition method includes: acquiring an image of a display panel, wherein the image includes gate lines extending in a first direction and data lines extending in a second direction, the gate lines and the data lines intersecting to define a plurality of sub-pixel regions, and the image further includes a defect pattern; and recognizing the defect pattern in the image by using an image recognition model to obtain defect information, wherein the defect information includes at least one of a defect type or a defect position of the defect pattern, the image recognition model comprises a first attention model configured to learn a weight proportion of a feature of the defect pattern in the image.

Abstract: Provided are a method and device for recommending goods, a method and device for training a goods knowledge graph, and a method and device for training a model. The method for training a goods knowledge graph includes: constructing an initial goods knowledge graph based on a first type of triples and a second type of triples, where a format of the first type of triples is head entity-relation-tail entity, and a format of the second type of triples is entity-attribute-attribute value (S101); and training the initial goods knowledge graph based on a graph embedding model to obtain embedding vectors of entities in the trained goods knowledge graph (S102).

Abstract: A ray scanning apparatus, including: a conveying device; a ray source assembly including a plurality of ray source modules arranged around the scanning area in a non-enclosed structure opened on one side of the scanning area; and a detector assembly including a plurality of detector sets arranged around the scanning area in a non-enclosed structure opened on one side of the scanning area, where the opening of the non-enclosed structure of the ray source assembly is opposite to the opening of the non-enclosed structure of the detector assembly, the plurality of detector sets are fixed in a same plane perpendicular to the conveying direction of the object under inspection, and the plurality of ray source modules of the ray source assembly are arranged in a plurality of different planes perpendicular to the conveying direction of the object under inspection.