13 results about "Cross processing" patented technology

The invention relates to the technical field of electrical digital data processing, in particular to an intelligent contract vulnerability detection method based on multi-modal selection state space fusion, and aims to solve the technical problem of low vulnerability detection accuracy in the prior art. The method comprises the following steps: firstly, fusing global and local features of a contract graph to obtain graph modal embedding features; performing feature alignment on the text context information of the intermediate representation text and the byte code text to obtain text modal embedding features; extracting spatial layout features from the color image and the grayscale image, and splicing the spatial layout features to obtain visual modal embedding features; thirdly, performing multiple rounds of feature cross processing on the graph modal embedding features, the text modal embedding features and the visual modal embedding features based on a selection state space to realize intra-modal and inter-modal information interaction, and then performing fusion to obtain multi-modal fusion features; and finally, performing feature normalization and regularization processing on the multi-modal fusion features, and performing classification processing to obtain a vulnerability detection result.

The invention discloses an intelligent discharging method and device based on array combinatorial optimization and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: analyzing a target graphic file to extract cutting piece information in the target graphic file; classifying the cutting piece information so as to classify the outline of the flexible material and sub cutting pieces in the flexible material; constructing an initial typesetting scheme according to the flexible material contour and the sub-cutting pieces; selecting a reference typesetting scheme from the initial typesetting schemes according to a preset fitness function; performing cross processing on the reference typesetting scheme; performing variation processing on the part of the reference typesetting schemes after the cross processing; carrying out correction processing on the reference typesetting scheme which is subjected to cross processing and is not subjected to variation processing and the reference typesetting scheme which is subjected to variation processing; and taking the reference typesetting scheme with the highest fitness as a target typesetting scheme. Typesetting of the sub-cutting pieces is regarded as a combinatorial optimization problem, and the material utilization rate is improved.

The present invention discloses an intelligent nesting method, device, and storage medium based on array combination optimization, relating to the field of image processing technology. The method comprises: parsing a target graphic file to extract piece information from the target graphic file; classifying the piece information to classify flexible material contours and sub-pieces within the flexible material; constructing an initial layout scheme based on the flexible material contours and sub-pieces; selecting a baseline layout scheme from the initial layout schemes based on a preset fitness function; cross-processing the baseline layout schemes; mutating some of the cross-processed baseline layout schemes; correcting the cross-processed and unmutated baseline layout schemes and the mutated baseline layout schemes; and selecting the baseline layout scheme with the highest fitness as the target layout scheme. The present invention regards the layout of sub-pieces as a combinatorial optimization problem, thereby improving material utilization.

The present invention relates to the field of electronic digital data processing technology, and specifically to a smart contract vulnerability detection method based on multimodal selection state space fusion. In order to solve the technical problem of low vulnerability detection accuracy in the prior art, the present invention first fuses the global and local features of the contract graph to obtain graph modality embedding features; and aligns the text context information of the intermediate representation text and the bytecode text to obtain text modality embedding features; and extracts spatial layout features from color images and grayscale images for splicing to obtain visual modality embedding features; then, the graph modality embedding features, text modality embedding features, and visual modality embedding features are subjected to several rounds of feature cross processing based on the selection state space to achieve intra-modal and inter-modal information interaction, and then are fused to obtain multimodal fusion features; finally, the multimodal fusion features are subjected to feature normalization and regularization, and then classified to obtain vulnerability detection results.

The application provides a lighting lamp distribution parameter determination method and related equipment, and the method comprises the following steps: randomly generating an initial population of lamp distribution in a digital twin space, the initial population comprising a first number of initial solutions, each initial solution corresponding to a first distribution parameter; performing cross processing on the first number of initial solutions and a second number of first mutation solutions to obtain a third number of first cross solutions, the first mutation solutions being obtained by mutation processing on the initial solutions; in the case that the change of fitness values between the third number of first cross solutions is less than or equal to a preset threshold, determining the third distribution parameter corresponding to a first target solution in the third number of first cross solutions as the lighting lamp distribution parameter of the digital twin space. In this way, the optimal lighting lamp distribution parameter can be found in a complex search space, and the lighting effect and energy utilization efficiency are improved.

The invention relates to the technical field of power generation prediction, and provides a photovoltaic power generation power short-term prediction method, device, equipment and medium, and the method comprises the steps: obtaining multi-modal data for a to-be-predicted photovoltaic power station, the multi-modal data being data describing the operation condition of the to-be-predicted photovoltaic power station, power station equipment and the environment of the to-be-predicted photovoltaic power station; generating power feature extraction and meteorological feature extraction are carried out according to the multi-modal data, and fusion processing is carried out on the extracted features to generate a fusion feature vector; performing explicit feature cross processing on the fusion feature vector to generate an attenuation coefficient; calculating clear sky power based on a clear sky model according to the multi-modal data; and according to the attenuation coefficient and the clear sky power, calculating a photovoltaic generation power prediction value of the photovoltaic power station to be predicted. According to the method, deep feature extraction and fusion are carried out on the multi-modal data, and the attenuation coefficient driven by the data is combined with the clear sky model based on the physical law, so that the prediction precision is remarkably improved.

This application relates to the field of image processing technology, providing an image processing method and a training method for a stable diffusion model of an image-generated image. The stable diffusion model of the image-generated image includes an image cross-attention mechanism layer. The method includes: inputting a first image with a first illumination effect and a target ambient light image into the trained stable diffusion model of the image-generated image; the stable diffusion model of the image-generated image performs cross-processing between the first image and the target ambient light image through the image cross-attention mechanism layer, enabling the stable diffusion model of the image-generated image to re-illuminate the first image under the guidance of the target ambient light image, outputting a second image with a second illumination effect. This achieves the reconstruction of the image illumination effect, improves the image illumination effect, and enhances image quality and image effect.

The embodiment of the invention provides an image processing method, device and equipment, a readable storage medium and a program product, and can be applied to the fields of artificial intelligence, image classification and the like, and the method comprises the steps: extracting the object feature of each to-be-classified object in a plurality of to-be-classified objects included in a to-be-processed image; performing feature cross processing on the first object feature and the second object feature to obtain a cross attention matrix between the first object feature and the second object feature, and determining correlation data between the first object feature and the second object feature according to the cross attention matrix; determining a correlation data set of the first object feature according to correlation data between the first object feature and each object feature except the first object feature; and performing image classification processing according to each object feature and the corresponding correlation data set to obtain a classification result of the to-be-processed image. According to the embodiment of the invention, the accuracy and integrity of classifying the plurality of objects in the image can be improved.

The embodiment of the invention discloses an information verification method and device, a program product and a storage medium, relates to the field of artificial intelligence and is also applicable to the field of financial science and technology, and the method comprises the following steps: receiving a verification instruction sent by a user, and obtaining an iris image of the user in response to the verification instruction; performing feature extraction on the iris image to obtain a first iris feature matrix of the iris image and a second iris feature matrix of the iris image; wherein the first iris feature matrix is a left eye iris feature matrix of the user or a right eye iris feature matrix of the user; the second iris feature matrix is a left eye iris feature matrix of the user or a right eye iris feature matrix of the user; performing cross processing on the first iris feature matrix and the second iris feature matrix to obtain a current iris feature of the user; and verifying the user according to the current iris feature and a predetermined user iris feature library. According to the method provided by the invention, the security of the system is improved, and the privacy of the user is effectively protected.

The present invention discloses a MiniLED defect detection method, electronic device, and medium, including: obtaining a defective MiniLED image and marking its key positions and categories as a training set; constructing and training a defect detection model; the defect detection model includes: extracting features from the MiniLED image, and obtaining first, second, and third scale features after multi-scale feature layer processing; aligning the first, second, and third scale features and performing global information fusion processing to obtain global fusion information; decomposing the global fusion information to obtain first and second fusion information; splicing the first fusion information with the first scale feature after detail enhancement processing to output a first prediction result; splicing the second fusion information with the second scale feature after spatial cross processing to output a second prediction result; directly outputting the third scale feature as a third prediction result; inputting the MiniLED image to be detected into the previously trained defect detection model to obtain a defect detection result.

The invention relates to the technical field of image processing, and provides an image processing method and a stable diffusion model training method for map generation. Wherein the stable diffusion model of the map comprises an image cross attention mechanism layer, and the method comprises the following steps: inputting a first image with a first illumination effect and a target ambient light image into the trained stable diffusion model of the map, and carrying out image cross processing on the first image and the target ambient light image by the stable diffusion model of the map through an image cross attention mechanism layer, so that the stable diffusion model of the map can carry out re-lighting on the first image under the traction of the target ambient light image, and a second image with a second illumination effect is output. Therefore, reconstruction of the image illumination effect is realized, the image illumination effect is improved, and the image quality and the image effect are improved.

The application discloses a lineup generation method and device, a storage medium and an electronic device. The method comprises the following steps: performing cross processing on an initial lineup set and a candidate lineup set to form a plurality of lineup pairs containing a first lineup and a second lineup from the two sets respectively. The lineup pairs are sequentially input into a target win rate prediction model with a cross network to determine the predicted fitness corresponding to each first lineup. The adaptive crossover probability and the adaptive mutation probability are calculated according to the predicted fitness, the initial lineup set is updated according to the two probabilities, and the iteration is continuously performed until the iteration number meets the preset condition, the target head lineup is determined, and the technical effects of improving the prediction accuracy and the mining efficiency are realized. The application solves the technical problems of poor evaluation reliability, poor generalization effect, low prediction lineup strength precision and low process efficiency of output head lineup result of the lineup strength model in the related art.