191results about How to "Generate accurately" patented technology

The invention discloses a context learning-based database query generation method and system and a storage medium, and the method comprises the steps: extracting keywords and phrases from a natural language query, retrieving values similar to the keywords in a database, and returning values most related to the keywords and corresponding columns thereof, retrieving description information related to the keywords and the phrases in a database directory; performing mode selection aiming at the most relevant value of each keyword and the corresponding column, namely screening out the column irrelevant to the natural language query, further selecting a table necessary for generating the SQL query, and finally reducing the mode to a minimum column set necessary for generating the SQL query; and correcting logic and grammar errors of the method model through the database mode, the natural language query, the generated candidate SQL query and the execution result thereof. According to the method, the efficient SQL query statement is generated by intelligently understanding the data context and the query requirement.

The invention provides a potential diffusion model-based building planning image generation method and system. The method comprises the steps of data preprocessing, potential diffusion model structure improvement, input condition optimization, model training, image generation and the like. The relevance between text description and architectural styles is enhanced by preprocessing data sets of architectural design images and urban planning images and introducing channel crossing and space crossing attention mechanisms. Meanwhile, an architectural style embedding vector, an architectural description text embedding vector and a geometric information embedding vector are combined as additional input conditions to ensure that the generated image contains complete information. In a model training process, a multi-scale strategy and depth separable convolution are adopted, so that the recognition capability and training efficiency of the model to a large scene are improved. The system comprises a data preprocessing module, a model optimization module, a model training module, an image generation module and the like, and can generate a high-resolution building image conforming to design specifications. The method and system can be widely applied to the fields of building design and urban planning, and design efficiency and accuracy are improved.

The invention provides a coal mine gas inspection task generation and distribution method and system, and relates to the technical field of coal mine safety management. The method specifically comprises the following steps: acquiring coal mine geologic structure distribution data and coal mine gas real-time concentration distribution data, and carrying out spatial topology simulation analysis and gas distribution matching to obtain a coal mine gas real-time distribution simulation space; performing wind power distribution detection and wind stopping area division processing on the coal mine to obtain a coal mine gas distribution space fixed wind stopping area and a coal mine gas distribution space temporary wind stopping area; and performing zoned gas leakage hot spot analysis and inspection task generation processing on the coal mine gas distribution space fixed blowing-out area and the coal mine gas distribution space temporary blowing-out area, and performing inspection capability detection and inspection task distribution processing to obtain a coal mine gas inspection task distribution result. According to the invention, the gas inspection efficiency and safety are effectively improved.

The invention belongs to the technical field of artificial intelligence, and relates to a fraud phone recognition method and device, computer equipment and a storage medium. Wherein the call data comprises audio data and metadata; respectively carrying out feature extraction on the audio data and the metadata to obtain audio features and metadata features; performing fraud analysis on the audio features and the metadata features based on a telephone analysis model to obtain a fraud analysis result; if the fraud analysis result is that the call corresponding to the call data is a suspected fraud call, converting the audio data into text data; performing semantic feature extraction on the text data to obtain semantic features; performing fusion processing on the audio features, the metadata features and the semantic features to obtain comprehensive features; and performing fraud recognition on the comprehensive features based on a telephone recognition model to obtain a fraud telephone recognition result corresponding to the call. According to the invention, based on a model combination processing mode, the accuracy and efficiency of telephone fraud identification are effectively improved.

The invention discloses a holographic display system based on an AI visual identification technology, and relates to the technical field of AI visual identification, and the holographic display system comprises the steps: obtaining real-time image data through at least one image sensor, and carrying out the preprocessing of the image data; analyzing the processed image data by using a deep learning model carried by an algorithm module, generating three-dimensional space information from the processed image data, constructing a holographic image based on the three-dimensional space information, and rendering the holographic image in combination with illumination and material information; the display angle of the holographic image is adjusted according to the position and visual angle information of the user, meanwhile, changes in a scene are monitored, display parameters of the holographic image in the holographic image display system are automatically adjusted, and the holographic image is dynamically modified through interactive input of the user. The method realizes efficient and accurate three-dimensional holographic image generation, has wide application potential, and is particularly suitable for the fields of virtual reality, augmented reality, holographic display and the like which need dynamic display and are high in interactivity.

The invention relates to a multi-modal automatic abstracting method based on deep learning, and belongs to the technical field of computer natural language processing. The method comprises the following steps: 1, extracting text features by utilizing BERT, and extracting image features by utilizing GoogleNet; 2, the extracted image-text features are connected in series to form a series vector; 3, fusing the text features and the image features; 4, inputting the fused text features into a decoder of the text abstract, calculating by adopting the cross attention and self-attention with masks of the decoder to obtain autoregression results of word vectors of the text abstract, and selecting the autoregression result with the maximum probability in an autoregression sequence by utilizing beam search to output the text abstract; 5, inputting the fused image features into a Softmax layer, and performing image abstract output on a sample image with the maximum probability in the samples; compared with the prior art, the method has the advantages that the text abstract is generated and the picture most related to the abstract is selected by adopting deep learning, so that the text abstract and the related picture can be accurately generated, and the practicability is very high.

The invention relates to the technical field of computer aided design, in particular to a fan model generation method and system. The method comprises the following steps: acquiring basic design parameters and constraint conditions of a fan, and performing fan model simulation generation and aerodynamics analogue simulation to generate aerodynamics simulation fields corresponding to different fan models; performing fan blade power curvature and airflow vibration evaluation analysis on the aerodynamic simulation fields corresponding to the different fan models, and performing fan airflow efficiency calculation at the same time to obtain fan air flow efficiency corresponding to the different fan models; performing operation acoustic noise evaluation analysis and performance curve simulation analysis on the corresponding fan three-dimensional models to generate airflow efficiency-noise performance curves corresponding to different fan models; a fan optimization model is generated by obtaining a corresponding fan constraint optimization target and performing time sequence energy efficiency ratio optimization design. The design effect and efficiency of the fan model can be improved.

The embodiment of the invention provides an automatic question and answer method and a tax knowledge question and answer method. The automatic question and answer method comprises the steps of obtaining target question information and a standard question and answer library comprising at least one piece of standard question and answer information; standard question and answer similarities corresponding to the standard question and answer information are generated, and the standard question and answer similarities are similarities between the standard question and answer information and the target question information; and generating answer information corresponding to the question information based on standard question and answer similarities corresponding to the standard question and answer information, a first similarity threshold used for judging whether the target question information is matched with the standard question and answer information or not and a second similarity threshold used for judging whether the target question information is related to the standard question and answer information or not. By combining and effectively integrating multi-channel target task related knowledge, the quality and efficiency of target task consultation are improved, and important support is provided for a user to understand the target task related knowledge.

The embodiment of the invention discloses a cable tunnel intelligent operation and maintenance method and device based on a digital twinning technology. A specific embodiment of the method comprises the steps of obtaining a point cloud data set and a tunnel drawing; constructing an initial three-dimensional cable tunnel model according to the point cloud data set and the tunnel drawing; according to each sensor arranged in the target cable tunnel, real-time sensing data corresponding to each sensor is obtained, and each piece of real-time sensing data is obtained; generating cable operation analysis information for the target cable tunnel according to the real-time sensing data; generating a cable twinborn model for the target cable tunnel according to the real-time sensing data and the cable operation analysis information; and executing at least one operation and maintenance processing task by using the cable twinning model. According to the implementation mode, the cable twinborn model can be accurately generated, so that intelligent operation and maintenance of the cable tunnel are realized, and cable accidents are avoided.

The invention discloses a cross-domain medical image segmentation method based on a discrete diffusion model and a related device. The segmentation method comprises the following steps: acquiring a to-be-segmented medical image and a pseudo tag of the to-be-segmented medical image; inputting the medical image to be segmented into a pre-trained domain generalization diffusion segmentation model to obtain a medical image segmentation result; the pre-trained domain generalization diffusion segmentation model comprises a forward diffusion processing module, a robust feature extraction sub-network and a segmentation label generation network which are connected in sequence; the forward diffusion processing module is used for adding noise to a to-be-segmented medical image to obtain a noise label, the robust feature extraction sub-network is used for extracting robust image features from the to-be-segmented medical image, and the segmentation label generation network is used for obtaining a segmentation label according to the robust image features, a pseudo label and the noise label. The method focuses on exploring and improving the cross-domain generalization ability of a domain generalization diffusion segmentation model, and adopts a robust feature extraction sub-network and a segmentation label generation network to realize accurate generation of a cross-domain segmentation label.

The invention discloses a Mal-POBM malicious code confrontation sample generation method based on a genetic algorithm. The Mal-POBM malicious code confrontation sample generation method comprises the following steps: modifying static characteristics of an original sample; in adversarial sample generation, grouping optimization is carried out on a population, and crossover and variation are carried out at a local adaptive probability to increase the diversity of the population and increase the probability of finding an optimal solution; and when the population is sunk into the local optimal solution, catastrophe is carried out on the population by using the Cauchy probability. The malicious code adversarial sample can be accurately and quickly generated, and the method has important significance for evaluating and improving a malicious code detection system.

The invention provides a visual question and answer method based on knowledge retrieval enhancement, and relates to the field of computer vision and natural language processing, and the method comprises the following steps: respectively encoding a multi-modal query and a knowledge text by using a multi-modal encoder; calculating the similarity between the multi-modal query and the feature vector of the knowledge text by using a vector dot product, and retrieving to obtain a plurality of related external knowledge; the method comprises the following steps of: extracting the most useful visual representation from a frozen image encoder by using a lightweight visual Transform module; using a T5 encoder to independently encode the joint input of a plurality of knowledge paragraphs, images and questions; the first N most representative marks are connected in series and input into a T5 decoder for joint decoding, and finally a correct answer is generated. According to the method, the problems of visual information loss and insufficient knowledge utilization in the existing method are solved, and the performance of the system in the knowledge-intensive visual question and answer task is remarkably improved.

The invention discloses an automatic driving vehicle test scene generation method, system and device and a medium, and relates to the technical field of dangerous scene generation, and the method comprises the steps: generating an initial scene based on a lane coordinate system; building a one-way single-lane car-following scene; the method specifically comprises: creating roads and vehicles based on an initial scene; the vehicle comprises an automatic driving vehicle and an intelligent body vehicle based on the IDM model; building a one-way double-lane vehicle switching scene; the method specifically comprises the steps of creating double lanes and vehicles based on an initial scene; the vehicle comprises an automatic driving vehicle and an intelligent body vehicle based on the IDM model; according to the established one-way single-lane car-following scene and the one-way double-lane car-switching scene, generating a dangerous scene for testing the automatic driving vehicle; according to the method, the dangerous scene of the automatic driving system is efficiently and accurately generated, so that the test is accelerated, and the performance and the safety of the automatic driving system in a complex and high-risk environment are verified.

The invention discloses a test data distribution method and device, equipment, a storage medium and a program product, and relates to the technical field of data processing.The test data distribution method comprises the steps that test data configuration information of a user is received; determining a target data structure, a data generation rule, a target distribution mode and target storage middleware according to the test data configuration information and a preset metadata warehouse; according to the data generation rule, constructing data conforming to the target data structure to obtain test data; and based on the target distribution mode, distributing the test data to the target storage middleware. According to the scheme, the test data can be automatically distributed to the multiple storage middleware after being generated at a time, repeated data generation of the multi-source storage middleware is effectively avoided, the accuracy of the test data is ensured, and the test efficiency is effectively improved.

The invention relates to the technical field of artificial intelligence, and provides an open source software network fingerprint generation method and system, electronic equipment and a storage medium, and the method comprises the steps: deploying target open source software of a to-be-generated fingerprint based on a large model; when the target open source software is operated, multiple different types of network communication characteristics are extracted based on the large model; the plurality of different types of network communication features at least comprise a network protocol feature, a flow feature and a load feature; aggregating the network communication features to generate an initial fingerprint of the target open source software; and verifying the initial fingerprint to obtain a target fingerprint of the target open source software. According to the open source software network fingerprint generation method provided by the invention, the fingerprint generation process of the target open source software can be efficiently and accurately completed without manual intervention under the multi-modal fusion capability of a large model, the automation level and accuracy of fingerprint generation are effectively improved, the investment of professionals is saved, and the user experience is improved. Therefore, the number and accuracy of fingerprint libraries are quickly improved.

The invention belongs to the technical field of artificial intelligence, relates to an artificial intelligence-based verbal skill generation method and device, computer equipment and a storage medium, is applied to the field of financial science and technology, and comprises the following steps: if a verbal skill generation request input by a user is received, extracting insurance type product information and scene description information from the verbal skill generation request; calling a preset verbal skill generation model; wherein the verbal skill generation model is a model generated by performing training and fine tuning processing on a pre-training language model based on a preset fine tuning sample set and a fine tuning algorithm; processing the insurance type product information and the scene description information based on a verbal skill generation model to obtain a verbal skill text; performing adjustment processing on the verbal skill text based on the adjustment strategy to obtain a target verbal skill text; and returning the target verbal skill text to the user. In addition, the invention also relates to a block chain technology, and the target verbal skill text can be stored in a block chain. Based on the use of the verbal skill generation model, the generation efficiency and the generation accuracy of the verbal skill text are effectively improved.

The invention relates to a large model corpus processing method and system. The method comprises the following steps: performing data acquisition on language data; value data identification is carried out on the collected data, and a valuable area is reserved; extracting the reserved area in the form of a paragraph document; the paragraph document is converted into a unified text format, and invalid content deletion, format and grammar error correction, content value screening and format unified processing are carried out; performing duplicate removal processing on the data; carrying out overall quality inspection on the data subjected to the duplicate removal processing so as to optimize the processing process; performing feature extraction on the data through a deep learning model to obtain corpus features; according to corpus features, semantic information is extracted through a semantic understanding model and used for training and application of a large model; and optimizing the data processing process according to the training and application results and feedback information of the large model. Compared with the prior art, the method has the advantages that unified processing of various large model training data is realized, and the accuracy and efficiency of data processing are improved.

The embodiment of the invention discloses a value data processing method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: determining a similar value product information set and user group information; for each piece of similar value product information, executing a first generation step: determining a first matching information set; performing data screening on the similar risk circulation data set to generate a screened data set; for each piece of user information, determining second matching information; screening out at least one piece of user information, and generating an extended value transfer data sequence set; generating an extended data set; generating value risk information; and storing various data or information, and sending information links and link instructions to the value monitoring terminal. According to the embodiment, the value risk information for the target user can be accurately generated, and the value loss for the target user is avoided by executing the corresponding value operation on the target user.

The invention relates to the technical field of reinforcement learning, in particular to a reinforcement learning method and device applied to a self-programming task, electronic equipment and a storage medium, and the method comprises the steps: obtaining an example data set applied to the self-programming task, and carrying out the fine adjustment of any open-source large language model according to the example data set; connecting a first value output end and a second value output end behind the fine-tuned large language model; encouraging the big language model to perform diversified code generation for the selected self-programming task by adjusting temperature parameters, and collecting positive example and counter example data; performing Monte Carlo tree search according to the generated diversified codes to generate a series of texts and recording the texts; and training a large language model by using data collected by Monte Carlo tree search to optimize the code generation and evaluation capability. The limitation of an existing framework in the self-programming field is overcome, and more efficient and more accurate code generation and optimization are achieved.

The invention relates to a virtual lane line generation method and device, a vehicle and a storage medium. The method comprises the steps of obtaining a road image and vehicle positioning information; determining a lane end point position and an intersection end point position from the road image based on the vehicle positioning information; extending the lane end point position according to the vehicle positioning information to generate a lane end point extension line; extending the intersection endpoint position according to the vehicle positioning information to generate an intersection endpoint extension line; determining a fitting rule according to a geometrical relationship between the lane end point extension line and the intersection end point extension line; and based on the fitting rule, fitting is carried out on the lane end point position and the intersection end point position, and a virtual lane line is generated. According to the method, the virtual lane line can be determined by identifying the image information, so that the virtual lane line can be accurately generated in a scene without lane mark guidance, such as a high-precision map or an intersection, and the driving control stability of a vehicle can be improved.

The invention discloses a business data chart generation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing or other related fields, the method comprises the following steps: receiving a data processing demand, the data processing demand being a chart generation demand for target business data, the target business data comprises static data and / or dynamic data; inputting the data processing demand into the target model, and outputting a data processing code and a task configuration file; compiling the data processing code to obtain a data processing task, and deploying the task configuration file and the data processing task to a real-time data processing platform; and running the data processing task based on the task configuration file in the real-time data processing platform, and generating a target display chart of the target business data. According to the invention, the technical problem of low efficiency of visualized display of real-time business data in a business data chart display method in the related art is solved.

The embodiment of the invention discloses a vehicle scheduling method, device and equipment, a computer readable medium and a program product. According to one specific embodiment, the method comprises the steps that an article demand order set, vehicle transportation information and transportation warehouse information in a target time period are acquired, and article demand orders are orders that demand warehouses in a front distribution center request warehouse articles from delivery warehouses in a regional distribution center; obtaining a vehicle scheduling model and at least one piece of corresponding vehicle scheduling constraint information; inputting the article demand order set, the vehicle transportation information and the transportation warehouse information into a vehicle scheduling model to generate vehicle scheduling information under at least one piece of vehicle scheduling constraint information; and performing a vehicle scheduling process for the vehicle scheduling information. The implementation mode is related to intelligent logistics, and the vehicle scheduling information can be accurately generated by utilizing the vehicle scheduling model and the at least one piece of vehicle scheduling constraint information, so that the vehicle loading rate is improved, and the transportation resource consumption is reduced.

The invention discloses an autonomous evolution type 5G protocol fuzzy test system based on a large language model, and the system comprises a protocol extraction unit which employs the large language model to analyze a 5G protocol specification document, and extracts structured protocol knowledge elements containing a message format, state machine logic and parameter constraints; the vulnerability analysis unit generates a potential vulnerability description list through big language model reasoning based on the knowledge elements; the test strategy generation unit drives a large language model to generate a targeted test case based on protocol knowledge, vulnerability description and historical test knowledge in the dynamic test knowledge base; the test execution unit executes the use case and captures a result; and feeding back an analysis result of the internalization unit, extracting a causal rule, generating new test knowledge and feeding back the new test knowledge to the knowledge base. According to the system disclosed by the invention, test targets and resources can be autonomously scheduled according to continuous updating of the knowledge base, so that the test process has an iterative optimization capability, and full-process automation and intelligentization of 5G protocol fuzzy testing are realized.

The invention relates to an object resource allocation information determination method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a resource allocation range, resource allocation time and information of each resource allocation object corresponding to a resource allocation task; determining a resource allocation index parameter corresponding to the resource allocation task according to the resource allocation range, the resource allocation time and the information of each resource allocation object; constructing a resource allocation processing model corresponding to the resource allocation task according to the resource allocation index parameter and the resource allocation task; and inputting the resource allocation basic information corresponding to each piece of resource allocation object information into a resource allocation processing model to obtain object resource allocation information corresponding to each piece of resource allocation object information. By adopting the method, the resource allocation accuracy can be improved.

The invention provides a dialogue generation method and device based on artificial intelligence, and the method comprises the steps: collecting a dialogue of a user in real time, carrying out the initial dialogue generation through a predefined basic reply template, replying the dialogue of the user, and obtaining the initial dialogue record information; performing context information fusion on the initial dialogue record information to obtain a fused context feature vector; performing semantic information fusion on the initial dialogue record information to obtain a fused semantic feature vector; performing secondary fusion on the fused context feature vector and the fused semantic feature vector to obtain a fused feature vector; and generating dialogue content for replying by combining the fused feature vector with the trained intention model. The device comprises modules corresponding to the steps of the method.

The invention discloses a vulnerability event intelligence early warning method and device based on a large model, and relates to the network security and artificial intelligence technology, and the method comprises the steps: obtaining data information related to a vulnerability event, and carrying out the knowledge extraction of the obtained data information, so as to obtain an entity and a relation related to a vulnerability; according to the extracted entity and relation related to the vulnerability, generating a vulnerability event by utilizing a set vulnerability information template; and inputting the generated vulnerability event into a pre-trained vulnerability intelligence large model to obtain a vulnerability brief report and a vulnerability early warning. According to the method, vulnerability early warning and brief reports can be quickly and accurately generated. The method can provide powerful support for the security management decision of the organization before the vulnerabilities are not widely utilized, and avoids the loss caused by the utilization of the vulnerabilities by attackers.

The embodiment of the invention discloses an overhead line safety information display method and device, equipment and a medium. One specific embodiment of the method comprises the following steps: in response to received overhead line verification information, obtaining at least one piece of overhead line information and a point cloud data set; performing point cloud data classification to generate a point cloud data set; generating point cloud data set proximity relation information corresponding to each point cloud data set; for each piece of overhead line information, executing a first generation step: determining a point cloud data set as a target point cloud data set; determining at least one adjacent point cloud data group; generating distance safety information; in response to determining that the overhead line information has a distance safety problem, generating distance safety information; performing information summarization on the at least one piece of distance safety information to generate distance safety summarized information; and sending the distance safety summary information to the target power data terminal. According to the embodiment, the safety detection of the overhead line in the target power area can be accurately and efficiently realized.

The invention relates to the technical field of brain-computer interfaces, and discloses a non-invasive brain-computer interface system based on deep learning and a signal processing method thereof, and the system comprises an EEG signal collection module, a signal preprocessing module, a deep learning model training module, and a decoding and control module. The EEG signal acquisition module is used for acquiring an EEG signal of a user; the signal preprocessing module is used for preprocessing the collected electroencephalogram signals, including noise suppression, filtering and feature extraction; the deep learning model training module is used for training a deep learning model and decoding the preprocessed electroencephalogram signal into a control instruction; the decoding module is used for decoding the electroencephalogram signals collected in real time into control instructions according to the trained model; and the control module is used for controlling external equipment according to the decoded control instruction. The method has the advantage of improving the performance and applicability of the non-invasive brain-computer interface.

The invention discloses an image generation method and device, a model training method and device, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining product description information and a product pattern corresponding to a target product of a to-be-generated display graph; generating background image description information corresponding to the product description information through a pre-trained text generation model based on the product description information; and based on the product pattern, through a pre-trained text guide image generation model, and by taking the background image description information as a guide text of the text guide image generation model, generating a display image for adding a background to the product pattern. Through the scheme provided by the invention, the high-quality display graph with the background matched with the product is more efficiently generated, and meanwhile, the labor cost required by background generation is saved.

The invention provides a combined protein generation method based on an electron density hidden space diffusion model. The method of the present disclosure utilizes a conditional diffusion model in a hidden space to generate a protein-bound electron density representation for structural related information of a given target spot. In the solution provided by the invention, related concepts of a reciprocal space are originally introduced to carry out encoding and decoding of a hidden space, and meanwhile, a mode of utilizing a guiding mask is provided to control the model to generate electron density with higher quality in a targeted manner. According to the method, unlabeled training is carried out by using a public data set (PDB), and a hidden space diffusion model capable of accurately generating protein electron density with binding characteristics in a specific region is obtained, so that a foundation is laid for subsequent realization of biological functions and design and development of related drugs.